diff --git a/openbb_platform/extensions/README.md b/openbb_platform/extensions/README.md new file mode 100644 index 0000000000000000000000000000000000000000..0b2b3217ea12a906f68167ad34d6cee49cb9cf3d --- /dev/null +++ b/openbb_platform/extensions/README.md @@ -0,0 +1,3 @@ +# Extensions + +In this folder you can find the extensions that were created or are supported by OpenBB. diff --git a/openbb_platform/extensions/__init__.py b/openbb_platform/extensions/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..a2fa55684fc129eda96c8d9d5752be6e32def5c7 --- /dev/null +++ b/openbb_platform/extensions/__init__.py @@ -0,0 +1 @@ +"""OpenBB Platform Extensions.""" diff --git a/openbb_platform/extensions/commodity/README.md b/openbb_platform/extensions/commodity/README.md new file mode 100644 index 0000000000000000000000000000000000000000..2d99987313e065d726fa107dd82147a6a9c04e72 --- /dev/null +++ b/openbb_platform/extensions/commodity/README.md @@ -0,0 +1,13 @@ +# Commodity Extension for OpenBB Platform + +This extension provides a set of commands for commodity-related data. + +## Installation + +To install the extension, run the following command in this folder: + +```bash +pip install openbb-commodity +``` + +Documentation available [here](https://docs.openbb.co/platform/developer_guide/contributing). diff --git a/openbb_platform/extensions/commodity/integration/test_commodity_api.py b/openbb_platform/extensions/commodity/integration/test_commodity_api.py new file mode 100644 index 0000000000000000000000000000000000000000..c63b3a4c54f2196515c7cd84796b23bb28157e37 --- /dev/null +++ b/openbb_platform/extensions/commodity/integration/test_commodity_api.py @@ -0,0 +1,112 @@ +"""Test Commodity API endpoints.""" + +import base64 + +import pytest +import requests +from openbb_core.env import Env +from openbb_core.provider.utils.helpers import get_querystring + +# pylint: disable=redefined-outer-name + + +@pytest.fixture(scope="session") +def headers(): + """Get the headers for the API request.""" + userpass = f"{Env().API_USERNAME}:{Env().API_PASSWORD}" + userpass_bytes = userpass.encode("ascii") + base64_bytes = base64.b64encode(userpass_bytes) + + return {"Authorization": f"Basic {base64_bytes.decode('ascii')}"} + + +@pytest.mark.parametrize( + "params", + [ + ( + { + "commodity": "all", + "start_date": None, + "end_date": None, + "frequency": None, + "transform": None, + "aggregation_method": None, + "provider": "fred", + } + ), + ], +) +@pytest.mark.integration +def test_commodity_price_spot(params, headers): + """Test the commodity spot prices endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/commodity/price/spot?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@pytest.mark.parametrize( + "params", + [ + ( + { + "category": "balance_sheet", + "table": "stocks", + "start_date": None, + "end_date": None, + "provider": "eia", + "use_cache": True, + } + ), + ( + { + "category": "weekly_estimates", + "table": "crude_production", + "start_date": "2020-01-01", + "end_date": "2023-12-31", + "provider": "eia", + "use_cache": True, + } + ), + ], +) +@pytest.mark.integration +def test_commodity_petroleum_status_report(params, headers): + """Test the Petroleum Status Report endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/commodity/petroleum_status_report?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@pytest.mark.parametrize( + "params", + [ + ( + { + "table": "01", + "symbol": None, + "start_date": "2024-09-01", + "end_date": "2024-10-01", + "provider": "eia", + "frequency": "month", + } + ), + ], +) +@pytest.mark.integration +def test_commodity_short_term_energy_outlook(params, headers): + """Test the Short Term Energy Outlook endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/commodity/short_term_energy_outlook?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 diff --git a/openbb_platform/extensions/commodity/integration/test_commodity_python.py b/openbb_platform/extensions/commodity/integration/test_commodity_python.py new file mode 100644 index 0000000000000000000000000000000000000000..f955c6417ea8dd5361e113f4e53233d4ef8337ba --- /dev/null +++ b/openbb_platform/extensions/commodity/integration/test_commodity_python.py @@ -0,0 +1,100 @@ +"""Test Commodity extension.""" + +import pytest +from openbb_core.app.model.obbject import OBBject + +# pylint: disable=redefined-outer-name + + +@pytest.fixture(scope="session") +def obb(pytestconfig): # pylint: disable=inconsistent-return-statements + """Fixture to setup obb.""" + if pytestconfig.getoption("markexpr") != "not integration": + import openbb # pylint: disable=import-outside-toplevel + + return openbb.obb + + +@pytest.mark.parametrize( + "params", + [ + ( + { + "commodity": "all", + "start_date": None, + "end_date": None, + "frequency": None, + "transform": None, + "aggregation_method": None, + "provider": "fred", + } + ), + ], +) +@pytest.mark.integration +def test_commodity_price_spot(params, obb): + """Test the commodity spot prices endpoint.""" + params = {p: v for p, v in params.items() if v} + + result = obb.commodity.price.spot(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@pytest.mark.parametrize( + "params", + [ + ( + { + "category": "balance_sheet", + "table": "stocks", + "start_date": None, + "end_date": None, + "provider": "eia", + "use_cache": True, + } + ), + ( + { + "category": "weekly_estimates", + "table": "crude_production", + "start_date": "2020-01-01", + "end_date": "2023-12-31", + "provider": "eia", + "use_cache": True, + } + ), + ], +) +@pytest.mark.integration +def test_commodity_petroleum_status_report(params, obb): + """Test Commodity Petroleum Status Report endpoint.""" + result = obb.commodity.petroleum_status_report(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@pytest.mark.parametrize( + "params", + [ + ( + { + "table": "01", + "symbol": None, + "start_date": "2024-09-01", + "end_date": "2024-10-01", + "provider": "eia", + "frequency": "month", + } + ), + ], +) +@pytest.mark.integration +def test_commodity_short_term_energy_outlook(params, obb): + """Test Commodity Short Term Energy Outlook endpoint.""" + result = obb.commodity.short_term_energy_outlook(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 diff --git a/openbb_platform/extensions/commodity/openbb_commodity/__init__.py b/openbb_platform/extensions/commodity/openbb_commodity/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..c38801bad6165bc51b5b1274876ce64ceae515b9 --- /dev/null +++ b/openbb_platform/extensions/commodity/openbb_commodity/__init__.py @@ -0,0 +1 @@ +"""OpenBB Commodity Extension.""" diff --git a/openbb_platform/extensions/commodity/openbb_commodity/commodity_router.py b/openbb_platform/extensions/commodity/openbb_commodity/commodity_router.py new file mode 100644 index 0000000000000000000000000000000000000000..a5a080b56afd00815116de0a812bf0dd668de558 --- /dev/null +++ b/openbb_platform/extensions/commodity/openbb_commodity/commodity_router.py @@ -0,0 +1,80 @@ +"""The Commodity router.""" + +# pylint: disable=unused-argument,unused-import +# flake8: noqa: F401 + +# pylint: disable=unused-argument + +from openbb_core.app.model.command_context import CommandContext +from openbb_core.app.model.example import APIEx +from openbb_core.app.model.obbject import OBBject +from openbb_core.app.provider_interface import ( + ExtraParams, + ProviderChoices, + StandardParams, +) +from openbb_core.app.query import Query +from openbb_core.app.router import Router + +from openbb_commodity.price.price_router import router as price_router + +router = Router(prefix="", description="Commodity market data.") + + +router.include_router(price_router) + + +@router.command( + model="PetroleumStatusReport", + examples=[ + APIEx( + description="Get the EIA's Weekly Petroleum Status Report.", + parameters={"provider": "eia"}, + ), + APIEx( + description="Select the category of data, and filter for a specific table within the report.", + parameters={ + "category": "weekly_estimates", + "table": "imports", + "provider": "eia", + }, + ), + ], +) +async def petroleum_status_report( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """EIA Weekly Petroleum Status Report.""" + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="ShortTermEnergyOutlook", + examples=[ + APIEx( + description="Get the EIA's Short Term Energy Outlook.", + parameters={"provider": "eia"}, + ), + APIEx( + description="Select the specific table of data from the STEO. Table 03d is World Crude Oil Production.", + parameters={ + "table": "03d", + "provider": "eia", + }, + ), + ], +) +async def short_term_energy_outlook( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Monthly short term (18 month) projections using EIA's STEO model. + + Source: www.eia.gov/steo/ + """ + return await OBBject.from_query(Query(**locals())) diff --git a/openbb_platform/extensions/commodity/openbb_commodity/price/__init__.py b/openbb_platform/extensions/commodity/openbb_commodity/price/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..a708c4e53368e3e7b3b11a44b726c581ee65db27 --- /dev/null +++ b/openbb_platform/extensions/commodity/openbb_commodity/price/__init__.py @@ -0,0 +1 @@ +"""Commodity Price.""" diff --git a/openbb_platform/extensions/commodity/openbb_commodity/price/price_router.py b/openbb_platform/extensions/commodity/openbb_commodity/price/price_router.py new file mode 100644 index 0000000000000000000000000000000000000000..a18ec4c159413b17ada9f9d69304ad6f1d20b6cd --- /dev/null +++ b/openbb_platform/extensions/commodity/openbb_commodity/price/price_router.py @@ -0,0 +1,33 @@ +"""Price Router.""" + +# pylint: disable=unused-argument + +from openbb_core.app.model.command_context import CommandContext +from openbb_core.app.model.example import APIEx +from openbb_core.app.model.obbject import OBBject +from openbb_core.app.provider_interface import ( + ExtraParams, + ProviderChoices, + StandardParams, +) +from openbb_core.app.query import Query +from openbb_core.app.router import Router + +router = Router(prefix="/price") + + +@router.command( + model="CommoditySpotPrices", + examples=[ + APIEx(parameters={"provider": "fred"}), + APIEx(parameters={"provider": "fred", "commodity": "wti"}), + ], +) +async def spot( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Commodity Spot Prices.""" + return await OBBject.from_query(Query(**locals())) diff --git a/openbb_platform/extensions/commodity/poetry.lock b/openbb_platform/extensions/commodity/poetry.lock new file mode 100644 index 0000000000000000000000000000000000000000..8b5cef68b5cd92a25e397a3668ab2711aa485aac --- /dev/null +++ b/openbb_platform/extensions/commodity/poetry.lock @@ -0,0 +1,1734 @@ +# This file is automatically @generated by Poetry 2.1.3 and should not be changed by hand. + +[[package]] +name = "aiohappyeyeballs" +version = "2.6.1" +description = "Happy Eyeballs for asyncio" +optional = false +python-versions = ">=3.9" +groups = ["main"] +files = [ + {file = "aiohappyeyeballs-2.6.1-py3-none-any.whl", hash = "sha256:f349ba8f4b75cb25c99c5c2d84e997e485204d2902a9597802b0371f09331fb8"}, + {file = "aiohappyeyeballs-2.6.1.tar.gz", hash = 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b/openbb_platform/extensions/commodity/pyproject.toml new file mode 100644 index 0000000000000000000000000000000000000000..2930a491114b6bc8dc3dbb45de7368dfae3d5abb --- /dev/null +++ b/openbb_platform/extensions/commodity/pyproject.toml @@ -0,0 +1,19 @@ +[tool.poetry] +name = "openbb-commodity" +version = "1.3.1" +description = "Commodity extension for OpenBB" +authors = ["OpenBB Team "] +license = "AGPL-3.0-only" +readme = "README.md" +packages = [{ include = "openbb_commodity" }] + +[tool.poetry.dependencies] +python = ">=3.9.21,<3.13" +openbb-core = "^1.4.6" + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" + +[tool.poetry.plugins."openbb_core_extension"] +commodity = "openbb_commodity.commodity_router:router" diff --git a/openbb_platform/extensions/crypto/README.md b/openbb_platform/extensions/crypto/README.md new file mode 100644 index 0000000000000000000000000000000000000000..6625076b5e1048d5fd3995e8c5b3f86a6967e02d --- /dev/null +++ b/openbb_platform/extensions/crypto/README.md @@ -0,0 +1,13 @@ +# Crypto data extension for OpenBB Platform + +This extension provides a set of commands for crypto data retrieval. + +## Installation + +To install the extension, run the following command in this folder: + +```bash +pip install openbb-crypto +``` + +Documentation available [here](https://docs.openbb.co/platform/developer_guide/contributing). diff --git a/openbb_platform/extensions/crypto/integration/test_crypto_api.py b/openbb_platform/extensions/crypto/integration/test_crypto_api.py new file mode 100644 index 0000000000000000000000000000000000000000..96a2fec38064d58c7cdddfe520d3248b457f31b6 --- /dev/null +++ b/openbb_platform/extensions/crypto/integration/test_crypto_api.py @@ -0,0 +1,126 @@ +"""Test crypto API endpoints.""" + +import base64 + +import pytest +import requests +from extensions.tests.conftest import parametrize +from openbb_core.env import Env +from openbb_core.provider.utils.helpers import get_querystring + +# pylint: disable=redefined-outer-name + + +@pytest.fixture(scope="session") +def headers(): + """Get the headers for the API request.""" + userpass = f"{Env().API_USERNAME}:{Env().API_PASSWORD}" + userpass_bytes = userpass.encode("ascii") + base64_bytes = base64.b64encode(userpass_bytes) + + return {"Authorization": f"Basic {base64_bytes.decode('ascii')}"} + + +@parametrize( + "params", + [ + ({"query": "asd"}), + ({"query": "btc", "provider": "fmp"}), + ], +) +@pytest.mark.integration +def test_crypto_search(params, headers): + """Test the crypto search endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/crypto/search?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ( + { + "interval": "1d", + "provider": "fmp", + "symbol": "BTCUSD", + "start_date": "2023-01-01", + "end_date": "2023-01-02", + } + ), + ( + { + "interval": "1h", + "provider": "fmp", + "symbol": "BTCUSD,ETHUSD", + "start_date": None, + "end_date": None, + } + ), + ( + { + "interval": "1m", + "sort": "desc", + "limit": 49999, + "provider": "polygon", + "symbol": "BTCUSD", + "start_date": "2023-01-01", + "end_date": "2023-01-02", + } + ), + ( + { + "interval": "1d", + "sort": "desc", + "limit": 49999, + "provider": "polygon", + "symbol": "BTCUSD", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + } + ), + ( + { + "interval": "1d", + "provider": "yfinance", + "symbol": "BTCUSD", + "start_date": "2023-01-01", + "end_date": "2023-01-04", + } + ), + ( + { + "provider": "tiingo", + "interval": "1d", + "exchanges": None, + "symbol": "BTCUSD", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + } + ), + ( + { + "provider": "tiingo", + "interval": "1h", + "exchanges": ["POLONIEX", "GDAX"], + "symbol": "BTCUSD", + "start_date": "2023-01-01", + "end_date": "2023-01-02", + } + ), + ], +) +@pytest.mark.integration +def test_crypto_price_historical(params, headers): + """Test the crypto historical price endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/crypto/price/historical?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 diff --git a/openbb_platform/extensions/crypto/integration/test_crypto_python.py b/openbb_platform/extensions/crypto/integration/test_crypto_python.py new file mode 100644 index 0000000000000000000000000000000000000000..7a3073620ef894006c8b0c9ab09700cc96c13b19 --- /dev/null +++ b/openbb_platform/extensions/crypto/integration/test_crypto_python.py @@ -0,0 +1,117 @@ +"""Test crypto extension.""" + +import pytest +from extensions.tests.conftest import parametrize +from openbb_core.app.model.obbject import OBBject + +# pylint: disable=redefined-outer-name + + +@pytest.fixture(scope="session") +def obb(pytestconfig): # pylint: disable=inconsistent-return-statements + """Fixture to setup obb.""" + if pytestconfig.getoption("markexpr") != "not integration": + import openbb # pylint: disable=import-outside-toplevel + + return openbb.obb + + +@parametrize( + "params", + [ + ({"query": "asd"}), + ({"query": "btc", "provider": "fmp"}), + ], +) +@pytest.mark.integration +def test_crypto_search(params, obb): + """Test the crypto search endpoint.""" + params = {p: v for p, v in params.items() if v} + + result = obb.crypto.search(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ( + { + "interval": "1d", + "provider": "fmp", + "symbol": "BTCUSD", + "start_date": "2023-01-01", + "end_date": "2023-01-02", + } + ), + ( + { + "interval": "1h", + "provider": "fmp", + "symbol": "BTCUSD,ETHUSD", + "start_date": None, + "end_date": None, + } + ), + ( + { + "interval": "1m", + "sort": "desc", + "limit": 49999, + "provider": "polygon", + "symbol": "BTCUSD", + "start_date": "2023-01-01", + "end_date": "2023-01-02", + } + ), + ( + { + "interval": "1d", + "sort": "desc", + "limit": 49999, + "provider": "polygon", + "symbol": "BTCUSD", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + } + ), + ( + { + "interval": "1d", + "provider": "yfinance", + "symbol": "BTCUSD", + "start_date": "2023-01-01", + "end_date": "2023-01-04", + } + ), + ( + { + "provider": "tiingo", + "interval": "1d", + "exchanges": None, + "symbol": "BTCUSD", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + } + ), + ( + { + "provider": "tiingo", + "interval": "1h", + "exchanges": ["POLONIEX", "GDAX"], + "symbol": "BTCUSD", + "start_date": "2023-01-01", + "end_date": "2023-01-02", + } + ), + ], +) +@pytest.mark.integration +def test_crypto_price_historical(params, obb): + """Test crypto price historical.""" + result = obb.crypto.price.historical(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 diff --git a/openbb_platform/extensions/crypto/openbb_crypto/__init__.py b/openbb_platform/extensions/crypto/openbb_crypto/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..2ce9b051267aed77acfca4a35df532ac15c84568 --- /dev/null +++ b/openbb_platform/extensions/crypto/openbb_crypto/__init__.py @@ -0,0 +1 @@ +"""OpenBB Crypto Extension.""" diff --git a/openbb_platform/extensions/crypto/openbb_crypto/crypto_router.py b/openbb_platform/extensions/crypto/openbb_crypto/crypto_router.py new file mode 100644 index 0000000000000000000000000000000000000000..bc81b7db88c2f427faf741c87bf94b18b407d0b3 --- /dev/null +++ b/openbb_platform/extensions/crypto/openbb_crypto/crypto_router.py @@ -0,0 +1,35 @@ +"""Crypto Router.""" + +from openbb_core.app.model.command_context import CommandContext +from openbb_core.app.model.example import APIEx +from openbb_core.app.model.obbject import OBBject +from openbb_core.app.provider_interface import ( + ExtraParams, + ProviderChoices, + StandardParams, +) +from openbb_core.app.query import Query +from openbb_core.app.router import Router + +from openbb_crypto.price.price_router import router as price_router + +router = Router(prefix="", description="Cryptocurrency market data.") +router.include_router(price_router) + + +# pylint: disable=unused-argument +@router.command( + model="CryptoSearch", + examples=[ + APIEx(parameters={"provider": "fmp"}), + APIEx(parameters={"query": "BTCUSD", "provider": "fmp"}), + ], +) +async def search( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Search available cryptocurrency pairs within a provider.""" + return await OBBject.from_query(Query(**locals())) diff --git a/openbb_platform/extensions/crypto/openbb_crypto/crypto_views.py b/openbb_platform/extensions/crypto/openbb_crypto/crypto_views.py new file mode 100644 index 0000000000000000000000000000000000000000..7293eb68c486bda96fed8160a262ac171a05f6fc --- /dev/null +++ b/openbb_platform/extensions/crypto/openbb_crypto/crypto_views.py @@ -0,0 +1,22 @@ +"""Views for the crypto Extension.""" + +from typing import TYPE_CHECKING, Any, Dict, Tuple + +if TYPE_CHECKING: + from openbb_charting.core.openbb_figure import ( + OpenBBFigure, + ) + + +class CryptoViews: + """Crypto Views.""" + + @staticmethod + def crypto_price_historical( # noqa: PLR0912 + **kwargs, + ) -> Tuple["OpenBBFigure", Dict[str, Any]]: + """Crypto Price Historical Chart.""" + # pylint: disable=import-outside-toplevel + from openbb_charting.charts.price_historical import price_historical + + return price_historical(**kwargs) diff --git a/openbb_platform/extensions/crypto/openbb_crypto/price/__init__.py b/openbb_platform/extensions/crypto/openbb_crypto/price/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..71f078e3f0acec1d8da09666057d572cdc322166 --- /dev/null +++ b/openbb_platform/extensions/crypto/openbb_crypto/price/__init__.py @@ -0,0 +1 @@ +"""OpenBB Crypto Price Router.""" diff --git a/openbb_platform/extensions/crypto/openbb_crypto/price/price_router.py b/openbb_platform/extensions/crypto/openbb_crypto/price/price_router.py new file mode 100644 index 0000000000000000000000000000000000000000..efce03361499b162f0c91c0b78ab5949c7b78fb1 --- /dev/null +++ b/openbb_platform/extensions/crypto/openbb_crypto/price/price_router.py @@ -0,0 +1,58 @@ +# pylint: disable=W0613:unused-argument +"""Crypto Price Router.""" + +from openbb_core.app.model.command_context import CommandContext +from openbb_core.app.model.example import APIEx +from openbb_core.app.model.obbject import OBBject +from openbb_core.app.provider_interface import ( + ExtraParams, + ProviderChoices, + StandardParams, +) +from openbb_core.app.query import Query +from openbb_core.app.router import Router + +router = Router(prefix="/price") + + +# pylint: disable=unused-argument,line-too-long +@router.command( + model="CryptoHistorical", + examples=[ + APIEx(parameters={"symbol": "BTCUSD", "provider": "fmp"}), + APIEx( + parameters={ + "symbol": "BTCUSD", + "start_date": "2024-01-01", + "end_date": "2024-01-31", + "provider": "fmp", + }, + ), + APIEx( + parameters={ + "symbol": "BTCUSD,ETHUSD", + "start_date": "2024-01-01", + "end_date": "2024-01-31", + "provider": "polygon", + }, + ), + APIEx( + description="Get monthly historical prices from Yahoo Finance for Ethereum.", + parameters={ + "symbol": "ETH-USD", + "interval": "1m", + "start_date": "2024-01-01", + "end_date": "2024-12-31", + "provider": "yfinance", + }, + ), + ], +) +async def historical( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Get historical price data for cryptocurrency pair(s) within a provider.""" + return await OBBject.from_query(Query(**locals())) diff --git a/openbb_platform/extensions/crypto/openbb_crypto/py.typed b/openbb_platform/extensions/crypto/openbb_crypto/py.typed new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/openbb_platform/extensions/crypto/poetry.lock b/openbb_platform/extensions/crypto/poetry.lock new file mode 100644 index 0000000000000000000000000000000000000000..8b5cef68b5cd92a25e397a3668ab2711aa485aac --- /dev/null +++ b/openbb_platform/extensions/crypto/poetry.lock @@ -0,0 +1,1734 @@ +# This file is automatically @generated by Poetry 2.1.3 and should not be changed by hand. + +[[package]] +name = "aiohappyeyeballs" +version = "2.6.1" +description = "Happy Eyeballs for asyncio" +optional = false +python-versions = ">=3.9" +groups = ["main"] +files = [ + {file = "aiohappyeyeballs-2.6.1-py3-none-any.whl", hash = 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b/openbb_platform/extensions/crypto/pyproject.toml new file mode 100644 index 0000000000000000000000000000000000000000..9e01a8fe828f104d8d6570e101ae75ec3a1c5e85 --- /dev/null +++ b/openbb_platform/extensions/crypto/pyproject.toml @@ -0,0 +1,22 @@ +[tool.poetry] +name = "openbb-crypto" +version = "1.4.1" +description = "Crypto extension for OpenBB" +authors = ["OpenBB Team "] +license = "AGPL-3.0-only" +readme = "README.md" +packages = [{ include = "openbb_crypto" }] + +[tool.poetry.dependencies] +python = ">=3.9.21,<3.13" +openbb-core = "^1.4.6" + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" + +[tool.poetry.plugins."openbb_core_extension"] +crypto = "openbb_crypto.crypto_router:router" + +[tool.poetry.plugins."openbb_charting_extension"] +crypto = "openbb_crypto.crypto_views:CryptoViews" diff --git a/openbb_platform/extensions/crypto/tests/.gitkeep b/openbb_platform/extensions/crypto/tests/.gitkeep new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/openbb_platform/extensions/currency/README.md b/openbb_platform/extensions/currency/README.md new file mode 100644 index 0000000000000000000000000000000000000000..4a9ea0b235d8df599e76f75aad8947db8d271d92 --- /dev/null +++ b/openbb_platform/extensions/currency/README.md @@ -0,0 +1,13 @@ +# OpenBB Currency Extension + +This extension provides currency exchange related data for the OpenBB Platform. + +## Installation + +To install the extension, run the following command in this folder: + +```bash +pip install openbb-currency +``` + +Documentation available [here](https://docs.openbb.co/platform/developer_guide/contributing). diff --git a/openbb_platform/extensions/currency/integration/test_currency_api.py b/openbb_platform/extensions/currency/integration/test_currency_api.py new file mode 100644 index 0000000000000000000000000000000000000000..58c47d3af72d23d0ba432dcbebf43e1cdd1749b1 --- /dev/null +++ b/openbb_platform/extensions/currency/integration/test_currency_api.py @@ -0,0 +1,198 @@ +"""Test currency API endpoints.""" + +import base64 + +import pytest +import requests +from extensions.tests.conftest import parametrize +from openbb_core.env import Env +from openbb_core.provider.utils.helpers import get_querystring + +# pylint: disable=redefined-outer-name + + +@pytest.fixture(scope="session") +def headers(): + """Get the headers for the API request.""" + userpass = f"{Env().API_USERNAME}:{Env().API_PASSWORD}" + userpass_bytes = userpass.encode("ascii") + base64_bytes = base64.b64encode(userpass_bytes) + + return {"Authorization": f"Basic {base64_bytes.decode('ascii')}"} + + +@parametrize( + "params", + [ + ( + { + "provider": "polygon", + "query": "eur", + } + ), + ( + { + "provider": "fmp", + "query": "eur", + } + ), + ( + { + "provider": "intrinio", + "query": "eur", + } + ), + ], +) +@pytest.mark.integration +def test_currency_search(params, headers): + """Test the currency search endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/currency/search?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ( + { + "symbol": "EURUSD", + "interval": "1d", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + "provider": "fmp", + } + ), + ( + { + "interval": "1h", + "provider": "fmp", + "symbol": "EURUSD,USDJPY", + "start_date": None, + "end_date": None, + } + ), + ( + { + "interval": "1m", + "sort": "desc", + "limit": 49999, + "provider": "polygon", + "symbol": "EURUSD", + "start_date": "2023-01-01", + "end_date": "2023-01-10", + } + ), + ( + { + "interval": "1d", + "sort": "desc", + "limit": 49999, + "provider": "polygon", + "symbol": "EURUSD", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + } + ), + ( + { + "interval": "1d", + "provider": "yfinance", + "symbol": "EURUSD", + "start_date": "2023-01-01", + "end_date": "2023-01-10", + } + ), + ( + { + "interval": "1m", + "provider": "yfinance", + "symbol": "EURUSD", + "start_date": None, + "end_date": None, + } + ), + ( + { + "interval": "1h", + "provider": "tiingo", + "symbol": "EURUSD", + "start_date": "2023-05-21", + "end_date": "2023-06-06", + } + ), + ( + { + "interval": "1d", + "provider": "tiingo", + "symbol": "EURUSD", + "start_date": "2023-05-21", + "end_date": "2023-06-06", + } + ), + ], +) +@pytest.mark.integration +def test_currency_price_historical(params, headers): + """Test the currency historical price endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/currency/price/historical?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [({"provider": "ecb"})], +) +@pytest.mark.integration +def test_currency_reference_rates(params, headers): + """Test the currency reference rates endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/currency/reference_rates?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ( + { + "provider": "fmp", + "base": "USD,XAU", + "counter_currencies": "EUR,JPY,GBP", + "quote_type": "indirect", + } + ), + ( + { + "provider": "polygon", + "base": "USD,XAU", + "counter_currencies": "EUR,JPY,GBP", + "quote_type": "indirect", + } + ), + ], +) +@pytest.mark.integration +def test_currency_snapshots(params, headers): + """Test the currency snapshots endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/currency/snapshots?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 diff --git a/openbb_platform/extensions/currency/integration/test_currency_python.py b/openbb_platform/extensions/currency/integration/test_currency_python.py new file mode 100644 index 0000000000000000000000000000000000000000..62069b8796cdd0c95d17d60445f63f540b127bf4 --- /dev/null +++ b/openbb_platform/extensions/currency/integration/test_currency_python.py @@ -0,0 +1,183 @@ +"""Test currency extension.""" + +import pytest +from extensions.tests.conftest import parametrize +from openbb_core.app.model.obbject import OBBject + +# pylint: disable=redefined-outer-name +# pylint: disable=inconsistent-return-statements + + +@pytest.fixture(scope="session") +def obb(pytestconfig): + """Fixture to setup obb.""" + + if pytestconfig.getoption("markexpr") != "not integration": + import openbb # pylint: disable=import-outside-toplevel + + return openbb.obb + + +@parametrize( + "params", + [ + ( + { + "provider": "polygon", + "query": "eur", + } + ), + ( + { + "provider": "fmp", + "query": "eur", + } + ), + ( + { + "provider": "intrinio", + "query": "eur", + } + ), + ], +) +@pytest.mark.integration +def test_currency_search(params, obb): + """Test the currency search endpoint.""" + result = obb.currency.search(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ( + { + "symbol": "EURUSD", + "interval": "1d", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + "provider": "fmp", + } + ), + ( + { + "interval": "1h", + "provider": "fmp", + "symbol": "EURUSD,USDJPY", + "start_date": None, + "end_date": None, + } + ), + ( + { + "interval": "1m", + "sort": "desc", + "limit": 49999, + "provider": "polygon", + "symbol": "EURUSD", + "start_date": "2023-01-01", + "end_date": "2023-01-10", + } + ), + ( + { + "interval": "1d", + "sort": "desc", + "limit": 49999, + "provider": "polygon", + "symbol": "EURUSD", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + } + ), + ( + { + "interval": "1d", + "provider": "yfinance", + "symbol": "EURUSD", + "start_date": "2023-01-01", + "end_date": "2023-01-10", + } + ), + ( + { + "interval": "1m", + "provider": "yfinance", + "symbol": "EURUSD", + "start_date": None, + "end_date": None, + } + ), + ( + { + "interval": "1h", + "provider": "tiingo", + "symbol": "EURUSD", + "start_date": "2023-05-21", + "end_date": "2023-06-06", + } + ), + ( + { + "interval": "1d", + "provider": "tiingo", + "symbol": "EURUSD", + "start_date": "2023-05-21", + "end_date": "2023-06-06", + } + ), + ], +) +@pytest.mark.integration +def test_currency_price_historical(params, obb): + """Test the currency historical price endpoint.""" + result = obb.currency.price.historical(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [({"provider": "ecb"})], +) +@pytest.mark.integration +def test_currency_reference_rates(params, obb): + """Test the currency reference rates endpoint.""" + result = obb.currency.reference_rates(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.model_dump()["results"].items()) > 0 + + +@parametrize( + "params", + [ + ( + { + "provider": "fmp", + "base": "USD,XAU", + "counter_currencies": "EUR,JPY,GBP", + "quote_type": "indirect", + } + ), + ( + { + "provider": "polygon", + "base": "USD,XAU", + "counter_currencies": "EUR,JPY,GBP", + "quote_type": "indirect", + } + ), + ], +) +@pytest.mark.integration +def test_currency_snapshots(params, obb): + """Test the currency snapshots endpoint.""" + result = obb.currency.snapshots(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 diff --git a/openbb_platform/extensions/currency/openbb_currency/__init__.py b/openbb_platform/extensions/currency/openbb_currency/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..afa19f607ee26dd70a6929279b23368defe9798c --- /dev/null +++ b/openbb_platform/extensions/currency/openbb_currency/__init__.py @@ -0,0 +1 @@ +"""The Currency router init.""" diff --git a/openbb_platform/extensions/currency/openbb_currency/currency_router.py b/openbb_platform/extensions/currency/openbb_currency/currency_router.py new file mode 100644 index 0000000000000000000000000000000000000000..b6d8c63f5f37b44f086dc10e6bb6e7cd541a330b --- /dev/null +++ b/openbb_platform/extensions/currency/openbb_currency/currency_router.py @@ -0,0 +1,99 @@ +"""The Currency router.""" + +from openbb_core.app.model.command_context import CommandContext +from openbb_core.app.model.example import APIEx +from openbb_core.app.model.obbject import OBBject +from openbb_core.app.provider_interface import ( + ExtraParams, + ProviderChoices, + StandardParams, +) +from openbb_core.app.query import Query +from openbb_core.app.router import Router + +from openbb_currency.price.price_router import router as price_router + +router = Router(prefix="", description="Foreign exchange (FX) market data.") +router.include_router(price_router) + + +# pylint: disable=unused-argument +@router.command( + model="CurrencyPairs", + examples=[ + APIEx(parameters={"provider": "fmp"}), + APIEx( + description="Search for 'EUR' currency pair using 'intrinio' as provider.", + parameters={"provider": "intrinio", "query": "EUR"}, + ), + APIEx( + description="Search for terms using 'polygon' as provider.", + parameters={"provider": "polygon", "query": "EUR"}, + ), + ], +) +async def search( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Currency Search. + + Search available currency pairs. + Currency pairs are the national currencies from two countries coupled for trading on + the foreign exchange (FX) marketplace. + Both currencies will have exchange rates on which the trade will have its position basis. + All trading within the forex market, whether selling, buying, or trading, will take place through currency pairs. + (ref: Investopedia) + Major currency pairs include pairs such as EUR/USD, USD/JPY, GBP/USD, etc. + """ + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="CurrencyReferenceRates", + examples=[APIEx(parameters={"provider": "ecb"})], +) +async def reference_rates( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Get current, official, currency reference rates. + + Foreign exchange reference rates are the exchange rates set by a major financial institution or regulatory body, + serving as a benchmark for the value of currencies around the world. + These rates are used as a standard to facilitate international trade and financial transactions, + ensuring consistency and reliability in currency conversion. + They are typically updated on a daily basis and reflect the market conditions at a specific time. + Central banks and financial institutions often use these rates to guide their own exchange rates, + impacting global trade, loans, and investments. + """ + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="CurrencySnapshots", + examples=[ + APIEx(parameters={"provider": "fmp"}), + APIEx( + description="Get exchange rates from USD and XAU to EUR, JPY, and GBP using 'fmp' as provider.", + parameters={ + "provider": "fmp", + "base": "USD,XAU", + "counter_currencies": "EUR,JPY,GBP", + "quote_type": "indirect", + }, + ), + ], +) +async def snapshots( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Snapshots of currency exchange rates from an indirect or direct perspective of a base currency.""" + return await OBBject.from_query(Query(**locals())) diff --git a/openbb_platform/extensions/currency/openbb_currency/currency_views.py b/openbb_platform/extensions/currency/openbb_currency/currency_views.py new file mode 100644 index 0000000000000000000000000000000000000000..6a4eeab3743a219aeba4baf841ebc81b477b9caa --- /dev/null +++ b/openbb_platform/extensions/currency/openbb_currency/currency_views.py @@ -0,0 +1,22 @@ +"""Views for the Currency Extension.""" + +from typing import TYPE_CHECKING, Any, Dict, Tuple + +if TYPE_CHECKING: + from openbb_charting.core.openbb_figure import ( + OpenBBFigure, + ) + + +class CurrencyViews: + """Currency Views.""" + + @staticmethod + def currency_price_historical( # noqa: PLR0912 + **kwargs, + ) -> Tuple["OpenBBFigure", Dict[str, Any]]: + """Currency Price Historical Chart.""" + # pylint: disable=import-outside-toplevel + from openbb_charting.charts.price_historical import price_historical + + return price_historical(**kwargs) diff --git a/openbb_platform/extensions/currency/openbb_currency/price/__init__.py b/openbb_platform/extensions/currency/openbb_currency/price/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..ae5f605d1df6e2feed2ca2e15022358f77e50fe4 --- /dev/null +++ b/openbb_platform/extensions/currency/openbb_currency/price/__init__.py @@ -0,0 +1 @@ +"""The Currency price router init.""" diff --git a/openbb_platform/extensions/currency/openbb_currency/price/price_router.py b/openbb_platform/extensions/currency/openbb_currency/price/price_router.py new file mode 100644 index 0000000000000000000000000000000000000000..fe84f8c7d8c652fa21fca7caf9b079a7fa209793 --- /dev/null +++ b/openbb_platform/extensions/currency/openbb_currency/price/price_router.py @@ -0,0 +1,53 @@ +"""Price router for Currency.""" + +# pylint: disable=unused-argument +from openbb_core.app.model.command_context import CommandContext +from openbb_core.app.model.example import APIEx +from openbb_core.app.model.obbject import OBBject +from openbb_core.app.provider_interface import ( + ExtraParams, + ProviderChoices, + StandardParams, +) +from openbb_core.app.query import Query +from openbb_core.app.router import Router + +router = Router(prefix="/price") + + +# pylint: disable=unused-argument +@router.command( + model="CurrencyHistorical", + examples=[ + APIEx(parameters={"symbol": "EURUSD", "provider": "fmp"}), + APIEx( + description="Filter historical data with specific start and end date.", + parameters={ + "symbol": "EURUSD", + "start_date": "2023-01-01", + "end_date": "2023-12-31", + "provider": "fmp", + }, + ), + APIEx( + description="Get data with different granularity.", + parameters={"symbol": "EURUSD", "provider": "polygon", "interval": "15m"}, + ), + ], +) +async def historical( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """ + Currency Historical Price. Currency historical data. + + Currency historical prices refer to the past exchange rates of one currency against + another over a specific period. + This data provides insight into the fluctuations and trends in the foreign exchange market, + helping analysts, traders, and economists understand currency performance, + evaluate economic health, and make predictions about future movements. + """ + return await OBBject.from_query(Query(**locals())) diff --git a/openbb_platform/extensions/currency/openbb_currency/py.typed b/openbb_platform/extensions/currency/openbb_currency/py.typed new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/openbb_platform/extensions/currency/poetry.lock b/openbb_platform/extensions/currency/poetry.lock new file mode 100644 index 0000000000000000000000000000000000000000..8b5cef68b5cd92a25e397a3668ab2711aa485aac --- /dev/null +++ b/openbb_platform/extensions/currency/poetry.lock @@ -0,0 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b/openbb_platform/extensions/currency/pyproject.toml new file mode 100644 index 0000000000000000000000000000000000000000..fb0bc788a8320dbb9f8872d8fe43dddc35cafbd4 --- /dev/null +++ b/openbb_platform/extensions/currency/pyproject.toml @@ -0,0 +1,22 @@ +[tool.poetry] +name = "openbb-currency" +version = "1.4.1" +description = "Currency extension for OpenBB" +authors = ["OpenBB Team "] +license = "AGPL-3.0-only" +readme = "README.md" +packages = [{ include = "openbb_currency" }] + +[tool.poetry.dependencies] +python = ">=3.9.21,<3.13" +openbb-core = "^1.4.6" + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" + +[tool.poetry.plugins."openbb_core_extension"] +currency = "openbb_currency.currency_router:router" + +[tool.poetry.plugins."openbb_charting_extension"] +currency = "openbb_currency.currency_views:CurrencyViews" diff --git a/openbb_platform/extensions/currency/tests/.gitkeep b/openbb_platform/extensions/currency/tests/.gitkeep new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/openbb_platform/extensions/derivatives/README.md b/openbb_platform/extensions/derivatives/README.md new file mode 100644 index 0000000000000000000000000000000000000000..82a234fb1dbd1cec82202608057e2f837f8f4e2d --- /dev/null +++ b/openbb_platform/extensions/derivatives/README.md @@ -0,0 +1,13 @@ +# OpenBB Derivatives Extension + +This extension provides derivatives data for the OpenBB Platform. + +## Installation + +To install the extension, run the following command in this folder: + +```bash +pip install openbb-derivatives +``` + +Documentation available [here](https://docs.openbb.co/sdk). diff --git a/openbb_platform/extensions/derivatives/integration/test_derivatives_api.py b/openbb_platform/extensions/derivatives/integration/test_derivatives_api.py new file mode 100644 index 0000000000000000000000000000000000000000..836ac4d62ec335dd606e0ac86ae9aafbb904a92a --- /dev/null +++ b/openbb_platform/extensions/derivatives/integration/test_derivatives_api.py @@ -0,0 +1,224 @@ +"""API integration tests for the derivatives extension.""" + +import base64 + +import pytest +import requests +from extensions.tests.conftest import parametrize +from openbb_core.env import Env +from openbb_core.provider.utils.helpers import get_querystring + +# pylint: disable=too-many-lines,redefined-outer-name + + +@pytest.fixture(scope="session") +def headers(): + """Get the headers for the API request.""" + userpass = f"{Env().API_USERNAME}:{Env().API_PASSWORD}" + userpass_bytes = userpass.encode("ascii") + base64_bytes = base64.b64encode(userpass_bytes) + + return {"Authorization": f"Basic {base64_bytes.decode('ascii')}"} + + +@parametrize( + "params", + [ + ( + { + "provider": "intrinio", + "symbol": "AAPL", + "date": "2023-01-25", + "option_type": None, + "moneyness": "all", + "strike_gt": None, + "strike_lt": None, + "volume_gt": None, + "volume_lt": None, + "oi_gt": None, + "oi_lt": None, + "model": "black_scholes", + "show_extended_price": False, + "include_related_symbols": False, + "delay": "delayed", + } + ), + ({"provider": "cboe", "symbol": "AAPL", "use_cache": False}), + ({"provider": "tradier", "symbol": "AAPL"}), + ({"provider": "yfinance", "symbol": "AAPL"}), + ({"provider": "deribit", "symbol": "BTC"}), + ( + { + "provider": "tmx", + "symbol": "SHOP", + "date": "2022-12-28", + "use_cache": False, + } + ), + ], +) +@pytest.mark.integration +def test_derivatives_options_chains(params, headers): + """Test the options chains endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/derivatives/options/chains?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ( + { + "symbol": "AAPL", + "provider": "intrinio", + "start_date": "2023-11-20", + "end_date": None, + "min_value": None, + "max_value": None, + "trade_type": None, + "sentiment": "neutral", + "limit": 1000, + "source": "delayed", + } + ) + ], +) +@pytest.mark.integration +def test_derivatives_options_unusual(params, headers): + """Test the unusual options endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/derivatives/options/unusual?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ( + { + "provider": "yfinance", + "interval": "1d", + "symbol": "CL,BZ", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + "expiration": "2025-12", + } + ), + ( + { + "provider": "deribit", + "interval": "1d", + "symbol": "BTC,ETH", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + } + ), + ], +) +@pytest.mark.integration +def test_derivatives_futures_historical(params, headers): + """Test the futures historical endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/derivatives/futures/historical?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ( + { + "provider": "yfinance", + "symbol": "ES", + "date": None, + } + ), + ( + { + "provider": "cboe", + "symbol": "VX_EOD", + "date": "2024-06-25", + } + ), + ({"provider": "deribit", "date": None, "symbol": "BTC", "hours_ago": 12}), + ], +) +@pytest.mark.integration +def test_derivatives_futures_curve(params, headers): + """Test the futures curve endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/derivatives/futures/curve?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ({"provider": "intrinio", "date": None, "only_traded": True}), + ], +) +@pytest.mark.skip( + reason="This test is skipped because the download is excessively large." +) +def test_derivatives_options_snapshots(params, headers): + """Test the options snapshots endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/derivatives/options/snapshots?{query_str}" + result = requests.get(url, headers=headers, timeout=60) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ({"provider": "deribit"}), + ], +) +@pytest.mark.integration +def test_derivatives_futures_instruments(params, headers): + """Test the futures instruments endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/derivatives/futures/instruments?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ({"provider": "deribit", "symbol": "ETH-PERPETUAL"}), + ], +) +@pytest.mark.integration +def test_derivatives_futures_info(params, headers): + """Test the futures info endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/derivatives/futures/info?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 diff --git a/openbb_platform/extensions/derivatives/integration/test_derivatives_python.py b/openbb_platform/extensions/derivatives/integration/test_derivatives_python.py new file mode 100644 index 0000000000000000000000000000000000000000..989a6bcd4367e0d4ab7e6040916502b79d7488f3 --- /dev/null +++ b/openbb_platform/extensions/derivatives/integration/test_derivatives_python.py @@ -0,0 +1,198 @@ +"""Python interface integration tests for the derivatives extension.""" + +import pytest +from extensions.tests.conftest import parametrize +from openbb_core.app.model.obbject import OBBject + +# pylint: disable=too-many-lines,redefined-outer-name +# pylint: disable=import-outside-toplevel,inconsistent-return-statements + + +@pytest.fixture(scope="session") +def obb(pytestconfig): + """Fixture to setup obb.""" + if pytestconfig.getoption("markexpr") != "not integration": + import openbb + + return openbb.obb + + +@parametrize( + "params", + [ + ( + { + "provider": "intrinio", + "symbol": "AAPL", + "date": "2023-01-25", + "option_type": None, + "moneyness": "all", + "strike_gt": None, + "strike_lt": None, + "volume_gt": None, + "volume_lt": None, + "oi_gt": None, + "oi_lt": None, + "model": "black_scholes", + "show_extended_price": False, + "include_related_symbols": False, + "delay": "delayed", + } + ), + ({"provider": "cboe", "symbol": "AAPL", "use_cache": False}), + ({"provider": "tradier", "symbol": "AAPL"}), + ({"provider": "yfinance", "symbol": "AAPL"}), + ({"provider": "deribit", "symbol": "BTC"}), + ( + { + "provider": "tmx", + "symbol": "SHOP", + "date": "2022-12-28", + "use_cache": False, + } + ), + ], +) +@pytest.mark.integration +def test_derivatives_options_chains(params, obb): + """Test the options chains endpoint.""" + result = obb.derivatives.options.chains(**params) + assert result + assert isinstance(result, OBBject) + result = result.results # type: ignore + list_msg = "Unexpected data format, expected List" + oi_msg = "Unexpected keys in total_oi property, expected ['total', 'expiration', 'strike']" + assert isinstance(result.expirations, list), list_msg # type: ignore + assert isinstance(result.strikes, list), list_msg # type: ignore + assert isinstance(result.contract_symbol, list), list_msg # type: ignore + assert hasattr(result, "total_oi"), "Missing total_oi property" # type: ignore + assert isinstance(result.total_oi, dict), "Unexpected property format, expected dictionary." # type: ignore + assert list(result.total_oi) == ["total", "expiration", "strike"], oi_msg # type: ignore + assert hasattr(result, "dataframe"), "Missing dataframe attribute" # type: ignore + assert result.has_iv, "Expected implied volatility data" # type: ignore + assert len(getattr(result, "dataframe", [])) == len(result.contract_symbol) # type: ignore + + +@parametrize( + "params", + [ + ( + { + "symbol": "AAPL", + "provider": "intrinio", + "start_date": "2023-11-20", + "end_date": None, + "min_value": None, + "max_value": None, + "trade_type": None, + "sentiment": "neutral", + "limit": 1000, + "source": "delayed", + } + ) + ], +) +@pytest.mark.integration +def test_derivatives_options_unusual(params, obb): + """Test the unusual options endpoint.""" + result = obb.derivatives.options.unusual(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ( + { + "provider": "yfinance", + "interval": "1d", + "symbol": "CL,BZ", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + "expiration": "2025-12", + } + ), + ( + { + "provider": "deribit", + "interval": "1d", + "symbol": "BTC,ETH", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + } + ), + ], +) +@pytest.mark.integration +def test_derivatives_futures_historical(params, obb): + """Test the futures historical endpoint.""" + result = obb.derivatives.futures.historical(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ({"provider": "yfinance", "symbol": "ES", "date": None}), + ({"provider": "cboe", "symbol": "VX", "date": "2024-06-25"}), + ({"provider": "deribit", "date": None, "symbol": "BTC", "hours_ago": 12}), + ], +) +@pytest.mark.integration +def test_derivatives_futures_curve(params, obb): + """Test the futures curve endpoint.""" + result = obb.derivatives.futures.curve(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ({"provider": "intrinio", "date": None, "only_traded": True}), + ], +) +@pytest.mark.skip( + reason="This test is skipped because the download is excessively large." +) +def test_derivatives_options_snapshots(params, obb): + """Test the options snapshots endpoint.""" + result = obb.derivatives.options.snapshots(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ({"provider": "deribit"}), + ], +) +@pytest.mark.integration +def test_derivatives_futures_instruments(params, obb): + """Test the futures instruments endpoint.""" + result = obb.derivatives.futures.instruments(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ({"provider": "deribit", "symbol": "ETH-PERPETUAL"}), + ], +) +@pytest.mark.integration +def test_derivatives_futures_info(params, obb): + """Test the futures info endpoint.""" + result = obb.derivatives.futures.info(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 diff --git a/openbb_platform/extensions/derivatives/openbb_derivatives/__init__.py b/openbb_platform/extensions/derivatives/openbb_derivatives/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..40c25febcec3ceac6e1efee6b1434327c6d455a9 --- /dev/null +++ b/openbb_platform/extensions/derivatives/openbb_derivatives/__init__.py @@ -0,0 +1 @@ +"""Options.""" diff --git a/openbb_platform/extensions/derivatives/openbb_derivatives/derivatives_router.py b/openbb_platform/extensions/derivatives/openbb_derivatives/derivatives_router.py new file mode 100644 index 0000000000000000000000000000000000000000..040720ecd60e661f9b08386246f88d92ffcc5c20 --- /dev/null +++ b/openbb_platform/extensions/derivatives/openbb_derivatives/derivatives_router.py @@ -0,0 +1,10 @@ +"""Derivatives Router.""" + +from openbb_core.app.router import Router + +from openbb_derivatives.futures.futures_router import router as futures_router +from openbb_derivatives.options.options_router import router as options_router + +router = Router(prefix="", description="Derivatives market data.") +router.include_router(options_router) +router.include_router(futures_router) diff --git a/openbb_platform/extensions/derivatives/openbb_derivatives/derivatives_views.py b/openbb_platform/extensions/derivatives/openbb_derivatives/derivatives_views.py new file mode 100644 index 0000000000000000000000000000000000000000..84dfbeda63cc1054571b34ef81c67a0f5c0ef28c --- /dev/null +++ b/openbb_platform/extensions/derivatives/openbb_derivatives/derivatives_views.py @@ -0,0 +1,244 @@ +"""Views for the Derivatives Extension.""" + +from typing import TYPE_CHECKING, Any, Dict, Tuple + +if TYPE_CHECKING: + from openbb_charting.core.openbb_figure import OpenBBFigure + + +class DerivativesViews: + """Derivatives Views.""" + + @staticmethod + def derivatives_futures_historical( # noqa: PLR0912 + **kwargs, + ) -> Tuple["OpenBBFigure", Dict[str, Any]]: + """Get Derivatives Price Historical Chart.""" + # pylint: disable=import-outside-toplevel + from openbb_charting.charts.price_historical import price_historical + + kwargs.update({"candles": False, "same_axis": False}) + + return price_historical(**kwargs) + + @staticmethod + def derivatives_futures_curve( # noqa: PLR0912 + **kwargs, + ) -> Tuple["OpenBBFigure", Dict[str, Any]]: + """Futures curve chart. All parameters are optional, and are kwargs. + Parameters can be directly accessed from the function end point by + entering as a nested dictionary to the 'chart_params' key. + + From the API, `chart_params` must be passed as a JSON in the request body with `extra_params`. + + If using the chart post-request, the parameters are passed directly + as `key=value` pairs in the `charting.to_chart` or `charting.show` methods. + + Parameters + ---------- + data : Optional[Union[List[Data], DataFrame]] + Data for the chart. Required fields are: 'expiration' and 'price'. + Multiple dates will be plotted on the same chart. + If not supplied, the original OBBject.results will be used. + If a DataFrame is supplied, flat data is expected, without a set index. + title: Optional[str] + Title for the chart. If not supplied, a default title will be used. + colors: Optional[List[str]] + List of colors to use for the chart. If not supplied, the default colorway will be used. + Colors should be in hex format, or named Plotly colors. Invalid colors will raise a Plotly error. + layout_kwargs: Optional[Dict[str, Any]] + Additional layout parameters for the chart, passed directly to `figure.update_layout` before output. + See Plotly documentation for available options. + + Returns + ------- + Tuple[OpenBBFigure, Dict[str, Any]] + Tuple with the OpenBBFigure object, and the JSON-serialized content. + If using the API, only the JSON content will be returned. + + Examples + -------- + ```python + from openbb import obb + data = obb.derivatives.futures.curve(symbol="vx", provider="cboe", date=["2020-03-31", "2024-06-28"], chart=True) + data.show() + ``` + + Redraw the chart, from the same data, with a custom colorway and title: + + ```python + data.charting.to_chart(colors=["green", "red"], title="VIX Futures Curve - 2020 vs. 2024") + ``` + """ + # pylint: disable=import-outside-toplevel + from openbb_charting.core.chart_style import ChartStyle + from openbb_charting.core.openbb_figure import OpenBBFigure + from openbb_charting.styles.colors import LARGE_CYCLER + from openbb_core.app.model.abstract.error import OpenBBError + from openbb_core.provider.abstract.data import Data + from pandas import DataFrame, to_datetime + + data = kwargs.get("data") + symbol = kwargs.get("standard_params", {}).get("symbol", "") + df: DataFrame = DataFrame() + if data: + if isinstance(data, DataFrame) and not data.empty: # noqa: SIM108 + df = data + elif isinstance(data, (list, Data)): + df = DataFrame([d.model_dump(exclude_none=True, exclude_unset=True) for d in data]) # type: ignore + else: + pass + else: + df = DataFrame( + [ + d.model_dump(exclude_none=True, exclude_unset=True) # type: ignore + for d in kwargs["obbject_item"] + ] + if isinstance(kwargs.get("obbject_item"), list) + else kwargs["obbject_item"].model_dump(exclude_none=True, exclude_unset=True) # type: ignore + ) + + if df.empty: + raise OpenBBError("Error: No data to plot.") + + if "expiration" not in df.columns: + raise OpenBBError("Expiration field not found in the data.") + + if "price" not in df.columns: + raise ValueError("Price field not found in the data.") + + provider = kwargs.get("provider", "") + + if provider != "deribit": + df["expiration"] = df["expiration"].apply(to_datetime).dt.strftime("%b-%Y") + + if ( + provider == "cboe" + and "date" in df.columns + and len(df["date"].unique()) > 1 + and "symbol" in df.columns + ): + df["expiration"] = df.symbol + + # Use a complete list of expirations to categorize the x-axis across all dates. + expirations = df["expiration"].unique().tolist() + + # Use the supplied colors, if any. + colors = kwargs.get("colors", []) + if not colors: + colors = LARGE_CYCLER + color_count = 0 + + figure = OpenBBFigure().create_subplots(shared_xaxes=True) + figure.update_layout(ChartStyle().plotly_template.get("layout", {})) + text_color = "white" if ChartStyle().plt_style == "dark" else "black" + + def create_fig(figure, df, dates, color_count): + """Create a scatter for each date in the data.""" + for date in dates: + color = colors[color_count % len(colors)] + plot_df = ( + df[df["date"].astype(str) == date].copy() + if "date" in df.columns + else df.copy() + ) + plot_df = plot_df.drop( + columns=["date"] if "date" in plot_df.columns else [] + ).rename(columns={"expiration": "Expiration", "price": "Price"}) + figure.add_scatter( + x=plot_df["Expiration"], + y=plot_df["Price"], + mode="lines+markers", + name=date, + line=dict(width=3, color=color), + marker=dict(size=10, color=color), + hovertemplate=( + "Expiration: %{x}
Price: $%{y}" + if len(dates) == 1 + else "%{fullData.name}
Expiration: %{x}
Price: $%{y}" + ), + ) + color_count += 1 + return figure, color_count + + dates = ( + df.date.astype(str).unique().tolist() + if "date" in df.columns + else ["Current"] + ) + + if provider == "deribit" and "hours_ago" in df.columns: + dates = [ + str(d) + " Hours Ago" if d > 0 else "Current" + for d in df["hours_ago"].unique().tolist() + ] + df.loc[:, "date"] = df["hours_ago"].apply( + lambda x: str(x) + " Hours Ago" if x > 0 else "Current" + ) + figure, color_count = create_fig(figure, df, dates, color_count) + + # Set the title for the chart + title: str = "" + if provider == "cboe": + vx_eod_symbols = ["vx", "vix", "vx_eod", "^vix"] + title = ( + "VIX EOD Futures Curve" + if symbol.lower() in vx_eod_symbols + else "VIX Mid-Morning TWAP Futures Curve" + ) + if len(dates) == 1 and dates[0] != "Current": + title = f"{title} for {dates[0]}" + else: + title = f"{symbol.upper()} Futures Curve" + + # Use the supplied title, if any. + title = kwargs.get("title", title) + + # Update the layout of the figure. + figure.update_layout( + title=dict(text=title, x=0.5, font=dict(size=20)), + plot_bgcolor=( + "rgba(0,0,0,0)" if text_color == "white" else "rgba(255,255,255,0)" + ), + paper_bgcolor=( + "rgba(0,0,0,0)" if text_color == "white" else "rgba(255,255,255,0)" + ), + xaxis=dict( + title="", + ticklen=0, + showgrid=False, + type="category", + categoryorder="array", + categoryarray=expirations, + ), + yaxis=dict( + title="Price ($)", + ticklen=0, + showgrid=True, + gridcolor="rgba(128,128,128,0.3)", + ), + legend=dict( + orientation="v", + yanchor="top", + xanchor="right", + y=0.95, + x=0, + xref="paper", + font=dict(size=12), + bgcolor=( + "rgba(0,0,0,0)" if text_color == "white" else "rgba(255,255,255,0)" + ), + ), + margin=dict( + b=10, + t=10, + ), + ) + + layout_kwargs = kwargs.get("layout_kwargs", {}) + if layout_kwargs: + figure.update_layout(layout_kwargs) + + content = figure.show(external=True).to_plotly_json() + + return figure, content diff --git a/openbb_platform/extensions/derivatives/openbb_derivatives/futures/__init__.py b/openbb_platform/extensions/derivatives/openbb_derivatives/futures/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..ef3ba3e56722da1eafba0d3fd7221455d2246599 --- /dev/null +++ b/openbb_platform/extensions/derivatives/openbb_derivatives/futures/__init__.py @@ -0,0 +1 @@ +"""Futures.""" diff --git a/openbb_platform/extensions/derivatives/openbb_derivatives/futures/futures_router.py b/openbb_platform/extensions/derivatives/openbb_derivatives/futures/futures_router.py new file mode 100644 index 0000000000000000000000000000000000000000..7e456263a9b9d51ebfdf960b64f78fc05ad68567 --- /dev/null +++ b/openbb_platform/extensions/derivatives/openbb_derivatives/futures/futures_router.py @@ -0,0 +1,97 @@ +"""Futures Router.""" + +from openbb_core.app.model.command_context import CommandContext +from openbb_core.app.model.example import APIEx +from openbb_core.app.model.obbject import OBBject +from openbb_core.app.provider_interface import ( + ExtraParams, + ProviderChoices, + StandardParams, +) +from openbb_core.app.query import Query +from openbb_core.app.router import Router + +router = Router(prefix="/futures") + + +# pylint: disable=unused-argument +@router.command( + model="FuturesHistorical", + examples=[ + APIEx(parameters={"symbol": "ES", "provider": "yfinance"}), + APIEx( + description="Enter multiple symbols.", + parameters={"symbol": "ES,NQ", "provider": "yfinance"}, + ), + APIEx( + description='Enter expiration dates as "YYYY-MM".', + parameters={ + "symbol": "ES", + "provider": "yfinance", + "expiration": "2025-12", + }, + ), + ], +) +async def historical( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Historical futures prices.""" + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="FuturesCurve", + examples=[ + APIEx(parameters={"symbol": "VX", "provider": "cboe", "date": "2024-06-25"}), + APIEx( + parameters={"symbol": "NG", "provider": "yfinance"}, + ), + ], +) +async def curve( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Futures Term Structure, current or historical.""" + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="FuturesInstruments", + examples=[ + APIEx(parameters={"provider": "deribit"}), + ], +) +async def instruments( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Get reference data for available futures instruments by provider.""" + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="FuturesInfo", + examples=[ + APIEx(parameters={"provider": "deribit", "symbol": "BTC"}), + APIEx(parameters={"provider": "deribit", "symbol": "SOLUSDC"}), + APIEx(parameters={"provider": "deribit", "symbol": "SOL_USDC-PERPETUAL"}), + APIEx(parameters={"provider": "deribit", "symbol": "BTC,ETH"}), + ], +) +async def info( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Get current trading statistics by futures contract symbol.""" + return await OBBject.from_query(Query(**locals())) diff --git a/openbb_platform/extensions/derivatives/openbb_derivatives/options/__init__.py b/openbb_platform/extensions/derivatives/openbb_derivatives/options/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..40c25febcec3ceac6e1efee6b1434327c6d455a9 --- /dev/null +++ b/openbb_platform/extensions/derivatives/openbb_derivatives/options/__init__.py @@ -0,0 +1 @@ +"""Options.""" diff --git a/openbb_platform/extensions/derivatives/openbb_derivatives/options/options_router.py b/openbb_platform/extensions/derivatives/openbb_derivatives/options/options_router.py new file mode 100644 index 0000000000000000000000000000000000000000..bcdb00be4b7eeb9d6e944b95af505350edd8d700 --- /dev/null +++ b/openbb_platform/extensions/derivatives/openbb_derivatives/options/options_router.py @@ -0,0 +1,74 @@ +"""Options Router.""" + +from openbb_core.app.model.command_context import CommandContext +from openbb_core.app.model.example import APIEx +from openbb_core.app.model.obbject import OBBject +from openbb_core.app.provider_interface import ( + ExtraParams, + ProviderChoices, + StandardParams, +) +from openbb_core.app.query import Query +from openbb_core.app.router import Router + +router = Router(prefix="/options") + +# pylint: disable=unused-argument + + +@router.command( + model="OptionsChains", + examples=[ + APIEx(parameters={"symbol": "AAPL", "provider": "intrinio"}), + APIEx( + description='Use the "date" parameter to get the end-of-day-data for a specific date, where supported.', + parameters={"symbol": "AAPL", "date": "2023-01-25", "provider": "intrinio"}, + ), + ], +) +async def chains( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Get the complete options chain for a ticker.""" + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="OptionsUnusual", + examples=[ + APIEx(parameters={"symbol": "TSLA", "provider": "intrinio"}), + APIEx( + description="Use the 'symbol' parameter to get the most recent activity for a specific symbol.", + parameters={"symbol": "TSLA", "provider": "intrinio"}, + ), + ], +) +async def unusual( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Get the complete options chain for a ticker.""" + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="OptionsSnapshots", + examples=[ + APIEx( + parameters={"provider": "intrinio"}, + ), + ], +) +async def snapshots( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Get a snapshot of the options market universe.""" + return await OBBject.from_query(Query(**locals())) diff --git a/openbb_platform/extensions/derivatives/poetry.lock b/openbb_platform/extensions/derivatives/poetry.lock new file mode 100644 index 0000000000000000000000000000000000000000..8b5cef68b5cd92a25e397a3668ab2711aa485aac --- /dev/null +++ b/openbb_platform/extensions/derivatives/poetry.lock @@ -0,0 +1,1734 @@ +# This file is automatically @generated by Poetry 2.1.3 and should not be changed by hand. + +[[package]] +name = "aiohappyeyeballs" +version = "2.6.1" +description = "Happy Eyeballs for asyncio" +optional = false +python-versions = ">=3.9" +groups = ["main"] +files = [ + {file = "aiohappyeyeballs-2.6.1-py3-none-any.whl", hash = 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b/openbb_platform/extensions/derivatives/pyproject.toml new file mode 100644 index 0000000000000000000000000000000000000000..708daaf72dfe52c2d730b4f045e14ac00fab129b --- /dev/null +++ b/openbb_platform/extensions/derivatives/pyproject.toml @@ -0,0 +1,22 @@ +[tool.poetry] +name = "openbb-derivatives" +version = "1.4.1" +description = "Derivatives extension for OpenBB" +authors = ["OpenBB Team "] +license = "AGPL-3.0-only" +readme = "README.md" +packages = [{ include = "openbb_derivatives" }] + +[tool.poetry.dependencies] +python = ">=3.9.21,<3.13" +openbb-core = "^1.4.6" + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" + +[tool.poetry.plugins."openbb_core_extension"] +derivatives = "openbb_derivatives.derivatives_router:router" + +[tool.poetry.plugins."openbb_charting_extension"] +derivatives = "openbb_derivatives.derivatives_views:DerivativesViews" diff --git a/openbb_platform/extensions/derivatives/tests/.gitkeep b/openbb_platform/extensions/derivatives/tests/.gitkeep new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/openbb_platform/extensions/devtools/README.md b/openbb_platform/extensions/devtools/README.md new file mode 100644 index 0000000000000000000000000000000000000000..cbfd9fb15f1491993449bb2eb29d6e744884b460 --- /dev/null +++ b/openbb_platform/extensions/devtools/README.md @@ -0,0 +1,22 @@ +# The OpenBB DevTools Extension + +This extension aggregates the dependencies that facilitate a nice development experience +for OpenBB. It does not contain any code itself, but rather pulls in the following dependencies: + +- Linters (ruff, pylint, mypy) +- Code formatters (black) +- Code quality tools (bandit) +- Pre-commit hooks (pre-commit) +- CI/CD configuration (tox, pytest, pytest-cov) +- Jupyter kernel (ipykernel) +- ... add your productivity booster here ... + +## Installation + +The extension is included into the dev_install.py script. + +Standalone installation: + +```bash +pip install openbb-devtools +``` diff --git a/openbb_platform/extensions/devtools/integration/.gitkeep b/openbb_platform/extensions/devtools/integration/.gitkeep new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/openbb_platform/extensions/devtools/openbb_devtools/__init__.py b/openbb_platform/extensions/devtools/openbb_devtools/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..3729885af2817c7ce825e85d7c527ec22d3016fb --- /dev/null +++ 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+readme = "README.md" +packages = [{ include = "openbb_devtools" }] + +[tool.poetry.dependencies] +python = ">=3.9.21,<3.13" # scipy forces <4.0 explicitly +ruff = "^0.7" +pylint = "^3.3" +mypy = "^1.12.1" +pydocstyle = "^6.3.0" +black = "^25.1.0" +bandit = "^1.7.5" +codespell = "^2.2.5" +pre-commit = "^3.5.0" +tox = "^4.11.3" +pytest = "^7.4.3" +pytest-subtests = "^0.11.0" +pytest-recorder = "^0.4.6" +pytest-asyncio = "^0.23.2" +pytest-cov = "^4.1.0" +ipykernel = "^6.29.5" +types-python-dateutil = "^2.8.19.14" +types-toml = "^0.10.8.7" +poetry = ">=1.8.0" + + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" diff --git a/openbb_platform/extensions/econometrics/README.md b/openbb_platform/extensions/econometrics/README.md new file mode 100644 index 0000000000000000000000000000000000000000..85e451a12e9324b21f2afe8f63488246253d026e --- /dev/null +++ b/openbb_platform/extensions/econometrics/README.md @@ -0,0 +1,13 @@ +# Econometrics extension for OpenBB Platform + +This extension provides a set of econometrics tools. + +## Installation + +To install the extension, run the following command in this folder: + +```bash +pip install openbb-econometrics +``` + +Documentation available [here](https://docs.openbb.co/platform/developer_guide/contributing). diff --git a/openbb_platform/extensions/econometrics/integration/test_econometrics_api.py b/openbb_platform/extensions/econometrics/integration/test_econometrics_api.py new file mode 100644 index 0000000000000000000000000000000000000000..2048f4486dbac8db7db35494a1e80381013c3091 --- /dev/null +++ b/openbb_platform/extensions/econometrics/integration/test_econometrics_api.py @@ -0,0 +1,278 @@ +"""Test econometrics extension.""" + +import base64 +import json +import random +from typing import Dict, Literal + +import pytest +import requests +from openbb_core.env import Env +from openbb_core.provider.utils.helpers import get_querystring + +data: Dict = {} + + +def get_headers(): + """Get the headers for the API request.""" + if "headers" in data: + return data["headers"] + + userpass = f"{Env().API_USERNAME}:{Env().API_PASSWORD}" + userpass_bytes = userpass.encode("ascii") + base64_bytes = base64.b64encode(userpass_bytes) + + data["headers"] = {"Authorization": f"Basic {base64_bytes.decode('ascii')}"} + return data["headers"] + + +def request_data(menu: str, symbol: str, provider: str): + """Randomly pick a symbol and a provider and get data from the selected menu.""" + url = f"http://0.0.0.0:8000/api/v1/{menu}/price/historical?symbol={symbol}&provider={provider}" + result = requests.get(url, headers=get_headers(), timeout=10) + return result.json()["results"] + + +def get_equity_data(): + """Get equity data.""" + if "equity_data" in data: + return data["equity_data"] + + symbol = random.choice(["AAPL", "NVDA", "MSFT", "TSLA", "AMZN", "V"]) # noqa: S311 + provider = random.choice(["fmp", "intrinio", "polygon"]) # noqa: S311 + + data["equity_data"] = request_data("equity", symbol=symbol, provider=provider) + return data["equity_data"] + + +def get_crypto_data(): + """Get crypto data.""" + if "crypto_data" in data: + return data["crypto_data"] + + # TODO : add more crypto providers and symbols + symbol = random.choice(["BTCUSD"]) # noqa: S311 + provider = random.choice(["fmp"]) # noqa: S311 + + data["crypto_data"] = request_data( + menu="crypto", + symbol=symbol, + provider=provider, + ) + return data["crypto_data"] + + +def get_data(menu: Literal["equity", "crypto"]): + """Get data based on the selected menu.""" + funcs = {"equity": get_equity_data, "crypto": get_crypto_data} + return funcs[menu]() + + +@pytest.mark.parametrize( + "params, data_type", + [ + ({"data": "", "method": "pearson"}, "equity"), + ({"data": "", "method": "pearson"}, "crypto"), + ], +) +@pytest.mark.integration +def test_econometrics_correlation_matrix(params, data_type): + """Test the correlation matrix endpoint.""" + params = {p: v for p, v in params.items() if v} + + body = json.dumps(get_data(data_type)) + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/econometrics/correlation_matrix?{query_str}" + result = requests.post(url, headers=get_headers(), timeout=10, data=body) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@pytest.mark.parametrize( + "params, data_type", + [ + ( + {"data": "", "y_column": "close", "x_columns": ["high"]}, + "equity", + ), + ( + {"data": "", "y_column": "close", "x_columns": ["high"]}, + "crypto", + ), + ], +) +@pytest.mark.integration +def test_econometrics_ols_regression_summary(params, data_type): + """Test the OLS regression summary endpoint.""" + params = {p: v for p, v in params.items() if v} + + body = json.dumps( + { + "data": get_data(data_type), + "x_columns": params.pop("x_columns"), + } + ) + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/econometrics/ols_regression_summary?{query_str}" + result = requests.post(url, headers=get_headers(), timeout=20, data=body) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@pytest.mark.parametrize( + "params, data_type", + [ + ( + {"data": "", "y_column": "volume", "x_columns": ["close"]}, + "equity", + ), + ( + {"data": "", "y_column": "volume", "x_columns": ["close"]}, + "crypto", + ), + ], +) +@pytest.mark.integration +def test_econometrics_autocorrelation(params, data_type): + """Test the autocorrelation endpoint.""" + params = {p: v for p, v in params.items() if v} + + body = json.dumps( + { + "data": get_data(data_type), + "x_columns": params.pop("x_columns"), + } + ) + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/econometrics/autocorrelation?{query_str}" + result = requests.post(url, headers=get_headers(), timeout=10, data=body) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@pytest.mark.parametrize( + "params, data_type", + [ + ( + { + "data": "", + "y_column": "volume", + "x_columns": ["close"], + "lags": "", + }, + "equity", + ), + ( + { + "data": "", + "y_column": "volume", + "x_columns": ["close"], + "lags": "2", + }, + "crypto", + ), + ], +) +@pytest.mark.integration +def test_econometrics_residual_autocorrelation(params, data_type): + """Test the residual autocorrelation endpoint.""" + params = {p: v for p, v in params.items() if v} + + body = json.dumps( + { + "data": get_data(data_type), + "x_columns": params.pop("x_columns"), + } + ) + + query_str = get_querystring(params, []) + url = ( + f"http://0.0.0.0:8000/api/v1/econometrics/residual_autocorrelation?{query_str}" + ) + result = requests.post(url, headers=get_headers(), timeout=10, data=body) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@pytest.mark.parametrize( + "params, data_type", + [ + ( + { + "data": "", + "columns": ["close", "volume"], + }, + "equity", + ), + ( + { + "data": "", + "columns": ["close", "volume"], + }, + "crypto", + ), + ], +) +@pytest.mark.integration +def test_econometrics_cointegration(params, data_type): + """Test the cointegration endpoint.""" + params = {p: v for p, v in params.items() if v} + + body = json.dumps( + { + "data": get_data(data_type), + "columns": params.pop("columns"), + } + ) + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/econometrics/cointegration?{query_str}" + result = requests.post(url, headers=get_headers(), timeout=10, data=body) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@pytest.mark.parametrize( + "params, data_type", + [ + ( + {"data": "", "y_column": "volume", "x_column": "close", "lag": ""}, + "equity", + ), + ( + {"data": "", "y_column": "volume", "x_column": "close", "lag": "2"}, + "crypto", + ), + ], +) +@pytest.mark.integration +def test_econometrics_causality(params, data_type): + """Test the causality endpoint.""" + params = {p: v for p, v in params.items() if v} + body = json.dumps(get_data(data_type)) + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/econometrics/causality?{query_str}" + result = requests.post(url, headers=get_headers(), timeout=10, data=body) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@pytest.mark.parametrize( + "params, data_type", + [({"data": "", "column": "high", "regression": "c"}, "equity")], +) +@pytest.mark.integration +def test_econometrics_unit_root(params, data_type): + """Test the unit root endpoint.""" + params = {p: v for p, v in params.items() if v} + body = json.dumps(get_data(data_type)) + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/econometrics/unit_root?{query_str}" + result = requests.post(url, headers=get_headers(), timeout=10, data=body) + assert isinstance(result, requests.Response) + assert result.status_code == 200 diff --git a/openbb_platform/extensions/econometrics/integration/test_econometrics_python.py b/openbb_platform/extensions/econometrics/integration/test_econometrics_python.py new file mode 100644 index 0000000000000000000000000000000000000000..f0635290bb110dff2bcaeb402baed1f98a7cc310 --- /dev/null +++ b/openbb_platform/extensions/econometrics/integration/test_econometrics_python.py @@ -0,0 +1,424 @@ +"""Test econometrics extension.""" + +import random +from typing import Literal + +import pytest +from extensions.tests.conftest import parametrize +from openbb_core.app.model.obbject import OBBject +from openbb_econometrics.utils import mock_multi_index_data + + +# pylint: disable=inconsistent-return-statements +@pytest.fixture(scope="session") +def obb(pytestconfig): + """Fixture to setup obb.""" + if pytestconfig.getoption("markexpr") != "not integration": + import openbb # pylint: disable=import-outside-toplevel + + return openbb.obb + + +# pylint: disable=redefined-outer-name + +data: dict = {} + + +def get_stocks_data(): + """Get stocks data.""" + import openbb # pylint:disable=import-outside-toplevel + + if "stocks_data" in data: + return data["stocks_data"] + + symbol = random.choice(["AAPL", "NVDA", "MSFT", "TSLA", "AMZN", "V"]) # noqa: S311 + provider = random.choice(["fmp", "polygon"]) # noqa: S311 + + data["stocks_data"] = openbb.obb.equity.price.historical( + symbol=symbol, provider=provider + ).results + return data["stocks_data"] + + +def get_crypto_data(): + """Get crypto data.""" + import openbb # pylint:disable=import-outside-toplevel + + if "crypto_data" in data: + return data["crypto_data"] + + # TODO : add more crypto providers and symbols + symbol = random.choice(["BTCUSD"]) # noqa: S311 + provider = random.choice(["fmp"]) # noqa: S311 + + data["crypto_data"] = openbb.obb.crypto.price.historical( + symbol=symbol, provider=provider + ).results + return data["crypto_data"] + + +def get_data(menu: Literal["equity", "crypto"]): + """Get data.""" + funcs = {"equity": get_stocks_data, "crypto": get_crypto_data} + return funcs[menu]() + + +@parametrize( + "params, data_type", + [ + ({"data": "", "method": "pearson"}, "equity"), + ({"data": "", "method": "pearson"}, "crypto"), + ], +) +@pytest.mark.integration +def test_econometrics_correlation_matrix(params, data_type, obb): + """Test the econometrics correlation matrix.""" + params = {p: v for p, v in params.items() if v} + + params["data"] = get_data(data_type) + + result = obb.econometrics.correlation_matrix(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params, data_type", + [ + ( + {"data": "", "y_column": "close", "x_columns": ["high"]}, + "equity", + ), + ( + {"data": "", "y_column": "close", "x_columns": ["high"]}, + "crypto", + ), + ], +) +@pytest.mark.integration +def test_econometrics_ols_regression(params, data_type, obb): + """Test the econometrics OLS regression.""" + params = {p: v for p, v in params.items() if v} + + params["data"] = get_data(data_type) + + result = obb.econometrics.ols_regression(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params, data_type", + [ + ( + {"data": "", "y_column": "close", "x_columns": ["high"]}, + "equity", + ), + ( + {"data": "", "y_column": "close", "x_columns": ["high"]}, + "crypto", + ), + ], +) +@pytest.mark.integration +def test_econometrics_ols_regression_summary(params, data_type, obb): + """Test the econometrics OLS regression summary.""" + params = {p: v for p, v in params.items() if v} + params["data"] = get_data(data_type) + + result = obb.econometrics.ols_regression_summary(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params, data_type", + [ + ( + {"data": "", "y_column": "volume", "x_columns": ["close"]}, + "equity", + ), + ( + {"data": "", "y_column": "volume", "x_columns": ["close"]}, + "crypto", + ), + ], +) +@pytest.mark.integration +def test_econometrics_autocorrelation(params, data_type, obb): + """Test the econometrics autocorrelation.""" + params = {p: v for p, v in params.items() if v} + + params["data"] = get_data(data_type) + + result = obb.econometrics.autocorrelation(**params) + assert result + assert isinstance(result, OBBject) + + +@parametrize( + "params, data_type", + [ + ( + { + "data": "", + "y_column": "volume", + "x_columns": ["close"], + "lags": "", + }, + "equity", + ), + ( + { + "data": "", + "y_column": "volume", + "x_columns": ["close"], + "lags": "2", + }, + "crypto", + ), + ], +) +@pytest.mark.integration +def test_econometrics_residual_autocorrelation(params, data_type, obb): + """Test the econometrics residual autocorrelation.""" + params = {p: v for p, v in params.items() if v} + + params["data"] = get_data(data_type) + + result = obb.econometrics.residual_autocorrelation(**params) + assert result + assert isinstance(result, OBBject) + + +@parametrize( + "params, data_type", + [ + ( + { + "data": "", + "columns": ["close", "volume"], + }, + "equity", + ), + ( + { + "data": "", + "columns": ["close", "volume"], + }, + "crypto", + ), + ], +) +@pytest.mark.integration +def test_econometrics_cointegration(params, data_type, obb): + """Test the econometrics cointegration.""" + params = {p: v for p, v in params.items() if v} + + params["data"] = get_data(data_type) + + result = obb.econometrics.cointegration(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params, data_type", + [ + ( + { + "data": "", + "y_column": "volume", + "x_column": "close", + "lag": "", + }, + "equity", + ), + ( + { + "data": "", + "y_column": "volume", + "x_column": "close", + "lag": "2", + }, + "crypto", + ), + ], +) +@pytest.mark.integration +def test_econometrics_causality(params, data_type, obb): + """Test the econometrics causality.""" + params = {p: v for p, v in params.items() if v} + + params["data"] = get_data(data_type) + + result = obb.econometrics.causality(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params, data_type", + [ + ({"data": "", "column": "close", "regression": "c"}, "equity"), + ( + {"data": "", "column": "volume", "regression": "ctt"}, + "crypto", + ), + ], +) +@pytest.mark.integration +def test_econometrics_unit_root(params, data_type, obb): + """Test the econometrics unit root.""" + params = {p: v for p, v in params.items() if v} + + params["data"] = get_data(data_type) + + result = obb.econometrics.unit_root(**params) + assert result + assert isinstance(result, OBBject) + + +@parametrize( + "params", + [ + {"data": "", "y_column": "income", "x_columns": ["age"]}, + {"data": "", "y_column": "income", "x_columns": ["age"]}, + ], +) +@pytest.mark.integration +def test_econometrics_panel_random_effects(params, obb): + """Test the econometrics panel random effects.""" + params = {p: v for p, v in params.items() if v} + + params["data"] = mock_multi_index_data() + + result = obb.econometrics.panel_random_effects(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + {"data": "", "y_column": "income", "x_columns": ["age"]}, + {"data": "", "y_column": "income", "x_columns": ["age"]}, + ], +) +@pytest.mark.integration +def test_econometrics_panel_between(params, obb): + """Test the econometrics panel between.""" + params = {p: v for p, v in params.items() if v} + + params["data"] = mock_multi_index_data() + + result = obb.econometrics.panel_between(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + {"data": "", "y_column": "income", "x_columns": ["age"]}, + {"data": "", "y_column": "income", "x_columns": ["age"]}, + ], +) +@pytest.mark.integration +def test_econometrics_panel_pooled(params, obb): + """Test the econometrics panel pooled.""" + params = {p: v for p, v in params.items() if v} + + params["data"] = mock_multi_index_data() + + result = obb.econometrics.panel_pooled(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + {"data": "", "y_column": "income", "x_columns": ["age"]}, + {"data": "", "y_column": "income", "x_columns": ["age"]}, + ], +) +@pytest.mark.integration +def test_econometrics_panel_fixed(params, obb): + """Test the econometrics panel fixed.""" + params = {p: v for p, v in params.items() if v} + + params["data"] = mock_multi_index_data() + + result = obb.econometrics.panel_fixed(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + {"data": "", "y_column": "income", "x_columns": ["age"]}, + {"data": "", "y_column": "income", "x_columns": ["age"]}, + ], +) +@pytest.mark.integration +def test_econometrics_panel_first_difference(params, obb): + """Test the econometrics panel first difference.""" + params = {p: v for p, v in params.items() if v} + + params["data"] = mock_multi_index_data() + + result = obb.econometrics.panel_first_difference(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + {"data": "", "y_column": "income", "x_columns": ["age"]}, + {"data": "", "y_column": "income", "x_columns": ["age"]}, + ], +) +@pytest.mark.integration +def test_econometrics_panel_fmac(params, obb): + """Test the econometrics panel fmac.""" + params = {p: v for p, v in params.items() if v} + + params["data"] = mock_multi_index_data() + + result = obb.econometrics.panel_fmac(**params) + """Test the econometrics panel fmac.""" + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@pytest.mark.parametrize( + "params", + [ + ({"data": "", "columns": ["income", "age"]}), + ({"data": "", "columns": ["education"]}), + ], +) +@pytest.mark.integration +def test_econometrics_variance_inflation_factor(params, obb): + params = {p: v for p, v in params.items() if v} + + params["data"] = mock_multi_index_data() + + result = obb.econometrics.variance_inflation_factor(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 diff --git a/openbb_platform/extensions/econometrics/openbb_econometrics/__init__.py b/openbb_platform/extensions/econometrics/openbb_econometrics/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..cd48520387fb28578cb1611bcd006279ec21c09c --- /dev/null +++ b/openbb_platform/extensions/econometrics/openbb_econometrics/__init__.py @@ -0,0 +1 @@ +"""OpenBB Econometrics Extension.""" diff --git a/openbb_platform/extensions/econometrics/openbb_econometrics/econometrics_router.py b/openbb_platform/extensions/econometrics/openbb_econometrics/econometrics_router.py new file mode 100644 index 0000000000000000000000000000000000000000..33b7b589883bda0b3345f0b670460666fbad1102 --- /dev/null +++ b/openbb_platform/extensions/econometrics/openbb_econometrics/econometrics_router.py @@ -0,0 +1,985 @@ +"""Econometrics Router.""" + +from itertools import combinations +from typing import Literal, Optional + +from openbb_core.app.model.example import APIEx, PythonEx +from openbb_core.app.model.obbject import OBBject +from openbb_core.app.router import Router +from openbb_core.provider.abstract.data import Data +from pydantic import PositiveInt + +router = Router(prefix="", description="Econometrics analysis tools.") + + +@router.command( + methods=["POST"], + examples=[ + PythonEx( + description="Get the correlation matrix of a dataset.", + code=[ + "stock_data = obb.equity.price.historical(symbol='TSLA', start_date='2023-01-01', provider='fmp').to_df()", # noqa: E501 + "obb.econometrics.correlation_matrix(data=stock_data)", + ], + ), + APIEx(parameters={"data": APIEx.mock_data("timeseries")}), + ], +) +def correlation_matrix( + data: list[Data], method: Literal["pearson", "kendall", "spearman"] = "pearson" +) -> OBBject[list[Data]]: + """Get the correlation matrix of an input dataset. + + The correlation matrix provides a view of how different variables in your dataset relate to one another. + By quantifying the degree to which variables move in relation to each other, this matrix can help identify patterns, + trends, and potential areas for deeper analysis. The correlation score ranges from -1 to 1, with -1 indicating a + perfect negative correlation, 0 indicating no correlation, and 1 indicating a perfect positive correlation. + + Parameters + ---------- + data : list[Data] + Input dataset. + method : Literal["pearson", "kendall", "spearman"] + Method to use for correlation calculation. Default is "pearson". + pearson : standard correlation coefficient + kendall : Kendall Tau correlation coefficient + spearman : Spearman rank correlation + + Returns + ------- + OBBject[list[Data]] + Correlation matrix. + """ + # pylint: disable=import-outside-toplevel + import numpy as np + from openbb_core.app.utils import basemodel_to_df + + df = basemodel_to_df(data) + # remove non float columns from the dataframe to perform the correlation + + if "symbol" in df.columns and len(df.symbol.unique()) > 1 and "close" in df.columns: + df = df.pivot( + columns="symbol", + values="close", + ) + + corr = df.corr(method=method, numeric_only=True) + + # replace nan values with None to allow for json serialization + corr = corr.replace(np.nan, None) + + ret = [] + for k, v in corr.items(): + v["comp_to"] = k + ret.append(Data(**v)) + return OBBject(results=ret) + + +@router.command( + methods=["POST"], + include_in_schema=False, + examples=[ + PythonEx( + description="Perform Ordinary Least Squares (OLS) regression.", + code=[ + "stock_data = obb.equity.price.historical(symbol='TSLA', start_date='2023-01-01', provider='fmp').to_df()", # noqa: E501 + 'obb.econometrics.ols_regression(data=stock_data, y_column="close", x_columns=["open", "high", "low"])', + ], + ), + APIEx( + parameters={ + "y_column": "close", + "x_columns": ["open", "high", "low"], + "data": APIEx.mock_data("timeseries"), + } + ), + ], +) +def ols_regression( + data: list[Data], + y_column: str, + x_columns: list[str], +) -> OBBject[dict]: + """Perform Ordinary Least Squares (OLS) regression. + + OLS regression is a fundamental statistical method to explore and model the relationship between a + dependent variable and one or more independent variables. By fitting the best possible linear equation to the data, + it helps uncover how changes in the independent variables are associated with changes in the dependent variable. + This returns the model and results objects from statsmodels library. + + Parameters + ---------- + data: list[Data] + Input dataset. + y_column: str + Target column. + x_columns: list[str] + list of columns to use as exogenous variables. + + Returns + ------- + OBBject[dict] + OBBject with the results being model and results objects. + """ + # pylint: disable=import-outside-toplevel + import statsmodels.api as sm + from openbb_core.app.utils import ( + basemodel_to_df, + get_target_column, + get_target_columns, + ) + + X = sm.add_constant(get_target_columns(basemodel_to_df(data), x_columns)) + y = get_target_column(basemodel_to_df(data), y_column) + model = sm.OLS(y, X) + results = model.fit() + return OBBject(results={"model": model, "results": results}) + + +@router.command( + methods=["POST"], + examples=[ + PythonEx( + description="Perform Ordinary Least Squares (OLS) regression and return the summary.", + code=[ + "stock_data = obb.equity.price.historical(symbol='TSLA', start_date='2023-01-01', provider='fmp').to_df()", # noqa: E501 pylint: disable=line-too-long + 'obb.econometrics.ols_regression_summary(data=stock_data, y_column="close", x_columns=["open", "high", "low"])', # noqa: E501 pylint: disable=line-too-long + ], + ), + APIEx( + parameters={ + "y_column": "close", + "x_columns": ["open", "high", "low"], + "data": APIEx.mock_data("timeseries"), + } + ), + ], +) +def ols_regression_summary( + data: list[Data], + y_column: str, + x_columns: list[str], +) -> OBBject[Data]: + """Perform Ordinary Least Squares (OLS) regression. + + This returns the summary object from statsmodels. + + Parameters + ---------- + data: list[Data] + Input dataset. + y_column: str + Target column. + x_columns: list[str] + list of columns to use as exogenous variables. + + Returns + ------- + OBBject[Data] + OBBject with the results being summary object. + """ + # pylint: disable=import-outside-toplevel + import re # noqa + import statsmodels.api as sm # noqa + from openbb_core.app.utils import ( + basemodel_to_df, + get_target_column, + get_target_columns, + ) + + X = sm.add_constant(get_target_columns(basemodel_to_df(data), x_columns)) + y = get_target_column(basemodel_to_df(data), y_column) + + try: + X = X.astype(float) + y = y.astype(float) + except ValueError as exc: + raise ValueError("All columns must be numeric") from exc + + results = sm.OLS(y, X).fit() + results_summary = results.summary() + results = {} + + for item in results_summary.tables[0].data: + results[item[0].strip()] = item[1].strip() + results[item[2].strip()] = str(item[3]).strip() + + table_1 = results_summary.tables[1] + headers = table_1.data[0] # Assuming the headers are in the first row + for i, row in enumerate(table_1.data): + if i == 0: # Skipping the header row + continue + for j, cell in enumerate(row): + if j == 0: # Skipping the row index + continue + key = f"{row[0].strip()}_{headers[j].strip()}" # Combining row index and column header + results[key] = cell.strip() + + for item in results_summary.tables[2].data: + results[item[0].strip()] = item[1].strip() + results[item[2].strip()] = str(item[3]).strip() + + results = {k: v for k, v in results.items() if v} + clean_results = {} + for k, v in results.items(): + new_key = re.sub(r"[.,\]\[:-]", "", k).lower().strip().replace(" ", "_") + clean_results[new_key] = v + + clean_results["raw"] = str(results_summary) + + return OBBject(results=clean_results) + + +@router.command( + methods=["POST"], + examples=[ + PythonEx( + description="Perform Durbin-Watson test for autocorrelation.", + code=[ + "stock_data = obb.equity.price.historical(symbol='TSLA', start_date='2023-01-01', provider='fmp').to_df()", # noqa: E501 + 'obb.econometrics.autocorrelation(data=stock_data, y_column="close", x_columns=["open", "high", "low"])', + ], + ), + APIEx( + parameters={ + "y_column": "close", + "x_columns": ["open", "high", "low"], + "data": APIEx.mock_data("timeseries"), + } + ), + ], +) +def autocorrelation( + data: list[Data], + y_column: str, + x_columns: list[str], +) -> OBBject[Data]: + """Perform Durbin-Watson test for autocorrelation. + + The Durbin-Watson test is a widely used method for detecting the presence of autocorrelation in the residuals + from a statistical or econometric model. Autocorrelation occurs when past values in the data series influence + future values, which can be a critical issue in time-series analysis, affecting the reliability of + model predictions. The test provides a statistic that ranges from 0 to 4, where a value around 2 suggests + no autocorrelation, values towards 0 indicate positive autocorrelation, and values towards 4 suggest + negative autocorrelation. Understanding the degree of autocorrelation helps in refining models to better capture + the underlying dynamics of the data, ensuring more accurate and trustworthy results. + + Parameters + ---------- + data: list[Data] + Input dataset. + y_column: str + Target column. + x_columns: list[str] + list of columns to use as exogenous variables. + + Returns + ------- + OBBject[dict] + OBBject with the results being the score from the test. + """ + # pylint: disable=import-outside-toplevel + import statsmodels.api as sm + from openbb_core.app.utils import ( + basemodel_to_df, + get_target_column, + get_target_columns, + ) + from statsmodels.stats.stattools import durbin_watson + + X = sm.add_constant(get_target_columns(basemodel_to_df(data), x_columns)) + y = get_target_column(basemodel_to_df(data), y_column) + results = sm.OLS(y, X).fit() + return OBBject(results=Data(score=durbin_watson(results.resid))) + + +@router.command( + methods=["POST"], + examples=[ + PythonEx( + description="Perform Breusch-Godfrey Lagrange Multiplier tests for residual autocorrelation.", + code=[ + "stock_data = obb.equity.price.historical(symbol='TSLA', start_date='2023-01-01', provider='fmp').to_df()", # noqa: E501 + 'obb.econometrics.residual_autocorrelation(data=stock_data, y_column="close", x_columns=["open", "high", "low"])', # noqa: E501 pylint: disable=line-too-long + ], + ), + APIEx( + parameters={ + "y_column": "close", + "x_columns": ["open", "high", "low"], + "data": APIEx.mock_data("timeseries"), + } + ), + ], +) +def residual_autocorrelation( + data: list[Data], + y_column: str, + x_columns: list[str], + lags: PositiveInt = 1, +) -> OBBject[Data]: + """Perform Breusch-Godfrey Lagrange Multiplier tests for residual autocorrelation. + + The Breusch-Godfrey Lagrange Multiplier test is a sophisticated tool for uncovering autocorrelation within the + residuals of a regression model. Autocorrelation in residuals can indicate that a model fails to capture some + aspect of the underlying data structure, possibly leading to biased or inefficient estimates. + By specifying the number of lags, you can control the depth of the test to check for autocorrelation, + allowing for a tailored analysis that matches the specific characteristics of your data. + This test is particularly valuable in econometrics and time-series analysis, where understanding the independence + of errors is crucial for model validity. + + Parameters + ---------- + data: list[Data] + Input dataset. + y_column: str + Target column. + x_columns: list[str] + list of columns to use as exogenous variables. + lags: PositiveInt + Number of lags to use in the test. + + Returns + ------- + OBBject[Data] + from statsmodels.stats.diagnostic import ( + acorr_breusch_godfrey, # type: ignore # pylint: disable=import-outside-toplevel + ) + """ + # pylint: disable=import-outside-toplevel + import statsmodels.api as sm + from openbb_core.app.utils import ( + basemodel_to_df, + get_target_column, + get_target_columns, + ) + from statsmodels.stats.diagnostic import ( + acorr_breusch_godfrey, + ) + + X = sm.add_constant(get_target_columns(basemodel_to_df(data), x_columns)) + y = get_target_column(basemodel_to_df(data), y_column) + model = sm.OLS(y, X) + results = model.fit() + lm_stat, p_value, f_stat, fp_value = acorr_breusch_godfrey(results, nlags=lags) + + results = { + "lm_stat": lm_stat, + "p_value": p_value, + "f_stat": f_stat, + "fp_value": fp_value, + } + + return OBBject(results=results) + + +@router.command( + methods=["POST"], + examples=[ + PythonEx( + description="Perform co-integration test between two timeseries.", + code=[ + "stock_data = obb.equity.price.historical(symbol='TSLA', start_date='2023-01-01', provider='fmp').to_df()", # noqa: E501 + 'obb.econometrics.cointegration(data=stock_data, columns=["open", "close"])', + ], + ), + ], +) +def cointegration( + data: list[Data], + columns: list[str], +) -> OBBject[Data]: + """Show co-integration between two timeseries using the two step Engle-Granger test. + + The two-step Engle-Granger test is a method designed to detect co-integration between two time series. + Co-integration is a statistical property indicating that two or more time series move together over the long term, + even if they are individually non-stationary. This concept is crucial in economics and finance, where identifying + pairs or groups of assets that share a common stochastic trend can inform long-term investment strategies + and risk management practices. The Engle-Granger test first checks for a stable, long-term relationship by + regressing one time series on the other and then tests the residuals for stationarity. + If the residuals are found to be stationary, it suggests that despite any short-term deviations, + the series are bound by an equilibrium relationship over time. + + Parameters + ---------- + data: list[Data] + Input dataset. + columns: list[str] + Data columns to check cointegration + maxlag: PositiveInt + Number of lags to use in the test. + + Returns + ------- + OBBject[Data] + OBBject with the results being the score from the test. + """ + # pylint: disable=import-outside-toplevel + from openbb_core.app.utils import basemodel_to_df, get_target_columns # noqa + from openbb_econometrics.utils import ( # noqa + get_engle_granger_two_step_cointegration_test, + ) + + pairs = list(combinations(columns, 2)) + dataset = get_target_columns(basemodel_to_df(data), columns) + result = {} + for x, y in pairs: + ( + c, + gamma, + alpha, + _, # z + adfstat, + pvalue, + ) = get_engle_granger_two_step_cointegration_test(dataset[x], dataset[y]) + result[f"{x}/{y}"] = { + "c": c, + "gamma": gamma, + "alpha": alpha, + "adfstat": adfstat, + "pvalue": pvalue, + } + + return OBBject(results=result) + + +@router.command( + methods=["POST"], + examples=[ + PythonEx( + description="Perform Granger causality test to determine if X 'causes' y.", + code=[ + "stock_data = obb.equity.price.historical(symbol='TSLA', start_date='2023-01-01', provider='fmp').to_df()", # noqa: E501 + 'obb.econometrics.causality(data=stock_data, y_column="close", x_column="open")', + ], + ), + APIEx( + description="Example with mock data.", + parameters={ + "y_column": "close", + "x_column": "open", + "lag": 1, + "data": APIEx.mock_data("timeseries"), + }, + ), + ], +) +def causality( + data: list[Data], + y_column: str, + x_column: str, + lag: PositiveInt = 3, +) -> OBBject[Data]: + """Perform Granger causality test to determine if X 'causes' y. + + The Granger causality test is a statistical hypothesis test to determine if one time series is useful in + forecasting another. While 'causality' in this context does not imply a cause-and-effect relationship in + the philosophical sense, it does test whether changes in one variable are systematically followed by changes + in another variable, suggesting a predictive relationship. By specifying a lag, you set the number of periods to + look back in the time series to assess this relationship. This test is particularly useful in economic and + financial data analysis, where understanding the lead-lag relationship between indicators can inform investment + decisions and policy making. + + Parameters + ---------- + data: list[Data] + Input dataset. + y_column: str + Target column. + x_column: str + Columns to use as exogenous variables. + lag: PositiveInt + Number of lags to use in the test. + + Returns + ------- + OBBject[Data] + OBBject with the results being the score from the test. + """ + # pylint: disable=import-outside-toplevel + from openbb_core.app.utils import basemodel_to_df, get_target_column + from pandas import DataFrame, concat + from statsmodels.tsa.stattools import grangercausalitytests + + X = get_target_column(basemodel_to_df(data), x_column) + y = get_target_column(basemodel_to_df(data), y_column) + + granger = grangercausalitytests(concat([y, X], axis=1), [lag], verbose=False) + + for test in granger[lag][0]: + # As ssr_chi2test and lrtest have one less value in the tuple, we fill + # this value with a '-' to allow the conversion to a DataFrame + if len(granger[lag][0][test]) != 4: + pars = granger[lag][0][test] + granger[lag][0][test] = (pars[0], pars[1], "-", pars[2]) + + df = DataFrame(granger[lag][0], index=["F-test", "P-value", "Count", "Lags"]).T + results = df.to_dict() + + return OBBject(results=results) + + +@router.command( + methods=["POST"], + examples=[ + PythonEx( + description="Perform Augmented Dickey-Fuller (ADF) unit root test.", + code=[ + "stock_data = obb.equity.price.historical(symbol='TSLA', start_date='2023-01-01', provider='fmp').to_df()", # noqa: E501 + 'obb.econometrics.unit_root(data=stock_data, column="close")', + 'obb.econometrics.unit_root(data=stock_data, column="close", regression="ct")', + ], + ), + APIEx( + parameters={ + "column": "close", + "data": APIEx.mock_data("timeseries"), + } + ), + ], +) +def unit_root( + data: list[Data], + column: str, + regression: Literal["c", "ct", "ctt"] = "c", +) -> OBBject[Data]: + """Perform Augmented Dickey-Fuller (ADF) unit root test. + + The ADF test is a popular method for testing the presence of a unit root in a time series. + A unit root indicates that the series may be non-stationary, meaning its statistical properties such as mean, + variance, and autocorrelation can change over time. The presence of a unit root suggests that the time series might + be influenced by a random walk process, making it unpredictable and challenging for modeling and forecasting. + The 'regression' parameter allows you to specify the model used in the test: 'c' for a constant term, + 'ct' for a constant and trend term, and 'ctt' for a constant, linear, and quadratic trend. + This flexibility helps tailor the test to the specific characteristics of your data, providing a more accurate + assessment of its stationarity. + + Parameters + ---------- + data: list[Data] + Input dataset. + column: str + Data columns to check unit root + regression: Literal["c", "ct", "ctt"] + Regression type to use in the test. Either "c" for constant only, "ct" for constant and trend, or "ctt" for + constant, trend, and trend-squared. + + Returns + ------- + OBBject[Data] + OBBject with the results being the score from the test. + """ + # pylint: disable=import-outside-toplevel + from openbb_core.app.utils import basemodel_to_df, get_target_column + from statsmodels.tsa.stattools import adfuller + + dataset = get_target_column(basemodel_to_df(data), column) + adfstat, pvalue, usedlag, nobs, _, icbest = adfuller(dataset, regression=regression) + results = { + "adfstat": adfstat, + "pvalue": pvalue, + "usedlag": usedlag, + "nobs": nobs, + "icbest": icbest, + } + return OBBject(results=results) + + +@router.command( + methods=["POST"], + examples=[ + APIEx( + parameters={ + "y_column": "portfolio_value", + "x_columns": ["risk_free_rate"], + "data": APIEx.mock_data("panel"), + } + ), + ], +) +def panel_random_effects( + data: list[Data], + y_column: str, + x_columns: list[str], +) -> OBBject[dict]: + """Perform One-way Random Effects model for panel data. + + One-way Random Effects model to panel data is offering a nuanced approach to analyzing data that spans across both + time and entities (such as individuals, companies, countries, etc.). By acknowledging and modeling the random + variation that exists within these entities, this method provides insights into the general patterns that + emerge across the dataset. + + Parameters + ---------- + data: list[Data] + Input dataset. + y_column: str + Target column. + x_columns: list[str] + list of columns to use as exogenous variables. + + Returns + ------- + OBBject[dict] + OBBject with the fit model returned + """ + # pylint: disable=import-outside-toplevel + import statsmodels.api as sm + from linearmodels.panel import RandomEffects + from openbb_core.app.utils import ( + basemodel_to_df, + get_target_column, + get_target_columns, + ) + + X = get_target_columns(basemodel_to_df(data), x_columns) + if len(X) < 3: + raise ValueError("This analysis requires at least 3 items in the dataset.") + y = get_target_column(basemodel_to_df(data), y_column) + exogenous = sm.add_constant(X) + results = RandomEffects(y, exogenous).fit() + return OBBject(results={"results": results}) + + +@router.command( + methods=["POST"], + examples=[ + APIEx( + parameters={ + "y_column": "portfolio_value", + "x_columns": ["risk_free_rate"], + "data": APIEx.mock_data("panel"), + } + ), + ], +) +def panel_between( + data: list[Data], + y_column: str, + x_columns: list[str], +) -> OBBject[dict]: + """Perform a Between estimator regression on panel data. + + The Between estimator for regression analysis on panel data is focusing on the differences between entities + (such as individuals, companies, or countries) over time. By aggregating the data for each entity and analyzing the + average outcomes, this method provides insights into the overall impact of explanatory variables (x_columns) on + the dependent variable (y_column) across all entities. + + Parameters + ---------- + data: list[Data] + Input dataset. + y_column: str + Target column. + x_columns: list[str] + list of columns to use as exogenous variables. + + Returns + ------- + OBBject[dict] + OBBject with the fit model returned + """ + # pylint: disable=import-outside-toplevel + import statsmodels.api as sm + from linearmodels.panel import BetweenOLS + from openbb_core.app.utils import ( + basemodel_to_df, + get_target_column, + get_target_columns, + ) + + X = get_target_columns(basemodel_to_df(data), x_columns) + y = get_target_column(basemodel_to_df(data), y_column) + exogenous = sm.add_constant(X) + results = BetweenOLS(y, exogenous).fit() + return OBBject(results={"results": results}) + + +@router.command( + methods=["POST"], + examples=[ + APIEx( + parameters={ + "y_column": "portfolio_value", + "x_columns": ["risk_free_rate"], + "data": APIEx.mock_data("panel"), + } + ), + ], +) +def panel_pooled( + data: list[Data], + y_column: str, + x_columns: list[str], +) -> OBBject[dict]: + """Perform a Pooled coefficient estimator regression on panel data. + + The Pooled coefficient estimator for regression analysis on panel data is treating the data as a large + cross-section without distinguishing between variations across time or entities + (such as individuals, companies, or countries). By assuming that the explanatory variables (x_columns) have a + uniform effect on the dependent variable (y_column) across all entities and time periods, this method simplifies + the analysis and provides a generalized view of the relationships within the data. + + Parameters + ---------- + data: list[Data] + Input dataset. + y_column: str + Target column. + x_columns: list[str] + list of columns to use as exogenous variables. + + Returns + ------- + OBBject[dict] + OBBject with the fit model returned + """ + # pylint: disable=import-outside-toplevel + import statsmodels.api as sm + from linearmodels.panel import PooledOLS + from openbb_core.app.utils import ( + basemodel_to_df, + get_target_column, + get_target_columns, + ) + + X = get_target_columns(basemodel_to_df(data), x_columns) + y = get_target_column(basemodel_to_df(data), y_column) + exogenous = sm.add_constant(X) + results = PooledOLS(y, exogenous).fit() + return OBBject(results={"results": results}) + + +@router.command( + methods=["POST"], + examples=[ + APIEx( + parameters={ + "y_column": "portfolio_value", + "x_columns": ["risk_free_rate"], + "data": APIEx.mock_data("panel"), + } + ), + ], +) +def panel_fixed( + data: list[Data], + y_column: str, + x_columns: list[str], +) -> OBBject[dict]: + """One- and two-way fixed effects estimator for panel data. + + The Fixed Effects estimator to panel data is enabling a focused analysis on the unique characteristics of entities + (such as individuals, companies, or countries) and/or time periods. By controlling for entity-specific and/or + time-specific influences, this method isolates the effect of explanatory variables (x_columns) on the dependent + variable (y_column), under the assumption that these entity or time effects capture unobserved heterogeneity. + + Parameters + ---------- + data: list[Data] + Input dataset. + y_column: str + Target column. + x_columns: list[str] + list of columns to use as exogenous variables. + + Returns + ------- + OBBject[dict] + OBBject with the fit model returned + """ + # pylint: disable=import-outside-toplevel + import statsmodels.api as sm + from linearmodels.panel import PanelOLS + from openbb_core.app.utils import ( + basemodel_to_df, + get_target_column, + get_target_columns, + ) + + X = get_target_columns(basemodel_to_df(data), x_columns) + y = get_target_column(basemodel_to_df(data), y_column) + exogenous = sm.add_constant(X) + results = PanelOLS(y, exogenous).fit() + return OBBject(results={"results": results}) + + +@router.command( + methods=["POST"], + examples=[ + APIEx( + parameters={ + "y_column": "portfolio_value", + "x_columns": ["risk_free_rate"], + "data": APIEx.mock_data("panel"), + } + ), + ], +) +def panel_first_difference( + data: list[Data], + y_column: str, + x_columns: list[str], +) -> OBBject[dict]: + """Perform a first-difference estimate for panel data. + + The First-Difference estimator for panel data analysis is focusing on the changes between consecutive observations + for each entity (such as individuals, companies, or countries). By differencing the data, this method effectively + removes entity-specific effects that are constant over time, allowing for the examination of the impact of changes + in explanatory variables (x_columns) on the change in the dependent variable (y_column). + + Parameters + ---------- + data: list[Data] + Input dataset. + y_column: str + Target column. + x_columns: list[str] + list of columns to use as exogenous variables. + + Returns + ------- + OBBject[dict] + OBBject with the fit model returned + """ + # pylint: disable=import-outside-toplevel + from linearmodels.panel import FirstDifferenceOLS + from openbb_core.app.utils import ( + basemodel_to_df, + get_target_column, + get_target_columns, + ) + + X = get_target_columns(basemodel_to_df(data), x_columns) + y = get_target_column(basemodel_to_df(data), y_column) + exogenous = X + results = FirstDifferenceOLS(y, exogenous).fit() + return OBBject(results={"results": results}) + + +@router.command( + methods=["POST"], + examples=[ + APIEx( + parameters={ + "y_column": "portfolio_value", + "x_columns": ["risk_free_rate"], + "data": APIEx.mock_data("panel"), + } + ), + ], +) +def panel_fmac( + data: list[Data], + y_column: str, + x_columns: list[str], +) -> OBBject[dict]: + """Fama-MacBeth estimator for panel data. + + The Fama-MacBeth estimator, a two-step procedure renowned for its application in finance to estimate the risk + premiums and evaluate the capital asset pricing model. By first estimating cross-sectional regressions for each + time period and then averaging the regression coefficients over time, this method provides insights into the + relationship between the dependent variable (y_column) and explanatory variables (x_columns) across different + entities (such as individuals, companies, or countries). + + Parameters + ---------- + data: list[Data] + Input dataset. + y_column: str + Target column. + x_columns: list[str] + list of columns to use as exogenous variables. + + Returns + ------- + OBBject[dict] + OBBject with the fit model returned + """ + # pylint: disable=import-outside-toplevel + import statsmodels.api as sm + from linearmodels.panel import FamaMacBeth + from openbb_core.app.utils import ( + basemodel_to_df, + get_target_column, + get_target_columns, + ) + + X = get_target_columns(basemodel_to_df(data), x_columns) + y = get_target_column(basemodel_to_df(data), y_column) + exogenous = sm.add_constant(X) + results = FamaMacBeth(y, exogenous).fit() + return OBBject(results={"results": results}) + + +@router.command( + methods=["POST"], + include_in_schema=False, + examples=[ + PythonEx( + description="Calculate the variance inflation factor.", + code=[ + "stock_data = obb.equity.price.historical(symbol='TSLA', start_date='2023-01-01', provider='yfinance').to_df()", # noqa: E501 pylint: disable= C0301 + 'obb.econometrics.variance_inflation_factor(data=stock_data, column="close")', + ], + ), + ], +) +def variance_inflation_factor( + data: list[Data], columns: Optional[list] = None +) -> OBBject[list[Data]]: + """Calculate VIF (variance inflation factor), which tests for collinearity. + + It quantifies the severity of multicollinearity in an ordinary least squares regression analysis. The square + root of the variance inflation factor indicates how much larger the standard error increases compared to if + that variable had 0 correlation to other predictor variables in the model. + + It is defined as: + + $ VIF_i = 1 / (1 - R_i^2) $ + where $ R_i $ is the coefficient of determination of the regression equation with the column i being the result + from the i:th series being the exogenous variable. + + A VIF over 5 indicates a high collinearity and correlation. Values over 10 indicates causes problems, while a + value of 1 indicates no correlation. Thus VIF values between 1 and 5 are most commonly considered acceptable. + In order to improve the results one can often remove a column with high VIF. + + For further information see: https://en.wikipedia.org/wiki/Variance_inflation_factor + + Parameters + ---------- + dataset: list[Data] + Dataset to calculate VIF on + columns: Optional[list] + The columns to calculate to test for collinearity + + Returns + ------- + OBBject[list[Data]] + The resulting VIF values for the selected columns + """ + # pylint: disable=import-outside-toplevel + from openbb_core.app.utils import ( + basemodel_to_df, + df_to_basemodel, + ) + from pandas import DataFrame + from statsmodels.stats.outliers_influence import variance_inflation_factor as vif + from statsmodels.tools.tools import add_constant + + # Convert to pandas dataframe + dataset = basemodel_to_df(data) + + # Add a constant + df = add_constant(dataset if columns is None else dataset[columns]) + + # Remove date and string type because VIF doesn't work for these types + df = df.select_dtypes(exclude=["object", "datetime", "timedelta"]) + + # Calculate the VIF values + vif_values: dict = {} + for i in range(len(df.columns))[1:]: + vif_values[f"{df.columns[i]}"] = vif(df.values, i) + + results = df_to_basemodel(DataFrame(vif_values, index=[0])) + return OBBject(results=results) diff --git a/openbb_platform/extensions/econometrics/openbb_econometrics/econometrics_views.py b/openbb_platform/extensions/econometrics/openbb_econometrics/econometrics_views.py new file mode 100644 index 0000000000000000000000000000000000000000..61c935a56b839838856b0b3ec15e63628043ad01 --- /dev/null +++ b/openbb_platform/extensions/econometrics/openbb_econometrics/econometrics_views.py @@ -0,0 +1,39 @@ +"""Views for the Econometrics Extension.""" + +from typing import TYPE_CHECKING, Any + +if TYPE_CHECKING: + from openbb_charting.core.openbb_figure import ( + OpenBBFigure, + ) + + +class EconometricsViews: + """Econometrics Views.""" + + @staticmethod + def econometrics_correlation_matrix( # noqa: PLR0912 + **kwargs, + ) -> tuple["OpenBBFigure", dict[str, Any]]: + """Correlation Matrix Chart. + + Parameters + ---------- + data : Union[list[Data], DataFrame] + Input dataset. + method : Literal["pearson", "kendall", "spearman"] + Method to use for correlation calculation. Default is "pearson". + pearson : standard correlation coefficient + kendall : Kendall Tau correlation coefficient + spearman : Spearman rank correlation + colorscale : str + Plotly colorscale to use for the heatmap. Default is "RdBu". + title : str + Title of the chart. Default is "Asset Correlation Matrix". + layout_kwargs : Dict[str, Any] + Additional keyword arguments to apply with figure.update_layout(), by default None. + """ + # pylint: disable=import-outside-toplevel + from openbb_charting.charts.correlation_matrix import correlation_matrix + + return correlation_matrix(**kwargs) diff --git a/openbb_platform/extensions/econometrics/openbb_econometrics/py.typed b/openbb_platform/extensions/econometrics/openbb_econometrics/py.typed new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/openbb_platform/extensions/econometrics/openbb_econometrics/utils.py b/openbb_platform/extensions/econometrics/openbb_econometrics/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..ff2211a3dbe517c7388603e9e0814578bc68bb89 --- /dev/null +++ b/openbb_platform/extensions/econometrics/openbb_econometrics/utils.py @@ -0,0 +1,115 @@ +"""Utility functions for the econometrics extension of the OpenBB platform.""" + +import warnings +from typing import TYPE_CHECKING, Tuple + +if TYPE_CHECKING: + from pandas import Series + + +def get_engle_granger_two_step_cointegration_test( + dependent_series: "Series", independent_series: "Series" +) -> Tuple[float, float, float, "Series", float, float]: + """Estimate long-run and short-run cointegration relationship for series y and x. + + Then apply the two-step Engle & Granger test for cointegration. + + Uses a 2-step process to first estimate coefficients for the long-run relationship + y_t = c + gamma * x_t + z_t + + and then the short-term relationship, + y_t - y_(t-1) = alpha * z_(t-1) + epsilon_t, + + with z the found residuals of the first equation. + + Then tests cointegration by Dickey-Fuller phi=1 vs phi < 1 in + z_t = phi * z_(t-1) + eta_t + + If this implies phi < 1, the z series is stationary is concluded to be + stationary, and thus the series y and x are concluded to be cointegrated. + + Parameters + ---------- + dependent_series : pd.Series + The first time series of the pair to analyse. + independent_series : pd.Series + The second time series of the pair to analyse. + + Returns + ------- + Tuple[float, float, float, pd.Series, float, float] + c : float + The constant term in the long-run relationship y_t = c + gamma * x_t + z_t. This + describes the static shift of y with respect to gamma * x. + + gamma : float + The gamma term in the long-run relationship y_t = c + gamma * x_t + z_t. This + describes the ratio between the const-shifted y and x. + + alpha : float + The alpha term in the short-run relationship y_t - y_(t-1) = alpha * z_(t-1) + epsilon. This + gives an indication of the strength of the error correction toward the long-run mean. + + z : pd.Series + Series of residuals z_t from the long-run relationship y_t = c + gamma * x_t + z_t, representing + the value of the error correction term. + + dfstat : float + The Dickey Fuller test-statistic for phi = 1 vs phi < 1 in the second equation. A more + negative value implies the existence of stronger cointegration. + + pvalue : float + The p-value corresponding to the Dickey Fuller test-statistic. A lower value implies + stronger rejection of no-cointegration, thus stronger evidence of cointegration. + + """ + # pylint: disable=import-outside-toplevel + import statsmodels.api as sm + from statsmodels.tsa.stattools import adfuller + + warnings.simplefilter(action="ignore", category=FutureWarning) + long_run_ols = sm.OLS(dependent_series, sm.add_constant(independent_series)) + warnings.simplefilter(action="default", category=FutureWarning) + + long_run_ols_fit = long_run_ols.fit() + + c, gamma = long_run_ols_fit.params + z = long_run_ols_fit.resid + + short_run_ols = sm.OLS(dependent_series.diff().iloc[1:], (z.shift().iloc[1:])) + short_run_ols_fit = short_run_ols.fit() + + alpha = short_run_ols_fit.params.iloc[0] + + # NOTE: The p-value returned by the adfuller function assumes we do not estimate z + # first, but test stationarity of an unestimated series directly. This assumption + # should have limited effect for high N, however. Critical values taking this into + # account more accurately are provided in e.g. McKinnon (1990) and Engle & Yoo (1987). + + adfstat, pvalue, _, _, _ = adfuller(z, maxlag=1, autolag=None) + + return c, gamma, alpha, z, adfstat, pvalue + + +def mock_multi_index_data(): + """Create a mock multi-index dataframe for testing purposes.""" + # pylint: disable=import-outside-toplevel + from numpy import random + from pandas import DataFrame, MultiIndex + + arrays = [ + ["individual_" + str(i) for i in range(1, 11) for _ in range(5)], + list(range(1, 6)) * 10, + ] + index = MultiIndex.from_arrays(arrays, names=("individual", "time")) + + df = DataFrame( + { + "income": random.randint(20000, 80000, size=50), + "age": random.randint(25, 60, size=50), + "education": random.randint(12, 21, size=50), + }, + index=index, + ) + + return df diff --git a/openbb_platform/extensions/econometrics/poetry.lock b/openbb_platform/extensions/econometrics/poetry.lock new file mode 100644 index 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b/openbb_platform/extensions/econometrics/pyproject.toml new file mode 100644 index 0000000000000000000000000000000000000000..0a600304dd8b64f7aa988669e2271101ef0b73e6 --- /dev/null +++ b/openbb_platform/extensions/econometrics/pyproject.toml @@ -0,0 +1,26 @@ +[tool.poetry] +name = "openbb-econometrics" +version = "1.5.3" +description = "Econometrics Toolkit for OpenBB" +authors = ["OpenBB Team "] +license = "AGPL-3.0-only" +readme = "README.md" +packages = [{ include = "openbb_econometrics" }] + +[tool.poetry.dependencies] +python = ">=3.9.21,<3.13" # scipy forces python <4.0 explicitly +openbb-core = "^1.4.6" +pandas-ta-openbb = "^0.4.20" +arch = "^7.2" +linearmodels = "^6" + + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" + +[tool.poetry.plugins."openbb_core_extension"] +econometrics = "openbb_econometrics.econometrics_router:router" + +[tool.poetry.plugins."openbb_charting_extension"] +econometrics = "openbb_econometrics.econometrics_views:EconometricsViews" \ No newline at end of file diff --git a/openbb_platform/extensions/econometrics/tests/test_econometrics_utils.py b/openbb_platform/extensions/econometrics/tests/test_econometrics_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..57ffd16fbeb8239f78bf82b0018da705cbd989cf --- /dev/null +++ b/openbb_platform/extensions/econometrics/tests/test_econometrics_utils.py @@ -0,0 +1,25 @@ +"""Test the econometrics utils module.""" + +import numpy as np +import pandas as pd +from extensions.econometrics.openbb_econometrics.utils import ( + get_engle_granger_two_step_cointegration_test, + mock_multi_index_data, +) + + +def test_get_engle_granger_two_step_cointegration_test(): + """Test the get_engle_granger_two_step_cointegration_test function.""" + x = pd.Series(np.random.randn(100)) + y = pd.Series(np.random.randn(100)) + + result = get_engle_granger_two_step_cointegration_test(x, y) + + assert result + + +def test_mock_multi_index_data(): + """Test the mock_multi_index_data function.""" + mi_data = mock_multi_index_data() + assert isinstance(mi_data, pd.DataFrame) + assert mi_data.index.nlevels == 2 diff --git a/openbb_platform/extensions/economy/README.md b/openbb_platform/extensions/economy/README.md new file mode 100644 index 0000000000000000000000000000000000000000..c0a8cb7a8ae888f4713da8babd23b5b5f9d68a3b --- /dev/null +++ b/openbb_platform/extensions/economy/README.md @@ -0,0 +1,13 @@ +# OpenBB Economy Extension + +The Economy extension provides global macroeconomic data access for the OpenBB Platform. + +## Installation + +To install the extension, run the following command in this folder: + +```bash +pip install openbb-economy +``` + +Documentation available [here](https://docs.openbb.co/platform/developer_guide/contributing). diff --git a/openbb_platform/extensions/economy/integration/test_economy_api.py b/openbb_platform/extensions/economy/integration/test_economy_api.py new file mode 100644 index 0000000000000000000000000000000000000000..76883635de71c4908dd9aa13ed11e157bc3c8792 --- /dev/null +++ b/openbb_platform/extensions/economy/integration/test_economy_api.py @@ -0,0 +1,1297 @@ +"""Test Economy API.""" + +import base64 + +import pytest +import requests +from extensions.tests.conftest import parametrize +from openbb_core.env import Env +from openbb_core.provider.utils.helpers import get_querystring + + +@pytest.fixture(scope="session") +def headers(): + """Get the headers for the API request.""" + userpass = f"{Env().API_USERNAME}:{Env().API_PASSWORD}" + userpass_bytes = userpass.encode("ascii") + base64_bytes = base64.b64encode(userpass_bytes) + + return {"Authorization": f"Basic {base64_bytes.decode('ascii')}"} + + +# pylint: disable=redefined-outer-name + + +@parametrize( + "params", + [ + ( + { + "provider": "nasdaq", + "start_date": "2023-10-24", + "end_date": "2023-11-03", + "country": "united_states,japan", + } + ), + ( + { + "provider": "tradingeconomics", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + "country": "mexico,sweden", + "importance": "low", + "group": "gdp", + "calendar_id": None, + } + ), + ( + { + "provider": "fmp", + "start_date": "2023-10-24", + "end_date": "2023-11-03", + } + ), + ], +) +@pytest.mark.integration +def test_economy_calendar(params, headers): + """Test the economy calendar endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/economy/calendar?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ( + { + "country": "spain", + "transform": "yoy", + "frequency": "annual", + "harmonized": False, + "start_date": "2020-01-01", + "end_date": "2023-06-06", + "provider": "fred", + } + ), + ( + { + "country": "portugal,spain", + "transform": "period", + "frequency": "monthly", + "harmonized": True, + "start_date": "2023-01-01", + "end_date": "2023-06-06", + "provider": "fred", + } + ), + ( + { + "country": "portugal,spain", + "transform": "yoy", + "frequency": "quarter", + "harmonized": False, + "start_date": "2020-01-01", + "end_date": "2023-06-06", + "provider": "oecd", + "expenditure": "transport", + } + ), + ], +) +@pytest.mark.integration +def test_economy_cpi(params, headers): + """Test the economy CPI endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/economy/cpi?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [{"provider": "fmp"}], +) +@pytest.mark.integration +def test_economy_risk_premium(params, headers): + """Test the economy risk premium endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/economy/risk_premium?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ( + { + "country": "united_states", + "provider": "oecd", + "frequency": "annual", + "start_date": None, + "end_date": None, + "units": "volume", + } + ), + ], +) +@pytest.mark.integration +def test_economy_gdp_forecast(params, headers): + """Test the economy GDP forecast endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/economy/gdp/forecast?{query_str}" + result = requests.get(url, headers=headers, timeout=20) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ( + { + "country": "united_states", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + "provider": "econdb", + "use_cache": False, + } + ), + ( + { + "country": "united_states", + "provider": "oecd", + "units": "level", + "frequency": "quarter", + "price_base": "volume", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + } + ), + ], +) +@pytest.mark.integration +def test_economy_gdp_nominal(params, headers): + """Test the economy GDP nominal endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/economy/gdp/nominal?{query_str}" + result = requests.get(url, headers=headers, timeout=20) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ( + { + "country": "united_states", + "frequency": "quarter", + "start_date": "2023-01-01", + "end_date": "2023-12-31", + "provider": "oecd", + } + ), + ( + { + "country": "united_states", + "provider": "econdb", + "start_date": "2023-01-01", + "end_date": "2023-12-31", + "use_cache": False, + } + ), + ], +) +@pytest.mark.integration +def test_economy_gdp_real(params, headers): + """Test the economy GDP real endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/economy/gdp/real?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ( + { + "report_type": "summary", + "frequency": "monthly", + "country": None, + "provider": "ecb", + } + ), + ( + { + "report_type": "direct_investment", + "frequency": "monthly", + "country": None, + "provider": "ecb", + } + ), + ( + { + "report_type": "main", + "frequency": "quarterly", + "country": "united_states", + "provider": "ecb", + } + ), + ( + { + "country": "united_states", + "start_date": None, + "end_date": None, + "provider": "fred", + } + ), + ], +) +@pytest.mark.integration +def test_economy_balance_of_payments(params, headers): + """Test the economy balance of payments endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/economy/balance_of_payments?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ( + { + "query": "GDP*", + "search_type": "series_id", + "release_id": None, + "offset": 0, + "limit": 10, + "order_by": "observation_end", + "sort_order": "desc", + "filter_variable": None, + "filter_value": None, + "tag_names": None, + "exclude_tag_names": None, + "series_id": None, + "provider": "fred", + } + ), + ( + { + "query": None, + "search_type": "release", + "release_id": None, + "offset": None, + "limit": None, + "order_by": "observation_end", + "sort_order": "desc", + "filter_variable": None, + "filter_value": None, + "tag_names": None, + "exclude_tag_names": None, + "series_id": None, + "provider": "fred", + } + ), + ( + { + "query": None, + "search_type": "full_text", + "release_id": None, + "offset": None, + "limit": None, + "order_by": "observation_end", + "sort_order": "desc", + "filter_variable": None, + "filter_value": None, + "tag_names": None, + "exclude_tag_names": None, + "series_id": "NYICLAIMS", + "provider": "fred", + } + ), + ], +) +@pytest.mark.integration +def test_economy_fred_search(params, headers): + """Test the economy FRED search endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/economy/fred_search?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ( + { + "symbol": "SP500", + "start_date": None, + "end_date": None, + "limit": 10000, + "frequency": "q", + "aggregation_method": "eop", + "transform": "chg", + "provider": "fred", + } + ), + ( + { + "symbol": "FEDFUNDS", + "start_date": None, + "end_date": None, + "limit": 10000, + "all_pages": True, + "provider": "intrinio", + "sleep": None, + } + ), + ], +) +@pytest.mark.integration +def test_economy_fred_series(params, headers): + """Test the economy FRED series endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/economy/fred_series?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ({"start_date": "2023-01-01", "end_date": "2023-06-06", "adjusted": True}), + ( + { + "provider": "federal_reserve", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + "adjusted": True, + } + ), + ], +) +@pytest.mark.integration +def test_economy_money_measures(params, headers): + """Test the economy money measures endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/economy/money_measures?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ( + { + "country": "united_states", + "sex": "total", + "frequency": "monthly", + "age": "total", + "seasonal_adjustment": True, + "provider": "oecd", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + } + ), + ], +) +@pytest.mark.integration +def test_economy_unemployment(params, headers): + """Test the economy unemployment endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/economy/unemployment?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ( + { + "country": "united_states", + "adjustment": "amplitude", + "growth_rate": False, + "provider": "oecd", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + } + ), + ], +) +@pytest.mark.integration +def test_economy_composite_leading_indicator(params, headers): + """Test the economy composite leading indicator endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/economy/composite_leading_indicator?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ({"start_date": "2023-01-01", "end_date": "2023-06-06", "provider": "oecd"}), + ( + { + "country": "united_states", + "frequency": "monthly", + "provider": "oecd", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + } + ), + ], +) +@pytest.mark.integration +def test_economy_short_term_interest_rate(params, headers): + """Test the economy short term interest rate endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/economy/short_term_interest_rate?{query_str}" + result = requests.get(url, headers=headers, timeout=30) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ({"start_date": "2023-01-01", "end_date": "2023-06-06", "provider": "oecd"}), + ( + { + "country": "united_states", + "frequency": "monthly", + "provider": "oecd", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + } + ), + ], +) +@pytest.mark.integration +def test_economy_long_term_interest_rate(params, headers): + """Test the economy long term interest rate endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/economy/long_term_interest_rate?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + argnames="params", + argvalues=[ + ( + { + "symbol": "156241", + "is_series_group": True, + "start_date": "2000-01-01", + "end_date": None, + "frequency": "w", + "units": "Number", + "region_type": "state", + "season": "nsa", + "aggregation_method": "eop", + "transform": "ch1", + "provider": "fred", + "limit": None, + } + ), + ( + { + "symbol": "CAICLAIMS", + "is_series_group": False, + "start_date": "1990-01-01", + "end_date": "2010-01-01", + "frequency": None, + "units": None, + "region_type": None, + "season": None, + "aggregation_method": None, + "transform": None, + "provider": "fred", + "limit": None, + } + ), + ], +) +@pytest.mark.integration +def test_economy_fred_regional(params, headers): + """Test the economy FRED regional endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/economy/fred_regional?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ( + { + "provider": "econdb", + "country": "us,uk,jp", + "symbol": "GDP,GDEBT", + "transform": None, + "start_date": "2022-01-01", + "end_date": "2024-01-01", + "use_cache": False, + "frequency": None, + } + ), + ( + { + "provider": "econdb", + "country": None, + "symbol": "MAIN", + "transform": None, + "start_date": "2022-01-01", + "end_date": "2024-01-01", + "use_cache": False, + "frequency": "quarter", + } + ), + ( + { + "provider": "imf", + "country": "us,uk,jp", + "symbol": "gold_reserves", + "start_date": "2022-01-01", + "end_date": "2023-12-31", + "frequency": "annual", + } + ), + ( + { + "provider": "imf", + "country": "all", + "symbol": "derivative_assets", + "start_date": "2022-01-01", + "end_date": "2023-12-31", + "frequency": "annual", + } + ), + ], +) +@pytest.mark.integration +def test_economy_indicators(params, headers): + """Test the economy indicators.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/economy/indicators?{query_str}" + result = requests.get(url, headers=headers, timeout=20) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ({"provider": "econdb", "use_cache": False}), + ({"provider": "imf", "query": "balance sheet;households;debt"}), + ], +) +@pytest.mark.integration +def test_economy_available_indicators(params, headers): + """Test the economy available indicators.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/economy/available_indicators?{query_str}" + result = requests.get(url, headers=headers, timeout=5) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ( + { + "provider": "econdb", + "country": "us,uk,jp", + "latest": True, + "use_cache": False, + } + ), + ], +) +@pytest.mark.integration +def test_economy_country_profile(params, headers): + """Test the economy country profile.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/economy/country_profile?{query_str}" + result = requests.get(url, headers=headers, timeout=30) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ( + { + "date": None, + "provider": "federal_reserve", + "holding_type": "all_treasury", + "summary": False, + "monthly": False, + "cusip": None, + "wam": False, + } + ), + ( + { + "date": None, + "provider": "federal_reserve", + "holding_type": "all_agency", + "summary": False, + "monthly": False, + "cusip": None, + "wam": True, + } + ), + ], +) +@pytest.mark.integration +def test_economy_central_bank_holdings(params, headers): + """Test the economy central bank holdings.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/economy/central_bank_holdings?{query_str}" + result = requests.get(url, headers=headers, timeout=5) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ( + { + "country": "united_states,united_kingdom", + "frequency": "monthly", + "provider": "oecd", + "start_date": "2022-01-01", + "end_date": "2024-04-01", + } + ), + ], +) +@pytest.mark.integration +def test_economy_share_price_index(params, headers): + """Test the economy share price index.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/economy/share_price_index?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ( + { + "country": "united_states,united_kingdom", + "frequency": "quarter", + "provider": "oecd", + "start_date": "2022-01-01", + "end_date": "2024-04-01", + "transform": "index", + } + ), + ], +) +@pytest.mark.integration +def test_economy_house_price_index(params, headers): + """Test the economy house price index.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/economy/house_price_index?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ( + { + "country": "united_states,united_kingdom", + "frequency": "monthly", + "provider": "oecd", + "start_date": "2022-01-01", + "end_date": "2024-04-01", + } + ), + ], +) +@pytest.mark.integration +def test_economy_immediate_interest_rate(params, headers): + """Test the economy immediate_interest_rate.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/economy/immediate_interest_rate?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ( + { + "country": "united_states", + "frequency": "monthly", + "provider": "oecd", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + "duration": "long", + } + ), + ], +) +@pytest.mark.integration +def test_economy_interest_rates(params, headers): + """Test the economy interest rates endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/economy/interest_rates?{query_str}" + result = requests.get(url, headers=headers, timeout=30) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ( + { + "country": "united_states", + "item": "meats", + "region": "all_city", + "frequency": "annual", + "provider": "fred", + "start_date": "2022-01-01", + "end_date": "2024-04-01", + "transform": "pc1", + } + ), + ], +) +@pytest.mark.integration +def test_economy_retail_prices(params, headers): + """Test the economy retail_prices.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/economy/retail_prices?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ( + { + "frequency": None, + "provider": "fred", + "start_date": "2022-01-01", + "end_date": "2024-04-01", + "transform": None, + "aggregation_method": None, + } + ), + ], +) +@pytest.mark.integration +def test_economy_survey_university_of_michigan(params, headers): + """Test the economy survey university_of_michigan endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = ( + f"http://0.0.0.0:8000/api/v1/economy/survey/university_of_michigan?{query_str}" + ) + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ( + { + "category": "auto", + "provider": "fred", + "start_date": "2022-01-01", + "end_date": "2024-04-01", + } + ), + ], +) +@pytest.mark.integration +def test_economy_survey_sloos(params, headers): + """Test the economy survey sloos endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/economy/survey/sloos?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ( + { + "provider": "fred", + "start_date": "2024-01-01", + "end_date": "2024-04-01", + "transform": None, + "aggregation_method": None, + "frequency": None, + } + ), + ], +) +@pytest.mark.integration +def test_economy_survey_economic_conditions_chicago(params, headers): + """Test the economy survey economic_conditions_chicago endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/economy/survey/economic_conditions_chicago?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ( + { + "provider": "fred", + "topic": "new_orders", + "start_date": "2024-01-01", + "end_date": "2024-04-01", + "transform": None, + "aggregation_method": None, + "frequency": None, + } + ), + ], +) +@pytest.mark.integration +def test_economy_survey_manufacturing_outlook_texas(params, headers): + """Test the economy survey manufacturing outlook texas endpoint""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/economy/survey/manufacturing_outlook_texas?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ( + { + "provider": "fred", + "topic": "new_orders", + "start_date": "2024-01-01", + "end_date": "2024-04-01", + "transform": None, + "aggregation_method": None, + "frequency": None, + } + ), + ], +) +@pytest.mark.integration +def test_economy_survey_manufacturing_outlook_ny(params, headers): + """Test the economy survey manufacturing outlook ny endpoint""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/economy/survey/manufacturing_outlook_ny?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ( + { + "provider": "federal_reserve", + "start_date": "2024-01-01", + "end_date": "2024-04-01", + "category": "cmbs", + } + ), + ], +) +@pytest.mark.integration +def test_economy_primary_dealer_positioning(params, headers): + """Test the economy primary dealer positioning endpoint""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/economy/primary_dealer_positioning?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ( + { + "provider": "fred", + "date": "2024-06-01,2023-06-01", + "category": "avg_earnings_hourly", + } + ), + ], +) +@pytest.mark.integration +def test_economy_survey_nonfarm_payrolls(params, headers): + """Test the economy survey nonfarm payrolls endpoint""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/economy/survey/nonfarm_payrolls?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ( + { + "provider": "fred", + "date": "2024-05-01,2024-04-01,2023-05-01", + "category": "pce_price_index", + } + ), + ], +) +@pytest.mark.integration +def test_economy_pce(params, headers): + """Test the economy pce endpoint""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/economy/pce?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ( + { + "provider": "fred", + "date": None, + "release_id": "14", + "element_id": "7930", + } + ), + ( + { + "provider": "fred", + "date": None, + "release_id": "14", + "element_id": None, + } + ), + ], +) +@pytest.mark.integration +def test_economy_fred_release_table(params, headers): + """Test the economy fred release table""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/economy/fred_release_table?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ( + { + "provider": "bls", + "query": "gasoline;seattle;average price", + "category": "cpi", + "include_extras": False, + "include_code_map": False, + } + ), + ], +) +@pytest.mark.integration +def test_economy_survey_bls_search(params, headers): + """Test the economy survey bls search endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/economy/survey/bls_search?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ( + { + "provider": "bls", + "symbol": "APUS49D74714,APUS49D74715,APUS49D74716", + "start_date": "2024-01-01", + "end_date": "2024-07-01", + "aspects": False, + "calculations": True, + "annual_average": True, + } + ), + ], +) +@pytest.mark.integration +def test_economy_survey_bls_series(params, headers): + """Test the economy survey bls search endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/economy/survey/bls_series?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ( + { + "provider": "econdb", + "country": "IN,CN", + } + ), + ], +) +@pytest.mark.integration +def test_economy_export_destinations(params, headers): + """Test the economy export destinations endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/economy/export_destinations?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ( + { + "provider": "federal_reserve", + "start_date": None, + "end_date": None, + "asset_class": "mbs", + "unit": "value", + } + ), + ], +) +@pytest.mark.integration +def test_economy_primary_dealer_fails(params, headers): + """Test the economy primary dealer fails endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/economy/primary_dealer_fails?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ( + { + "provider": "econdb", + "start_date": None, + "end_date": None, + } + ), + ], +) +@pytest.mark.integration +def test_economy_port_volume(params, headers): + """Test the economy port volume endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/economy/port_volume?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ( + { + "provider": "imf", + "country": "us", + "counterpart": "world,eu", + "frequency": "annual", + "direction": "exports", + "start_date": "2020-01-01", + "end_date": "2023-01-01", + } + ), + ], +) +@pytest.mark.integration +def test_economy_direction_of_trade(params, headers): + """Test the economy direction of trade endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/economy/direction_of_trade?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ( + { + "provider": "federal_reserve", + "year": None, + "document_type": None, + "pdf_only": False, + "as_choices": False, + "url": None, + } + ), + ( + { + "provider": "federal_reserve", + "year": None, + "document_type": None, + "pdf_only": False, + "as_choices": False, + "url": "https://www.federalreserve.gov/monetarypolicy/files/fomcminutes20250129.pdf", + } + ), + ( + { + "provider": "federal_reserve", + "year": 2022, + "document_type": "minutes", + "pdf_only": True, + "as_choices": True, + "url": None, + } + ), + ], +) +@pytest.mark.integration +def test_economy_fomc_documents(params, headers): + """Test the economy fomc documentsendpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/economy/fomc_documents?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 diff --git a/openbb_platform/extensions/economy/integration/test_economy_python.py b/openbb_platform/extensions/economy/integration/test_economy_python.py new file mode 100644 index 0000000000000000000000000000000000000000..7e16b169d64038fbc9292abf30b8e8911cb2fc39 --- /dev/null +++ b/openbb_platform/extensions/economy/integration/test_economy_python.py @@ -0,0 +1,1249 @@ +"""Test economy extension.""" + +import pytest +from extensions.tests.conftest import parametrize +from openbb_core.app.model.obbject import OBBject + + +@pytest.fixture(scope="session") +def obb(pytestconfig): # pylint: disable=inconsistent-return-statements + """Fixture to setup obb.""" + + if pytestconfig.getoption("markexpr") != "not integration": + import openbb # pylint: disable=import-outside-toplevel + + return openbb.obb + + +# pylint: disable=redefined-outer-name + + +@parametrize( + "params", + [ + ( + { + "provider": "nasdaq", + "start_date": "2023-10-24", + "end_date": "2023-11-03", + "country": "united_states,japan", + } + ), + ( + { + "provider": "tradingeconomics", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + "country": "mexico,sweden", + "importance": "low", + "group": "gdp", + "calendar_id": None, + } + ), + ( + { + "provider": "fmp", + "start_date": "2023-10-24", + "end_date": "2023-11-03", + } + ), + ], +) +@pytest.mark.integration +def test_economy_calendar(params, obb): + """Test economy calendar.""" + params = {p: v for p, v in params.items() if v} + + result = obb.economy.calendar(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ( + { + "country": "spain", + "transform": "yoy", + "frequency": "annual", + "harmonized": False, + "start_date": "2020-01-01", + "end_date": "2023-06-06", + "provider": "fred", + } + ), + ( + { + "country": "portugal,spain", + "transform": "period", + "frequency": "monthly", + "harmonized": True, + "start_date": "2023-01-01", + "end_date": "2023-06-06", + "provider": "fred", + } + ), + ( + { + "country": "portugal,spain", + "transform": "yoy", + "frequency": "quarter", + "harmonized": False, + "start_date": "2020-01-01", + "end_date": "2023-06-06", + "provider": "oecd", + "expenditure": "transport", + } + ), + ], +) +@pytest.mark.integration +def test_economy_cpi(params, obb): + """Test economy cpi.""" + result = obb.economy.cpi(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ({"provider": "fmp"}), + ], +) +@pytest.mark.integration +def test_economy_risk_premium(params, obb): + """Test economy risk premium.""" + result = obb.economy.risk_premium(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ( + { + "country": "united_states", + "provider": "oecd", + "frequency": "annual", + "start_date": None, + "end_date": None, + "units": "volume", + } + ), + ], +) +@pytest.mark.integration +def test_economy_gdp_forecast(params, obb): + """Test economy gdp forecast.""" + params = {p: v for p, v in params.items() if v} + + result = obb.economy.gdp.forecast(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ( + { + "country": "united_states", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + "provider": "econdb", + "use_cache": False, + } + ), + ( + { + "country": "united_states", + "provider": "oecd", + "units": "level", + "price_base": "volume", + "frequency": "quarter", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + } + ), + ], +) +@pytest.mark.integration +def test_economy_gdp_nominal(params, obb): + """Test economy gdp nominal.""" + params = {p: v for p, v in params.items() if v} + + result = obb.economy.gdp.nominal(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ( + { + "country": "united_states", + "frequency": "quarter", + "start_date": "2023-01-01", + "end_date": "2023-12-31", + "provider": "oecd", + } + ), + ( + { + "country": "united_states", + "provider": "econdb", + "start_date": "2023-01-01", + "end_date": "2023-12-31", + "use_cache": False, + } + ), + ], +) +@pytest.mark.integration +def test_economy_gdp_real(params, obb): + """Test economy gdp real.""" + params = {p: v for p, v in params.items() if v} + + result = obb.economy.gdp.real(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ( + { + "report_type": "summary", + "frequency": "monthly", + "country": None, + "provider": "ecb", + } + ), + ( + { + "report_type": "direct_investment", + "frequency": "monthly", + "country": None, + "provider": "ecb", + } + ), + ( + { + "report_type": "main", + "frequency": "quarterly", + "country": "united_states", + "provider": "ecb", + } + ), + ( + { + "country": "united_states", + "start_date": None, + "end_date": None, + "provider": "fred", + } + ), + ], +) +@pytest.mark.integration +def test_economy_balance_of_payments(params, obb): + """Test economy balance of payments.""" + params = {p: v for p, v in params.items() if v} + + result = obb.economy.balance_of_payments(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ( + { + "query": "GDP*", + "search_type": "series_id", + "release_id": None, + "offset": 0, + "limit": 10, + "order_by": "observation_end", + "sort_order": "desc", + "filter_variable": None, + "filter_value": None, + "tag_names": None, + "exclude_tag_names": None, + "series_id": None, + "provider": "fred", + } + ), + ( + { + "query": None, + "search_type": "release", + "release_id": None, + "offset": None, + "limit": None, + "order_by": "observation_end", + "sort_order": "desc", + "filter_variable": None, + "filter_value": None, + "tag_names": None, + "exclude_tag_names": None, + "series_id": None, + "provider": "fred", + } + ), + ( + { + "query": None, + "search_type": "full_text", + "release_id": None, + "offset": None, + "limit": None, + "order_by": "observation_end", + "sort_order": "desc", + "filter_variable": None, + "filter_value": None, + "tag_names": None, + "exclude_tag_names": None, + "series_id": "NYICLAIMS", + "provider": "fred", + } + ), + ], +) +@pytest.mark.integration +def test_economy_fred_search(params, obb): + """Test economy fred search.""" + params = {p: v for p, v in params.items() if v} + + result = obb.economy.fred_search(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ( + { + "symbol": "SP500", + "start_date": None, + "end_date": None, + "limit": 10000, + "frequency": "q", + "aggregation_method": "eop", + "transform": "chg", + "provider": "fred", + } + ), + ( + { + "symbol": "FEDFUNDS", + "start_date": None, + "end_date": None, + "limit": 10000, + "all_pages": True, + "provider": "intrinio", + "sleep": None, + } + ), + ], +) +@pytest.mark.integration +def test_economy_fred_series(params, obb): + """Test economy fred series.""" + params = {p: v for p, v in params.items() if v} + + result = obb.economy.fred_series(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ({"start_date": "2023-01-01", "end_date": "2023-06-06", "adjusted": True}), + ( + { + "provider": "federal_reserve", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + "adjusted": True, + } + ), + ], +) +@pytest.mark.integration +def test_economy_money_measures(params, obb): + """Test economy money measures.""" + params = {p: v for p, v in params.items() if v} + + result = obb.economy.money_measures(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ( + { + "country": "united_states", + "sex": "total", + "frequency": "monthly", + "age": "total", + "seasonal_adjustment": True, + "provider": "oecd", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + } + ), + ], +) +@pytest.mark.integration +def test_economy_unemployment(params, obb): + """Test economy unemployment.""" + params = {p: v for p, v in params.items() if v} + + result = obb.economy.unemployment(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ( + { + "country": "united_states", + "adjustment": "amplitude", + "growth_rate": False, + "provider": "oecd", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + } + ), + ], +) +@pytest.mark.integration +def test_economy_composite_leading_indicator(params, obb): + """Test economy composite leading indicator.""" + params = {p: v for p, v in params.items() if v} + + result = obb.economy.composite_leading_indicator(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ({"start_date": "2023-01-01", "end_date": "2023-06-06"}), + ( + { + "country": "united_states", + "frequency": "monthly", + "provider": "oecd", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + } + ), + ], +) +@pytest.mark.integration +def test_economy_short_term_interest_rate(params, obb): + """Test economy short term interest rate.""" + params = {p: v for p, v in params.items() if v} + + result = obb.economy.short_term_interest_rate(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ({"start_date": "2023-01-01", "end_date": "2023-06-06"}), + ( + { + "country": "united_states", + "frequency": "monthly", + "provider": "oecd", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + } + ), + ], +) +@pytest.mark.integration +def test_economy_long_term_interest_rate(params, obb): + """Test economy long term interest rate.""" + params = {p: v for p, v in params.items() if v} + + result = obb.economy.long_term_interest_rate(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + argnames="params", + argvalues=[ + ( + { + "symbol": "156241", + "is_series_group": True, + "start_date": "2000-01-01", + "end_date": None, + "frequency": "w", + "units": "Number", + "region_type": "state", + "season": "nsa", + "aggregation_method": "eop", + "transform": "ch1", + "provider": "fred", + "limit": None, + } + ), + ( + { + "symbol": "CAICLAIMS", + "is_series_group": False, + "start_date": "1990-01-01", + "end_date": "2010-01-01", + "frequency": None, + "units": None, + "region_type": None, + "season": None, + "aggregation_method": "avg", + "transform": "chg", + "provider": "fred", + "limit": None, + } + ), + ], +) +@pytest.mark.integration +def test_economy_fred_regional(params, obb): + """Test economy fred regional.""" + params = {p: v for p, v in params.items() if v} + + result = obb.economy.fred_regional(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ( + { + "provider": "econdb", + "country": "us,uk,jp", + "latest": True, + "use_cache": False, + } + ), + ], +) +@pytest.mark.integration +def test_economy_country_profile(params, obb): + """Test economy country profile.""" + params = {p: v for p, v in params.items() if v} + + result = obb.economy.country_profile(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ({"provider": "econdb", "use_cache": False}), + ({"provider": "imf", "query": "balance sheet;households;debt"}), + ], +) +@pytest.mark.integration +def test_economy_available_indicators(params, obb): + """Test economy available indicators.""" + params = {p: v for p, v in params.items() if v} + + result = obb.economy.available_indicators(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ( + { + "provider": "econdb", + "country": "us,uk,jp", + "symbol": "GDP,GDEBT", + "transform": None, + "start_date": "2022-01-01", + "end_date": "2024-01-01", + "use_cache": False, + "frequency": None, + } + ), + ( + { + "provider": "econdb", + "country": None, + "symbol": "MAIN", + "transform": None, + "start_date": "2022-01-01", + "end_date": "2024-01-01", + "use_cache": False, + "frequency": "quarter", + } + ), + ( + { + "provider": "imf", + "country": "us,uk,jp", + "symbol": "gold_reserves", + "start_date": "2022-01-01", + "end_date": "2023-12-31", + "frequency": "annual", + } + ), + ( + { + "provider": "imf", + "country": "all", + "symbol": "derivative_assets", + "start_date": "2022-01-01", + "end_date": "2023-12-31", + "frequency": "annual", + } + ), + ], +) +@pytest.mark.integration +def test_economy_indicators(params, obb): + """Test economy indicators.""" + params = {p: v for p, v in params.items() if v} + + result = obb.economy.indicators(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ( + { + "date": None, + "provider": "federal_reserve", + "holding_type": "all_treasury", + "summary": False, + "monthly": False, + "cusip": None, + "wam": False, + } + ), + ( + { + "date": None, + "provider": "federal_reserve", + "holding_type": "all_agency", + "summary": False, + "monthly": False, + "cusip": None, + "wam": True, + } + ), + ], +) +@pytest.mark.integration +def test_economy_central_bank_holdings(params, obb): + """Test economy central bank holdings.""" + params = {p: v for p, v in params.items() if v} + + result = obb.economy.central_bank_holdings(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ( + { + "country": "united_states,united_kingdom", + "frequency": "monthly", + "provider": "oecd", + "start_date": "2022-01-01", + "end_date": "2024-04-01", + } + ), + ], +) +@pytest.mark.integration +def test_economy_share_price_index(params, obb): + """Test economy share price index.""" + params = {p: v for p, v in params.items() if v} + + result = obb.economy.share_price_index(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ( + { + "country": "united_states,united_kingdom", + "frequency": "quarter", + "provider": "oecd", + "start_date": "2022-01-01", + "end_date": "2024-04-01", + "transform": "index", + } + ), + ], +) +@pytest.mark.integration +def test_economy_house_price_index(params, obb): + """Test economy house price index.""" + params = {p: v for p, v in params.items() if v} + + result = obb.economy.house_price_index(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ( + { + "country": "united_states,united_kingdom", + "frequency": "monthly", + "provider": "oecd", + "start_date": "2022-01-01", + "end_date": "2024-04-01", + } + ), + ], +) +@pytest.mark.integration +def test_economy_immediate_interest_rate(params, obb): + """Test economy immediate interest rate.""" + params = {p: v for p, v in params.items() if v} + + result = obb.economy.immediate_interest_rate(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ( + { + "country": "united_states", + "frequency": "monthly", + "provider": "oecd", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + "duration": "long", + } + ), + ], +) +@pytest.mark.integration +def test_economy_interest_rates(params, obb): + """Test economy country interest rates endpoint.""" + params = {p: v for p, v in params.items() if v} + + result = obb.economy.interest_rates(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ( + { + "country": "united_states", + "item": "meats", + "region": "all_city", + "frequency": "annual", + "provider": "fred", + "start_date": "2022-01-01", + "end_date": "2024-04-01", + "transform": "pc1", + } + ), + ], +) +@pytest.mark.integration +def test_economy_retail_prices(params, obb): + """Test economy retail prices.""" + params = {p: v for p, v in params.items() if v} + + result = obb.economy.retail_prices(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ( + { + "frequency": None, + "provider": "fred", + "start_date": "2022-01-01", + "end_date": "2024-04-01", + "transform": None, + "aggregation_method": None, + } + ), + ], +) +@pytest.mark.integration +def test_economy_survey_university_of_michigan(params, obb): + """Test the economy survey university_of_michigan endpoint""" + params = {p: v for p, v in params.items() if v} + + result = obb.economy.survey.university_of_michigan(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ( + { + "category": "auto", + "provider": "fred", + "start_date": "2022-01-01", + "end_date": "2024-04-01", + } + ), + ], +) +@pytest.mark.integration +def test_economy_survey_sloos(params, obb): + """Test the economy survey sloos endpoint""" + params = {p: v for p, v in params.items() if v} + + result = obb.economy.survey.sloos(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ( + { + "provider": "fred", + "start_date": "2024-01-01", + "end_date": "2024-04-01", + "transform": None, + "aggregation_method": None, + "frequency": None, + } + ), + ], +) +@pytest.mark.integration +def test_economy_survey_economic_conditions_chicago(params, obb): + """Test the economy survey economic conditions chicago endpoint""" + params = {p: v for p, v in params.items() if v} + + result = obb.economy.survey.economic_conditions_chicago(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ( + { + "provider": "fred", + "topic": "new_orders", + "start_date": "2024-01-01", + "end_date": "2024-04-01", + "transform": None, + "aggregation_method": None, + "frequency": None, + } + ), + ], +) +@pytest.mark.integration +def test_economy_survey_manufacturing_outlook_texas(params, obb): + """Test the economy survey manufacturing outlook texas endpoint""" + params = {p: v for p, v in params.items() if v} + + result = obb.economy.survey.manufacturing_outlook_texas(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ( + { + "provider": "fred", + "topic": "new_orders", + "start_date": "2024-01-01", + "end_date": "2024-04-01", + "transform": None, + "aggregation_method": None, + "frequency": None, + } + ), + ], +) +@pytest.mark.integration +def test_economy_survey_manufacturing_outlook_ny(params, obb): + """Test the economy survey manufacturing outlook ny endpoint""" + params = {p: v for p, v in params.items() if v} + + result = obb.economy.survey.manufacturing_outlook_ny(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ( + { + "provider": "federal_reserve", + "start_date": "2024-01-01", + "end_date": "2024-04-01", + "category": "cmbs", + } + ), + ], +) +@pytest.mark.integration +def test_economy_primary_dealer_positioning(params, obb): + """Test the economy primary dealer positioning endpoint""" + params = {p: v for p, v in params.items() if v} + + result = obb.economy.primary_dealer_positioning(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ( + { + "provider": "fred", + "date": "2024-06-01,2023-06-01", + "category": "avg_earnings_hourly", + } + ), + ], +) +@pytest.mark.integration +def test_economy_survey_nonfarm_payrolls(params, obb): + """Test the economy survery nonfarm payrolls endpoint""" + params = {p: v for p, v in params.items() if v} + + result = obb.economy.survey.nonfarm_payrolls(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ( + { + "provider": "fred", + "date": "2024-05-01,2024-04-01,2023-05-01", + "category": "pce_price_index", + } + ), + ], +) +@pytest.mark.integration +def test_economy_pce(params, obb): + """Test the economy pce endpoint""" + params = {p: v for p, v in params.items() if v} + + result = obb.economy.pce(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ( + { + "provider": "fred", + "date": None, + "release_id": "14", + "element_id": "7930", + } + ), + ( + { + "provider": "fred", + "date": None, + "release_id": "14", + "element_id": None, + } + ), + ], +) +@pytest.mark.integration +def test_economy_fred_release_table(params, obb): + """Test the economy fred release table endpoint""" + params = {p: v for p, v in params.items() if v} + + result = obb.economy.fred_release_table(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ( + { + "provider": "bls", + "query": "gasoline;seattle;average price", + "category": "cpi", + "include_extras": False, + "include_code_map": False, + } + ), + ], +) +@pytest.mark.integration +def test_economy_survey_bls_search(params, obb): + """Test the economy survey bls search endpoint""" + params = {p: v for p, v in params.items() if v} + + result = obb.economy.survey.bls_search(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ( + { + "provider": "bls", + "symbol": "APUS49D74714,APUS49D74715,APUS49D74716", + "start_date": "2024-01-01", + "end_date": "2024-07-01", + "aspects": False, + "calculations": True, + "annual_average": True, + } + ), + ], +) +@pytest.mark.integration +def test_economy_survey_bls_series(params, obb): + """Test the economy survey bls series endpoint""" + params = {p: v for p, v in params.items() if v} + + result = obb.economy.survey.bls_series(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ( + { + "provider": "econdb", + "country": "IN,CN", + } + ), + ], +) +@pytest.mark.integration +def test_economy_export_destinations(params, obb): + """Test the economy export destinations endpoint""" + params = {p: v for p, v in params.items() if v} + + result = obb.economy.export_destinations(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ( + { + "provider": "federal_reserve", + "start_date": None, + "end_date": None, + "asset_class": "mbs", + "unit": "value", + } + ), + ], +) +@pytest.mark.integration +def test_economy_primary_dealer_fails(params, obb): + """Test the economy primary dealer fails endpoint""" + params = {p: v for p, v in params.items() if v} + + result = obb.economy.primary_dealer_fails(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ( + { + "provider": "econdb", + "start_date": None, + "end_date": None, + } + ), + ], +) +@pytest.mark.integration +def test_economy_port_volume(params, obb): + """Test the economy port volume endpoint""" + params = {p: v for p, v in params.items() if v} + + result = obb.economy.port_volume(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ( + { + "provider": "imf", + "country": "us", + "counterpart": "world,eu", + "frequency": "annual", + "direction": "exports", + "start_date": "2020-01-01", + "end_date": "2023-01-01", + } + ), + ], +) +@pytest.mark.integration +def test_economy_direction_of_trade(params, obb): + """Test the economy direction of trade endpoint""" + params = {p: v for p, v in params.items() if v} + + result = obb.economy.direction_of_trade(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ( + { + "provider": "federal_reserve", + "year": None, + "document_type": None, + "pdf_only": False, + "as_choices": False, + "url": None, + } + ), + ( + { + "provider": "federal_reserve", + "year": None, + "document_type": None, + "pdf_only": False, + "as_choices": False, + "url": "https://www.federalreserve.gov/monetarypolicy/files/fomcminutes20250129.pdf", + } + ), + ( + { + "provider": "federal_reserve", + "year": 2022, + "document_type": "minutes", + "pdf_only": True, + "as_choices": True, + "url": None, + } + ), + ], +) +@pytest.mark.integration +def test_economy_fomc_documents(params, obb): + """Test the economy fomc documents endpoint""" + params = {p: v for p, v in params.items() if v} + + result = obb.economy.fomc_documents(**params) + assert result + assert isinstance(result, (list, dict)) + assert len(result) > 0 diff --git a/openbb_platform/extensions/economy/openbb_economy/__init__.py b/openbb_platform/extensions/economy/openbb_economy/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..8eb8dbd57c856a94450ac2a553f2bd944fd7df23 --- /dev/null +++ b/openbb_platform/extensions/economy/openbb_economy/__init__.py @@ -0,0 +1 @@ +"""OpenBB Economy Extension.""" diff --git a/openbb_platform/extensions/economy/openbb_economy/economy_router.py b/openbb_platform/extensions/economy/openbb_economy/economy_router.py new file mode 100644 index 0000000000000000000000000000000000000000..3bb5232eaaf78d10ccf7fe9058f252f9b3d62d94 --- /dev/null +++ b/openbb_platform/extensions/economy/openbb_economy/economy_router.py @@ -0,0 +1,863 @@ +"""Economy Router.""" + +from typing import Union + +from openbb_core.app.deprecation import OpenBBDeprecationWarning +from openbb_core.app.model.command_context import CommandContext +from openbb_core.app.model.example import APIEx +from openbb_core.app.model.obbject import OBBject +from openbb_core.app.provider_interface import ( + ExtraParams, + ProviderChoices, + StandardParams, +) +from openbb_core.app.query import Query +from openbb_core.app.router import Router + +from openbb_economy.gdp.gdp_router import router as gdp_router +from openbb_economy.survey.survey_router import router as survey_router + +router = Router(prefix="", description="Economic data.") +router.include_router(gdp_router) +router.include_router(survey_router) + +# pylint: disable=unused-argument + + +@router.command( + model="EconomicCalendar", + examples=[ + APIEx( + parameters={"provider": "fmp"}, + description="By default, the calendar will be forward-looking.", + ), + APIEx( + parameters={ + "provider": "fmp", + "start_date": "2020-03-01", + "end_date": "2020-03-31", + } + ), + APIEx( + description="By default, the calendar will be forward-looking.", + parameters={"provider": "nasdaq"}, + ), + ], +) +async def calendar( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Get the upcoming, or historical, economic calendar of global events.""" + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="ConsumerPriceIndex", + examples=[ + APIEx(parameters={"country": "japan,china,turkey", "provider": "fred"}), + APIEx( + description="Use the `transform` parameter to define the reference period for the change in values." + + " Default is YoY.", + parameters={ + "country": "united_states,united_kingdom", + "transform": "period", + "provider": "oecd", + }, + ), + ], +) +async def cpi( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Get Consumer Price Index (CPI). + + Returns either the rescaled index value, or a rate of change (inflation). + """ + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="RiskPremium", + examples=[APIEx(parameters={"provider": "fmp"})], +) +async def risk_premium( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Get Market Risk Premium by country.""" + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="BalanceOfPayments", + examples=[ + APIEx(parameters={"provider": "fred"}), + APIEx(parameters={"provider": "fred", "country": "brazil"}), + APIEx(parameters={"provider": "ecb"}), + APIEx(parameters={"report_type": "summary", "provider": "ecb"}), + APIEx( + description="The `country` parameter will override the `report_type`.", + parameters={"country": "united_states", "provider": "ecb"}, + ), + ], +) +async def balance_of_payments( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Balance of Payments Reports.""" + return await OBBject.from_query(Query(**locals())) + + +@router.command(model="FredSearch", examples=[APIEx(parameters={"provider": "fred"})]) +async def fred_search( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Search for FRED series or economic releases by ID or string. + + This does not return the observation values, only the metadata. + Use this function to find series IDs for `fred_series()`. + """ + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="FredSeries", + examples=[ + APIEx(parameters={"symbol": "NFCI", "provider": "fred"}), + APIEx( + description="Multiple series can be passed in as a list.", + parameters={"symbol": "NFCI,STLFSI4", "provider": "fred"}, + ), + APIEx( + description="Use the `transform` parameter to transform the data as change, log, or percent change.", + parameters={"symbol": "CBBTCUSD", "transform": "pc1", "provider": "fred"}, + ), + ], +) +async def fred_series( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Get data by series ID from FRED.""" + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="FredReleaseTable", + examples=[ + APIEx( + description="Get the top-level elements of a release by not supplying an element ID.", + parameters={"release_id": "50", "provider": "fred"}, + ), + APIEx( + description="Drill down on a specific section of the release.", + parameters={"release_id": "50", "element_id": "4880", "provider": "fred"}, + ), + APIEx( + description="Drill down on a specific table of the release.", + parameters={"release_id": "50", "element_id": "4881", "provider": "fred"}, + ), + ], +) +async def fred_release_table( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Get economic release data by ID and/or element from FRED.""" + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="MoneyMeasures", + examples=[ + APIEx(parameters={"provider": "federal_reserve"}), + APIEx(parameters={"adjusted": False, "provider": "federal_reserve"}), + ], +) +async def money_measures( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Get Money Measures (M1/M2 and components). + + The Federal Reserve publishes as part of the H.6 Release. + """ + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="Unemployment", + examples=[ + APIEx(parameters={"provider": "oecd"}), + APIEx( + parameters={"country": "all", "frequency": "quarter", "provider": "oecd"} + ), + APIEx( + description="Demographics for the statistics are selected with the `age` parameter.", + parameters={ + "country": "all", + "frequency": "quarter", + "age": "total", + "provider": "oecd", + }, + ), + ], +) +async def unemployment( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Get global unemployment data.""" + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="CompositeLeadingIndicator", + examples=[ + APIEx(parameters={"provider": "oecd"}), + APIEx(parameters={"country": "all", "provider": "oecd", "growth_rate": True}), + ], +) +async def composite_leading_indicator( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Get the composite leading indicator (CLI). + + It is designed to provide early signals of turning points + in business cycles showing fluctuation of the economic activity around its long term potential level. + + CLIs show short-term economic movements in qualitative rather than quantitative terms. + """ + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="STIR", + deprecated=True, + deprecation=OpenBBDeprecationWarning( + message="This endpoint will be removed in a future version. Use, `/economy/interest_rates`, instead.", + since=(4, 3), + expected_removal=(4, 5), + ), + examples=[ + APIEx(parameters={"provider": "oecd"}), + APIEx( + parameters={"country": "all", "frequency": "quarterly", "provider": "oecd"} + ), + ], +) +async def short_term_interest_rate( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Get Short-term interest rates. + + They are the rates at which short-term borrowings are effected between + financial institutions or the rate at which short-term government paper is issued or traded in the market. + + Short-term interest rates are generally averages of daily rates, measured as a percentage. + Short-term interest rates are based on three-month money market rates where available. + Typical standardised names are "money market rate" and "treasury bill rate". + """ + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="LTIR", + deprecated=True, + deprecation=OpenBBDeprecationWarning( + message="This endpoint will be removed in a future version. Use, `/economy/interest_rates`, instead.", + since=(4, 3), + expected_removal=(4, 5), + ), + examples=[ + APIEx(parameters={"provider": "oecd"}), + APIEx( + parameters={"country": "all", "frequency": "quarterly", "provider": "oecd"} + ), + ], +) +async def long_term_interest_rate( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Get Long-term interest rates that refer to government bonds maturing in ten years. + + Rates are mainly determined by the price charged by the lender, the risk from the borrower and the + fall in the capital value. Long-term interest rates are generally averages of daily rates, + measured as a percentage. These interest rates are implied by the prices at which the government bonds are + traded on financial markets, not the interest rates at which the loans were issued. + In all cases, they refer to bonds whose capital repayment is guaranteed by governments. + Long-term interest rates are one of the determinants of business investment. + Low long-term interest rates encourage investment in new equipment and high interest rates discourage it. + Investment is, in turn, a major source of economic growth. + """ + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="FredRegional", + examples=[ + APIEx( + parameters={"symbol": "NYICLAIMS", "provider": "fred"}, + ), + APIEx( + description="With a date, time series data is returned.", + parameters={ + "symbol": "NYICLAIMS", + "start_date": "2021-01-01", + "end_date": "2021-12-31", + "limit": 10, + "provider": "fred", + }, + ), + ], +) +async def fred_regional( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Query the Geo Fred API for regional economic data by series group. + + The series group ID is found by using `fred_search` and the `series_id` parameter. + """ + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="CountryProfile", + examples=[ + APIEx(parameters={"provider": "econdb", "country": "united_kingdom"}), + APIEx( + description="Enter the country as the full name, or iso code." + + " If `latest` is False, the complete history for each series is returned.", + parameters={ + "country": "united_states,jp", + "latest": False, + "provider": "econdb", + }, + ), + ], +) +async def country_profile( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Get a profile of country statistics and economic indicators.""" + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="AvailableIndicators", + examples=[ + APIEx(parameters={"provider": "econdb"}), + ], +) +async def available_indicators( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Get the available economic indicators for a provider.""" + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="EconomicIndicators", + examples=[ + APIEx(parameters={"provider": "econdb", "symbol": "PCOCO"}), + APIEx( + description="Enter the country as the full name, or iso code." + + " Use `available_indicators()` to get a list of supported indicators from EconDB.", + parameters={ + "symbol": "CPI", + "country": "united_states,jp", + "provider": "econdb", + }, + ), + APIEx( + description="Use the `main` symbol to get the group of main indicators for a country.", + parameters={"provider": "econdb", "symbol": "main", "country": "eu"}, + ), + APIEx( + description="When the provider is 'imf', the absence of a symbol will default to 'irfcl_top_lines'." + + " Use 'IRFCL' to get all the data from the set of indicators.", + parameters={"provider": "imf"}, + ), + APIEx( + description="When the provider is 'imf', complete tables are returned by using a 'preset'." + + " Refer to the function's docstring for descriptions of each preset." + + " When no country is supplied, the data is returned for all countries.", + parameters={"provider": "imf", "symbol": "gold_reserves"}, + ), + APIEx( + description="When the provider is 'imf', multiple countries and symbols can be supplied." + + " Enter countries as a two-letter ISO country code, or the country name in lower_snake_case.", + parameters={ + "provider": "imf", + "symbol": "RAFA_USD,RAPFA_USD,RAFA_RAPFA_RO", + "country": "us,china,jp,4f,gb", + "start_date": "2010-01-01", + "end_date": "2020-12-31", + "frequency": "annual", + }, + ), + APIEx( + description=( + "When the provider is 'imf', additional presets return the core Financial Soundness Indicators." + "\n 'fsi_core' - Core FSIs" + "\n 'fsi_encouraged_set' - Encouraged Set of FSIs," + "\n 'fsi_core_underlying' - Underlying data for the Core FSIs." + "\n 'fsi_other' - Additional/Other FSIs that are not in the Core or Encouraged Set." + "\n 'fsi_all' - all FSI data for a single country." + ), + parameters={ + "provider": "imf", + "symbol": "fsi_encouraged_set", + "country": "us,fr,gb", + "start_date": "2022-01-01", + "end_date": "2023-12-31", + "frequency": "annual", + }, + ), + ], +) +async def indicators( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Get economic indicators by country and indicator.""" + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="CentralBankHoldings", + examples=[ + APIEx( + description="The default is the latest Treasury securities held by the Federal Reserve.", + parameters={"provider": "federal_reserve"}, + ), + APIEx( + description="Get historical summaries of the Fed's holdings.", + parameters={"provider": "federal_reserve", "summary": True}, + ), + APIEx( + description="Get the balance sheet holdings as-of a historical date.", + parameters={"provider": "federal_reserve", "date": "2019-05-21"}, + ), + APIEx( + description="Use the `holding_type` parameter to select Agency securities," + + " or specific categories or Treasury securities.", + parameters={"provider": "federal_reserve", "holding_type": "agency_debts"}, + ), + ], +) +async def central_bank_holdings( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Get the balance sheet holdings of a central bank.""" + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="SharePriceIndex", + examples=[ + APIEx(parameters={"provider": "oecd"}), + APIEx( + description="Multiple countries can be passed in as a list.", + parameters={ + "country": "united_kingdom,germany", + "frequency": "quarter", + "provider": "oecd", + }, + ), + ], +) +async def share_price_index( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Get the Share Price Index by country from the OECD Short-Term Economics Statistics.""" + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="HousePriceIndex", + examples=[ + APIEx(parameters={"provider": "oecd"}), + APIEx( + description="Multiple countries can be passed in as a list.", + parameters={ + "country": "united_kingdom,germany", + "frequency": "quarter", + "provider": "oecd", + }, + ), + ], +) +async def house_price_index( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Get the House Price Index by country from the OECD Short-Term Economics Statistics.""" + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="ImmediateInterestRate", + deprecated=True, + deprecation=OpenBBDeprecationWarning( + message="This endpoint will be removed in a future version. Use, `/economy/interest_rates`, instead.", + since=(4, 3), + expected_removal=(4, 5), + ), + examples=[ + APIEx(parameters={"provider": "oecd"}), + APIEx( + description="Multiple countries can be passed in as a list.", + parameters={ + "country": "united_kingdom,germany", + "frequency": "monthly", + "provider": "oecd", + }, + ), + ], +) +async def immediate_interest_rate( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Get immediate interest rates by country.""" + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="CountryInterestRates", + examples=[ + APIEx(parameters={"provider": "oecd"}), + APIEx( + description="For OECD, duration can be 'immediate', 'short', or 'long'." + + " Default is 'short', which is the 3-month rate." + + " Overnight interbank rate is 'immediate', and 10-year rate is 'long'.", + parameters={ + "provider": "oecd", + "country": "all", + "duration": "immediate", + "frequency": "quarter", + }, + ), + APIEx( + description="Multiple countries can be passed in as a list.", + parameters={ + "duration": "long", + "country": "united_kingdom,germany", + "frequency": "monthly", + "provider": "oecd", + }, + ), + ], +) +async def interest_rates( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Get interest rates by country(s) and duration. + Most OECD countries publish short-term, a long-term, and immediate rates monthly. + """ + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="RetailPrices", + examples=[ + APIEx(parameters={"provider": "fred"}), + APIEx( + description="The price of eggs in the northeast census region.", + parameters={ + "item": "eggs", + "region": "northeast", + "provider": "fred", + }, + ), + APIEx( + description="The percentage change in price, from one-year ago, of various meats, US City Average.", + parameters={ + "item": "meats", + "transform": "pc1", + "provider": "fred", + }, + ), + ], +) +async def retail_prices( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Get retail prices for common items.""" + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="PrimaryDealerPositioning", + examples=[ + APIEx(parameters={"provider": "federal_reserve"}), + APIEx( + parameters={ + "category": "abs", + "provider": "federal_reserve", + }, + ), + ], +) +async def primary_dealer_positioning( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Get Primary dealer positioning statistics.""" + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="PersonalConsumptionExpenditures", + examples=[ + APIEx(parameters={"provider": "fred"}), + APIEx( + description="Get reports for multiple dates, entered as a comma-separated string.", + parameters={ + "provider": "fred", + "date": "2024-05-01,2024-04-01,2023-05-01", + "category": "pce_price_index", + }, + ), + ], +) +async def pce( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Get Personal Consumption Expenditures (PCE) reports.""" + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="ExportDestinations", + examples=[ + APIEx(parameters={"provider": "econdb", "country": "us"}), + ], +) +async def export_destinations( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Get top export destinations by country from the UN Comtrade International Trade Statistics Database.""" + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="PrimaryDealerFails", + examples=[ + APIEx(parameters={"provider": "federal_reserve"}), + APIEx( + description="Transform the data to be percentage totals by asset class", + parameters={"provider": "federal_reserve", "unit": "percent"}, + ), + ], +) +async def primary_dealer_fails( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Primary Dealer Statistics for Fails to Deliver and Fails to Receive. + + Data from the NY Federal Reserve are updated on Thursdays at approximately + 4:15 p.m. with the previous week's statistics. + + For research on the topic, see: + https://www.federalreserve.gov/econres/notes/feds-notes/the-systemic-nature-of-settlement-fails-20170703.html + + "Large and protracted settlement fails are believed to undermine the liquidity + and well-functioning of securities markets. + + Near-100 percent pass-through of fails suggests a high degree of collateral + re-hypothecation together with the inability or unwillingness to borrow or buy the needed securities." + """ + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="PortVolume", + examples=[ + APIEx(parameters={"provider": "econdb"}), + ], +) +async def port_volume( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Get average dwelling times and TEU volumes from the top ports.""" + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="DirectionOfTrade", + examples=[ + APIEx(parameters={"provider": "imf", "country": "all", "counterpart": "china"}), + APIEx( + description="Select multiple countries or counterparts by entering a comma-separated list." + + " The direction of trade can be 'exports', 'imports', 'balance', or 'all'.", + parameters={ + "provider": "imf", + "country": "us", + "counterpart": "world,eu", + "frequency": "annual", + "direction": "exports", + }, + ), + ], +) +async def direction_of_trade( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Get Direction Of Trade Statistics from the IMF database. + + The Direction of Trade Statistics (DOTS) presents the value of merchandise exports and + imports disaggregated according to a country's primary trading partners. + Area and world aggregates are included in the display of trade flows between major areas of the world. + Reported data is supplemented by estimates whenever such data is not available or current. + Imports are reported on a cost, insurance and freight (CIF) basis + and exports are reported on a free on board (FOB) basis. + Time series data includes estimates derived from reports of partner countries + for non-reporting and slow-reporting countries. + """ + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="FomcDocuments", + examples=[ + APIEx(parameters={"provider": "federal_reserve"}), + APIEx( + description="Filter all documents by year.", + parameters={"provider": "federal_reserve", "year": 2022}, + ), + APIEx( + description="Filter all documents by year and document type.", + parameters={ + "provider": "federal_reserve", + "year": 2022, + "document_type": "minutes", + }, + ), + APIEx( + description="The `url` parameter will override all other parameters to download the document." + + " The response will be a dictionary with keys `filename`, `content`, and `data_format`." + + " PDF content will be a base64 encoded string of the document.", + parameters={ + "provider": "federal_reserve", + "url": "https://www.federalreserve.gov/monetarypolicy/files/fomcminutes20220126.pdf", + }, + ), + ], + openapi_extra={ + "widget_config": { + "type": "multi_file_viewer", + "name": "FOMC Document Viewer", + "description": "View FOMC materials.", + "gridData": { + "w": 30, + "h": 27, + }, + "refetchInterval": False, + } + }, + response_model=Union[list, dict], +) +async def fomc_documents( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """ + Get FOMC documents by year and document type. + Optionally, download the file directly from the Federal Reserve's website. + + Source: https://www.federalreserve.gov/monetarypolicy/fomc_historical.htm + Source: https://www.federalreserve.gov/monetarypolicy/fomccalendars.htm + + This function does not return the typical OBBject response. + + The response is `list[dict[str, str]]` of FOMC documents and their URLs. + + Each dictionary entry has keys: `date`, `url`, `doc_type`, and `doc_format`. + + If `as_choices` is True, the response is a list of valid Workspace parameter choices. + Keys, `label` and `value`, correspond with the `doc_type` + `date`, and the `url`, respectively. + + If `url` was provided, the response is a `dict[str, Any]` with keys `filename`, `content`, and `data_format`. + """ + results = await OBBject.from_query(Query(**locals())) + + return results.results.content diff --git a/openbb_platform/extensions/economy/openbb_economy/economy_views.py b/openbb_platform/extensions/economy/openbb_economy/economy_views.py new file mode 100644 index 0000000000000000000000000000000000000000..c602858ed0ac93caabd77c99e368687e1ff424ba --- /dev/null +++ b/openbb_platform/extensions/economy/openbb_economy/economy_views.py @@ -0,0 +1,533 @@ +"""Views for the Economy Extension.""" + +# flake8: noqa: PLR0912 +# pylint: disable=too-many-branches + +from typing import TYPE_CHECKING, Any, Dict, Optional, Tuple +from warnings import warn + +if TYPE_CHECKING: + from openbb_charting.core.openbb_figure import ( + OpenBBFigure, + ) + + +class EconomyViews: + """economy Views.""" + + @staticmethod + def economy_fred_series( + **kwargs, + ) -> Tuple["OpenBBFigure", Dict[str, Any]]: + """FRED Series Chart.""" + # pylint: disable=import-outside-toplevel + from openbb_charting.charts.generic_charts import bar_chart + from openbb_charting.charts.helpers import ( + z_score_standardization, + ) + from openbb_charting.core.chart_style import ChartStyle + from openbb_charting.core.openbb_figure import OpenBBFigure + from openbb_charting.styles.colors import LARGE_CYCLER + from openbb_core.app.utils import basemodel_to_df + from pandas import DataFrame + + ytitle_dict = { + "chg": "Change", + "ch1": "Change From Year Ago", + "pch": "Percent Change", + "pc1": "Percent Change From Year Ago", + "pca": "Compounded Annual Rate Of Change", + "cch": "Continuously Compounded Rate Of Change", + "cca": "Continuously Compounded Annual Rate Of Change", + "log": "Natural Log", + } + + provider = kwargs.get("provider") + + if provider != "fred": + raise RuntimeError( + f"This charting method does not support {provider}. Supported providers: fred." + ) + + columns = basemodel_to_df(kwargs["obbject_item"], index=None).columns.to_list() # type: ignore + + allow_unsafe = kwargs.get("allow_unsafe", False) + dropnan = kwargs.get("dropna", True) + normalize = kwargs.get("normalize", False) + + data_cols = [] + data = kwargs.get("data") + + if isinstance(data, DataFrame) and not data.empty: + data_cols = data.columns.to_list() + df_ta = data + + else: + df_ta = basemodel_to_df(kwargs["obbject_item"], index="date") # type: ignore + + # Check for unsupported external data injection. + if allow_unsafe is False and data_cols: + for data_col in data_cols: + if data_col not in columns: + raise RuntimeError( + f"Column '{data_col}' was not found in the original data." + + " External data injection is not supported unless `allow_unsafe = True`." + ) + + # Align the data so each column has the same index and length. + if dropnan: + df_ta = df_ta.dropna(how="any") + + if df_ta.empty or len(df_ta) < 2: + raise ValueError( + "No data is left after dropping NaN values. Try setting `dropnan = False`," + + " or use the `frequency` parameter on request." + ) + + columns = df_ta.columns.to_list() + + metadata = kwargs["extra"].get("results_metadata", {}) # type: ignore + + # Check if the request was transformed by the FRED API. + params = kwargs["extra_params"] if kwargs.get("extra_params") else {} + has_params = hasattr(params, "transform") and params.transform is not None # type: ignore + + # Get a unique list of all units of measurement in the DataFrame. + y_units = list({metadata.get(col).get("units") for col in columns if col in metadata}) # type: ignore + if has_params is True and not y_units: + y_units = [ytitle_dict.get(params.transform)] # type: ignore + + if normalize or ( + kwargs.get("bar") is True + and len(y_units) > 1 + and ( + has_params is False + or not any( + i in params.transform for i in ["pc1", "pch", "pca", "cch", "cca", "log"] # type: ignore + ) + ) + ): + normalize = True + df_ta = df_ta.apply(z_score_standardization) + + if len(y_units) > 2 and has_params is False and allow_unsafe is False: + raise RuntimeError( + "This method supports up to 2 y-axis units." + + " Please use the 'transform' parameter, in the data request," + + " to compare all series on the same scale, or set `normalize = True`." + + " Override this error by setting `allow_unsafe = True`." + ) + + y1_units = y_units[0] if y_units else None + y1title = y1_units + y2title = y_units[1] if len(y_units) > 1 else None + xtitle = str(kwargs.get("xtitle", "")) + + # If the request was transformed, the y-axis will be shared under these conditions. + if has_params and any( + i in params.transform for i in ["pc1", "pch", "pca", "cch", "cca", "log"] # type: ignore + ): + y1title = "Log" if params.transform == "Log" else "Percent" # type: ignore + y2title = None + + # Set the title for the chart. + title: str = "" + if isinstance(kwargs, dict) and title in kwargs: + title = kwargs["title"] # type: ignore + else: + if metadata.get(columns[0]): # type: ignore + title = metadata.get(columns[0]).get("title") if len(columns) == 1 else "FRED Series" # type: ignore + else: + title = "FRED Series" + transform_title = ytitle_dict.get(params.transform) if has_params is True else "" # type: ignore + title = f"{title} - {transform_title}" if transform_title else title + + # Define this to use as a check. + y3title: Optional[str] = "" + + if kwargs.get("plot_bar") is True or len(df_ta.index) < 100: + margin = dict(l=10, r=5, b=75 if xtitle else 30) + try: + if normalize: + y1title = None + title = f"{title} - Normalized" if title else "Normalized" + bar_mode = kwargs.get("barmode", "group") + fig = bar_chart( + df_ta.reset_index(), + "date", + df_ta.columns.to_list(), + title=title, + xtitle=xtitle, + ytitle=y1title, + barmode=bar_mode, # type: ignore + layout_kwargs=dict(margin=margin), # type: ignore + ) + if kwargs.get("layout_kwargs"): + fig.update_layout(kwargs.get("layout_kwargs")) + + if kwargs.get("title"): + fig.set_title(str(kwargs.get("title"))) # type: ignore + + content = fig.to_plotly_json() + + return fig, content # type: ignore + except Exception as _: + warn("Bar chart failed. Attempting line chart.") + + # Create the figure object with subplots. + fig = OpenBBFigure().create_subplots( + rows=1, cols=1, shared_xaxes=True, shared_yaxes=False + ) + fig.update_layout(ChartStyle().plotly_template.get("layout", {})) + text_color = "white" if ChartStyle().plt_style == "dark" else "black" + # For each series in the DataFrame, add a scatter plot. + for i, col in enumerate(df_ta.columns): + + # Check if the y-axis should be shared for this series. + on_y1 = ( + ( + metadata.get(col).get("units") == y1_units # type: ignore + or y2title is None # type: ignore + or kwargs.get("same_axis") is True + ) + if metadata.get(col) # type: ignore + else False + ) + if normalize: + on_y1 = True + + yaxes = "y2" if not on_y1 else "y1" + on_y3 = not metadata.get(col) and normalize is False # type: ignore + if on_y3: + yaxes = "y3" + y3title = df_ta[col].name # type: ignore + fig.add_scatter( + x=df_ta.index, + y=df_ta[col], + name=df_ta[col].name, + mode="lines", + hovertemplate=f"{df_ta[col].name}: %{{y}}", + line=dict(width=2, color=LARGE_CYCLER[i % len(LARGE_CYCLER)]), + yaxis="y1" if kwargs.get("same_axis") is True else yaxes, + ) + + # Set the y-axis titles, if supplied. + if kwargs.get("y1title"): + y1title = kwargs.get("y1title") + if kwargs.get("y2title") and y2title is not None: + y2title = kwargs.get("y2title") + # Set the x-axis title, if suppiled. + if isinstance(kwargs, dict) and "xtitle" in kwargs: + xtitle = kwargs["xtitle"] + # If the data was normalized, set the title to reflect this. + if normalize: + y1title = None + y2title = None + y3title = None + title = f"{title} - Normalized" if title else "Normalized" + + # Now update the layout of the complete figure. + fig.update_layout( + title=dict(text=title, x=0.5, font=dict(size=16)), + paper_bgcolor=( + "rgba(0,0,0,0)" if text_color == "white" else "rgba(255,255,255,0)" + ), + plot_bgcolor=( + "rgba(0,0,0,0)" if text_color == "white" else "rgba(255,255,255,0)" + ), + legend=dict( + orientation="h", + yanchor="bottom", + xanchor="right", + y=1.02, + x=0.95, + bgcolor=( + "rgba(0,0,0,0)" if text_color == "white" else "rgba(255,255,255,0)" + ), + font=dict(size=12), + ), + yaxis=( + dict( + ticklen=0, + side="right", + showline=True, + mirror=True, + title=dict(text=y1title, standoff=30, font=dict(size=16)), + tickfont=dict(size=14), + anchor="x", + gridcolor="rgba(128,128,128,0.3)", + ) + if y1title + else None + ), + yaxis2=( + dict( + overlaying="y", + side="left", + ticklen=0, + showgrid=False, + title=dict( + text=y2title if y2title else None, + standoff=10, + font=dict(size=16), + ), + tickfont=dict(size=14), + anchor="x", + ) + if y2title + else None + ), + yaxis3=( + dict( + overlaying="y", + side="left", + ticklen=0, + position=0, + showgrid=False, + showticklabels=True, + title=( + dict(text=y3title, standoff=10, font=dict(size=16)) + if y3title + else None + ), + tickfont=dict(size=12, color="rgba(128,128,128,0.9)"), + anchor="free", + ) + if y3title + else None + ), + xaxis=dict( + ticklen=0, + showgrid=True, + showline=True, + mirror=True, + title=( + dict(text=xtitle, standoff=30, font=dict(size=16)) + if xtitle + else None + ), + gridcolor="rgba(128,128,128,0.3)", + domain=[0.095, 0.95] if y3title else None, + ), + margin=( + dict(r=25, l=25, b=75 if xtitle else 30) if normalize is False else None + ), + font=dict(color=text_color), + autosize=True, + dragmode="pan", + ) + if kwargs.get("layout_kwargs"): + fig.update_layout(kwargs.get("layout_kwargs")) + if kwargs.get("title"): + fig.set_title(str(kwargs.get("title"))) + + content = fig.to_plotly_json() + + return fig, content + + @staticmethod + def economy_survey_bls_series( + **kwargs, + ) -> Tuple["OpenBBFigure", Dict[str, Any]]: + """Economy Survey BLS Series Chart. + + Parameters + ---------- + data: Optional[Union[DataFrame, List[Data]]] + Filtered subset of the parent results. + target_symbol: Optional[str] + The target symbol(s) to plot. Plot multiple symbols by separating them with a comma. Max 10 symbols. + target_col: Optional[str] + The target column to plot. Default is 'value'. + plot_type: Literal["line", "bar"] + The type of plot to display. Default is 'line', unless the data is significantly small. + normalize: bool + Normalize the data before displaying. Default is False. + title: Optional[str] + The title of the chart. + xtitle: Optional[str] + The title of the x-axis. + ytitle: Optional[str] + The title of the y-axis. + bar_kwargs: Optional[Dict] + Additional keyword arguments applied to `fig.add_bar`. + scatter_kwargs: Optional[Dict] + Additional keyword arguments applied to `fig.add_scatter`. + layout_kwargs: Optional[Dict] + Additional keyword arguments applied to `fig.update_layout`. + """ + # pylint: disable=import-outside-toplevel + from openbb_charting.charts.generic_charts import bar_chart, line_chart + from openbb_charting.charts.helpers import ( + z_score_standardization, + ) + from openbb_core.app.utils import basemodel_to_df + from pandas import DataFrame + + provider = kwargs.get("provider") + + if provider != "bls": + raise RuntimeError( + f"This charting method does not support {provider}. Supported providers: bls." + ) + + _data = ( + kwargs.pop("data", None) + if "data" in kwargs and kwargs["data"] is not None + else kwargs.get("obbject_item") + ) + df = DataFrame() + + if isinstance(_data, DataFrame) and not _data.empty: + df = _data.reset_index() if _data.index.name == "date" else _data + else: + try: + df = basemodel_to_df(_data, index=None) # type: ignore + except Exception as e: + raise RuntimeError("Unable to process supplied data.") from e + + if df.empty or len(df) < 2: + raise RuntimeError("No data found to plot.") + + cols = df.columns.to_list() + target_col = kwargs.get("target_col", "value") + if target_col not in cols: + raise RuntimeError(f"Column '{target_col}' not found in the data.") + + new_df = df.pivot(columns="symbol", values=target_col, index="date") + target_symbols = kwargs.get("target_symbol", "").split(",")[:10] # type: ignore + + if not target_symbols or len(target_symbols) == 0 or target_symbols[0] == "": + target_symbols = new_df.columns.to_list()[:10] + + metadata = kwargs["extra"].get("results_metadata", {}) # type: ignore + ytitle = kwargs.get("ytitle", "") + + new_df = new_df.filter(target_symbols, axis=1) + + if "percent" in target_col.lower(): # type: ignore + ytitle = ( + ytitle + if ytitle + else target_col.replace("change_percent_", "").replace("M", " Month") # type: ignore + + " Change (%)" + ) + new_df = new_df.apply(lambda x: x * 100) + elif "change" in target_col.lower() and "percent" not in target_col.lower(): # type: ignore + ytitle = ( + ytitle + if ytitle + else target_col.replace("change_", "").replace("M", " Month") # type: ignore + + " Change" + ) + + title_map: Dict = {} + for symbol in target_symbols: + if symbol not in new_df.columns: + continue + survey_name = metadata.get(symbol, {}).get("survey_name", symbol) # type: ignore + series_title = metadata.get(symbol, {}).get("series_title", symbol) # type: ignore + + if survey_name != series_title: + title_map[symbol] = f"{survey_name} \n {series_title}" + + normalize = kwargs.get("normalize", False) + same_axis = kwargs.get("same_axis", False) + + if normalize: + new_df = new_df.apply(z_score_standardization) + same_axis = True + if ytitle: + ytitle = f"Normalized {ytitle.replace('(%)', '')}" # type: ignore + + plot_type = kwargs.get("plot_type") + + if plot_type is None: + plot_type = ( + "line" # type: ignore + if (len(new_df.index) > 36 and len(new_df.columns.to_list()) >= 1) + else "bar" + ) + + layout_kwargs: Dict = kwargs.pop("layout_kwargs", {}) # type: ignore + scatter_kwargs: Dict = kwargs.pop("scatter_kwargs", {}) # type: ignore + bar_kwargs: Dict = kwargs.pop("bar_kwargs", {}) # type: ignore + hovertemplate = scatter_kwargs.pop("hovertemplate", None) # type: ignore + trace_titles = { + symbol: metadata.get(symbol, {}) + .get("series_title", symbol) + .replace(",", " -") + for symbol in target_symbols + } + new_df.columns = [trace_titles.get(col, col) for col in new_df.columns] + scatter_kwargs["hovertemplate"] = ( # type: ignore + hovertemplate if hovertemplate else "%{fullData.name}:%{y}" + ) + + if len(target_symbols) == 1: + title = title_map.get(target_symbols[0], target_symbols[0]) + fig = ( + line_chart( + data=new_df, + title=title, + ytitle=ytitle, + y=list(trace_titles.values()), + scatter_kwargs=scatter_kwargs, + layout_kwargs=layout_kwargs, + **kwargs, + ) + if plot_type == "line" + else bar_chart( + data=new_df, + title=title, + ytitle=ytitle, + x=new_df.index, + y=list(trace_titles.values()), + layout_kwargs=layout_kwargs, + bar_kwargs=bar_kwargs, + **kwargs, + ) + ) + else: + survey_name = metadata.get(target_symbols[0], {}).get("survey_name", target_symbols[0]).split("\n")[0].strip() # type: ignore + _t = kwargs.pop("title", None) + title = _t if _t else f"{survey_name} - {ytitle}" if ytitle else survey_name + fig = ( + line_chart( + data=new_df, + y=list(trace_titles.values()), + title=title, + ytitle=ytitle, + same_axis=same_axis, + normalize=False, + scatter_kwargs=scatter_kwargs, + layout_kwargs=layout_kwargs, + **kwargs, + ) + if plot_type == "line" + else bar_chart( + data=new_df, + title=title, + ytitle=ytitle, + x=new_df.index, + y=list(trace_titles.values()), + layout_kwargs=layout_kwargs, + bar_kwargs=bar_kwargs, + **kwargs, + ) + ) + + fig.update_layout( + margin=dict(b=20), + legend=dict( + orientation="h", + yanchor="top", + xanchor="left", + y=-0.075, + x=0, + font=dict(size=12), + ), + ) + content = fig.to_plotly_json() + + return fig, content diff --git a/openbb_platform/extensions/economy/openbb_economy/gdp/gdp_router.py b/openbb_platform/extensions/economy/openbb_economy/gdp/gdp_router.py new file mode 100644 index 0000000000000000000000000000000000000000..b2aa1666e7cab3a673a8376a2437034e413bc31e --- /dev/null +++ b/openbb_platform/extensions/economy/openbb_economy/gdp/gdp_router.py @@ -0,0 +1,83 @@ +"""Economy GDP Router.""" + +from openbb_core.app.model.command_context import CommandContext +from openbb_core.app.model.example import APIEx +from openbb_core.app.model.obbject import OBBject +from openbb_core.app.provider_interface import ( + ExtraParams, + ProviderChoices, + StandardParams, +) +from openbb_core.app.query import Query +from openbb_core.app.router import Router + +router = Router(prefix="/gdp") + +# pylint: disable=unused-argument + + +@router.command( + model="GdpForecast", + examples=[ + APIEx(parameters={"provider": "oecd"}), + APIEx( + parameters={ + "country": "united_states,germany,france", + "frequency": "annual", + "units": "capita", + "provider": "oecd", + } + ), + ], +) +async def forecast( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Get Forecasted GDP Data.""" + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="GdpNominal", + examples=[ + APIEx(parameters={"provider": "oecd"}), + APIEx( + parameters={ + "units": "capita", + "country": "all", + "frequency": "annual", + "provider": "oecd", + } + ), + ], +) +async def nominal( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Get Nominal GDP Data.""" + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="GdpReal", + examples=[ + APIEx(parameters={"provider": "oecd"}), + APIEx( + parameters={"country": "united_states,germany,japan", "provider": "econdb"} + ), + ], +) +async def real( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Get Real GDP Data.""" + return await OBBject.from_query(Query(**locals())) diff --git a/openbb_platform/extensions/economy/openbb_economy/py.typed b/openbb_platform/extensions/economy/openbb_economy/py.typed new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/openbb_platform/extensions/economy/openbb_economy/survey/survey_router.py b/openbb_platform/extensions/economy/openbb_economy/survey/survey_router.py new file mode 100644 index 0000000000000000000000000000000000000000..854961ccdf2e06f3e849ba653f6b8316a4b7943d --- /dev/null +++ b/openbb_platform/extensions/economy/openbb_economy/survey/survey_router.py @@ -0,0 +1,198 @@ +"""Economy Survey Router.""" + +from openbb_core.app.model.command_context import CommandContext +from openbb_core.app.model.example import APIEx +from openbb_core.app.model.obbject import OBBject +from openbb_core.app.provider_interface import ( + ExtraParams, + ProviderChoices, + StandardParams, +) +from openbb_core.app.query import Query +from openbb_core.app.router import Router + +router = Router(prefix="/survey") + +# pylint: disable=unused-argument + + +@router.command( + model="BlsSeries", + examples=[ + APIEx(parameters={"provider": "bls", "symbol": "CES0000000001"}), + ], +) +async def bls_series( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Get time series data for one, or more, BLS series IDs.""" + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="BlsSearch", + examples=[ + APIEx( + parameters={ + "provider": "bls", + "category": "cpi", + } + ), + APIEx( + description="Use semi-colon to separate multiple queries as an & operator.", + parameters={ + "provider": "bls", + "category": "cpi", + "query": "seattle;gasoline", + }, + ), + ], +) +async def bls_search( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Search BLS surveys by category and keyword or phrase to identify BLS series IDs.""" + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="SeniorLoanOfficerSurvey", + examples=[ + APIEx(parameters={"provider": "fred"}), + APIEx(parameters={"category": "credit_card", "provider": "fred"}), + ], +) +async def sloos( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Get Senior Loan Officers Opinion Survey.""" + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="UniversityOfMichigan", + examples=[ + APIEx(parameters={"provider": "fred"}), + ], +) +async def university_of_michigan( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Get University of Michigan Consumer Sentiment and Inflation Expectations Surveys.""" + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="SurveyOfEconomicConditionsChicago", + examples=[ + APIEx(parameters={"provider": "fred"}), + ], +) +async def economic_conditions_chicago( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Get The Survey Of Economic Conditions For The Chicago Region.""" + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="ManufacturingOutlookTexas", + examples=[ + APIEx(parameters={"provider": "fred"}), + APIEx( + parameters={ + "topic": "business_outlook,new_orders", + "transform": "pc1", + "provider": "fred", + } + ), + ], +) +async def manufacturing_outlook_texas( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Get The Manufacturing Outlook Survey For The Texas Region.""" + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="ManufacturingOutlookNY", + examples=[ + APIEx(parameters={"provider": "fred"}), + APIEx( + parameters={ + "topic": "hours_worked,new_orders", + "transform": "pc1", + "provider": "fred", + "seasonally_adjusted": True, + } + ), + ], + openapi_extra={ + "widget_config": { + "name": "Empire State Manufacturing Survey", + } + }, +) +async def manufacturing_outlook_ny( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Get the Empire State Manufacturing Survey. + + It is a monthly survey of manufacturers in New York State conducted by the Federal Reserve Bank of New York. + + Participants from across the state in a variety of industries respond to a questionnaire + and report the change in a variety of indicators from the previous month. + + Respondents also state the likely direction of these same indicators six months ahead. + April 2002 is the first report, although survey data date back to July 2001. + + The survey is sent on the first day of each month to the same pool of about 200 + manufacturing executives in New York State, typically the president or CEO. + + About 100 responses are received. Most are completed by the tenth, although surveys are accepted until the fifteenth. + """ + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="NonFarmPayrolls", + examples=[ + APIEx(parameters={"provider": "fred"}), + APIEx( + parameters={ + "category": "avg_hours", + "provider": "fred", + } + ), + ], +) +async def nonfarm_payrolls( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Get Nonfarm Payrolls Survey.""" + return await OBBject.from_query(Query(**locals())) diff --git a/openbb_platform/extensions/economy/poetry.lock b/openbb_platform/extensions/economy/poetry.lock new file mode 100644 index 0000000000000000000000000000000000000000..8b5cef68b5cd92a25e397a3668ab2711aa485aac --- /dev/null +++ b/openbb_platform/extensions/economy/poetry.lock @@ -0,0 +1,1734 @@ +# This file is automatically @generated by Poetry 2.1.3 and should not be changed by hand. + 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b/openbb_platform/extensions/economy/pyproject.toml new file mode 100644 index 0000000000000000000000000000000000000000..850e7a7cb71b5f6f5ca6d36a7b0271d1877f86c2 --- /dev/null +++ b/openbb_platform/extensions/economy/pyproject.toml @@ -0,0 +1,22 @@ +[tool.poetry] +name = "openbb-economy" +version = "1.4.2" +description = "Economy extension for OpenBB" +authors = ["OpenBB Team "] +license = "AGPL-3.0-only" +readme = "README.md" +packages = [{ include = "openbb_economy" }] + +[tool.poetry.dependencies] +python = ">=3.9.21,<3.13" +openbb-core = "^1.4.6" + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" + +[tool.poetry.plugins."openbb_core_extension"] +economy = "openbb_economy.economy_router:router" + +[tool.poetry.plugins."openbb_charting_extension"] +economy = "openbb_economy.economy_views:EconomyViews" diff --git a/openbb_platform/extensions/economy/tests/.gitkeep b/openbb_platform/extensions/economy/tests/.gitkeep new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/openbb_platform/extensions/equity/README.md b/openbb_platform/extensions/equity/README.md new file mode 100644 index 0000000000000000000000000000000000000000..615df4128d53e016e446fe3a840d90bcaec50a77 --- /dev/null +++ b/openbb_platform/extensions/equity/README.md @@ -0,0 +1,28 @@ +# OpenBB Equity Extension + +This extension provides equity market data tools for the OpenBB Platform. + +Features of the Equity extension include: + +- Access to various equity market data sources +- Sub-modules such as: + - `calendar` for equity-specific events + - `compare` for peer analysis + - `darkpool` for dark pool shorts data + - `discovery` for equity discovery + - `estimates` for analyst estimates + - `fundamental` for fundamental analysis + - `options` for options + - `ownership` for internal and external ownership + - `price` for historical pricing data + - `shorts` for shorts data + +## Installation + +To install the extension, run the following command in this folder: + +```bash +pip install openbb-equity +``` + +Documentation available [here](https://docs.openbb.co/platform/developer_guide/contributing). diff --git a/openbb_platform/extensions/equity/integration/test_equity_api.py b/openbb_platform/extensions/equity/integration/test_equity_api.py new file mode 100644 index 0000000000000000000000000000000000000000..1a7587dd0d85a263fcd04fae404fe6bd3777f492 --- /dev/null +++ b/openbb_platform/extensions/equity/integration/test_equity_api.py @@ -0,0 +1,2258 @@ +"""API integration tests for equity extension.""" + +import base64 + +import pytest +import requests +from extensions.tests.conftest import parametrize +from openbb_core.env import Env +from openbb_core.provider.utils.helpers import get_querystring + +# pylint: disable=too-many-lines,redefined-outer-name + + +@pytest.fixture(scope="session") +def headers(): + """Get the headers for the API request.""" + userpass = f"{Env().API_USERNAME}:{Env().API_PASSWORD}" + userpass_bytes = userpass.encode("ascii") + base64_bytes = base64.b64encode(userpass_bytes) + + return {"Authorization": f"Basic {base64_bytes.decode('ascii')}"} + + +@parametrize( + "params", + [ + ({"symbol": "AAPL", "period": "annual", "limit": 12, "provider": "fmp"}), + ( + { + "provider": "intrinio", + "symbol": "AAPL", + "period": "annual", + "fiscal_year": None, + "limit": 2, + } + ), + ( + { + "provider": "polygon", + "include_sources": True, + "order": "asc", + "sort": "filing_date", + "symbol": "AAPL", + "period": "annual", + "limit": 12, + "filing_date": "2022-10-27", + "filing_date_lt": "2022-11-01", + "filing_date_lte": "2022-11-01", + "filing_date_gt": "2022-10-10", + "filing_date_gte": "2022-10-10", + "period_of_report_date": "2022-09-24", + "period_of_report_date_lt": "2022-11-01", + "period_of_report_date_lte": "2022-11-01", + "period_of_report_date_gt": "2022-10-10", + "period_of_report_date_gte": "2022-10-10", + } + ), + ( + { + "symbol": "AAPL", + "period": "annual", + "limit": 12, + "provider": "fmp", + } + ), + ( + { + "symbol": "AAPL", + "period": "annual", + "limit": 5, + "provider": "yfinance", + } + ), + ], +) +@pytest.mark.integration +def test_equity_fundamental_balance(params, headers): + """Test the equity fundamental balance endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/equity/fundamental/balance?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [{"symbol": "AAPL", "limit": 10, "provider": "fmp", "period": "annual"}], +) +@pytest.mark.integration +def test_equity_fundamental_balance_growth(params, headers): + """Test the equity fundamental balance growth endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/equity/fundamental/balance_growth?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ({"start_date": "2023-11-05", "end_date": "2023-11-10", "provider": "fmp"}), + ({"start_date": "2023-11-05", "end_date": "2023-11-10", "provider": "nasdaq"}), + ], +) +@pytest.mark.integration +def test_equity_calendar_dividend(params, headers): + """Test the equity calendar dividend endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/equity/calendar/dividend?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ({"start_date": "2023-11-05", "end_date": "2023-11-10", "provider": "fmp"}), + ], +) +@pytest.mark.integration +def test_equity_calendar_splits(params, headers): + """Test the equity calendar splits endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/equity/calendar/splits?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ({"start_date": "2023-11-09", "end_date": "2023-11-10", "provider": "fmp"}), + ({"start_date": "2023-11-09", "end_date": "2023-11-10", "provider": "nasdaq"}), + ({"start_date": "2023-11-09", "end_date": "2023-11-10", "provider": "tmx"}), + ( + { + "start_date": None, + "end_date": None, + "provider": "seeking_alpha", + "country": "us", + } + ), + ], +) +@pytest.mark.integration +def test_equity_calendar_earnings(params, headers): + """Test the equity calendar earnings endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/equity/calendar/earnings?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ( + { + "provider": "intrinio", + "symbol": "AAPL", + "period": "ttm", + "fiscal_year": 2015, + "limit": 4, + } + ), + ( + { + "provider": "polygon", + "include_sources": True, + "order": "asc", + "sort": "filing_date", + "symbol": "AAPL", + "period": "annual", + "limit": 12, + "filing_date": "2022-10-27", + "filing_date_lt": "2022-11-01", + "filing_date_lte": "2022-11-01", + "filing_date_gt": "2022-10-10", + "filing_date_gte": "2022-10-10", + "period_of_report_date": "2022-09-24", + "period_of_report_date_lt": "2022-11-01", + "period_of_report_date_lte": "2022-11-01", + "period_of_report_date_gt": "2022-10-10", + "period_of_report_date_gte": "2022-10-10", + } + ), + ( + { + "symbol": "AAPL", + "period": "annual", + "limit": 12, + "provider": "fmp", + } + ), + ( + { + "symbol": "AAPL", + "period": "annual", + "limit": 5, + "provider": "yfinance", + } + ), + ], +) +@pytest.mark.integration +def test_equity_fundamental_cash(params, headers): + """Test the equity fundamental cash endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/equity/fundamental/cash?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [{"symbol": "AAPL", "limit": 10, "provider": "fmp", "period": "annual"}], +) +@pytest.mark.integration +def test_equity_fundamental_cash_growth(params, headers): + """Test the equity fundamental cash growth endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/equity/fundamental/cash_growth?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ( + { + "symbol": "AAPL", + "year": 2022, + "provider": "fmp", + } + ), + ( + { + "symbol": "AAPL", + "provider": "fmp", + "year": None, + } + ), + ( + { + "symbol": "AAPL,MSFT", + "provider": "fmp", + "year": None, + } + ), + ], +) +@pytest.mark.integration +def test_equity_fundamental_management_compensation(params, headers): + """Test the equity fundamental management compensation endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/equity/fundamental/management_compensation?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [{"symbol": "AAPL", "provider": "fmp"}], +) +@pytest.mark.integration +def test_equity_fundamental_historical_splits(params, headers): + """Test the equity fundamental historical splits endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/equity/fundamental/historical_splits?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ( + { + "symbol": "AAPL", + "start_date": "2021-01-01", + "end_date": "2023-06-06", + "limit": 100, + "provider": "intrinio", + } + ), + ({"symbol": "RY", "provider": "tmx"}), + ( + { + "symbol": "AAPL", + "start_date": "2021-01-01", + "end_date": "2023-06-06", + "provider": "fmp", + } + ), + ( + { + "symbol": "AAPL", + "limit": 3, + "provider": "intrinio", + } + ), + ( + { + "symbol": "AAPL", + "provider": "fmp", + } + ), + ( + { + "symbol": "AAPL", + "start_date": "2021-01-01", + "end_date": "2023-06-06", + "provider": "nasdaq", + } + ), + ( + { + "symbol": "AAPL", + "provider": "yfinance", + } + ), + ], +) +@pytest.mark.integration +def test_equity_fundamental_dividends(params, headers): + """Test the equity fundamental dividends endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/equity/fundamental/dividends?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [{"symbol": "AAPL", "provider": "fmp"}], +) +@pytest.mark.integration +def test_equity_fundamental_employee_count(params, headers): + """Test the equity fundamental employee count endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/equity/fundamental/employee_count?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [{"symbol": "AAPL,MSFT", "period": "annual", "limit": 30}], +) +@pytest.mark.integration +def test_equity_estimates_historical(params, headers): + """Test the equity estimates historical endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/equity/estimates/historical?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ( + { + "symbol": "AAPL,MSFT", + "fiscal_period": "fy", + "fiscal_year": None, + "calendar_year": None, + "calendar_period": None, + "provider": "intrinio", + } + ), + ( + { + "symbol": "AAPL,BAM:CA", + "period": "annual", + "provider": "seeking_alpha", + } + ), + ], +) +@pytest.mark.integration +def test_equity_estimates_forward_sales(params, headers): + """Test the equity estimates forward sales endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/equity/estimates/forward_sales?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ( + { + "symbol": "AAPL,MSFT", + "fiscal_period": "fy", + "fiscal_year": None, + "calendar_year": None, + "calendar_period": None, + "provider": "intrinio", + } + ), + ( + { + "symbol": "AAPL,MSFT", + "fiscal_period": "annual", + "limit": None, + "include_historical": False, + "provider": "fmp", + } + ), + ( + { + "symbol": "AAPL,BAM:CA", + "period": "annual", + "provider": "seeking_alpha", + } + ), + ], +) +@pytest.mark.integration +def test_equity_estimates_forward_eps(params, headers): + """Test the equity estimates forward EPS endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/equity/estimates/forward_eps?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ({"symbol": "AAPL", "period": "annual", "limit": 12, "provider": "fmp"}), + ( + { + "provider": "intrinio", + "symbol": "AAPL", + "period": "ytd", + "fiscal_year": 2020, + "limit": 4, + } + ), + ( + { + "provider": "polygon", + "include_sources": True, + "order": "asc", + "sort": "filing_date", + "symbol": "AAPL", + "period": "annual", + "limit": 12, + "filing_date": "2022-10-27", + "filing_date_lt": "2022-11-01", + "filing_date_lte": "2022-11-01", + "filing_date_gt": "2022-10-10", + "filing_date_gte": "2022-10-10", + "period_of_report_date": "2022-09-24", + "period_of_report_date_lt": "2022-11-01", + "period_of_report_date_lte": "2022-11-01", + "period_of_report_date_gt": "2022-10-10", + "period_of_report_date_gte": "2022-10-10", + } + ), + ( + { + "symbol": "AAPL", + "period": "annual", + "limit": 12, + "provider": "fmp", + } + ), + ( + { + "symbol": "AAPL", + "period": "annual", + "limit": 5, + "provider": "yfinance", + } + ), + ], +) +@pytest.mark.integration +def test_equity_fundamental_income(params, headers): + """Test the equity fundamental income endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/equity/fundamental/income?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [{"symbol": "AAPL", "limit": 10, "period": "annual", "provider": "fmp"}], +) +@pytest.mark.integration +def test_equity_fundamental_income_growth(params, headers): + """Test the equity fundamental income growth endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/equity/fundamental/income_growth?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ( + { + "symbol": "RY", + "provider": "tmx", + "limit": 0, + } + ), + ( + { + "provider": "fmp", + "symbol": "AAPL", + "limit": 10, + "transaction_type": None, + } + ), + ( + { + "provider": "intrinio", + "symbol": "AAPL", + "limit": 10, + "start_date": "2021-01-01", + "end_date": "2023-06-06", + "ownership_type": None, + "sort_by": "updated_on", + } + ), + ( + { + "provider": "sec", + "symbol": "AAPL", + "limit": 10, + "start_date": "2024-06-30", + "end_date": "2024-09-30", + "use_cache": True, + } + ), + ], +) +@pytest.mark.integration +def test_equity_ownership_insider_trading(params, headers): + """Test the equity ownership insider trading endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/equity/ownership/insider_trading?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ( + { + "symbol": "AAPL", + "include_current_quarter": True, + "date": "2021-09-30", + "provider": "fmp", + } + ), + # Disabled due to unreliable Intrinio endpoint + # ( + # { + # "provider": "intrinio", + # "symbol": "AAPL", + # "limit": 100, + # } + # ), + ], +) +@pytest.mark.integration +def test_equity_ownership_institutional(params, headers): + """Test the equity ownership institutional endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/equity/ownership/institutional?{query_str}" + result = requests.get(url, headers=headers, timeout=20) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ( + { + "symbol": None, + "start_date": "2023-01-01", + "end_date": "2023-06-06", + "limit": 100, + "provider": "intrinio", + } + ), + ( + { + "start_date": "2023-01-01", + "end_date": "2023-11-01", + "status": "priced", + "provider": "nasdaq", + "is_spo": False, + } + ), + ], +) +@pytest.mark.integration +def test_equity_calendar_ipo(params, headers): + """Test the equity calendar IPO endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/equity/calendar/ipo?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ( + { + "provider": "fmp", + "symbol": "AAPL", + "period": "annual", + "limit": 100, + "with_ttm": False, + } + ), + ({"provider": "intrinio", "symbol": "AAPL", "limit": 100}), + ({"provider": "yfinance", "symbol": "AAPL"}), + ({"provider": "finviz", "symbol": "AAPL,GOOG"}), + ], +) +@pytest.mark.integration +def test_equity_fundamental_metrics(params, headers): + """Test the equity fundamental metrics endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/equity/fundamental/metrics?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ({"symbol": "AAPL", "provider": "fmp"}), + ({"symbol": "AAPL", "provider": "yfinance"}), + ], +) +@pytest.mark.integration +def test_equity_fundamental_management(params, headers): + """Test the equity fundamental management endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/equity/fundamental/management?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [{"symbol": "AAPL", "date": "2023-01-01", "page": 1, "provider": "fmp"}], +) +@pytest.mark.integration +def test_equity_ownership_major_holders(params, headers): + """Test the equity ownership major holders endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/equity/ownership/major_holders?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ({"symbol": "AAPL", "limit": 10, "provider": "fmp", "with_grade": True}), + ({"symbol": "AAPL", "provider": "finviz"}), + ( + { + "symbol": "AAPL", + "limit": 10, + "provider": "benzinga", + # optional provider params + "fields": None, + "date": None, + "start_date": None, + "end_date": None, + "importance": None, + "updated": None, + "action": None, + "analyst_ids": None, + "firm_ids": None, + "page": 0, + } + ), + ], +) +@pytest.mark.integration +def test_equity_estimates_price_target(params, headers): + """Test the equity estimates price target endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/equity/estimates/price_target?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ( + { + "limit": 10, + "provider": "benzinga", + # optional provider params + "fields": None, + "analyst_ids": None, + "firm_ids": None, + "firm_name": "Barclays", + "analyst_name": None, + "page": 0, + } + ), + ( + { + "limit": 3, + "provider": "benzinga", + # optional provider params + "fields": None, + "analyst_ids": None, + "firm_ids": None, + "firm_name": "Barclays,Credit Suisse", + "analyst_name": None, + "page": 1, + } + ), + ], +) +@pytest.mark.integration +def test_equity_estimates_analyst_search(params, headers): + """Test the equity estimates analyst search endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/equity/estimates/analyst_search?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ({"symbol": "AAPL", "provider": "fmp"}), + ({"symbol": "AAPL,AMZN,RELIANCE.NS", "provider": "yfinance"}), + ({"symbol": "TD:US", "provider": "tmx"}), + ( + { + "symbol": "AAPL,MSFT", + "industry_group_number": None, + "provider": "intrinio", + } + ), + ], +) +@pytest.mark.integration +def test_equity_estimates_consensus(params, headers): + """Test the equity estimates consensus endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/equity/estimates/consensus?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ({"symbol": "AAPL", "period": "annual", "limit": 12, "provider": "fmp"}), + ( + { + "symbol": "AAPL", + "period": "ttm", + "fiscal_year": 2019, + "limit": 4, + "provider": "intrinio", + } + ), + ], +) +@pytest.mark.integration +def test_equity_fundamental_ratios(params, headers): + """Test the equity fundamental ratios endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/equity/fundamental/ratios?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [{"symbol": "AAPL", "period": "annual", "provider": "fmp"}], +) +@pytest.mark.integration +def test_equity_fundamental_revenue_per_geography(params, headers): + """Test the equity fundamental revenue per geography endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/equity/fundamental/revenue_per_geography?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [{"symbol": "AAPL", "period": "annual", "provider": "fmp"}], +) +@pytest.mark.integration +def test_equity_fundamental_revenue_per_segment(params, headers): + """Test the equity fundamental revenue per segment endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = ( + f"http://0.0.0.0:8000/api/v1/equity/fundamental/revenue_per_segment?{query_str}" + ) + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ( + { + "symbol": "AAPL", + "form_type": "144", + "limit": 100, + "provider": "fmp", + } + ), + ( + { + "provider": "intrinio", + "symbol": "AAPL", + "start_date": "2021-01-01", + "end_date": "2023-01-01", + "form_type": "4", + "limit": 100, + "thea_enabled": None, + } + ), + ( + { + "symbol": "AAPL", + "limit": 3, + "form_type": "8-K", + "start_date": None, + "end_date": None, + "cik": None, + "provider": "sec", + "use_cache": False, + } + ), + ( + { + "cik": "0001067983", + "limit": 3, + "form_type": "10-Q", + "symbol": None, + "start_date": None, + "end_date": None, + "provider": "sec", + "use_cache": False, + } + ), + ( + { + "provider": "tmx", + "symbol": "IBM:US", + "start_date": "2023-09-30", + "end_date": "2023-12-31", + } + ), + ( + { + "provider": "nasdaq", + "symbol": "AAPL", + "form_group": "annual", + "year": 2024, + } + ), + ], +) +@pytest.mark.integration +def test_equity_fundamental_filings(params, headers): + """Test the equity fundamental filings endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/equity/fundamental/filings?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ({"symbol": "AAPL", "provider": "fmp"}), + ({"symbol": "AAPL", "provider": "intrinio"}), + ({"symbol": "AAPL", "provider": "yfinance"}), + ], +) +@pytest.mark.integration +def test_equity_ownership_share_statistics(params, headers): + """Test the equity ownership share statistics endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/equity/ownership/share_statistics?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [{"symbol": "AAPL", "year": 2023}], +) +@pytest.mark.integration +def test_equity_fundamental_transcript(params, headers): + """Test the equity fundamental transcript endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/equity/fundamental/transcript?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [{"symbol": "AAPL"}], +) +@pytest.mark.integration +def test_equity_compare_peers(params, headers): + """Test the equity compare peers endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/equity/compare/peers?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [{"group": "country", "metric": "overview", "provider": "finviz"}], +) +@pytest.mark.integration +def test_equity_compare_groups(params, headers): + """Test the equity compare groups endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/equity/compare/groups?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ( + { + "adjustment": "unadjusted", + "extended_hours": True, + "provider": "alpha_vantage", + "symbol": "AAPL", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + "interval": "15m", + } + ), + ( + { + "provider": "cboe", + "symbol": "AAPL", + "start_date": None, + "end_date": None, + "interval": "1m", + "use_cache": False, + } + ), + ( + { + "provider": "cboe", + "symbol": "AAPL", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + "interval": "1d", + "use_cache": False, + } + ), + ( + { + "provider": "fmp", + "symbol": "AAPL", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + "interval": "1d", + } + ), + ( + { + "interval": "1h", + "provider": "fmp", + "symbol": "AAPL,MSFT", + "start_date": None, + "end_date": None, + } + ), + ( + { + "timezone": "UTC", + "source": "realtime", + "start_time": None, + "end_time": None, + "provider": "intrinio", + "symbol": "AAPL", + "start_date": "2023-06-01", + "end_date": "2023-06-03", + "interval": "1h", + } + ), + ( + { + "timezone": None, + "source": "delayed", + "start_time": None, + "end_time": None, + "provider": "intrinio", + "symbol": "AAPL", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + "interval": "1d", + } + ), + ( + { + "sort": "desc", + "limit": "49999", + "adjustment": "unadjusted", + "provider": "polygon", + "symbol": "AAPL", + "start_date": "2023-01-01", + "end_date": "2023-01-03", + "interval": "1m", + "extended_hours": False, + } + ), + ( + { + "sort": "desc", + "limit": "49999", + "adjustment": "splits_only", + "provider": "polygon", + "symbol": "AAPL", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + "interval": "1d", + "extended_hours": False, + } + ), + ( + { + "extended_hours": False, + "include_actions": False, + "adjustment": "splits_only", + "provider": "yfinance", + "symbol": "AAPL", + "start_date": None, + "end_date": None, + "interval": "1h", + } + ), + ( + { + "extended_hours": False, + "include_actions": True, + "adjustment": "splits_only", + "provider": "yfinance", + "symbol": "AAPL", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + "interval": "1d", + } + ), + ( + { + "provider": "tiingo", + "symbol": "AAPL", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + "interval": "1d", + } + ), + ( + { + "provider": "tiingo", + "symbol": "AAPL", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + "interval": "1M", + } + ), + ( + { + "provider": "tradier", + "symbol": "AAPL,MSFT", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + "interval": "1M", + "extended_hours": False, + } + ), + ( + { + "provider": "tradier", + "symbol": "AAPL,MSFT", + "start_date": None, + "end_date": None, + "interval": "15m", + "extended_hours": False, + } + ), + ( + { + "provider": "tmx", + "symbol": "AAPL:US", + "start_date": "2023-01-01", + "end_date": "2023-12-31", + "interval": "1d", + "adjustment": "splits_only", + } + ), + ], +) +@pytest.mark.integration +def test_equity_price_historical(params, headers): + """Test the equity price historical endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/equity/price/historical?{query_str}" + result = requests.get(url, headers=headers, timeout=40) + result = requests.get(url, headers=headers, timeout=40) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [{"symbol": "AAPL", "provider": "fmp"}], +) +@pytest.mark.integration +def test_equity_fundamental_multiples(params, headers): + """Test the equity fundamental multiples endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/equity/fundamental/multiples?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ({"query": "ebit", "limit": 100, "provider": "intrinio"}), + ], +) +@pytest.mark.integration +def test_equity_fundamental_search_attributes(params, headers): + """Test the equity fundamental search attributes endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/equity/fundamental/search_attributes?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ( + { + "provider": "intrinio", + "symbol": "AAPL", + "tag": "ebit", + "frequency": "yearly", + "limit": 1000, + "tag_type": None, + "start_date": "2013-01-01", + "end_date": "2023-01-01", + "sort": "desc", + } + ), + ( + { + "provider": "intrinio", + "symbol": "AAPL", + "tag": "ebit,ebitda,marketcap", + "frequency": "yearly", + "limit": 1000, + "tag_type": None, + "start_date": "2013-01-01", + "end_date": "2023-01-01", + "sort": "desc", + } + ), + ( + { + "provider": "intrinio", + "symbol": "AAPL", + "tag": ["ebit", "ebitda", "marketcap"], + "frequency": "yearly", + "limit": 1000, + "tag_type": None, + "start_date": "2013-01-01", + "end_date": "2023-01-01", + "sort": "desc", + } + ), + ( + { + "provider": "intrinio", + "symbol": "AAPL,MSFT", + "tag": "ebit,ebitda,marketcap", + "frequency": "yearly", + "limit": 1000, + "tag_type": None, + "start_date": "2013-01-01", + "end_date": "2023-01-01", + "sort": "desc", + } + ), + ( + { + "provider": "intrinio", + "symbol": ["AAPL", "MSFT"], + "tag": ["ebit", "ebitda", "marketcap"], + "frequency": "yearly", + "limit": 1000, + "tag_type": None, + "start_date": "2013-01-01", + "end_date": "2023-01-01", + "sort": "desc", + } + ), + ], +) +@pytest.mark.integration +def test_equity_fundamental_historical_attributes(params, headers): + """Test the equity fundamental historical attributes endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/equity/fundamental/historical_attributes?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ( + { + "provider": "intrinio", + "symbol": "AAPL", + "tag": "ceo", + } + ), + ( + { + "provider": "intrinio", + "symbol": "AAPL", + "tag": "ebitda", + } + ), + ( + { + "provider": "intrinio", + "symbol": "AAPL", + "tag": "ceo,ebitda", + } + ), + ( + { + "provider": "intrinio", + "symbol": "AAPL", + "tag": ["ceo", "ebitda"], + } + ), + ( + { + "provider": "intrinio", + "symbol": "AAPL,MSFT", + "tag": ["ceo", "ebitda"], + } + ), + ( + { + "provider": "intrinio", + "symbol": ["AAPL", "MSFT"], + "tag": ["ceo", "ebitda"], + } + ), + ], +) +@pytest.mark.integration +def test_equity_fundamental_latest_attributes(params, headers): + """Test the equity fundamental latest attributes endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/equity/fundamental/latest_attributes?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ({"query": "AAPl", "is_symbol": True, "provider": "cboe", "use_cache": False}), + ({"query": "Apple", "provider": "sec", "use_cache": False, "is_fund": False}), + ({"query": "", "provider": "nasdaq", "is_etf": True}), + ({"query": "gold", "provider": "tmx", "use_cache": False}), + ({"query": "gold", "provider": "tradier", "is_symbol": False}), + ( + { + "query": "gold", + "provider": "intrinio", + "active": True, + "limit": 100, + } + ), + ], +) +@pytest.mark.integration +def test_equity_search(params, headers): + """Test the equity search endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/equity/search?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ( + { + "industry": "REIT", + "sector": "real_estate", + "mktcap_min": None, + "mktcap_max": None, + "price_min": None, + "price_max": None, + "volume_min": None, + "volume_max": None, + "dividend_min": None, + "dividend_max": None, + "is_active": True, + "is_etf": False, + "beta_min": None, + "beta_max": None, + "country": "US", + "exchange": "nyse", + "limit": None, + "provider": "fmp", + } + ), + ( + { + "sector": "consumer_staples,consumer_discretionary", + "exchange": "all", + "exsubcategory": "all", + "region": "all", + "country": "all", + "mktcap": "large", + "recommendation": "all", + "limit": None, + "provider": "nasdaq", + } + ), + ( + { + "metric": "overview", + "signal": None, + "preset": None, + "filters_dict": None, + "sector": "consumer_defensive", + "industry": "grocery_stores", + "index": "all", + "exchange": "all", + "mktcap": "all", + "recommendation": "all", + "limit": None, + "provider": "finviz", + } + ), + ( + { + "country": "us", + "sector": "consumer_cyclical", + "industry": "auto_manufacturers", + "exchange": None, + "mktcap_min": 60000000000, + "mktcap_max": None, + "price_min": 10, + "price_max": None, + "volume_min": 5000000, + "volume_max": None, + "beta_min": None, + "beta_max": None, + "provider": "yfinance", + "limit": 200, + } + ), + ], +) +@pytest.mark.integration +def test_equity_screener(params, headers): + """Test the equity screener endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/equity/screener?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ({"source": "iex", "provider": "intrinio", "symbol": "AAPL"}), + ({"symbol": "AAPL", "provider": "fmp"}), + ({"symbol": "AAPL", "provider": "cboe", "use_cache": False}), + ({"symbol": "AAPL", "provider": "yfinance"}), + ({"symbol": "AAPL:US", "provider": "tmx"}), + ({"symbol": "AAPL,MSFT", "provider": "tradier"}), + ], +) +@pytest.mark.integration +def test_equity_price_quote(params, headers): + """Test the equity price quote endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/equity/price/quote?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ({"symbol": "MSFT", "provider": "intrinio"}), + ({"symbol": "AAPL,MSFT", "provider": "intrinio"}), + ({"symbol": "AAPL,MSFT", "provider": "finviz"}), + ({"symbol": "AAPL,MSFT", "provider": "yfinance"}), + ({"provider": "tmx", "symbol": "AAPL:US"}), + ({"symbol": "AAPL,MSFT", "provider": "fmp"}), + ], +) +@pytest.mark.integration +def test_equity_profile(params, headers): + """Test the equity profile endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/equity/profile?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ({"sort": "desc", "provider": "yfinance", "limit": 10}), + ({"provider": "tmx", "category": "52w_high"}), + ], +) +@pytest.mark.integration +def test_equity_discovery_gainers(params, headers): + """Test the equity discovery gainers endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/equity/discovery/gainers?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [{"sort": "desc", "provider": "yfinance", "limit": 10}], +) +@pytest.mark.integration +def test_equity_discovery_losers(params, headers): + """Test the equity discovery losers endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/equity/discovery/losers?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [{"sort": "desc", "provider": "yfinance", "limit": 10}], +) +@pytest.mark.integration +def test_equity_discovery_active(params, headers): + """Test the equity discovery active endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/equity/discovery/active?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ({"symbol": "AAPL", "provider": "fmp"}), + ({"symbol": "AAPL,MSFT", "provider": "finviz"}), + ], +) +@pytest.mark.integration +def test_equity_price_performance(params, headers): + """Test the equity price performance endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/equity/price/performance?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [{"sort": "desc", "provider": "yfinance", "limit": 10}], +) +@pytest.mark.integration +def test_equity_discovery_undervalued_large_caps(params, headers): + """Test the equity discovery undervalued large caps endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/equity/discovery/undervalued_large_caps?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [{"sort": "desc", "provider": "yfinance", "limit": 10}], +) +@pytest.mark.integration +def test_equity_discovery_undervalued_growth(params, headers): + """Test the equity discovery undervalued growth endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/equity/discovery/undervalued_growth?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [{"sort": "desc", "provider": "yfinance", "limit": 10}], +) +@pytest.mark.integration +def test_equity_discovery_aggressive_small_caps(params, headers): + """Test the equity discovery aggressive small caps endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = ( + f"http://0.0.0.0:8000/api/v1/equity/discovery/aggressive_small_caps?{query_str}" + ) + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [{"sort": "desc", "provider": "yfinance", "limit": 10}], +) +@pytest.mark.integration +def test_equity_discovery_growth_tech(params, headers): + """Test the equity discovery growth tech endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/equity/discovery/growth_tech?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [{"limit": 10, "provider": "nasdaq"}], +) +@pytest.mark.integration +def test_equity_discovery_top_retail(params, headers): + """Test the equity discovery top retail endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/equity/discovery/top_retail?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ( + { + "start_date": None, + "end_date": None, + "limit": 10, + "form_type": None, + "is_done": None, + "provider": "fmp", + } + ), + ( + { + "start_date": "2023-11-06", + "end_date": "2023-11-07", + "limit": 50, + "form_type": "10-Q", + "is_done": "true", + "provider": "fmp", + } + ), + ], +) +@pytest.mark.integration +def test_equity_discovery_filings(params, headers): + """Test the equity discovery filings endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/equity/discovery/filings?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ({"symbol": "AAPL"}), + ( + { + "limit": 24, + "provider": "sec", + "symbol": "AAPL", + "skip_reports": 1, + "use_cache": False, + } + ), + ], +) +@pytest.mark.integration +def test_equity_shorts_fails_to_deliver(params, headers): + """Test the equity shorts fails to deliver endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/equity/shorts/fails_to_deliver?{query_str}" + result = requests.get(url, headers=headers, timeout=20) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [{"symbol": "AAPL", "provider": "stockgrid"}], +) +@pytest.mark.integration +def test_equity_shorts_short_volume(params, headers): + """Test the equity shorts short volume endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/equity/shorts/short_volume?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [{"symbol": "AAPL", "provider": "finra"}], +) +@pytest.mark.integration +def test_equity_shorts_short_interest(params, headers): + """Test the equity shorts short interest endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/equity/shorts/short_interest?{query_str}" + result = requests.get(url, headers=headers, timeout=60) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ( + { + "symbol": "CLOV", + "provider": "polygon", # premium endpoint + "timestamp_gt": "2023-10-26T15:20:00.000000000-04:00", + "timestamp_lt": "2023-10-26T15:30:00.000000000-04:00", + "limit": 5000, + "timestamp_gte": None, + "timestamp_lte": None, + "date": None, + } + ), + ], +) +@pytest.mark.integration +def test_equity_price_nbbo(params, headers): + """Test the equity price NBBO endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/equity/price/nbbo?{query_str}" + result = requests.get(url, headers=headers, timeout=40) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ({"symbol": "AAPL"}), + ({"tier": "T1", "is_ats": True, "provider": "finra", "symbol": "AAPL"}), + ], +) +@pytest.mark.integration +def test_equity_darkpool_otc(params, headers): + """Test the equity darkpool otc endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/equity/darkpool/otc?{query_str}" + + try: + result = requests.get(url, headers=headers, timeout=30) + except requests.exceptions.Timeout: + pytest.skip("Timeout: `equity/darkpool/otc` took too long to respond.") + + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ({"provider": "fmp", "market": "euronext"}), + ({"provider": "polygon"}), + ({"provider": "intrinio", "date": "2022-06-30"}), + ], +) +@pytest.mark.integration +def test_equity_market_snapshots(params, headers): + """Test the equity market snapshots endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/equity/market_snapshots?{query_str}" + result = requests.get(url, headers=headers, timeout=20) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ({"symbol": "AAPL", "limit": 5, "provider": "fmp"}), + ( + { + "symbol": "AAPL", + "period": "quarter", + "limit": 5, + "provider": "alpha_vantage", + } + ), + ], +) +@pytest.mark.integration +def test_equity_fundamental_historical_eps(params, headers): + """Test the equity fundamental historical eps endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/equity/fundamental/historical_eps?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [{"provider": "tiingo", "symbol": "AAPL", "limit": 10}], +) +@pytest.mark.integration +def test_equity_fundamental_trailing_dividend_yield(params, headers): + """Test the equity fundamental trailing dividend yield endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/equity/fundamental/trailing_dividend_yield?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ( + { + "symbol": "AAPL", + "statement_type": "income", + "period": "quarter", + "limit": 5, + "fiscal_year": None, + "provider": "intrinio", + } + ), + ( + { + "symbol": "AAPL", + "statement_type": "cash", + "period": "annual", + "limit": 1, + "fiscal_year": 2015, + "provider": "intrinio", + } + ), + ( + { + "symbol": "AAPL", + "statement_type": "balance", + "period": "annual", + "fiscal_year": None, + "limit": 10, + "provider": "intrinio", + } + ), + ], +) +@pytest.mark.integration +def test_equity_fundamental_reported_financials(params, headers): + """Test the equity fundamental reported financials endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = ( + f"http://0.0.0.0:8000/api/v1/equity/fundamental/reported_financials?{query_str}" + ) + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ( + { + "symbol": "NVDA", + "date": None, + "limit": 1, + "provider": "sec", + } + ), + ], +) +@pytest.mark.integration +def test_equity_ownership_form_13f(params, headers): + """Test the equity ownership form 13f endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/equity/ownership/form_13f?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ( + { + "symbol": "NVDA,MSFT", + "provider": "intrinio", + } + ), + ( + { + "symbol": None, + "provider": "intrinio", + } + ), + ], +) +@pytest.mark.integration +def test_equity_estimates_forward_pe(params, headers): + """Test the equity estimates forward_pe endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/equity/estimates/forward_pe?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ( + { + "symbol": "AAPL,MSFT", + "fiscal_period": "quarter", + "provider": "intrinio", + } + ), + ( + { + "symbol": "AAPL,MSFT", + "fiscal_period": "annual", + "limit": None, + "include_historical": False, + "provider": "fmp", + } + ), + ], +) +@pytest.mark.integration +def test_equity_estimates_forward_ebitda(params, headers): + """Test the equity estimates forward_ebitda endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/equity/estimates/forward_ebitda?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ( + { + "provider": "sec", + "symbol": "NVDA,AAPL,AMZN,MSFT,GOOG,SMCI", + "fact": "RevenueFromContractWithCustomerExcludingAssessedTax", + "year": 2024, + "fiscal_period": None, + "instantaneous": False, + "use_cache": False, + } + ), + ( + { + "provider": "sec", + "symbol": None, + "fact": None, + "year": None, + "fiscal_period": None, + "instantaneous": False, + "use_cache": False, + } + ), + ], +) +@pytest.mark.integration +def test_equity_compare_company_facts(params, headers): + """Test the equity compare company_facts endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/equity/compare/company_facts?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ( + { + "symbol": "AAPL,MSFT", + "start_date": None, + "end_date": None, + "provider": "fmp", + } + ), + ( + { + "symbol": "AAPL,MSFT", + "start_date": None, + "end_date": None, + "provider": "intrinio", + "interval": "week", + } + ), + ], +) +@pytest.mark.integration +def test_equity_historical_market_cap(params, headers): + """Test the equity historical market cap endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/equity/historical_market_cap?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ( + { + "date": None, + "report_type": None, + "provider": "sec", + } + ), + ], +) +@pytest.mark.integration +def test_equity_discovery_latest_financial_reports(params, headers): + """Test the equity discovery latest financial reports endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/equity/discovery/latest_financial_reports?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ( + { + "chamber": "all", + "symbol": "AAPL", + "provider": "fmp", + "limit": None, + } + ), + ( + { + "symbol": None, + "chamber": "all", + "limit": 300, + "provider": "fmp", + } + ), + ], +) +@pytest.mark.integration +def test_equity_ownership_government_trades(params, headers): + """Test the equity ownership government trades endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/equity/ownership/government_trades?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ( + { + "symbol": "AAPL", + "calendar_year": 2024, + "calendar_period": "Q2", + "wrap_length": 120, + "include_tables": False, + "use_cache": True, + "raw_html": False, + "strategy": "trafilatura", + "provider": "sec", + } + ), + ], +) +@pytest.mark.integration +def test_equity_fundamental_management_discussion_analysis(params, headers): + """Test the equity fundamental management discussion analysis endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/equity/fundamental/management_discussion_analysis?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ( + { + "start_date": "2024-01-07", + "end_date": "2024-01-10", + "provider": "fmp", + } + ), + ], +) +@pytest.mark.integration +def test_equity_calendar_events(params, headers): + """Test the equity calendar events endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/equity/calendar/events?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 diff --git a/openbb_platform/extensions/equity/integration/test_equity_python.py b/openbb_platform/extensions/equity/integration/test_equity_python.py new file mode 100644 index 0000000000000000000000000000000000000000..d0476beb1f66034a3511f0dbffd560e1658e8231 --- /dev/null +++ b/openbb_platform/extensions/equity/integration/test_equity_python.py @@ -0,0 +1,2114 @@ +"""Python interface integration tests for the equity extension.""" + +from datetime import date, timedelta + +import pytest +from extensions.tests.conftest import parametrize +from openbb_core.app.model.obbject import OBBject + +# pylint: disable=too-many-lines,redefined-outer-name + + +# pylint: disable=import-outside-toplevel,inconsistent-return-statements +@pytest.fixture(scope="session") +def obb(pytestconfig): + """Fixture to setup obb.""" + if pytestconfig.getoption("markexpr") != "not integration": + import openbb + + return openbb.obb + + +@parametrize( + "params", + [ + ({"symbol": "AAPL", "period": "annual", "limit": 12}), + ( + { + "provider": "intrinio", + "symbol": "AAPL", + "period": "quarter", + "fiscal_year": 2014, + "limit": 2, + } + ), + ( + { + "provider": "polygon", + "include_sources": True, + "order": "asc", + "sort": "filing_date", + "symbol": "AAPL", + "period": "annual", + "limit": 12, + "filing_date": "2022-10-27", + "filing_date_lt": "2022-11-01", + "filing_date_lte": "2022-11-01", + "filing_date_gt": "2022-10-10", + "filing_date_gte": "2022-10-10", + "period_of_report_date": "2022-09-24", + "period_of_report_date_lt": "2022-11-01", + "period_of_report_date_lte": "2022-11-01", + "period_of_report_date_gt": "2022-10-10", + "period_of_report_date_gte": "2022-10-10", + } + ), + ( + { + "symbol": "AAPL", + "period": "annual", + "limit": 12, + "provider": "fmp", + } + ), + ( + { + "symbol": "AAPL", + "period": "annual", + "limit": 5, + "provider": "yfinance", + } + ), + ], +) +@pytest.mark.integration +def test_equity_fundamental_balance(params, obb): + """Test the equity fundamental balance endpoint.""" + result = obb.equity.fundamental.balance(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ({"symbol": "AAPL", "limit": 10, "provider": "fmp", "period": "annual"}), + ], +) +@pytest.mark.integration +def test_equity_fundamental_balance_growth(params, obb): + """Test the equity fundamental balance growth endpoint.""" + result = obb.equity.fundamental.balance_growth(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ({"start_date": "2023-11-05", "end_date": "2023-11-10", "provider": "fmp"}), + ({"start_date": "2023-11-05", "end_date": "2023-11-10", "provider": "nasdaq"}), + ], +) +@pytest.mark.integration +def test_equity_calendar_dividend(params, obb): + """Test the equity calendar dividend endpoint.""" + result = obb.equity.calendar.dividend(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ({"start_date": "2023-11-05", "end_date": "2023-11-10", "provider": "fmp"}), + ], +) +@pytest.mark.integration +def test_equity_calendar_splits(params, obb): + """Test the equity calendar splits endpoint.""" + result = obb.equity.calendar.splits(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ({"start_date": "2023-11-09", "end_date": "2023-11-10", "provider": "fmp"}), + ({"start_date": "2023-11-09", "end_date": "2023-11-10", "provider": "nasdaq"}), + ({"start_date": "2023-11-09", "end_date": "2023-11-10", "provider": "tmx"}), + ( + { + "start_date": None, + "end_date": None, + "provider": "seeking_alpha", + "country": "us", + } + ), + ], +) +@pytest.mark.integration +def test_equity_calendar_earnings(params, obb): + """Test the equity calendar earnings endpoint.""" + result = obb.equity.calendar.earnings(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ( + { + "provider": "intrinio", + "symbol": "AAPL", + "period": "annual", + "fiscal_year": None, + "limit": 2, + } + ), + ( + { + "provider": "polygon", + "include_sources": True, + "order": "asc", + "sort": "filing_date", + "symbol": "AAPL", + "period": "annual", + "limit": 12, + "filing_date": "2022-10-27", + "filing_date_lt": "2022-11-01", + "filing_date_lte": "2022-11-01", + "filing_date_gt": "2022-10-10", + "filing_date_gte": "2022-10-10", + "period_of_report_date": "2022-09-24", + "period_of_report_date_lt": "2022-11-01", + "period_of_report_date_lte": "2022-11-01", + "period_of_report_date_gt": "2022-10-10", + "period_of_report_date_gte": "2022-10-10", + } + ), + ( + { + "symbol": "AAPL", + "period": "annual", + "limit": 12, + "provider": "fmp", + } + ), + ( + { + "symbol": "AAPL", + "period": "annual", + "limit": 5, + "provider": "yfinance", + } + ), + ], +) +@pytest.mark.integration +def test_equity_fundamental_cash(params, obb): + """Test the equity fundamental cash endpoint.""" + result = obb.equity.fundamental.cash(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ({"symbol": "AAPL", "limit": 10, "provider": "fmp", "period": "annual"}), + ], +) +@pytest.mark.integration +def test_equity_fundamental_cash_growth(params, obb): + """Test the equity fundamental cash growth endpoint.""" + result = obb.equity.fundamental.cash_growth(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ( + { + "symbol": "AAPL", + "year": 2022, + "provider": "fmp", + } + ), + ( + { + "symbol": "AAPL", + "provider": "fmp", + "year": None, + } + ), + ( + { + "symbol": "AAPL,MSFT", + "provider": "fmp", + "year": None, + } + ), + ], +) +@pytest.mark.integration +def test_equity_fundamental_management_compensation(params, obb): + """Test the equity fundamental management compensation endpoint.""" + result = obb.equity.fundamental.management_compensation(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ({"symbol": "AAPL", "provider": "fmp"}), + ], +) +@pytest.mark.integration +def test_equity_fundamental_historical_splits(params, obb): + """Test the equity fundamental historical splits endpoint.""" + result = obb.equity.fundamental.historical_splits(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ( + { + "symbol": "AAPL", + "start_date": "2021-01-01", + "end_date": "2023-06-06", + "limit": 100, + "provider": "intrinio", + } + ), + ({"symbol": "RY", "provider": "tmx"}), + ( + { + "symbol": "AAPL", + "start_date": "2021-01-01", + "end_date": "2023-06-06", + "provider": "fmp", + } + ), + ( + { + "symbol": "AAPL", + "limit": 3, + "provider": "intrinio", + } + ), + ( + { + "symbol": "AAPL", + "provider": "fmp", + } + ), + ( + { + "symbol": "AAPL", + "start_date": "2021-01-01", + "end_date": "2023-06-06", + "provider": "nasdaq", + } + ), + ( + { + "symbol": "AAPL", + "provider": "yfinance", + } + ), + ], +) +@pytest.mark.integration +def test_equity_fundamental_dividends(params, obb): + """Test the equity fundamental dividends endpoint.""" + result = obb.equity.fundamental.dividends(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ({"symbol": "AAPL", "provider": "fmp"}), + ], +) +@pytest.mark.integration +def test_equity_fundamental_employee_count(params, obb): + """Test the equity fundamental employee count endpoint.""" + result = obb.equity.fundamental.employee_count(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ({"symbol": "AAPL,MSFT", "period": "annual", "limit": 30}), + ], +) +@pytest.mark.integration +def test_equity_estimates_historical(params, obb): + """Test the equity estimates historical endpoint.""" + result = obb.equity.estimates.historical(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ({"symbol": "AAPL", "period": "annual", "limit": 12}), + ( + { + "provider": "intrinio", + "symbol": "AAPL", + "period": "quarter", + "fiscal_year": 2020, + "limit": 4, + } + ), + ( + { + "provider": "polygon", + "include_sources": True, + "order": "asc", + "sort": "filing_date", + "symbol": "AAPL", + "period": "annual", + "limit": 12, + "filing_date": "2022-10-27", + "filing_date_lt": "2022-11-01", + "filing_date_lte": "2022-11-01", + "filing_date_gt": "2022-10-10", + "filing_date_gte": "2022-10-10", + "period_of_report_date": "2022-09-24", + "period_of_report_date_lt": "2022-11-01", + "period_of_report_date_lte": "2022-11-01", + "period_of_report_date_gt": "2022-10-10", + "period_of_report_date_gte": "2022-10-10", + } + ), + ( + { + "provider": "fmp", + "symbol": "AAPL", + "limit": 12, + "period": "annual", + } + ), + ( + { + "provider": "yfinance", + "symbol": "AAPL", + "limit": 5, + "period": "annual", + } + ), + ], +) +@pytest.mark.integration +def test_equity_fundamental_income(params, obb): + """Test the equity fundamental income endpoint.""" + result = obb.equity.fundamental.income(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [{"symbol": "AAPL", "limit": 10, "period": "annual", "provider": "fmp"}], +) +@pytest.mark.integration +def test_equity_fundamental_income_growth(params, obb): + """Test the equity fundamental income growth endpoint.""" + result = obb.equity.fundamental.income_growth(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ( + { + "symbol": "RY", + "provider": "tmx", + "limit": 0, + } + ), + ( + { + "provider": "fmp", + "symbol": "AAPL", + "limit": 10, + "transaction_type": None, + } + ), + ( + { + "provider": "intrinio", + "symbol": "AAPL", + "limit": 10, + "start_date": "2021-01-01", + "end_date": "2023-06-06", + "ownership_type": None, + "sort_by": "updated_on", + } + ), + ( + { + "provider": "sec", + "symbol": "AAPL", + "limit": 10, + "start_date": "2024-06-30", + "end_date": "2024-09-30", + "use_cache": True, + } + ), + ], +) +@pytest.mark.integration +def test_equity_ownership_insider_trading(params, obb): + """Test the equity ownership insider trading endpoint.""" + result = obb.equity.ownership.insider_trading(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ( + { + "symbol": "AAPL", + "include_current_quarter": True, + "date": "2021-09-30", + "provider": "fmp", + } + ), + # Disabled due to unreliable Intrinio endpoint + # ( + # { + # "provider": "intrinio", + # "symbol": "AAPL", + # "limit": 100, + # } + # ), + ], +) +@pytest.mark.integration +def test_equity_ownership_institutional(params, obb): + """Test the equity ownership institutional endpoint.""" + result = obb.equity.ownership.institutional(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ( + { + "symbol": None, + "start_date": "2023-01-01", + "end_date": "2023-06-06", + "limit": 100, + "provider": "intrinio", + } + ), + ( + { + "start_date": "2023-01-01", + "end_date": "2023-11-01", + "status": "priced", + "provider": "nasdaq", + "is_spo": False, + } + ), + ], +) +@pytest.mark.integration +def test_equity_calendar_ipo(params, obb): + """Test the equity calendar IPO endpoint.""" + result = obb.equity.calendar.ipo(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ({"symbol": "AAPL", "period": "annual", "limit": 100}), + ( + { + "provider": "fmp", + "symbol": "AAPL", + "period": "annual", + "limit": 100, + "with_ttm": False, + } + ), + ({"provider": "intrinio", "symbol": "AAPL", "limit": 100}), + ({"provider": "yfinance", "symbol": "AAPL"}), + ({"provider": "finviz", "symbol": "AAPL,GOOG"}), + ], +) +@pytest.mark.integration +def test_equity_fundamental_metrics(params, obb): + """Test the equity fundamental metrics endpoint.""" + result = obb.equity.fundamental.metrics(**params) + assert result + assert isinstance(result, OBBject) + if isinstance(result.results, list): + assert len(result.results) > 0 + else: + assert result.results is not None + + +@parametrize( + "params", + [ + ({"symbol": "AAPL", "provider": "fmp"}), + ({"symbol": "AAPL", "provider": "yfinance"}), + ], +) +@pytest.mark.integration +def test_equity_fundamental_management(params, obb): + """Test the equity fundamental management endpoint.""" + result = obb.equity.fundamental.management(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ({"symbol": "AAPL", "date": "2023-01-01", "page": 1, "provider": "fmp"}), + ], +) +@pytest.mark.integration +def test_equity_ownership_major_holders(params, obb): + """Test the equity ownership major holders endpoint.""" + result = obb.equity.ownership.major_holders(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ({"symbol": "AAPL", "limit": 10, "provider": "fmp", "with_grade": True}), + ( + { + "symbol": "AAPL", + "limit": 10, + "provider": "benzinga", + # optional provider params + "fields": None, + "date": None, + "start_date": None, + "end_date": None, + "importance": None, + "updated": None, + "action": None, + "analyst_ids": None, + "firm_ids": None, + "page": 0, + } + ), + ({"symbol": "AAPL", "provider": "finviz"}), + ], +) +@pytest.mark.integration +def test_equity_estimates_price_target(params, obb): + """Test the equity estimates price target endpoint.""" + result = obb.equity.estimates.price_target(**params) + assert result + assert isinstance(result, OBBject) + assert result.results is not None + + +@parametrize( + "params", + [ + ( + { + "limit": 10, + "provider": "benzinga", + # optional provider params + "fields": None, + "analyst_ids": None, + "firm_ids": None, + "firm_name": "Barclays", + "analyst_name": None, + } + ), + ], +) +@pytest.mark.integration +def test_equity_estimates_analyst_search(params, obb): + """Test the equity estimates analyst search endpoint.""" + result = obb.equity.estimates.analyst_search(**params) + assert result + assert isinstance(result, OBBject) + assert result.results is not None + + +@parametrize( + "params", + [ + ({"symbol": "AAPL", "provider": "fmp"}), + ({"symbol": "AAPL,AMZN,RELIANCE.NS", "provider": "yfinance"}), + ({"symbol": "TD:US", "provider": "tmx"}), + ( + { + "symbol": "AAPL,MSFT", + "industry_group_number": None, + "provider": "intrinio", + } + ), + ], +) +@pytest.mark.integration +def test_equity_estimates_consensus(params, obb): + """Test the equity estimates consensus endpoint.""" + result = obb.equity.estimates.consensus(**params) + assert result + assert isinstance(result, OBBject) + assert result.results is not None + + +@parametrize( + "params", + [ + ( + { + "symbol": "AAPL,MSFT", + "fiscal_period": "fy", + "fiscal_year": None, + "calendar_year": None, + "calendar_period": None, + "provider": "intrinio", + } + ), + ( + { + "symbol": "AAPL,BAM:CA", + "period": "annual", + "provider": "seeking_alpha", + } + ), + ], +) +@pytest.mark.integration +def test_equity_estimates_forward_sales(params, obb): + """Test the equity estimates forward sales endpoint.""" + result = obb.equity.estimates.forward_sales(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ( + { + "symbol": "AAPL,MSFT", + "fiscal_period": "fy", + "fiscal_year": None, + "calendar_year": None, + "calendar_period": None, + "provider": "intrinio", + } + ), + ( + { + "symbol": "AAPL,MSFT", + "fiscal_period": "annual", + "limit": None, + "include_historical": False, + "provider": "fmp", + } + ), + ( + { + "symbol": "AAPL,BAM:CA", + "period": "annual", + "provider": "seeking_alpha", + } + ), + ], +) +@pytest.mark.integration +def test_equity_estimates_forward_eps(params, obb): + """Test the equity estimates forward EPS endpoint.""" + result = obb.equity.estimates.forward_eps(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ( + { + "symbol": "AAPL,MSFT", + "fiscal_period": "quarter", + "provider": "intrinio", + } + ), + ( + { + "symbol": "AAPL,MSFT", + "fiscal_period": "annual", + "limit": None, + "include_historical": False, + "provider": "fmp", + } + ), + ], +) +@pytest.mark.integration +def test_equity_estimates_forward_ebitda(params, obb): + """Test the equity estimates forward EBITDA endpoint.""" + result = obb.equity.estimates.forward_ebitda(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ({"symbol": "AAPL", "period": "annual", "limit": 12, "provider": "fmp"}), + ( + { + "symbol": "AAPL", + "period": "ttm", + "fiscal_year": None, + "limit": 12, + "provider": "intrinio", + } + ), + ], +) +@pytest.mark.integration +def test_equity_fundamental_ratios(params, obb): + """Test the equity fundamental ratios endpoint.""" + result = obb.equity.fundamental.ratios(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ( + { + "symbol": "AAPL", + "period": "annual", + "provider": "fmp", + } + ), + ], +) +@pytest.mark.integration +def test_equity_fundamental_revenue_per_geography(params, obb): + """Test the equity fundamental revenue per geography endpoint.""" + result = obb.equity.fundamental.revenue_per_geography(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ( + { + "symbol": "AAPL", + "period": "annual", + "provider": "fmp", + } + ), + ], +) +@pytest.mark.integration +def test_equity_fundamental_revenue_per_segment(params, obb): + """Test the equity fundamental revenue per segment endpoint.""" + result = obb.equity.fundamental.revenue_per_segment(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ( + { + "symbol": "AAPL", + "form_type": "144", + "limit": 100, + "provider": "fmp", + } + ), + ( + { + "provider": "intrinio", + "symbol": "AAPL", + "start_date": "2021-01-01", + "end_date": "2023-01-01", + "form_type": "4", + "limit": 100, + "thea_enabled": None, + } + ), + ( + { + "symbol": "AAPL", + "limit": 3, + "form_type": "8-K", + "start_date": None, + "end_date": None, + "cik": None, + "provider": "sec", + "use_cache": False, + } + ), + ( + { + "cik": "0001067983", + "limit": 3, + "form_type": "10-Q", + "symbol": None, + "start_date": None, + "end_date": None, + "provider": "sec", + "use_cache": False, + } + ), + ( + { + "provider": "tmx", + "symbol": "IBM:US", + "start_date": "2023-09-30", + "end_date": "2023-12-31", + } + ), + ( + { + "provider": "nasdaq", + "symbol": "AAPL", + "form_group": "annual", + "year": 2024, + } + ), + ], +) +@pytest.mark.integration +def test_equity_fundamental_filings(params, obb): + """Test the equity fundamental filings endpoint.""" + result = obb.equity.fundamental.filings(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ({"symbol": "AAPL", "provider": "fmp"}), + ({"symbol": "AAPL", "provider": "intrinio"}), + ({"symbol": "AAPL", "provider": "yfinance"}), + ], +) +@pytest.mark.integration +def test_equity_ownership_share_statistics(params, obb): + """Test the equity ownership share statistics endpoint.""" + result = obb.equity.ownership.share_statistics(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ({"symbol": "AAPL", "year": 2023}), + ], +) +@pytest.mark.integration +def test_equity_fundamental_transcript(params, obb): + """Test the equity fundamental transcript endpoint.""" + result = obb.equity.fundamental.transcript(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ({"symbol": "AAPL"}), + ], +) +@pytest.mark.integration +def test_equity_compare_peers(params, obb): + """Test the equity compare peers endpoint.""" + result = obb.equity.compare.peers(**params) + assert result + assert isinstance(result, OBBject) + assert result.results is not None + + +@parametrize( + "params", + [{"group": "country", "metric": "overview", "provider": "finviz"}], +) +@pytest.mark.integration +def test_equity_compare_groups(params, obb): + """Test the equity compare groups endpoint.""" + result = obb.equity.compare.groups(**params) + assert result + assert isinstance(result, OBBject) + assert result.results is not None + + +@parametrize( + "params", + [ + ( + { + "adjustment": "unadjusted", + "extended_hours": True, + "provider": "alpha_vantage", + "symbol": "AAPL", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + "interval": "15m", + } + ), + ( + { + "provider": "cboe", + "symbol": "AAPL", + "start_date": (date.today() - timedelta(days=1)).strftime("%Y-%m-%d"), + "end_date": date.today().strftime("%Y-%m-%d"), + "interval": "1m", + "use_cache": False, + } + ), + ( + { + "provider": "cboe", + "symbol": "AAPL", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + "interval": "1d", + "use_cache": False, + } + ), + ( + { + "provider": "fmp", + "symbol": "AAPL", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + "interval": "1d", + } + ), + ( + { + "interval": "1h", + "provider": "fmp", + "symbol": "AAPL,MSFT", + "start_date": None, + "end_date": None, + } + ), + ( + { + "timezone": "UTC", + "source": "realtime", + "start_time": None, + "end_time": None, + "provider": "intrinio", + "symbol": "AAPL", + "start_date": "2023-06-01", + "end_date": "2023-06-03", + "interval": "1h", + } + ), + ( + { + "timezone": None, + "source": "delayed", + "start_time": None, + "end_time": None, + "provider": "intrinio", + "symbol": "AAPL", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + "interval": "1d", + } + ), + ( + { + "sort": "desc", + "limit": "49999", + "adjustment": "unadjusted", + "provider": "polygon", + "symbol": "AAPL", + "start_date": "2023-01-01", + "end_date": "2023-01-03", + "interval": "1m", + "extended_hours": False, + } + ), + ( + { + "sort": "desc", + "limit": "49999", + "adjustment": "splits_only", + "provider": "polygon", + "symbol": "AAPL", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + "interval": "1d", + "extended_hours": False, + } + ), + ( + { + "extended_hours": False, + "include_actions": False, + "adjustment": "splits_and_dividends", + "provider": "yfinance", + "symbol": "AAPL", + "start_date": None, + "end_date": None, + "interval": "1h", + } + ), + ( + { + "extended_hours": False, + "include_actions": True, + "adjustment": "splits_only", + "provider": "yfinance", + "symbol": "AAPL", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + "interval": "1d", + } + ), + ( + { + "provider": "tiingo", + "symbol": "AAPL", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + "interval": "1d", + } + ), + ( + { + "provider": "tiingo", + "symbol": "AAPL", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + "interval": "1M", + } + ), + ( + { + "provider": "tradier", + "symbol": "AAPL,MSFT", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + "interval": "1M", + "extended_hours": False, + } + ), + ( + { + "provider": "tradier", + "symbol": "AAPL,MSFT", + "start_date": None, + "end_date": None, + "interval": "15m", + "extended_hours": False, + } + ), + ( + { + "provider": "tmx", + "symbol": "AAPL:US", + "start_date": "2023-01-01", + "end_date": "2023-12-31", + "interval": "1d", + "adjustment": "splits_only", + } + ), + ], +) +@pytest.mark.integration +def test_equity_price_historical(params, obb): + """Test the equity price historical endpoint.""" + result = obb.equity.price.historical(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ({"symbol": "AAPL", "provider": "fmp"}), + ], +) +@pytest.mark.integration +def test_equity_fundamental_multiples(params, obb): + """Test the equity fundamental multiples endpoint.""" + result = obb.equity.fundamental.multiples(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ({"query": "ebit", "limit": 100, "provider": "intrinio"}), + ], +) +@pytest.mark.integration +def test_equity_fundamental_search_attributes(params, obb): + """Test the equity fundamental search attributes endpoint.""" + result = obb.equity.fundamental.search_attributes(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ( + { + "provider": "intrinio", + "symbol": "AAPL", + "tag": "ebit", + "frequency": "yearly", + "limit": 1000, + "tag_type": None, + "start_date": "2013-01-01", + "end_date": "2023-01-01", + "sort": "desc", + } + ), + ( + { + "provider": "intrinio", + "symbol": "AAPL", + "tag": "ebit,ebitda,marketcap", + "frequency": "yearly", + "limit": 1000, + "tag_type": None, + "start_date": "2013-01-01", + "end_date": "2023-01-01", + "sort": "desc", + } + ), + ( + { + "provider": "intrinio", + "symbol": "AAPL", + "tag": ["ebit", "ebitda", "marketcap"], + "frequency": "yearly", + "limit": 1000, + "tag_type": None, + "start_date": "2013-01-01", + "end_date": "2023-01-01", + "sort": "desc", + } + ), + ( + { + "provider": "intrinio", + "symbol": "AAPL,MSFT", + "tag": "ebit,ebitda,marketcap", + "frequency": "yearly", + "limit": 1000, + "tag_type": None, + "start_date": "2013-01-01", + "end_date": "2023-01-01", + "sort": "desc", + } + ), + ( + { + "provider": "intrinio", + "symbol": ["AAPL", "MSFT"], + "tag": ["ebit", "ebitda", "marketcap"], + "frequency": "yearly", + "limit": 1000, + "tag_type": None, + "start_date": "2013-01-01", + "end_date": "2023-01-01", + "sort": "desc", + } + ), + ], +) +@pytest.mark.integration +def test_equity_fundamental_historical_attributes(params, obb): + """Test the equity fundamental historical attributes endpoint.""" + result = obb.equity.fundamental.historical_attributes(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ( + { + "provider": "intrinio", + "symbol": "AAPL", + "tag": "ceo", + } + ), + ( + { + "provider": "intrinio", + "symbol": "AAPL", + "tag": "ebitda", + } + ), + ( + { + "provider": "intrinio", + "symbol": "AAPL", + "tag": "ceo,ebitda", + } + ), + ( + { + "provider": "intrinio", + "symbol": "AAPL", + "tag": ["ceo", "ebitda"], + } + ), + ( + { + "provider": "intrinio", + "symbol": "AAPL,MSFT", + "tag": ["ceo", "ebitda"], + } + ), + ( + { + "provider": "intrinio", + "symbol": ["MSFT", "AAPL"], + "tag": ["ceo", "ebitda"], + } + ), + ], +) +@pytest.mark.integration +def test_equity_fundamental_latest_attributes(params, obb): + """Test the equity fundamental latest attributes endpoint.""" + result = obb.equity.fundamental.latest_attributes(**params) + assert result + assert isinstance(result, OBBject) + if isinstance(result.results, list): + assert len(result.results) > 0 + else: + assert result.results is not None + + +@parametrize( + "params", + [ + ({"query": "AAPL", "is_symbol": True, "provider": "cboe", "use_cache": False}), + ({"query": "Apple", "provider": "sec", "use_cache": False, "is_fund": False}), + ({"query": "", "provider": "nasdaq", "is_etf": True}), + ({"query": "gold", "provider": "tmx", "use_cache": False}), + ({"query": "gold", "provider": "tradier", "is_symbol": False}), + ( + { + "query": "gold", + "provider": "intrinio", + "active": True, + "limit": 100, + } + ), + ], +) +@pytest.mark.integration +def test_equity_search(params, obb): + """Test the equity search endpoint.""" + result = obb.equity.search(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ( + { + "industry": "REIT", + "sector": "real_estate", + "mktcap_min": None, + "mktcap_max": None, + "price_min": None, + "price_max": None, + "volume_min": None, + "volume_max": None, + "dividend_min": None, + "dividend_max": None, + "is_active": True, + "is_etf": False, + "beta_min": None, + "beta_max": None, + "country": "US", + "exchange": "nyse", + "limit": None, + "provider": "fmp", + } + ), + ( + { + "sector": "consumer_staples,consumer_discretionary", + "exchange": "all", + "exsubcategory": "all", + "region": "all", + "country": "all", + "mktcap": "large", + "recommendation": "all", + "limit": None, + "provider": "nasdaq", + } + ), + ( + { + "metric": "overview", + "signal": None, + "preset": None, + "filters_dict": None, + "sector": "consumer_defensive", + "industry": "grocery_stores", + "index": "all", + "exchange": "all", + "mktcap": "all", + "recommendation": "all", + "limit": None, + "provider": "finviz", + } + ), + ( + { + "country": "us", + "sector": "consumer_cyclical", + "industry": "auto_manufacturers", + "exchange": None, + "mktcap_min": 60000000000, + "mktcap_max": None, + "price_min": 10, + "price_max": None, + "volume_min": 5000000, + "volume_max": None, + "beta_min": None, + "beta_max": None, + "provider": "yfinance", + "limit": 200, + } + ), + ], +) +@pytest.mark.integration +def test_equity_screener(params, obb): + """Test the equity screener endpoint.""" + result = obb.equity.screener(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ({"symbol": "AAPL"}), + ({"source": "iex", "provider": "intrinio", "symbol": "AAPL"}), + ({"symbol": "AAPL", "provider": "fmp"}), + ({"symbol": "AAPL", "provider": "cboe", "use_cache": False}), + ({"symbol": "AAPL", "provider": "yfinance"}), + ({"symbol": "AAPL:US", "provider": "tmx"}), + ({"symbol": "AAPL,MSFT", "provider": "tradier"}), + ], +) +@pytest.mark.integration +def test_equity_price_quote(params, obb): + """Test the equity price quote endpoint.""" + result = obb.equity.price.quote(**params) + assert result + assert isinstance(result, OBBject) + assert result.results is not None + + +@parametrize( + "params", + [ + ({"symbol": "MSFT", "provider": "intrinio"}), + ({"symbol": "AAPL,MSFT", "provider": "intrinio"}), + ({"symbol": "AAPL,MSFT", "provider": "finviz"}), + ({"symbol": "AAPL,MSFT", "provider": "yfinance"}), + ({"symbol": "AAPL,MSFT", "provider": "fmp"}), + ({"provider": "tmx", "symbol": "AAPL:US"}), + ], +) +@pytest.mark.integration +def test_equity_profile(params, obb): + """Test the equity profile endpoint.""" + result = obb.equity.profile(**params) + assert result + assert isinstance(result, OBBject) + if isinstance(result.results, list): + assert len(result.results) > 0 + else: + assert result.results is not None + + +@parametrize( + "params", + [ + ({"sort": "desc", "provider": "yfinance", "limit": 10}), + ({"provider": "tmx", "category": "52w_high"}), + ], +) +@pytest.mark.integration +def test_equity_discovery_gainers(params, obb): + """Test the equity discovery gainers endpoint.""" + params = {p: v for p, v in params.items() if v} + + result = obb.equity.discovery.gainers(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [{"sort": "desc", "provider": "yfinance", "limit": 10}], +) +@pytest.mark.integration +def test_equity_discovery_losers(params, obb): + """Test the equity discovery losers endpoint.""" + params = {p: v for p, v in params.items() if v} + + result = obb.equity.discovery.losers(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [{"sort": "desc", "provider": "yfinance", "limit": 10}], +) +@pytest.mark.integration +def test_equity_discovery_active(params, obb): + """Test the equity discovery active endpoint.""" + params = {p: v for p, v in params.items() if v} + + result = obb.equity.discovery.active(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ({"symbol": "AAPL", "provider": "fmp"}), + ({"symbol": "AAPL,MSFT", "provider": "finviz"}), + ], +) +@pytest.mark.integration +def test_equity_price_performance(params, obb): + """Test the equity price performance endpoint.""" + params = {p: v for p, v in params.items() if v} + + result = obb.equity.price.performance(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [{"sort": "desc", "provider": "yfinance", "limit": 10}], +) +@pytest.mark.integration +def test_equity_discovery_undervalued_large_caps(params, obb): + """Test the equity discovery undervalued large caps endpoint.""" + params = {p: v for p, v in params.items() if v} + + result = obb.equity.discovery.undervalued_large_caps(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [{"sort": "desc", "provider": "yfinance", "limit": 10}], +) +@pytest.mark.integration +def test_equity_discovery_undervalued_growth(params, obb): + """Test the equity discovery undervalued growth endpoint.""" + params = {p: v for p, v in params.items() if v} + + result = obb.equity.discovery.undervalued_growth(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [{"sort": "desc", "provider": "yfinance", "limit": 10}], +) +@pytest.mark.integration +def test_equity_discovery_aggressive_small_caps(params, obb): + """Test the equity discovery aggressive small caps endpoint.""" + params = {p: v for p, v in params.items() if v} + + result = obb.equity.discovery.aggressive_small_caps(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [{"sort": "desc", "provider": "yfinance", "limit": 10}], +) +@pytest.mark.integration +def test_equity_discovery_growth_tech(params, obb): + """Test the equity discovery growth tech endpoint.""" + params = {p: v for p, v in params.items() if v} + + result = obb.equity.discovery.growth_tech(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [{"limit": 10, "provider": "nasdaq"}], +) +@pytest.mark.integration +def test_equity_discovery_top_retail(params, obb): + """Test the equity discovery top retail endpoint.""" + params = {p: v for p, v in params.items() if v} + + result = obb.equity.discovery.top_retail(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ( + { + "start_date": None, + "end_date": None, + "limit": 10, + "form_type": "1-A", + "is_done": None, + "provider": "fmp", + } + ), + ( + { + "start_date": "2023-11-06", + "end_date": "2023-11-07", + "limit": 50, + "form_type": "10-Q", + "is_done": "true", + "provider": "fmp", + } + ), + ], +) +@pytest.mark.integration +def test_equity_discovery_filings(params, obb): + """Test the equity discovery filings endpoint.""" + params = {p: v for p, v in params.items() if v} + + result = obb.equity.discovery.filings(**params) + assert result + assert isinstance(result, OBBject) + if isinstance(result.results, list): + assert len(result.results) > 0 + else: + assert result.results is not None + + +@parametrize( + "params", + [ + ({"symbol": "AAPL"}), + ( + { + "limit": 24, + "provider": "sec", + "symbol": "AAPL", + "skip_reports": 1, + "use_cache": False, + } + ), + ], +) +@pytest.mark.integration +def test_equity_shorts_fails_to_deliver(params, obb): + """Test the equity shorts fails to deliver endpoint.""" + params = {p: v for p, v in params.items() if v} + + result = obb.equity.shorts.fails_to_deliver(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [{"symbol": "AAPL", "provider": "stockgrid"}], +) +@pytest.mark.integration +def test_equity_shorts_short_volume(params, obb): + """Test the equity shorts short volume endpoint.""" + params = {p: v for p, v in params.items() if v} + + result = obb.equity.shorts.short_volume(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [{"symbol": "AAPL", "provider": "finra"}], +) +@pytest.mark.integration +def test_equity_shorts_short_interest(params, obb): + """Test the equity shorts short interest endpoint.""" + params = {p: v for p, v in params.items() if v} + + result = obb.equity.shorts.short_interest(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ( + { + "symbol": "CLOV", + "provider": "polygon", # premium endpoint + "timestamp_gt": "2023-10-26T15:20:00.000000000-04:00", + "timestamp_lt": "2023-10-26T15:30:00.000000000-04:00", + "limit": 5000, + "timestamp_gte": None, + "timestamp_lte": None, + "date": None, + } + ), + ], +) +@pytest.mark.integration +def test_equity_price_nbbo(params, obb): + """Test the equity price nbbo endpoint.""" + result = obb.equity.price.nbbo(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ({"symbol": "AAPL"}), + ({"tier": "T1", "is_ats": True, "provider": "finra", "symbol": "AAPL"}), + ], +) +@pytest.mark.integration +def test_equity_darkpool_otc(params, obb): + """Test the equity darkpool otc endpoint.""" + params = {p: v for p, v in params.items() if v} + + result = obb.equity.darkpool.otc(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ({"provider": "fmp", "market": "euronext"}), + ({"provider": "polygon"}), + ({"provider": "intrinio", "date": "2022-06-30"}), + ], +) +@pytest.mark.integration +def test_equity_market_snapshots(params, obb): + """Test the equity market snapshots endpoint.""" + result = obb.equity.market_snapshots(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ({"symbol": "AAPL", "limit": 5, "provider": "fmp"}), + ( + { + "symbol": "AAPL", + "period": "quarter", + "limit": 5, + "provider": "alpha_vantage", + } + ), + ], +) +@pytest.mark.integration +def test_equity_fundamental_historical_eps(params, obb): + """Test the equity fundamental historical eps endpoint.""" + params = {p: v for p, v in params.items() if v} + + result = obb.equity.fundamental.historical_eps(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [{"provider": "tiingo", "symbol": "AAPL", "limit": 10}], +) +@pytest.mark.integration +def test_equity_fundamental_trailing_dividend_yield(params, obb): + """Test the equity fundamental trailing dividend yield endpoint.""" + params = {p: v for p, v in params.items() if v} + + result = obb.equity.fundamental.trailing_dividend_yield(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ( + { + "symbol": "AAPL", + "statement_type": "income", + "period": "quarter", + "limit": 5, + "fiscal_year": None, + "provider": "intrinio", + } + ), + ( + { + "symbol": "AAPL", + "statement_type": "cash", + "period": "annual", + "limit": 1, + "fiscal_year": 2015, + "provider": "intrinio", + } + ), + ( + { + "symbol": "AAPL", + "statement_type": "balance", + "period": "annual", + "fiscal_year": None, + "limit": 10, + "provider": "intrinio", + } + ), + ], +) +@pytest.mark.integration +def test_equity_fundamental_reported_financials(params, obb): + """Test the equity fundamental reported financials endpoint.""" + params = {p: v for p, v in params.items() if v} + + result = obb.equity.fundamental.reported_financials(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ( + { + "symbol": "NVDA", + "date": None, + "limit": 1, + "provider": "sec", + } + ), + ], +) +@pytest.mark.integration +def test_equity_ownership_form_13f(params, obb): + """Test the equity ownership form 13f endpoint.""" + params = {p: v for p, v in params.items() if v} + + result = obb.equity.ownership.form_13f(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ( + { + "symbol": "NVDA,MSFT", + "provider": "intrinio", + } + ), + ( + { + "symbol": None, + "provider": "intrinio", + } + ), + ], +) +@pytest.mark.integration +def test_equity_estimates_forward_pe(params, obb): + """Test the equity estimates forward_pe endpoint.""" + params = {p: v for p, v in params.items() if v} + + result = obb.equity.estimates.forward_pe(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ( + { + "provider": "sec", + "symbol": "NVDA,AAPL,AMZN,MSFT,GOOG,SMCI", + "fact": "RevenueFromContractWithCustomerExcludingAssessedTax", + "year": 2024, + "fiscal_period": None, + "instantaneous": False, + "use_cache": False, + } + ), + ( + { + "provider": "sec", + "symbol": None, + "fact": None, + "year": None, + "fiscal_period": None, + "instantaneous": False, + "use_cache": False, + } + ), + ], +) +@pytest.mark.integration +def test_equity_compare_company_facts(params, obb): + """Test the equity compare company_facts endpoint.""" + params = {p: v for p, v in params.items() if v} + + result = obb.equity.compare.company_facts(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ( + { + "symbol": "AAPL,MSFT", + "start_date": None, + "end_date": None, + "provider": "fmp", + } + ), + ( + { + "symbol": "AAPL,MSFT", + "start_date": None, + "end_date": None, + "provider": "intrinio", + "interval": "week", + } + ), + ], +) +@pytest.mark.integration +def test_equity_historical_market_cap(params, obb): + """Test the equity historical market cap endpoint.""" + params = {p: v for p, v in params.items() if v} + + result = obb.equity.historical_market_cap(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ( + { + "date": None, + "report_type": None, + "provider": "sec", + } + ), + ], +) +@pytest.mark.integration +def test_equity_discovery_latest_financial_reports(params, obb): + """Test the equity discovery latest financial reports endpoint.""" + params = {p: v for p, v in params.items() if v} + + result = obb.equity.discovery.latest_financial_reports(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ( + { + "chamber": "all", + "symbol": "AAPL", + "provider": "fmp", + "limit": None, + } + ), + ( + { + "symbol": None, + "chamber": "all", + "limit": 300, + "provider": "fmp", + } + ), + ], +) +@pytest.mark.integration +def test_equity_ownership_government_trades(params, obb): + """Test the equity ownership government trades endpoint.""" + params = {p: v for p, v in params.items() if v} + + result = obb.equity.ownership.government_trades(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ( + { + "symbol": "AAPL", + "calendar_year": 2024, + "calendar_period": "Q2", + "wrap_length": 120, + "include_tables": False, + "use_cache": True, + "raw_html": False, + "strategy": "trafilatura", + "provider": "sec", + } + ), + ], +) +@pytest.mark.integration +def test_equity_fundamental_management_discussion_analysis(params, obb): + """Test the equity fundamental management discussion analysis endpoint.""" + result = obb.equity.fundamental.management_discussion_analysis(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results.content) > 0 + + +@parametrize( + "params", + [ + ( + { + "start_date": "2024-01-07", + "end_date": "2024-01-10", + "provider": "fmp", + } + ), + ], +) +@pytest.mark.integration +def test_equity_calendar_events(params, obb): + """Test the equity calendar events endpoint.""" + result = obb.equity.calendar.events(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 diff --git a/openbb_platform/extensions/equity/openbb_equity/__init__.py b/openbb_platform/extensions/equity/openbb_equity/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..ad14014d423971d9915f5e15fb44c3f8e5363bc7 --- /dev/null +++ b/openbb_platform/extensions/equity/openbb_equity/__init__.py @@ -0,0 +1 @@ +"""Equity Data.""" diff --git a/openbb_platform/extensions/equity/openbb_equity/calendar/__init__.py b/openbb_platform/extensions/equity/openbb_equity/calendar/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..79d248b489bc84f9bf98e1b5b2392d76f777bdf6 --- /dev/null +++ b/openbb_platform/extensions/equity/openbb_equity/calendar/__init__.py @@ -0,0 +1 @@ +"""Equity Calendar.""" diff --git a/openbb_platform/extensions/equity/openbb_equity/calendar/calendar_router.py b/openbb_platform/extensions/equity/openbb_equity/calendar/calendar_router.py new file mode 100644 index 0000000000000000000000000000000000000000..5e9e7f64326b28016b5b736426b7e5951e10dda0 --- /dev/null +++ b/openbb_platform/extensions/equity/openbb_equity/calendar/calendar_router.py @@ -0,0 +1,140 @@ +"""Calendar Router.""" + +from openbb_core.app.model.command_context import CommandContext +from openbb_core.app.model.example import APIEx +from openbb_core.app.model.obbject import OBBject +from openbb_core.app.provider_interface import ( + ExtraParams, + ProviderChoices, + StandardParams, +) +from openbb_core.app.query import Query +from openbb_core.app.router import Router + +router = Router(prefix="/calendar") + +# pylint: disable=unused-argument + + +@router.command( + model="CalendarIpo", + examples=[ + APIEx(parameters={"provider": "intrinio"}), + APIEx(parameters={"limit": 100, "provider": "nasdaq"}), + APIEx( + description="Get all IPOs available.", parameters={"provider": "intrinio"} + ), + APIEx( + description="Get IPOs for specific dates.", + parameters={ + "start_date": "2024-02-01", + "end_date": "2024-02-07", + "provider": "nasdaq", + }, + ), + ], +) +async def ipo( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Get historical and upcoming initial public offerings (IPOs).""" + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="CalendarDividend", + examples=[ + APIEx(parameters={"provider": "fmp"}), + APIEx( + description="Get dividend calendar for specific dates.", + parameters={ + "start_date": "2024-02-01", + "end_date": "2024-02-07", + "provider": "nasdaq", + }, + ), + ], +) +async def dividend( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Get historical and upcoming dividend payments. Includes dividend amount, ex-dividend and payment dates.""" + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="CalendarSplits", + examples=[ + APIEx(parameters={"provider": "fmp"}), + APIEx( + description="Get stock splits calendar for specific dates.", + parameters={ + "start_date": "2024-02-01", + "end_date": "2024-02-07", + "provider": "fmp", + }, + ), + ], +) +async def splits( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Get historical and upcoming stock split operations.""" + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="CalendarEvents", + examples=[ + APIEx(parameters={"provider": "fmp"}), + APIEx( + description="Get company events calendar for specific dates.", + parameters={ + "start_date": "2024-02-01", + "end_date": "2024-02-07", + "provider": "fmp", + }, + ), + ], +) +async def events( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Get historical and upcoming company events, such as Investor Day, Conference Call, Earnings Release.""" + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="CalendarEarnings", + examples=[ + APIEx(parameters={"provider": "fmp"}), + APIEx( + description="Get earnings calendar for specific dates.", + parameters={ + "start_date": "2024-02-01", + "end_date": "2024-02-07", + "provider": "fmp", + }, + ), + ], +) +async def earnings( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Get historical and upcoming company earnings releases. Includes earnings per share (EPS) and revenue data.""" + return await OBBject.from_query(Query(**locals())) diff --git a/openbb_platform/extensions/equity/openbb_equity/compare/__init__.py b/openbb_platform/extensions/equity/openbb_equity/compare/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..2648a1695506ba9d05fb7fecd270a7455b4fa385 --- /dev/null +++ b/openbb_platform/extensions/equity/openbb_equity/compare/__init__.py @@ -0,0 +1 @@ +"""Comparison Analysis.""" diff --git a/openbb_platform/extensions/equity/openbb_equity/compare/compare_router.py b/openbb_platform/extensions/equity/openbb_equity/compare/compare_router.py new file mode 100644 index 0000000000000000000000000000000000000000..60b9bea411c5ef3d81e92e72e47963517e272ad3 --- /dev/null +++ b/openbb_platform/extensions/equity/openbb_equity/compare/compare_router.py @@ -0,0 +1,104 @@ +# pylint: disable=W0613:unused-argument +"""Comparison Analysis Router.""" + +from openbb_core.app.model.command_context import CommandContext +from openbb_core.app.model.example import APIEx +from openbb_core.app.model.obbject import OBBject +from openbb_core.app.provider_interface import ( + ExtraParams, + ProviderChoices, + StandardParams, +) +from openbb_core.app.query import Query +from openbb_core.app.router import Router + +router = Router(prefix="/compare") + + +@router.command( + model="EquityPeers", + examples=[APIEx(parameters={"symbol": "AAPL", "provider": "fmp"})], +) +async def peers( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Get the closest peers for a given company. + + Peers consist of companies trading on the same exchange, operating within the same sector + and with comparable market capitalizations. + """ + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="CompareGroups", + examples=[ + APIEx(parameters={"provider": "finviz"}), + APIEx( + description="Group by sector and analyze valuation.", + parameters={"group": "sector", "metric": "valuation", "provider": "finviz"}, + ), + APIEx( + description="Group by industry and analyze performance.", + parameters={ + "group": "industry", + "metric": "performance", + "provider": "finviz", + }, + ), + APIEx( + description="Group by country and analyze valuation.", + parameters={ + "group": "country", + "metric": "valuation", + "provider": "finviz", + }, + ), + ], +) +async def groups( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Get company data grouped by sector, industry or country and display either performance or valuation metrics. + + Valuation metrics include price to earnings, price to book, price to sales ratios and price to cash flow. + Performance metrics include the stock price change for different time periods. + """ + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="CompareCompanyFacts", + examples=[ + APIEx(parameters={"provider": "sec"}), + APIEx( + parameters={ + "provider": "sec", + "fact": "PaymentsForRepurchaseOfCommonStock", + "year": 2023, + } + ), + APIEx( + parameters={ + "provider": "sec", + "symbol": "NVDA,AAPL,AMZN,MSFT,GOOG,SMCI", + "fact": "RevenueFromContractWithCustomerExcludingAssessedTax", + "year": 2024, + } + ), + ], +) +async def company_facts( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Compare reported company facts and fundamental data points.""" + return await OBBject.from_query(Query(**locals())) diff --git a/openbb_platform/extensions/equity/openbb_equity/darkpool/__init__.py b/openbb_platform/extensions/equity/openbb_equity/darkpool/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..8b3133def59dc834bc9716ab35925bf86d89a8b8 --- /dev/null +++ b/openbb_platform/extensions/equity/openbb_equity/darkpool/__init__.py @@ -0,0 +1 @@ +"""Dark Pool.""" diff --git a/openbb_platform/extensions/equity/openbb_equity/darkpool/darkpool_router.py b/openbb_platform/extensions/equity/openbb_equity/darkpool/darkpool_router.py new file mode 100644 index 0000000000000000000000000000000000000000..e9db292bcebb37dc909f9f28b041d8e0eb9de5dc --- /dev/null +++ b/openbb_platform/extensions/equity/openbb_equity/darkpool/darkpool_router.py @@ -0,0 +1,40 @@ +"""Dark Pool Router.""" + +from openbb_core.app.model.command_context import CommandContext +from openbb_core.app.model.example import APIEx +from openbb_core.app.model.obbject import OBBject +from openbb_core.app.provider_interface import ( + ExtraParams, + ProviderChoices, + StandardParams, +) +from openbb_core.app.query import Query +from openbb_core.app.router import Router + +router = Router(prefix="/darkpool") + +# pylint: disable=unused-argument + + +@router.command( + model="OTCAggregate", + examples=[ + APIEx(parameters={"provider": "finra"}), + APIEx( + description="Get OTC data for a symbol", + parameters={"symbol": "AAPL", "provider": "finra"}, + ), + ], +) +async def otc( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Get the weekly aggregate trade data for Over The Counter deals. + + ATS and non-ATS trading data for each ATS/firm + with trade reporting obligations under FINRA rules. + """ + return await OBBject.from_query(Query(**locals())) diff --git a/openbb_platform/extensions/equity/openbb_equity/discovery/__init__.py b/openbb_platform/extensions/equity/openbb_equity/discovery/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..2e85c310105bb69bdd30025471cb5660e773f189 --- /dev/null +++ b/openbb_platform/extensions/equity/openbb_equity/discovery/__init__.py @@ -0,0 +1 @@ +"""Discovery.""" diff --git a/openbb_platform/extensions/equity/openbb_equity/discovery/discovery_router.py b/openbb_platform/extensions/equity/openbb_equity/discovery/discovery_router.py new file mode 100644 index 0000000000000000000000000000000000000000..d7d94605b0449d8f4d5a03b985ce3363824dcf56 --- /dev/null +++ b/openbb_platform/extensions/equity/openbb_equity/discovery/discovery_router.py @@ -0,0 +1,200 @@ +"""Disc router for Equities.""" + +# pylint: disable=unused-argument +from openbb_core.app.model.command_context import CommandContext +from openbb_core.app.model.example import APIEx +from openbb_core.app.model.obbject import OBBject +from openbb_core.app.provider_interface import ( + ExtraParams, + ProviderChoices, + StandardParams, +) +from openbb_core.app.query import Query +from openbb_core.app.router import Router + +router = Router(prefix="/discovery") + + +@router.command( + model="EquityGainers", + examples=[ + APIEx(parameters={"provider": "yfinance"}), + APIEx(parameters={"sort": "desc", "provider": "yfinance"}), + ], +) +async def gainers( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Get the top price gainers in the stock market.""" + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="EquityLosers", + examples=[ + APIEx(parameters={"provider": "yfinance"}), + APIEx(parameters={"sort": "desc", "provider": "yfinance"}), + ], +) +async def losers( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Get the top price losers in the stock market.""" + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="EquityActive", + examples=[ + APIEx(parameters={"provider": "yfinance"}), + APIEx(parameters={"sort": "desc", "provider": "yfinance"}), + ], +) +async def active( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Get the most actively traded stocks based on volume.""" + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="EquityUndervaluedLargeCaps", + examples=[ + APIEx(parameters={"provider": "yfinance"}), + APIEx(parameters={"sort": "desc", "provider": "yfinance"}), + ], +) +async def undervalued_large_caps( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Get potentially undervalued large cap stocks.""" + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="EquityUndervaluedGrowth", + examples=[ + APIEx(parameters={"provider": "yfinance"}), + APIEx(parameters={"sort": "desc", "provider": "yfinance"}), + ], +) +async def undervalued_growth( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Get potentially undervalued growth stocks.""" + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="EquityAggressiveSmallCaps", + examples=[ + APIEx(parameters={"provider": "yfinance"}), + APIEx(parameters={"sort": "desc", "provider": "yfinance"}), + ], +) +async def aggressive_small_caps( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Get top small cap stocks based on earnings growth.""" + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="GrowthTechEquities", + examples=[ + APIEx(parameters={"provider": "yfinance"}), + APIEx(parameters={"sort": "desc", "provider": "yfinance"}), + ], +) +async def growth_tech( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Get top tech stocks based on revenue and earnings growth.""" + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="TopRetail", + examples=[APIEx(parameters={"provider": "nasdaq"})], +) +async def top_retail( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Track over $30B USD/day of individual investors trades. + + It gives a daily view into retail activity and sentiment for over 9,500 US traded stocks, + ADRs, and ETPs. + """ + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="DiscoveryFilings", + examples=[ + APIEx(parameters={"provider": "fmp"}), + APIEx( + description="Get filings for the year 2023, limited to 100 results", + parameters={ + "start_date": "2023-01-01", + "end_date": "2023-12-31", + "limit": 100, + "provider": "fmp", + }, + ), + ], +) +async def filings( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Get the URLs to SEC filings reported to EDGAR database, such as 10-K, 10-Q, 8-K, and more. + + SEC filings include Form 10-K, Form 10-Q, Form 8-K, the proxy statement, Forms 3, 4, and 5, Schedule 13, Form 114, + Foreign Investment Disclosures and others. The annual 10-K report is required to be + filed annually and includes the company's financial statements, management discussion and analysis, + and audited financial statements. + """ + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="LatestFinancialReports", + examples=[ + APIEx(parameters={"provider": "sec"}), + APIEx(parameters={"provider": "sec", "date": "2024-09-30"}), + ], +) +async def latest_financial_reports( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Get the newest quarterly, annual, and current reports for all companies.""" + return await OBBject.from_query(Query(**locals())) diff --git a/openbb_platform/extensions/equity/openbb_equity/equity_router.py b/openbb_platform/extensions/equity/openbb_equity/equity_router.py new file mode 100644 index 0000000000000000000000000000000000000000..c34423cb2661889b8e7f37e4b30bbe7230644f53 --- /dev/null +++ b/openbb_platform/extensions/equity/openbb_equity/equity_router.py @@ -0,0 +1,116 @@ +"""Equity Router.""" + +from openbb_core.app.model.command_context import CommandContext +from openbb_core.app.model.example import APIEx +from openbb_core.app.model.obbject import OBBject +from openbb_core.app.provider_interface import ( + ExtraParams, + ProviderChoices, + StandardParams, +) +from openbb_core.app.query import Query +from openbb_core.app.router import Router + +from openbb_equity.calendar.calendar_router import router as calendar_router +from openbb_equity.compare.compare_router import router as compare_router +from openbb_equity.darkpool.darkpool_router import router as darkpool_router +from openbb_equity.discovery.discovery_router import router as discovery_router +from openbb_equity.estimates.estimates_router import router as estimates_router +from openbb_equity.fundamental.fundamental_router import router as fundamental_router +from openbb_equity.ownership.ownership_router import router as ownership_router +from openbb_equity.price.price_router import router as price_router +from openbb_equity.shorts.shorts_router import router as shorts_router + +router = Router(prefix="", description="Equity market data.") +router.include_router(calendar_router) +router.include_router(compare_router) +router.include_router(estimates_router) +router.include_router(darkpool_router) +router.include_router(discovery_router) +router.include_router(fundamental_router) +router.include_router(ownership_router) +router.include_router(price_router) +router.include_router(shorts_router) + +# pylint: disable=import-outside-toplevel, W0613:unused-argument + + +@router.command( + model="EquitySearch", + examples=[ + APIEx(parameters={"provider": "intrinio"}), + APIEx( + parameters={ + "query": "AAPL", + "is_symbol": False, + "use_cache": True, + "provider": "nasdaq", + } + ), + ], +) +async def search( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Search for stock symbol, CIK, LEI, or company name.""" + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="EquityScreener", examples=[APIEx(parameters={"provider": "fmp"})] +) +async def screener( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Screen for companies meeting various criteria. + + These criteria include market cap, price, beta, volume, and dividend yield. + """ + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="EquityInfo", + examples=[APIEx(parameters={"symbol": "AAPL", "provider": "fmp"})], +) +async def profile( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Get general information about a company. This includes company name, industry, sector and price data.""" + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="MarketSnapshots", examples=[APIEx(parameters={"provider": "fmp"})] +) +async def market_snapshots( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Get an updated equity market snapshot. This includes price data for thousands of stocks.""" + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="HistoricalMarketCap", + examples=[APIEx(parameters={"provider": "fmp", "symbol": "AAPL"})], +) +async def historical_market_cap( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Get the historical market cap of a ticker symbol.""" + return await OBBject.from_query(Query(**locals())) diff --git a/openbb_platform/extensions/equity/openbb_equity/equity_views.py b/openbb_platform/extensions/equity/openbb_equity/equity_views.py new file mode 100644 index 0000000000000000000000000000000000000000..f52f110857494e0f43c762a9387a7480883b082c --- /dev/null +++ b/openbb_platform/extensions/equity/openbb_equity/equity_views.py @@ -0,0 +1,85 @@ +"""Views for the Equity Extension.""" + +from typing import TYPE_CHECKING, Any, Dict, Tuple + +if TYPE_CHECKING: + from openbb_charting.core.openbb_figure import ( + OpenBBFigure, + ) + + +class EquityViews: + """Equity Views.""" + + @staticmethod + def equity_price_historical( # noqa: PLR0912 + **kwargs, + ) -> Tuple["OpenBBFigure", Dict[str, Any]]: + """Equity Price Historical Chart.""" + # pylint: disable=import-outside-toplevel + from openbb_charting.charts.price_historical import price_historical + + return price_historical(**kwargs) + + @staticmethod + def equity_price_performance( # noqa: PLR0912 + **kwargs, + ) -> Tuple["OpenBBFigure", Dict[str, Any]]: + """Equity Price Performance Chart.""" + # pylint: disable=import-outside-toplevel + from openbb_charting.charts.price_performance import price_performance + + return price_performance(**kwargs) + + @staticmethod + def equity_historical_market_cap( # noqa: PLR0912 + **kwargs, + ) -> Tuple["OpenBBFigure", Dict[str, Any]]: + """Equity Historical Market Cap Chart.""" + # pylint: disable=import-outside-toplevel + from openbb_charting.charts.generic_charts import line_chart + from openbb_core.app.utils import basemodel_to_df + from pandas import DataFrame + + title = kwargs.pop("title", "Historical Market Cap") + + data = DataFrame() + + if "data" in kwargs and isinstance(kwargs["data"], DataFrame): + data = kwargs["data"] + elif "data" in kwargs and isinstance(kwargs["data"], list): + data = basemodel_to_df(kwargs["data"], index=kwargs.get("index", "date")) # type: ignore + else: + data = basemodel_to_df( + kwargs["obbject_item"], + index=kwargs.get("index", "date"), # type: ignore + ) + + if "date" in data.columns: + data = data.set_index("date") + + if data.empty: + raise ValueError("Data is empty") + + df = data.pivot(columns="symbol", values="market_cap") + + scatter_kwargs = kwargs.pop("scatter_kwargs", {}) + + if "hovertemplate" not in scatter_kwargs: + scatter_kwargs["hovertemplate"] = "%{y}" + + ytital = kwargs.pop("ytitle", "Market Cap ($)") + y = kwargs.pop("y", df.columns.tolist()) + + fig = line_chart( + data=df, + title=title, + y=y, + ytitle=ytital, + same_axis=True, + scatter_kwargs=scatter_kwargs, + **kwargs, + ) + content = fig.show(external=True).to_plotly_json() + + return fig, content diff --git a/openbb_platform/extensions/equity/openbb_equity/estimates/__init__.py b/openbb_platform/extensions/equity/openbb_equity/estimates/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..d68d27247744a6b1338c30def38abc84210b5ab1 --- /dev/null +++ b/openbb_platform/extensions/equity/openbb_equity/estimates/__init__.py @@ -0,0 +1 @@ +"""Estimates.""" diff --git a/openbb_platform/extensions/equity/openbb_equity/estimates/estimates_router.py b/openbb_platform/extensions/equity/openbb_equity/estimates/estimates_router.py new file mode 100644 index 0000000000000000000000000000000000000000..c779c2d8dc5c309570536a82884ae6b181a7d76c --- /dev/null +++ b/openbb_platform/extensions/equity/openbb_equity/estimates/estimates_router.py @@ -0,0 +1,191 @@ +"""Estimates Router.""" + +from openbb_core.app.model.command_context import CommandContext +from openbb_core.app.model.example import APIEx +from openbb_core.app.model.obbject import OBBject +from openbb_core.app.provider_interface import ( + ExtraParams, + ProviderChoices, + StandardParams, +) +from openbb_core.app.query import Query +from openbb_core.app.router import Router + +router = Router(prefix="/estimates") + +# pylint: disable=unused-argument + + +@router.command( + model="PriceTarget", + examples=[ + APIEx(parameters={"provider": "benzinga"}), + APIEx( + description="Get price targets for Microsoft using 'benzinga' as provider.", + parameters={ + "start_date": "2020-01-01", + "end_date": "2024-02-16", + "limit": 10, + "symbol": "msft", + "provider": "benzinga", + "action": "downgrades", + }, + ), + ], +) +async def price_target( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Get analyst price targets by company.""" + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="AnalystEstimates", + examples=[ + APIEx(parameters={"symbol": "AAPL", "provider": "fmp"}), + ], +) +async def historical( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Get historical analyst estimates for earnings and revenue.""" + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="PriceTargetConsensus", + examples=[ + APIEx(parameters={"symbol": "AAPL", "provider": "fmp"}), + APIEx(parameters={"symbol": "AAPL,MSFT", "provider": "yfinance"}), + ], +) +async def consensus( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Get consensus price target and recommendation.""" + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="AnalystSearch", + examples=[ + APIEx(parameters={"provider": "benzinga"}), + APIEx(parameters={"firm_name": "Wedbush", "provider": "benzinga"}), + ], +) +async def analyst_search( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Search for specific analysts and get their forecast track record.""" + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="ForwardSalesEstimates", + examples=[ + APIEx(parameters={"symbol": "AAPL", "provider": "intrinio"}), + APIEx( + parameters={ + "fiscal_year": 2025, + "fiscal_period": "fy", + "provider": "intrinio", + } + ), + ], +) +async def forward_sales( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Get forward sales estimates.""" + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="ForwardEbitdaEstimates", + examples=[ + APIEx(parameters={"provider": "intrinio"}), + APIEx( + parameters={ + "symbol": "AAPL", + "fiscal_period": "annual", + "provider": "intrinio", + } + ), + APIEx( + parameters={ + "symbol": "AAPL,MSFT", + "fiscal_period": "quarter", + "provider": "fmp", + } + ), + ], +) +async def forward_ebitda( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Get forward EBITDA estimates.""" + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="ForwardEpsEstimates", + examples=[ + APIEx(parameters={"symbol": "AAPL", "provider": "intrinio"}), + APIEx( + parameters={ + "fiscal_year": 2025, + "fiscal_period": "fy", + "provider": "intrinio", + } + ), + ], +) +async def forward_eps( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Get forward EPS estimates.""" + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="ForwardPeEstimates", + examples=[ + APIEx(parameters={"provider": "intrinio"}), + APIEx( + parameters={ + "symbol": "AAPL,MSFT,GOOG", + "provider": "intrinio", + } + ), + ], +) +async def forward_pe( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Get forward PE estimates.""" + return await OBBject.from_query(Query(**locals())) diff --git a/openbb_platform/extensions/equity/openbb_equity/fundamental/__init__.py b/openbb_platform/extensions/equity/openbb_equity/fundamental/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..3a15fb472b19545dfea2f402cba7249db5d4e232 --- /dev/null +++ b/openbb_platform/extensions/equity/openbb_equity/fundamental/__init__.py @@ -0,0 +1 @@ +"""Fundamentals.""" diff --git a/openbb_platform/extensions/equity/openbb_equity/fundamental/fundamental_router.py b/openbb_platform/extensions/equity/openbb_equity/fundamental/fundamental_router.py new file mode 100644 index 0000000000000000000000000000000000000000..5b99a8bb25923a30e058d5ea47745c36076fcda1 --- /dev/null +++ b/openbb_platform/extensions/equity/openbb_equity/fundamental/fundamental_router.py @@ -0,0 +1,528 @@ +# pylint: disable=W0613:unused-argument +"""Fundamental Analysis Router.""" + +from openbb_core.app.model.command_context import CommandContext +from openbb_core.app.model.example import APIEx +from openbb_core.app.model.obbject import OBBject +from openbb_core.app.provider_interface import ( + ExtraParams, + ProviderChoices, + StandardParams, +) +from openbb_core.app.query import Query +from openbb_core.app.router import Router + +router = Router(prefix="/fundamental") + + +@router.command( + model="EquityValuationMultiples", + examples=[APIEx(parameters={"symbol": "AAPL", "provider": "fmp"})], +) +async def multiples( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Get equity valuation multiples for a given company.""" + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="BalanceSheet", + examples=[ + APIEx(parameters={"symbol": "AAPL", "provider": "fmp"}), + APIEx( + parameters={ + "symbol": "AAPL", + "period": "annual", + "limit": 5, + "provider": "intrinio", + } + ), + ], +) +async def balance( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Get the balance sheet for a given company.""" + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="BalanceSheetGrowth", + examples=[ + APIEx(parameters={"symbol": "AAPL", "provider": "fmp"}), + APIEx(parameters={"symbol": "AAPL", "limit": 10, "provider": "fmp"}), + ], +) +async def balance_growth( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Get the growth of a company's balance sheet items over time.""" + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="CashFlowStatement", + examples=[ + APIEx(parameters={"symbol": "AAPL", "provider": "fmp"}), + APIEx( + parameters={ + "symbol": "AAPL", + "period": "annual", + "limit": 5, + "provider": "intrinio", + } + ), + ], +) +async def cash( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Get the cash flow statement for a given company.""" + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="ReportedFinancials", + examples=[ + APIEx(parameters={"symbol": "AAPL", "provider": "intrinio"}), + APIEx( + description="Get AAPL balance sheet with a limit of 10 items.", + parameters={ + "symbol": "AAPL", + "period": "annual", + "statement_type": "balance", + "limit": 10, + "provider": "intrinio", + }, + ), + APIEx( + description="Get reported income statement", + parameters={ + "symbol": "AAPL", + "statement_type": "income", + "provider": "intrinio", + }, + ), + APIEx( + description="Get reported cash flow statement", + parameters={ + "symbol": "AAPL", + "statement_type": "cash", + "provider": "intrinio", + }, + ), + ], +) +async def reported_financials( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Get financial statements as reported by the company.""" + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="CashFlowStatementGrowth", + examples=[ + APIEx(parameters={"symbol": "AAPL", "provider": "fmp"}), + APIEx(parameters={"symbol": "AAPL", "limit": 10, "provider": "fmp"}), + ], +) +async def cash_growth( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Get the growth of a company's cash flow statement items over time.""" + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="HistoricalDividends", + examples=[APIEx(parameters={"symbol": "AAPL", "provider": "intrinio"})], +) +async def dividends( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Get historical dividend data for a given company.""" + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="HistoricalEps", + examples=[APIEx(parameters={"symbol": "AAPL", "provider": "fmp"})], +) +async def historical_eps( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Get historical earnings per share data for a given company.""" + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="HistoricalEmployees", + examples=[APIEx(parameters={"symbol": "AAPL", "provider": "fmp"})], +) +async def employee_count( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Get historical employee count data for a given company.""" + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="SearchAttributes", + examples=[APIEx(parameters={"query": "ebitda", "provider": "intrinio"})], +) +async def search_attributes( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Search Intrinio data tags to search in latest or historical attributes.""" + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="LatestAttributes", + examples=[ + APIEx(parameters={"symbol": "AAPL", "tag": "ceo", "provider": "intrinio"}) + ], +) +async def latest_attributes( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Get the latest value of a data tag from Intrinio.""" + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="HistoricalAttributes", + examples=[ + APIEx(parameters={"symbol": "AAPL", "tag": "ebitda", "provider": "intrinio"}) + ], +) +async def historical_attributes( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Get the historical values of a data tag from Intrinio.""" + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="IncomeStatement", + examples=[ + APIEx(parameters={"symbol": "AAPL", "provider": "fmp"}), + APIEx( + parameters={ + "symbol": "AAPL", + "period": "annual", + "limit": 5, + "provider": "intrinio", + } + ), + ], +) +async def income( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Get the income statement for a given company.""" + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="IncomeStatementGrowth", + examples=[ + APIEx(parameters={"symbol": "AAPL", "provider": "fmp"}), + APIEx( + parameters={ + "symbol": "AAPL", + "limit": 10, + "period": "annual", + "provider": "fmp", + } + ), + ], +) +async def income_growth( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Get the growth of a company's income statement items over time.""" + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="KeyMetrics", + examples=[ + APIEx(parameters={"symbol": "AAPL", "provider": "fmp"}), + APIEx( + parameters={ + "symbol": "AAPL", + "period": "annual", + "limit": 100, + "provider": "intrinio", + } + ), + ], +) +async def metrics( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Get fundamental metrics for a given company.""" + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="KeyExecutives", + examples=[APIEx(parameters={"symbol": "AAPL", "provider": "fmp"})], +) +async def management( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Get executive management team data for a given company.""" + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="ExecutiveCompensation", + examples=[APIEx(parameters={"symbol": "AAPL", "provider": "fmp"})], +) +async def management_compensation( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Get executive management team compensation for a given company over time.""" + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="FinancialRatios", + examples=[ + APIEx(parameters={"symbol": "AAPL", "provider": "fmp"}), + APIEx( + parameters={ + "symbol": "AAPL", + "period": "annual", + "limit": 12, + "provider": "intrinio", + } + ), + ], +) +async def ratios( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Get an extensive set of financial and accounting ratios for a given company over time.""" + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="RevenueGeographic", + examples=[ + APIEx(parameters={"symbol": "AAPL", "provider": "fmp"}), + APIEx( + parameters={ + "symbol": "AAPL", + "period": "quarter", + "provider": "fmp", + } + ), + ], +) +async def revenue_per_geography( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Get the geographic breakdown of revenue for a given company over time.""" + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="RevenueBusinessLine", + examples=[ + APIEx(parameters={"symbol": "AAPL", "provider": "fmp"}), + APIEx( + parameters={ + "symbol": "AAPL", + "period": "quarter", + "provider": "fmp", + } + ), + ], +) +async def revenue_per_segment( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Get the revenue breakdown by business segment for a given company over time.""" + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="CompanyFilings", + examples=[ + APIEx(parameters={"provider": "fmp"}), + APIEx(parameters={"limit": 100, "provider": "fmp"}), + ], +) +async def filings( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Get public company filings.""" + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="HistoricalSplits", + examples=[APIEx(parameters={"symbol": "AAPL", "provider": "fmp"})], +) +async def historical_splits( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Get historical stock splits for a given company.""" + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="EarningsCallTranscript", + examples=[APIEx(parameters={"symbol": "AAPL", "year": 2020, "provider": "fmp"})], +) +async def transcript( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Get earnings call transcripts for a given company.""" + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="TrailingDividendYield", + examples=[ + APIEx(parameters={"symbol": "AAPL", "provider": "tiingo"}), + APIEx(parameters={"symbol": "AAPL", "limit": 252, "provider": "tiingo"}), + ], +) +async def trailing_dividend_yield( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Get the 1 year trailing dividend yield for a given company over time.""" + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="ManagementDiscussionAnalysis", + examples=[ + APIEx(parameters={"symbol": "AAPL", "provider": "sec"}), + APIEx( + description="Get the Management Discussion & Analysis section by calendar year and period.", + parameters={ + "symbol": "AAPL", + "calendar_year": 2020, + "calendar_period": "Q4", + "provider": "sec", + }, + ), + APIEx( + description="Setting 'include_tables' to True will attempt to extract all tables in valid Markdown.", + parameters={ + "symbol": "AAPL", + "calendar_year": 2020, + "calendar_period": "Q4", + "provider": "sec", + "include_tables": True, + }, + ), + APIEx( + description="Setting 'raw_html' to True will bypass extraction and return the raw HTML file, as is." + + " Use this for custom parsing or to access the entire HTML filing.", + parameters={ + "symbol": "AAPL", + "calendar_year": 2020, + "calendar_period": "Q4", + "provider": "sec", + "raw_html": True, + }, + ), + ], + openapi_extra={ + "widget_config": { + "type": "markdown", + "data": {"dataKey": "results.content", "columnsDefs": []}, + "staleTime": 86400000, + "refetchInterval": 86400000, + "source": "SEC", + } + }, +) +async def management_discussion_analysis( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Get the Management Discussion & Analysis section from the financial statements for a given company.""" + return await OBBject.from_query(Query(**locals())) diff --git a/openbb_platform/extensions/equity/openbb_equity/ownership/__init__.py b/openbb_platform/extensions/equity/openbb_equity/ownership/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..496ed5003ed9d3126c5e6c89fb908b62a734e367 --- /dev/null +++ b/openbb_platform/extensions/equity/openbb_equity/ownership/__init__.py @@ -0,0 +1 @@ +"""Equity Ownership.""" diff --git a/openbb_platform/extensions/equity/openbb_equity/ownership/ownership_router.py b/openbb_platform/extensions/equity/openbb_equity/ownership/ownership_router.py new file mode 100644 index 0000000000000000000000000000000000000000..b4dccde57b967f430e6d9666f879c0d00d7846a8 --- /dev/null +++ b/openbb_platform/extensions/equity/openbb_equity/ownership/ownership_router.py @@ -0,0 +1,133 @@ +"""Ownership Router.""" + +from openbb_core.app.model.command_context import CommandContext +from openbb_core.app.model.example import APIEx, PythonEx +from openbb_core.app.model.obbject import OBBject +from openbb_core.app.provider_interface import ( + ExtraParams, + ProviderChoices, + StandardParams, +) +from openbb_core.app.query import Query +from openbb_core.app.router import Router + +router = Router(prefix="/ownership") + +# pylint: disable=unused-argument + + +@router.command( + model="EquityOwnership", + examples=[ + APIEx(parameters={"symbol": "AAPL", "provider": "fmp"}), + APIEx(parameters={"symbol": "AAPL", "page": 0, "provider": "fmp"}), + ], +) +async def major_holders( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Get data about major holders for a given company over time.""" + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="InstitutionalOwnership", + examples=[APIEx(parameters={"symbol": "AAPL", "provider": "fmp"})], +) +async def institutional( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Get data about institutional ownership for a given company over time.""" + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="InsiderTrading", + examples=[ + APIEx(parameters={"symbol": "AAPL", "provider": "fmp"}), + APIEx(parameters={"symbol": "AAPL", "limit": 500, "provider": "intrinio"}), + ], +) +async def insider_trading( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Get data about trading by a company's management team and board of directors.""" + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="ShareStatistics", + examples=[APIEx(parameters={"symbol": "AAPL", "provider": "fmp"})], +) +async def share_statistics( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Get data about share float for a given company.""" + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="Form13FHR", + examples=[ + APIEx(parameters={"symbol": "NVDA", "provider": "sec"}), + APIEx( + description="Enter a date (calendar quarter ending) for a specific report.", + parameters={"symbol": "BRK-A", "date": "2016-09-30", "provider": "sec"}, + ), + PythonEx( + description="Example finding Michael Burry's filings.", + code=[ + 'cik = obb.regulators.sec.institutions_search("Scion Asset Management").results[0].cik', + "# Use the `limit` parameter to return N number of reports from the most recent.", + "obb.equity.ownership.form_13f(cik, limit=2).to_df()", + ], + ), + ], +) +async def form_13f( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Get the form 13F. + + The Securities and Exchange Commission's (SEC) Form 13F is a quarterly report + that is required to be filed by all institutional investment managers with at least + $100 million in assets under management. + Managers are required to file Form 13F within 45 days after the last day of the calendar quarter. + Most funds wait until the end of this period in order to conceal + their investment strategy from competitors and the public. + """ + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="GovernmentTrades", + examples=[ + APIEx(parameters={"symbol": "AAPL", "chamber": "all", "provider": "fmp"}), + APIEx(parameters={"limit": 500, "chamber": "all", "provider": "fmp"}), + ], +) +async def government_trades( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Obtain government transaction data, including data from the Senate + and the House of Representatives. + """ + return await OBBject.from_query(Query(**locals())) diff --git a/openbb_platform/extensions/equity/openbb_equity/price/__init__.py b/openbb_platform/extensions/equity/openbb_equity/price/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e210843604bb38ab19e35f7b88beb0f35f9ebe60 --- /dev/null +++ b/openbb_platform/extensions/equity/openbb_equity/price/__init__.py @@ -0,0 +1 @@ +"""Equity Price.""" diff --git a/openbb_platform/extensions/equity/openbb_equity/price/price_router.py b/openbb_platform/extensions/equity/openbb_equity/price/price_router.py new file mode 100644 index 0000000000000000000000000000000000000000..b52b0f7a730bb73ae3dda5d1b9dd59125036576d --- /dev/null +++ b/openbb_platform/extensions/equity/openbb_equity/price/price_router.py @@ -0,0 +1,75 @@ +"""Price Router.""" + +from openbb_core.app.model.command_context import CommandContext +from openbb_core.app.model.example import APIEx +from openbb_core.app.model.obbject import OBBject +from openbb_core.app.provider_interface import ( + ExtraParams, + ProviderChoices, + StandardParams, +) +from openbb_core.app.query import Query +from openbb_core.app.router import Router + +router = Router(prefix="/price") + +# pylint: disable=unused-argument + + +@router.command( + model="EquityQuote", + examples=[APIEx(parameters={"symbol": "AAPL", "provider": "fmp"})], +) +async def quote( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Get the latest quote for a given stock. Quote includes price, volume, and other data.""" + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="EquityNBBO", + examples=[APIEx(parameters={"symbol": "AAPL", "provider": "polygon"})], +) +async def nbbo( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Get the National Best Bid and Offer for a given stock.""" + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="EquityHistorical", + examples=[ + APIEx(parameters={"symbol": "AAPL", "provider": "fmp"}), + APIEx(parameters={"symbol": "AAPL", "interval": "1d", "provider": "intrinio"}), + ], +) +async def historical( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Get historical price data for a given stock. This includes open, high, low, close, and volume.""" + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="PricePerformance", + examples=[APIEx(parameters={"symbol": "AAPL", "provider": "fmp"})], +) +async def performance( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Get price performance data for a given stock. This includes price changes for different time periods.""" + return await OBBject.from_query(Query(**locals())) diff --git a/openbb_platform/extensions/equity/openbb_equity/py.typed b/openbb_platform/extensions/equity/openbb_equity/py.typed new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/openbb_platform/extensions/equity/openbb_equity/shorts/__init__.py b/openbb_platform/extensions/equity/openbb_equity/shorts/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..bd8609b9f44b75bda4f2b6f0075969374702088f --- /dev/null +++ b/openbb_platform/extensions/equity/openbb_equity/shorts/__init__.py @@ -0,0 +1 @@ +"""Shorts.""" diff --git a/openbb_platform/extensions/equity/openbb_equity/shorts/shorts_router.py b/openbb_platform/extensions/equity/openbb_equity/shorts/shorts_router.py new file mode 100644 index 0000000000000000000000000000000000000000..1a08323b7bd235f056ba65c0485cfcbbb03dbdd6 --- /dev/null +++ b/openbb_platform/extensions/equity/openbb_equity/shorts/shorts_router.py @@ -0,0 +1,58 @@ +"""Shorts Router.""" + +from openbb_core.app.model.command_context import CommandContext +from openbb_core.app.model.example import APIEx +from openbb_core.app.model.obbject import OBBject +from openbb_core.app.provider_interface import ( + ExtraParams, + ProviderChoices, + StandardParams, +) +from openbb_core.app.query import Query +from openbb_core.app.router import Router + +router = Router(prefix="/shorts") + +# pylint: disable=unused-argument + + +@router.command( + model="EquityFTD", + examples=[APIEx(parameters={"symbol": "AAPL", "provider": "sec"})], +) +async def fails_to_deliver( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Get reported Fail-to-deliver (FTD) data.""" + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="ShortVolume", + examples=[APIEx(parameters={"symbol": "AAPL", "provider": "stockgrid"})], +) +async def short_volume( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Get reported Fail-to-deliver (FTD) data.""" + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="EquityShortInterest", + examples=[APIEx(parameters={"symbol": "AAPL", "provider": "finra"})], +) +async def short_interest( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Get reported short volume and days to cover data.""" + return await OBBject.from_query(Query(**locals())) diff --git a/openbb_platform/extensions/equity/poetry.lock b/openbb_platform/extensions/equity/poetry.lock new file mode 100644 index 0000000000000000000000000000000000000000..8b5cef68b5cd92a25e397a3668ab2711aa485aac --- /dev/null +++ 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b/openbb_platform/extensions/equity/pyproject.toml new file mode 100644 index 0000000000000000000000000000000000000000..516de832055594f0d5054414683d135526dbb9e4 --- /dev/null +++ b/openbb_platform/extensions/equity/pyproject.toml @@ -0,0 +1,22 @@ +[tool.poetry] +name = "openbb-equity" +version = "1.4.1" +description = "Equity extension for OpenBB" +authors = ["OpenBB Team "] +license = "AGPL-3.0-only" +readme = "README.md" +packages = [{ include = "openbb_equity" }] + +[tool.poetry.dependencies] +python = ">=3.9.21,<3.13" +openbb-core = "^1.4.6" + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" + +[tool.poetry.plugins."openbb_core_extension"] +equity = "openbb_equity.equity_router:router" + +[tool.poetry.plugins."openbb_charting_extension"] +equity = "openbb_equity.equity_views:EquityViews" diff --git a/openbb_platform/extensions/equity/tests/.gitkeep b/openbb_platform/extensions/equity/tests/.gitkeep new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/openbb_platform/extensions/etf/README.md b/openbb_platform/extensions/etf/README.md new file mode 100644 index 0000000000000000000000000000000000000000..1448428d9bc40388d90db2244872d27fffb98113 --- /dev/null +++ b/openbb_platform/extensions/etf/README.md @@ -0,0 +1,11 @@ +# ETF data extension for OpenBB SDK + +This extension provides a set of commands for ETF data retrieval. + +## Installation + +To install the extension, run the following command in this folder: + +```bash +pip install openbb-etf +``` diff --git a/openbb_platform/extensions/etf/integration/test_etf_api.py b/openbb_platform/extensions/etf/integration/test_etf_api.py new file mode 100644 index 0000000000000000000000000000000000000000..b4226d6c8171dbc5a04852a9b2bdbbe0c6db6cae --- /dev/null +++ b/openbb_platform/extensions/etf/integration/test_etf_api.py @@ -0,0 +1,477 @@ +"""Integration tests for the ETF API.""" + +import base64 + +import pytest +import requests +from extensions.tests.conftest import parametrize +from openbb_core.env import Env +from openbb_core.provider.utils.helpers import get_querystring + + +@pytest.fixture(scope="session") +def headers(): + """Get the headers for the API request.""" + userpass = f"{Env().API_USERNAME}:{Env().API_PASSWORD}" + userpass_bytes = userpass.encode("ascii") + base64_bytes = base64.b64encode(userpass_bytes) + + return {"Authorization": f"Basic {base64_bytes.decode('ascii')}"} + + +# pylint: disable=redefined-outer-name + + +@parametrize( + "params", + [ + ({"query": "", "provider": "fmp"}), + ( + { + "query": "vanguard", + "provider": "tmx", + "div_freq": "quarterly", + "sort_by": "return_1y", + "use_cache": False, + } + ), + ( + { + "query": "vanguard", + "provider": "intrinio", + "exchange": "arcx", + } + ), + ], +) +@pytest.mark.integration +def test_etf_search(params, headers): + """Test the ETF search endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/etf/search?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ( + { + "adjustment": "unadjusted", + "extended_hours": True, + "provider": "alpha_vantage", + "symbol": "SPY", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + "interval": "15m", + } + ), + ( + { + "provider": "cboe", + "symbol": "SPY", + "start_date": None, + "end_date": None, + "interval": "1m", + "use_cache": False, + } + ), + ( + { + "provider": "cboe", + "symbol": "SPY", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + "interval": "1d", + "use_cache": False, + } + ), + ( + { + "provider": "fmp", + "symbol": "SPY", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + "interval": "1d", + } + ), + ( + { + "timezone": "UTC", + "source": "realtime", + "start_time": None, + "end_time": None, + "provider": "intrinio", + "symbol": "SPY", + "start_date": "2023-06-01", + "end_date": "2023-06-03", + "interval": "1h", + } + ), + ( + { + "timezone": None, + "source": "delayed", + "start_time": None, + "end_time": None, + "provider": "intrinio", + "symbol": "AAPL", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + "interval": "1d", + } + ), + ( + { + "sort": "desc", + "limit": "49999", + "adjustment": "unadjusted", + "provider": "polygon", + "symbol": "SPY", + "start_date": "2023-01-01", + "end_date": "2023-01-03", + "interval": "1m", + "extended_hours": False, + } + ), + ( + { + "sort": "desc", + "limit": "49999", + "adjustment": "splits_only", + "provider": "polygon", + "symbol": "SPY", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + "interval": "1d", + "extended_hours": False, + } + ), + ( + { + "extended_hours": False, + "include_actions": False, + "adjustment": "splits_and_dividends", + "provider": "yfinance", + "symbol": "SPY", + "start_date": None, + "end_date": None, + "interval": "1h", + } + ), + ( + { + "extended_hours": False, + "include_actions": True, + "adjustment": "splits_only", + "provider": "yfinance", + "symbol": "SPY", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + "interval": "1d", + } + ), + ( + { + "provider": "tiingo", + "symbol": "SPY", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + "interval": "1d", + } + ), + ( + { + "provider": "tiingo", + "symbol": "SPY", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + "interval": "1M", + } + ), + ( + { + "provider": "tradier", + "symbol": "SPY", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + "interval": "1M", + "extended_hours": False, + } + ), + ( + { + "provider": "tradier", + "symbol": "SPY,DJIA", + "start_date": None, + "end_date": None, + "interval": "15m", + "extended_hours": False, + } + ), + ( + { + "provider": "tmx", + "symbol": "SPY:US", + "start_date": "2023-01-01", + "end_date": "2023-12-31", + "interval": "1d", + "adjustment": "splits_only", + } + ), + ], +) +@pytest.mark.integration +def test_etf_historical(params, headers): + """Test the ETF historical endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/etf/historical?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ({"symbol": "IOO", "provider": "fmp"}), + ({"symbol": "XIU", "provider": "tmx", "use_cache": False}), + ({"symbol": "QQQ", "provider": "yfinance"}), + ({"symbol": "IOO,QQQ", "provider": "intrinio"}), + ], +) +@pytest.mark.integration +def test_etf_info(params, headers): + """Test the ETF info endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/etf/info?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ({"symbol": "IOO", "provider": "fmp"}), + ({"symbol": "XIU", "provider": "tmx", "use_cache": False}), + ], +) +@pytest.mark.integration +def test_etf_sectors(params, headers): + """Test the ETF sectors endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/etf/sectors?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ({"symbol": "QQQ", "cik": None, "provider": "fmp"}), + ], +) +@pytest.mark.integration +def test_etf_holdings_date(params, headers): + """Test the ETF holdings date endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/etf/holdings_date?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ( + { + "symbol": "IOO", + "date": "2023-03-31", + "cik": None, + "provider": "fmp", + } + ), + ( + { + "symbol": "VOO", + "date": "2023-03-31", + "cik": None, + "provider": "fmp", + } + ), + ( + { + "symbol": "TQQQ", + "date": None, + "provider": "sec", + "use_cache": False, + } + ), + ( + { + "symbol": "QQQ", + "date": "2021-06-30", + "provider": "sec", + "use_cache": False, + } + ), + ( + { + "symbol": "XIU", + "provider": "tmx", + "use_cache": False, + } + ), + ( + { + "symbol": "DJIA", + "provider": "intrinio", + "date": None, + } + ), + ( + { + "symbol": "QQQ", + "provider": "intrinio", + "date": "2020-04-03", + } + ), + ], +) +@pytest.mark.integration +def test_etf_holdings(params, headers): + """Test the ETF holdings endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/etf/holdings?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ({"symbol": "SPY,VOO,QQQ,IWM,IWN,GOVT,JNK", "provider": "fmp"}), + ({"symbol": "SPY,VOO,QQQ,IWM,IWN,GOVT,JNK", "provider": "finviz"}), + ( + { + "symbol": "SPY,VOO,QQQ,IWM,IWN,GOVT,JNK", + "return_type": "trailing", + "adjustment": "splits_and_dividends", + "provider": "intrinio", + } + ), + ], +) +@pytest.mark.integration +def test_etf_price_performance(params, headers): + """Test the ETF price performance endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/etf/price_performance?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ({"symbol": "IOO", "provider": "fmp"}), + ({"symbol": "XIU", "use_cache": False, "provider": "tmx"}), + ], +) +@pytest.mark.integration +def test_etf_countries(params, headers): + """Test the ETF countries endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/etf/countries?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [({"sort": "desc", "limit": 10})], +) +@pytest.mark.integration +def test_etf_discovery_gainers(params, headers): + """Test the ETF discovery gainers endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/etf/discovery/gainers?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [({"sort": "desc", "limit": 10})], +) +@pytest.mark.integration +def test_etf_discovery_losers(params, headers): + """Test the ETF discovery losers endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/etf/discovery/losers?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [({"sort": "desc", "limit": 10})], +) +@pytest.mark.integration +def test_etf_discovery_active(params, headers): + """Test the ETF discovery active endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/etf/discovery/active?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ({"symbol": "SPY,VOO,QQQ,IWM,IWN", "provider": "fmp"}), + ], +) +@pytest.mark.integration +def test_etf_equity_exposure(params, headers): + """Test the ETF equity exposure endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/etf/equity_exposure?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 diff --git a/openbb_platform/extensions/etf/integration/test_etf_python.py b/openbb_platform/extensions/etf/integration/test_etf_python.py new file mode 100644 index 0000000000000000000000000000000000000000..dbbea4981b4ad314177c8b68c2ad60cd4ea122c5 --- /dev/null +++ b/openbb_platform/extensions/etf/integration/test_etf_python.py @@ -0,0 +1,459 @@ +"""Test etf extension.""" + +import pytest +from extensions.tests.conftest import parametrize +from openbb_core.app.model.obbject import OBBject + + +@pytest.fixture(scope="session") +def obb(pytestconfig): # pylint: disable=inconsistent-return-statements + """Fixture to setup obb.""" + + if pytestconfig.getoption("markexpr") != "not integration": + import openbb # pylint: disable=import-outside-toplevel + + return openbb.obb + + +# pylint: disable=redefined-outer-name + + +@parametrize( + "params", + [ + ({"query": None, "provider": "fmp"}), + ( + { + "query": "vanguard", + "provider": "tmx", + "div_freq": "quarterly", + "sort_by": "return_1y", + "use_cache": False, + } + ), + ( + { + "query": "vanguard", + "provider": "intrinio", + "exchange": "arcx", + } + ), + ], +) +@pytest.mark.integration +def test_etf_search(params, obb): + """Test the ETF search endpoint.""" + params = {p: v for p, v in params.items() if v} + + result = obb.etf.search(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ( + { + "adjustment": "unadjusted", + "extended_hours": True, + "provider": "alpha_vantage", + "symbol": "SPY", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + "interval": "15m", + } + ), + ( + { + "provider": "cboe", + "symbol": "SPY", + "start_date": None, + "end_date": None, + "interval": "1m", + "use_cache": False, + } + ), + ( + { + "provider": "cboe", + "symbol": "SPY", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + "interval": "1d", + "use_cache": False, + } + ), + ( + { + "provider": "fmp", + "symbol": "SPY", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + "interval": "1d", + } + ), + ( + { + "timezone": "UTC", + "source": "realtime", + "start_time": None, + "end_time": None, + "provider": "intrinio", + "symbol": "SPY", + "start_date": "2023-06-01", + "end_date": "2023-06-03", + "interval": "1h", + } + ), + ( + { + "timezone": None, + "source": "delayed", + "start_time": None, + "end_time": None, + "provider": "intrinio", + "symbol": "AAPL", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + "interval": "1d", + } + ), + ( + { + "sort": "desc", + "limit": "49999", + "adjustment": "unadjusted", + "provider": "polygon", + "symbol": "SPY", + "start_date": "2023-01-01", + "end_date": "2023-01-03", + "interval": "1m", + "extended_hours": False, + } + ), + ( + { + "sort": "desc", + "limit": "49999", + "adjustment": "splits_only", + "provider": "polygon", + "symbol": "SPY", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + "interval": "1d", + "extended_hours": False, + } + ), + ( + { + "extended_hours": False, + "include_actions": False, + "adjustment": "splits_and_dividends", + "provider": "yfinance", + "symbol": "SPY", + "start_date": None, + "end_date": None, + "interval": "1h", + } + ), + ( + { + "extended_hours": False, + "include_actions": True, + "adjustment": "splits_only", + "provider": "yfinance", + "symbol": "SPY", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + "interval": "1d", + } + ), + ( + { + "provider": "tiingo", + "symbol": "SPY", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + "interval": "1d", + } + ), + ( + { + "provider": "tiingo", + "symbol": "SPY", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + "interval": "1M", + } + ), + ( + { + "provider": "tradier", + "symbol": "SPY", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + "interval": "1M", + "extended_hours": False, + } + ), + ( + { + "provider": "tradier", + "symbol": "SPY,DJIA", + "start_date": None, + "end_date": None, + "interval": "15m", + "extended_hours": False, + } + ), + ( + { + "provider": "tmx", + "symbol": "SPY:US", + "start_date": "2023-01-01", + "end_date": "2023-12-31", + "interval": "1d", + "adjustment": "splits_only", + } + ), + ], +) +@pytest.mark.integration +def test_etf_historical(params, obb): + """Test the ETF historical endpoint.""" + result = obb.equity.price.historical(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ({"symbol": "IOO", "provider": "fmp"}), + ({"symbol": "XIU", "provider": "tmx", "use_cache": False}), + ({"symbol": "QQQ", "provider": "yfinance"}), + ({"symbol": "IOO,QQQ", "provider": "intrinio"}), + ], +) +@pytest.mark.integration +def test_etf_info(params, obb): + """Test the ETF info endpoint.""" + params = {p: v for p, v in params.items() if v} + + result = obb.etf.info(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ({"symbol": "IOO", "provider": "fmp"}), + ({"symbol": "XIU", "provider": "tmx", "use_cache": False}), + ], +) +@pytest.mark.integration +def test_etf_sectors(params, obb): + """Test the ETF sectors endpoint.""" + params = {p: v for p, v in params.items() if v} + + result = obb.etf.sectors(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ({"symbol": "QQQ", "cik": None, "provider": "fmp"}), + ], +) +@pytest.mark.integration +def test_etf_holdings_date(params, obb): + """Test the ETF holdings date endpoint.""" + params = {p: v for p, v in params.items() if v} + + result = obb.etf.holdings_date(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ( + { + "symbol": "IOO", + "date": "2023-03-31", + "cik": None, + "provider": "fmp", + } + ), + ( + { + "symbol": "SILJ", + "date": "2019-12-31", + "cik": None, + "provider": "fmp", + } + ), + ( + { + "symbol": "TQQQ", + "date": None, + "provider": "sec", + "use_cache": False, + } + ), + ( + { + "symbol": "QQQ", + "date": "2021-06-30", + "provider": "sec", + "use_cache": False, + } + ), + ( + { + "symbol": "XIU", + "provider": "tmx", + "use_cache": False, + } + ), + ( + { + "symbol": "DJIA", + "provider": "intrinio", + "date": None, + } + ), + ( + { + "symbol": "QQQ", + "provider": "intrinio", + "date": "2020-04-03", + } + ), + ], +) +@pytest.mark.integration +def test_etf_holdings(params, obb): + """Test the ETF holdings endpoint.""" + params = {p: v for p, v in params.items() if v} + + result = obb.etf.holdings(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ({"symbol": "SPY,VOO,QQQ,IWM,IWN,GOVT,JNK", "provider": "fmp"}), + ({"symbol": "SPY,VOO,QQQ,IWM,IWN,GOVT,JNK", "provider": "finviz"}), + ( + { + "symbol": "SPY,VOO,QQQ,IWM,IWN,GOVT,JNK", + "return_type": "trailing", + "adjustment": "splits_and_dividends", + "provider": "intrinio", + } + ), + ], +) +@pytest.mark.integration +def test_etf_price_performance(params, obb): + """Test the ETF price performance endpoint.""" + params = {p: v for p, v in params.items() if v} + + result = obb.etf.price_performance(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ({"symbol": "IOO", "provider": "fmp"}), + ({"symbol": "XIU", "provider": "tmx", "use_cache": False}), + ], +) +@pytest.mark.integration +def test_etf_countries(params, obb): + """Test the ETF countries endpoint.""" + params = {p: v for p, v in params.items() if v} + + result = obb.etf.countries(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [({"sort": "desc", "limit": 10})], +) +@pytest.mark.integration +def test_etf_discovery_gainers(params, obb): + """Test the ETF discovery gainers endpoint.""" + params = {p: v for p, v in params.items() if v} + + result = obb.etf.discovery.gainers(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [({"sort": "desc", "limit": 10})], +) +@pytest.mark.integration +def test_etf_discovery_losers(params, obb): + """Test the ETF discovery losers endpoint.""" + params = {p: v for p, v in params.items() if v} + + result = obb.etf.discovery.losers(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [({"sort": "desc", "limit": 10})], +) +@pytest.mark.integration +def test_etf_discovery_active(params, obb): + """Test the ETF discovery active endpoint.""" + params = {p: v for p, v in params.items() if v} + + result = obb.etf.discovery.active(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ({"symbol": "SPY,VOO,QQQ,IWM,IWN", "provider": "fmp"}), + ], +) +@pytest.mark.integration +def test_etf_equity_exposure(params, obb): + """Test the ETF equity exposure endpoint.""" + params = {p: v for p, v in params.items() if v} + + result = obb.etf.equity_exposure(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 diff --git a/openbb_platform/extensions/etf/openbb_etf/__init__.py b/openbb_platform/extensions/etf/openbb_etf/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..0151e352e7786b74c091b63ccc3a0a72a18bcc7c --- /dev/null +++ b/openbb_platform/extensions/etf/openbb_etf/__init__.py @@ -0,0 +1 @@ +"""OpenBB ETF Extension.""" diff --git a/openbb_platform/extensions/etf/openbb_etf/discovery/__init__.py b/openbb_platform/extensions/etf/openbb_etf/discovery/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..648950b311fa78bdf212e2f05fcccfaa041c1362 --- /dev/null +++ b/openbb_platform/extensions/etf/openbb_etf/discovery/__init__.py @@ -0,0 +1 @@ +"""ETF Discovery.""" diff --git a/openbb_platform/extensions/etf/openbb_etf/discovery/discovery_router.py b/openbb_platform/extensions/etf/openbb_etf/discovery/discovery_router.py new file mode 100644 index 0000000000000000000000000000000000000000..7963e71294759919ab37fce593792279b5bb7721 --- /dev/null +++ b/openbb_platform/extensions/etf/openbb_etf/discovery/discovery_router.py @@ -0,0 +1,67 @@ +"""Disc router for ETFs.""" + +from openbb_core.app.model.command_context import CommandContext +from openbb_core.app.model.example import APIEx +from openbb_core.app.model.obbject import OBBject +from openbb_core.app.provider_interface import ( + ExtraParams, + ProviderChoices, + StandardParams, +) +from openbb_core.app.query import Query +from openbb_core.app.router import Router + +router = Router(prefix="/discovery") + +# pylint: disable=unused-argument + + +@router.command( + model="ETFGainers", + operation_id="etf_gainers", + examples=[ + APIEx(description="Get the top ETF gainers.", parameters={"provider": "wsj"}), + ], +) +async def gainers( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Get the top ETF gainers.""" + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="ETFLosers", + operation_id="etf_losers", + examples=[ + APIEx(description="Get the top ETF losers.", parameters={"provider": "wsj"}), + ], +) +async def losers( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Get the top ETF losers.""" + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="ETFActive", + operation_id="etf_active", + examples=[ + APIEx(description="Get the most active ETFs.", parameters={"provider": "wsj"}), + ], +) +async def active( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Get the most active ETFs.""" + return await OBBject.from_query(Query(**locals())) diff --git a/openbb_platform/extensions/etf/openbb_etf/etf_router.py b/openbb_platform/extensions/etf/openbb_etf/etf_router.py new file mode 100644 index 0000000000000000000000000000000000000000..b74645dedac2e545e8bd40824e0d6ddbefe02ea2 --- /dev/null +++ b/openbb_platform/extensions/etf/openbb_etf/etf_router.py @@ -0,0 +1,190 @@ +"""ETF Router.""" + +from openbb_core.app.model.command_context import CommandContext +from openbb_core.app.model.example import APIEx +from openbb_core.app.model.obbject import OBBject +from openbb_core.app.provider_interface import ( + ExtraParams, + ProviderChoices, + StandardParams, +) +from openbb_core.app.query import Query +from openbb_core.app.router import Router + +from openbb_etf.discovery.discovery_router import router as discovery_router + +router = Router(prefix="", description="Exchange Traded Funds market data.") +router.include_router(discovery_router) + +# pylint: disable=unused-argument + + +@router.command( + model="EtfSearch", + examples=[ + APIEx( + description="An empty query returns the full list of ETFs from the provider.", + parameters={"provider": "fmp"}, + ), + APIEx( + description="The query will return results from text-based fields containing the term.", + parameters={"query": "commercial real estate", "provider": "fmp"}, + ), + ], +) +async def search( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Search for ETFs. + + An empty query returns the full list of ETFs from the provider. + """ + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="EtfHistorical", + operation_id="etf_historical", + examples=[ + APIEx(parameters={"symbol": "SPY", "provider": "fmp"}), + APIEx(parameters={"symbol": "SPY", "provider": "yfinance"}), + APIEx( + description="This function accepts multiple tickers.", + parameters={"symbol": "SPY,IWM,QQQ,DJIA", "provider": "yfinance"}, + ), + ], +) +async def historical( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """ETF Historical Market Price.""" + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="EtfInfo", + examples=[ + APIEx(parameters={"symbol": "SPY", "provider": "fmp"}), + APIEx( + description="This function accepts multiple tickers.", + parameters={"symbol": "SPY,IWM,QQQ,DJIA", "provider": "fmp"}, + ), + ], +) +async def info( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """ETF Information Overview.""" + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="EtfSectors", + examples=[APIEx(parameters={"symbol": "SPY", "provider": "fmp"})], +) +async def sectors( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """ETF Sector weighting.""" + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="EtfCountries", + examples=[APIEx(parameters={"symbol": "VT", "provider": "fmp"})], +) +async def countries( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """ETF Country weighting.""" + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="EtfPricePerformance", + examples=[ + APIEx(parameters={"symbol": "QQQ", "provider": "fmp"}), + APIEx(parameters={"symbol": "SPY,QQQ,IWM,DJIA", "provider": "fmp"}), + ], +) +async def price_performance( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Price performance as a return, over different periods.""" + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="EtfHoldings", + examples=[ + APIEx(parameters={"symbol": "XLK", "provider": "fmp"}), + APIEx( + description="Including a date (FMP, SEC) will return the holdings as per NPORT-P filings.", + parameters={"symbol": "XLK", "date": "2022-03-31", "provider": "fmp"}, + ), + APIEx( + description="The same data can be returned from the SEC directly.", + parameters={"symbol": "XLK", "date": "2022-03-31", "provider": "sec"}, + ), + ], +) +async def holdings( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Get the holdings for an individual ETF.""" + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="EtfHoldingsDate", + examples=[APIEx(parameters={"symbol": "XLK", "provider": "fmp"})], +) +async def holdings_date( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Use this function to get the holdings dates, if available.""" + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="EtfEquityExposure", + examples=[ + APIEx(parameters={"symbol": "MSFT", "provider": "fmp"}), + APIEx( + description="This function accepts multiple tickers.", + parameters={"symbol": "MSFT,AAPL", "provider": "fmp"}, + ), + ], +) +async def equity_exposure( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Get the exposure to ETFs for a specific stock.""" + return await OBBject.from_query(Query(**locals())) diff --git a/openbb_platform/extensions/etf/openbb_etf/etf_views.py b/openbb_platform/extensions/etf/openbb_etf/etf_views.py new file mode 100644 index 0000000000000000000000000000000000000000..fe74776ed1bde1524cd6ecf443a82c7bcf2b454e --- /dev/null +++ b/openbb_platform/extensions/etf/openbb_etf/etf_views.py @@ -0,0 +1,99 @@ +"""Views for the ETF Extension.""" + +# pylint: disable=unused-argument + +from typing import TYPE_CHECKING, Any, Dict, Tuple, Union + +if TYPE_CHECKING: + from openbb_charting.core.openbb_figure import ( + OpenBBFigure, + ) + from plotly.graph_objs import Figure + + +class EtfViews: + """Etf Views.""" + + @staticmethod + def etf_historical( + **kwargs, + ) -> Tuple["OpenBBFigure", Dict[str, Any]]: + """Etf Price Historical Chart.""" + # pylint: disable=import-outside-toplevel + + from openbb_charting.charts.price_historical import price_historical + + return price_historical(**kwargs) + + @staticmethod + def etf_price_performance( + **kwargs, + ) -> Tuple["OpenBBFigure", Dict[str, Any]]: + """Etf Price Performance Chart.""" + # pylint: disable=import-outside-toplevel + from openbb_charting.charts.price_performance import price_performance + + return price_performance(**kwargs) + + @staticmethod + def etf_holdings( + **kwargs, + ) -> Tuple[Union["OpenBBFigure", "Figure"], Dict[str, Any]]: + """Equity Compare Groups Chart.""" + # pylint: disable=import-outside-toplevel + from pandas import DataFrame # noqa + from openbb_core.app.utils import basemodel_to_df # noqa + from openbb_core.app.model.abstract.error import OpenBBError # noqa + from openbb_charting.charts.generic_charts import bar_chart # noqa + + if "data" in kwargs and isinstance(kwargs["data"], DataFrame): + data = kwargs["data"] + elif "data" in kwargs and isinstance(kwargs["data"], list): + data = basemodel_to_df(kwargs["data"], index=None) # type: ignore + else: + data = basemodel_to_df(kwargs["obbject_item"], index=None) # type: ignore + + if "weight" not in data.columns: + raise OpenBBError("No 'weight' column found in the data.") + + orientation = kwargs.get("orientation", "h") + limit = kwargs.get("limit", 20) + symbol = kwargs["standard_params"].get("symbol") # type: ignore + title = kwargs.get("title", f"Top {limit} {symbol} Holdings") + layout_kwargs = kwargs.get("layout_kwargs", {}) + + data = data.sort_values("weight", ascending=False) + limit = min(limit, len(data)) # type: ignore + target = data.head(limit)[["symbol", "weight"]].set_index("symbol") + target = target.multiply(100) + axis_title = "Weight (%)" + + fig = bar_chart( + target.reset_index(), + "symbol", + ["weight"], + title=title, # type: ignore + xtitle=axis_title if orientation == "h" else None, + ytitle=axis_title if orientation == "v" else None, + orientation=orientation, # type: ignore + ) + + fig.update_layout( + hovermode="x" if orientation == "v" else "y", + margin=dict(r=0, l=50) if orientation == "h" else None, + ) + + fig.update_traces( + hovertemplate=( + "%{y:.3f}%" + if orientation == "v" + else "%{x:.3f}%" + ) + ) + + if layout_kwargs: + fig.update_layout(**layout_kwargs) # type: ignore + + content = fig.show(external=True).to_plotly_json() # type: ignore + + return fig, content diff --git a/openbb_platform/extensions/etf/openbb_etf/py.typed b/openbb_platform/extensions/etf/openbb_etf/py.typed new file mode 100644 index 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b/openbb_platform/extensions/etf/pyproject.toml new file mode 100644 index 0000000000000000000000000000000000000000..f1659d86adcd746618e5e77ddbf1bd974627d070 --- /dev/null +++ b/openbb_platform/extensions/etf/pyproject.toml @@ -0,0 +1,22 @@ +[tool.poetry] +name = "openbb-etf" +version = "1.4.1" +description = "ETF extension for OpenBB" +authors = ["OpenBB Team "] +license = "AGPL-3.0-only" +readme = "README.md" +packages = [{ include = "openbb_etf" }] + +[tool.poetry.dependencies] +python = ">=3.9.21,<3.13" +openbb-core = "^1.4.6" + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" + +[tool.poetry.plugins."openbb_core_extension"] +etf = "openbb_etf.etf_router:router" + +[tool.poetry.plugins."openbb_charting_extension"] +etf = "openbb_etf.etf_views:EtfViews" diff --git a/openbb_platform/extensions/etf/tests/.gitkeep b/openbb_platform/extensions/etf/tests/.gitkeep new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/openbb_platform/extensions/fixedincome/README.md b/openbb_platform/extensions/fixedincome/README.md new file mode 100644 index 0000000000000000000000000000000000000000..d212040506d1983d8d6e3539c0fddad68632899d --- /dev/null +++ b/openbb_platform/extensions/fixedincome/README.md @@ -0,0 +1,15 @@ +# OpenBB Fixed Income Extension + +This extension provides fixed income data for the OpenBB Platform. + +Features of the Fixed Income extension include information on government bonds and central bank rates. + +## Installation + +To install the extension, run the following command in this folder: + +```bash +pip install openbb-fixedincome +``` + +Documentation available [here](https://docs.openbb.co/platform/developer_guide/contributing). diff --git a/openbb_platform/extensions/fixedincome/integration/test_fixedincome_api.py b/openbb_platform/extensions/fixedincome/integration/test_fixedincome_api.py new file mode 100644 index 0000000000000000000000000000000000000000..98c6193209889a914a429caabb997e160edc7101 --- /dev/null +++ b/openbb_platform/extensions/fixedincome/integration/test_fixedincome_api.py @@ -0,0 +1,789 @@ +"""Test fixedincome API endpoints.""" + +import base64 + +import pytest +import requests +from extensions.tests.conftest import parametrize +from openbb_core.env import Env +from openbb_core.provider.utils.helpers import get_querystring + +# pylint: disable=redefined-outer-name + + +@pytest.fixture(scope="session") +def headers(): + """Get the headers for the API request.""" + userpass = f"{Env().API_USERNAME}:{Env().API_PASSWORD}" + userpass_bytes = userpass.encode("ascii") + base64_bytes = base64.b64encode(userpass_bytes) + + return {"Authorization": f"Basic {base64_bytes.decode('ascii')}"} + + +@parametrize( + "params", + [({"start_date": "2023-01-01", "end_date": "2023-06-06", "provider": "fmp"})], +) +@pytest.mark.integration +def test_fixedincome_government_treasury_rates(params, headers): + """Test the treasury rates endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = ( + f"http://0.0.0.0:8000/api/v1/fixedincome/government/treasury_rates?{query_str}" + ) + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ( + { + "start_date": "2023-01-01", + "end_date": "2023-06-06", + "provider": "federal_reserve", + } + ), + ( + { + "frequency": None, + "transform": None, + "aggregation_method": None, + "provider": "fred", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + } + ), + ], +) +@pytest.mark.integration +def test_fixedincome_sofr(params, headers): + """Test the SOFR endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/fixedincome/sofr?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ( + { + "start_date": "2023-01-01", + "end_date": "2023-06-06", + "provider": "federal_reserve", + } + ), + ( + { + "frequency": None, + "transform": None, + "aggregation_method": None, + "provider": "fred", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + } + ), + ], +) +@pytest.mark.integration +def test_fixedincome_rate_sofr(params, headers): + """Test the SOFR endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/fixedincome/rate/sofr?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ( + { + "provider": "fred", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + "transform": None, + "aggregation_method": None, + "frequency": None, + } + ), + ], +) +@pytest.mark.integration +def test_fixedincome_rate_estr(params, headers): + """Test the ESTR rate endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/fixedincome/rate/estr?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ({"start_date": "2023-01-01", "end_date": "2023-06-06"}), + ( + { + "parameter": "rate", + "provider": "fred", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + } + ), + ], +) +@pytest.mark.integration +def test_fixedincome_rate_sonia(params, headers): + """Test the SONIA rate endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/fixedincome/rate/sonia?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ( + { + "maturity": "overnight", + "provider": "fred", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + "transform": None, + "aggregation_method": None, + "frequency": None, + } + ), + ], +) +@pytest.mark.integration +def test_fixedincome_rate_ameribor(params, headers): + """Test the Ameribor rate endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/fixedincome/rate/ameribor?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ( + { + "frequency": "w", + "transform": None, + "aggregation_method": "avg", + "effr_only": False, + "provider": "fred", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + } + ), + ( + { + "start_date": "2023-01-01", + "end_date": "2023-06-06", + "provider": "federal_reserve", + } + ), + ], +) +@pytest.mark.integration +def test_fixedincome_rate_effr(params, headers): + """Test the EFFR rate endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/fixedincome/rate/effr?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [({}), ({"long_run": True, "provider": "fred"})], +) +@pytest.mark.integration +def test_fixedincome_rate_effr_forecast(params, headers): + """Test the EFFR forecast rate endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/fixedincome/rate/effr_forecast?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [({"start_date": "2023-01-01", "end_date": "2023-06-06"})], +) +@pytest.mark.integration +def test_fixedincome_rate_iorb(params, headers): + """Test the IORB rate endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/fixedincome/rate/iorb?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ({"start_date": "2023-01-01", "end_date": "2023-06-06"}), + ( + { + "parameter": "daily_excl_weekend", + "provider": "fred", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + } + ), + ], +) +@pytest.mark.integration +def test_fixedincome_rate_dpcredit(params, headers): + """Test the DPCredit rate endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/fixedincome/rate/dpcredit?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ( + { + "start_date": "2023-01-01", + "end_date": "2023-06-06", + "interest_rate_type": "lending", + } + ) + ], +) +@pytest.mark.integration +def test_fixedincome_rate_ecb(params, headers): + """Test the ECB rate endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/fixedincome/rate/ecb?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ({"start_date": "2023-01-01", "end_date": "2023-06-06", "index_type": "yield"}), + ( + { + "category": "all", + "area": "us", + "grade": "non_sovereign", + "options": True, + "provider": "fred", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + "index_type": "yield", + } + ), + ], +) +@pytest.mark.integration +def test_fixedincome_corporate_ice_bofa(params, headers): + """Test the ICE BofA corporate yield index endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/fixedincome/corporate/ice_bofa?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [({"start_date": "2023-01-01", "end_date": "2023-06-06", "index_type": "aaa"})], +) +@pytest.mark.integration +def test_fixedincome_corporate_moody(params, headers): + """Test the Moody's corporate yield index endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/fixedincome/corporate/moody?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ( + { + "start_date": "2023-01-01", + "end_date": "2023-06-06", + "maturity": "overnight", + "category": "financial", + "transform": None, + "aggregation_method": None, + "frequency": None, + "provider": "fred", + } + ) + ], +) +@pytest.mark.integration +def test_fixedincome_corporate_commercial_paper(params, headers): + """Test the commercial paper endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = ( + f"http://0.0.0.0:8000/api/v1/fixedincome/corporate/commercial_paper?{query_str}" + ) + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ( + { + "start_date": "2023-01-01", + "end_date": "2023-06-06", + "maturity": [10.0], + "category": "spot_rate", + "provider": "fred", + } + ), + ( + { + "start_date": None, + "end_date": None, + "maturity": 5.5, + "category": ["spot_rate"], + } + ), + ( + { + "start_date": None, + "end_date": None, + "maturity": "1,5.5,10", + "category": "spot_rate,par_yield", + } + ), + ], +) +@pytest.mark.integration +def test_fixedincome_corporate_spot_rates(params, headers): + """Test the corporate spot rates endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/fixedincome/corporate/spot_rates?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [({"date": "2023-01-01", "yield_curve": "spot"})], +) +@pytest.mark.integration +def test_fixedincome_corporate_hqm(params, headers): + """Test the HQM corporate yield curve endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/fixedincome/corporate/hqm?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [({"start_date": "2023-01-01", "end_date": "2023-06-06", "maturity": "3m"})], +) +@pytest.mark.integration +def test_fixedincome_spreads_tcm(params, headers): + """Test the TCM spreads endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/fixedincome/spreads/tcm?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ( + { + "start_date": "2023-01-01", + "end_date": "2023-06-06", + "maturity": "10y", + "provider": "fred", + } + ) + ], +) +@pytest.mark.integration +def test_fixedincome_spreads_tcm_effr(params, headers): + """Test the TCM EFFR spreads endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/fixedincome/spreads/tcm_effr?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ( + { + "start_date": "2023-01-01", + "end_date": "2023-06-06", + "maturity": "3m", + "provider": "fred", + } + ) + ], +) +@pytest.mark.integration +def test_fixedincome_spreads_treasury_effr(params, headers): + """Test the treasury EFFR spreads endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/fixedincome/spreads/treasury_effr?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ( + { + "start_date": "2023-09-01", + "end_date": "2023-11-16", + "cusip": None, + "page_size": None, + "page_num": None, + "security_type": None, + "provider": "government_us", + } + ), + ( + { + "start_date": "2023-09-01", + "end_date": "2023-11-16", + "cusip": None, + "page_size": None, + "page_num": None, + "security_type": "bond", + "provider": "government_us", + } + ), + ], +) +@pytest.mark.integration +def test_fixedincome_government_treasury_auctions(params, headers): + """Test the treasury auctions endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/fixedincome/government/treasury_auctions?{query_str}" + result = requests.get(url, headers=headers, timeout=30) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@pytest.mark.parametrize( + "params", + [ + ( + { + "date": "2023-11-16", + "cusip": None, + "security_type": "bond", + "provider": "government_us", + } + ), + ( + { + "date": "2023-12-28", + "cusip": None, + "security_type": "bill", + "provider": "government_us", + } + ), + ( + { + "date": None, + "provider": "tmx", + "govt_type": "federal", + "issue_date_min": None, + "issue_date_max": None, + "last_traded_min": None, + "maturity_date_min": None, + "maturity_date_max": None, + "use_cache": True, + } + ), + ], +) +@pytest.mark.integration +def test_fixedincome_government_treasury_prices(params, headers): + """Test the treasury prices endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = ( + f"http://0.0.0.0:8000/api/v1/fixedincome/government/treasury_prices?{query_str}" + ) + result = requests.get(url, headers=headers, timeout=40) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@pytest.mark.parametrize( + "params", + [ + ( + { + "provider": "tmx", + "issuer_name": "federal", + "issue_date_min": None, + "issue_date_max": None, + "last_traded_min": None, + "coupon_min": 3, + "coupon_max": None, + "currency": None, + "issued_amount_min": None, + "issued_amount_max": None, + "maturity_date_min": None, + "maturity_date_max": None, + "isin": None, + "lei": None, + "country": None, + "use_cache": False, + } + ) + ], +) +@pytest.mark.integration +def test_fixedincome_corporate_bond_prices(params, headers): + """Test the corporate bond prices endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/fixedincome/corporate/bond_prices?{query_str}" + result = requests.get(url, headers=headers, timeout=40) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ({"date": "2023-05-01,2024-05-01", "provider": "fmp"}), + ( + { + "date": "2023-05-01", + "country": "united_kingdom", + "provider": "econdb", + "use_cache": True, + } + ), + ( + { + "provider": "ecb", + "yield_curve_type": "par_yield", + "date": None, + "rating": "aaa", + "use_cache": True, + } + ), + ( + { + "provider": "fred", + "yield_curve_type": "nominal", + "date": "2023-05-01,2024-05-01", + } + ), + ({"provider": "federal_reserve", "date": "2023-05-01,2024-05-01"}), + ], +) +@pytest.mark.integration +def test_fixedincome_government_yield_curve(params, headers): + """Test the treasury rates endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/fixedincome/government/yield_curve?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + { + "provider": "fred", + "category": "high_yield", + "index": "us,europe,emerging", + "index_type": "total_return", + "start_date": "2023-05-31", + "end_date": "2024-06-01", + "transform": None, + "frequency": None, + "aggregation_method": "avg", + }, + ], +) +@pytest.mark.integration +def test_fixedincome_bond_indices(params, headers): + """Test the bond indices endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/fixedincome/bond_indices?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + { + "provider": "fred", + "index": "usda_30y,fha_30y", + "start_date": "2023-05-31", + "end_date": "2024-06-01", + "transform": None, + "frequency": None, + "aggregation_method": "avg", + }, + ], +) +@pytest.mark.integration +def test_fixedincome_mortgage_indices(params, headers): + """Test the mortgage indices endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/fixedincome/mortgage_indices?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ( + { + "start_date": "2023-01-01", + "end_date": "2023-06-06", + "transform": None, + "aggregation_method": None, + "frequency": None, + "provider": "fred", + } + ), + ( + { + "start_date": "2023-01-01", + "end_date": "2023-06-06", + "provider": "federal_reserve", + } + ), + ], +) +@pytest.mark.integration +def test_fixedincome_rate_overnight_bank_funding(params, headers): + """Test the Overnight Bank Funding Rate endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/fixedincome/rate/overnight_bank_funding?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ( + { + "maturity": None, + "start_date": None, + "end_date": None, + "transform": None, + "aggregation_method": None, + "frequency": None, + "provider": "fred", + } + ), + ], +) +@pytest.mark.integration +def test_fixedincome_government_tips_yields(params, headers): + """Test the TIPS Yields endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/fixedincome/government/tips_yields?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 diff --git a/openbb_platform/extensions/fixedincome/integration/test_fixedincome_python.py b/openbb_platform/extensions/fixedincome/integration/test_fixedincome_python.py new file mode 100644 index 0000000000000000000000000000000000000000..4269425733a6b3d00cb110d1c1a69a93ebd58aa0 --- /dev/null +++ b/openbb_platform/extensions/fixedincome/integration/test_fixedincome_python.py @@ -0,0 +1,738 @@ +"""Test fixed income extension.""" + +import pytest +from extensions.tests.conftest import parametrize +from openbb_core.app.model.obbject import OBBject + +# pylint: disable=redefined-outer-name +# pylint: disable=inconsistent-return-statements + + +@pytest.fixture(scope="session") +def obb(pytestconfig): + """Fixture to setup obb.""" + + if pytestconfig.getoption("markexpr") != "not integration": + import openbb # pylint: disable=import-outside-toplevel + + return openbb.obb + + +@parametrize( + "params", + [ + ({"start_date": "2023-01-01", "end_date": "2023-06-06", "provider": "fmp"}), + ], +) +@pytest.mark.integration +def test_fixedincome_government_treasury_rates(params, obb): + """Test the treasury rates endpoint.""" + result = obb.fixedincome.government.treasury_rates(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ( + { + "start_date": "2023-01-01", + "end_date": "2023-06-06", + "provider": "federal_reserve", + } + ), + ( + { + "frequency": None, + "transform": None, + "aggregation_method": None, + "provider": "fred", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + } + ), + ], +) +@pytest.mark.integration +def test_fixedincome_sofr(params, obb): + """Test the SOFR endpoint.""" + result = obb.fixedincome.sofr(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ( + { + "start_date": "2023-01-01", + "end_date": "2023-06-06", + "provider": "federal_reserve", + } + ), + ( + { + "frequency": None, + "transform": None, + "aggregation_method": None, + "provider": "fred", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + } + ), + ], +) +@pytest.mark.integration +def test_fixedincome_rate_sofr(params, obb): + """Test the fixedincome rate sofr endpoint.""" + result = obb.fixedincome.rate.sofr(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ( + { + "provider": "fred", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + "transform": None, + "aggregation_method": None, + "frequency": None, + } + ), + ], +) +@pytest.mark.integration +def test_fixedincome_rate_estr(params, obb): + """Test the ESTR endpoint.""" + result = obb.fixedincome.rate.estr(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ({"start_date": "2023-01-01", "end_date": "2023-06-06"}), + ( + { + "parameter": "rate", + "provider": "fred", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + } + ), + ], +) +@pytest.mark.integration +def test_fixedincome_rate_sonia(params, obb): + """Test the SONIA endpoint.""" + result = obb.fixedincome.rate.sonia(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ( + { + "maturity": "overnight", + "provider": "fred", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + "transform": None, + "aggregation_method": None, + "frequency": None, + } + ), + ], +) +@pytest.mark.integration +def test_fixedincome_rate_ameribor(params, obb): + """Test the Ameribor endpoint.""" + result = obb.fixedincome.rate.ameribor(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ( + { + "frequency": "w", + "transform": None, + "aggregation_method": "avg", + "effr_only": False, + "provider": "fred", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + } + ), + ( + { + "start_date": "2023-01-01", + "end_date": "2023-06-06", + "provider": "federal_reserve", + } + ), + ], +) +@pytest.mark.integration +def test_fixedincome_rate_effr(params, obb): + """Test the EFFR endpoint.""" + result = obb.fixedincome.rate.effr(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ({}), + ({"long_run": True, "provider": "fred"}), + ], +) +@pytest.mark.integration +def test_fixedincome_rate_effr_forecast(params, obb): + """Test the EFFR forecast endpoint.""" + result = obb.fixedincome.rate.effr_forecast(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ({"start_date": "2023-01-01", "end_date": "2023-06-06"}), + ], +) +@pytest.mark.integration +def test_fixedincome_rate_iorb(params, obb): + """Test the IORB endpoint.""" + result = obb.fixedincome.rate.iorb(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ({"start_date": "2023-01-01", "end_date": "2023-06-06"}), + ( + { + "parameter": "daily_excl_weekend", + "provider": "fred", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + } + ), + ], +) +@pytest.mark.integration +def test_fixedincome_rate_dpcredit(params, obb): + """Test the DPCREDIT endpoint.""" + params = {p: v for p, v in params.items() if v} + + result = obb.fixedincome.rate.dpcredit(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ( + { + "start_date": "2023-01-01", + "end_date": "2023-06-06", + "interest_rate_type": "lending", + } + ) + ], +) +@pytest.mark.integration +def test_fixedincome_rate_ecb(params, obb): + """Test the ECB endpoint.""" + params = {p: v for p, v in params.items() if v} + + result = obb.fixedincome.rate.ecb(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ({"start_date": "2023-01-01", "end_date": "2023-06-06", "index_type": "yield"}), + ( + { + "category": "all", + "area": "us", + "grade": "non_sovereign", + "options": True, + "provider": "fred", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + "index_type": "yield", + } + ), + ], +) +@pytest.mark.integration +def test_fixedincome_corporate_ice_bofa(params, obb): + """Test the ICE BofA endpoint.""" + params = {p: v for p, v in params.items() if v} + + result = obb.fixedincome.corporate.ice_bofa(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [({"start_date": "2023-01-01", "end_date": "2023-06-06", "index_type": "aaa"})], +) +@pytest.mark.integration +def test_fixedincome_corporate_moody(params, obb): + """Test the Moody endpoint.""" + params = {p: v for p, v in params.items() if v} + + result = obb.fixedincome.corporate.moody(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ( + { + "start_date": "2023-01-01", + "end_date": "2023-06-06", + "maturity": "overnight", + "category": "financial", + "transform": None, + "aggregation_method": None, + "frequency": None, + "provider": "fred", + } + ) + ], +) +@pytest.mark.integration +def test_fixedincome_corporate_commercial_paper(params, obb): + """Test the commercial paper endpoint.""" + params = {p: v for p, v in params.items() if v} + + result = obb.fixedincome.corporate.commercial_paper(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ( + { + "start_date": "2023-01-01", + "end_date": "2023-06-06", + "maturity": [10.0], + "category": "spot_rate", + "provider": "fred", + } + ), + ( + { + "start_date": None, + "end_date": None, + "maturity": 5.5, + "category": ["spot_rate"], + } + ), + ( + { + "start_date": None, + "end_date": None, + "maturity": "1,5.5,10", + "category": "spot_rate,par_yield", + } + ), + ], +) +@pytest.mark.integration +def test_fixedincome_corporate_spot_rates(params, obb): + """Test the spot rates endpoint.""" + params = {p: v for p, v in params.items() if v} + + result = obb.fixedincome.corporate.spot_rates(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [({"date": "2023-01-01", "yield_curve": "spot"})], +) +@pytest.mark.integration +def test_fixedincome_corporate_hqm(params, obb): + """Test the HQM endpoint.""" + params = {p: v for p, v in params.items() if v} + + result = obb.fixedincome.corporate.hqm(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [({"start_date": "2023-01-01", "end_date": "2023-06-06", "maturity": "3m"})], +) +@pytest.mark.integration +def test_fixedincome_spreads_tcm(params, obb): + """Test the TCM endpoint.""" + params = {p: v for p, v in params.items() if v} + + result = obb.fixedincome.spreads.tcm(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ( + { + "start_date": "2023-01-01", + "end_date": "2023-06-06", + "maturity": "10y", + "provider": "fred", + } + ) + ], +) +@pytest.mark.integration +def test_fixedincome_spreads_tcm_effr(params, obb): + """Test the TCM EFFR endpoint.""" + params = {p: v for p, v in params.items() if v} + + result = obb.fixedincome.spreads.tcm_effr(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ( + { + "start_date": "2023-01-01", + "end_date": "2023-06-06", + "maturity": "3m", + "provider": "fred", + } + ) + ], +) +@pytest.mark.integration +def test_fixedincome_spreads_treasury_effr(params, obb): + """Test the treasury EFFR endpoint.""" + params = {p: v for p, v in params.items() if v} + + result = obb.fixedincome.spreads.treasury_effr(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ( + { + "start_date": "2023-09-01", + "end_date": "2023-11-16", + "cusip": None, + "page_size": None, + "page_num": None, + "security_type": None, + "provider": "government_us", + } + ), + ( + { + "start_date": "2023-09-01", + "end_date": "2023-11-16", + "cusip": None, + "page_size": None, + "page_num": None, + "security_type": "bond", + "provider": "government_us", + } + ), + ], +) +@pytest.mark.integration +def test_fixedincome_government_treasury_auctions(params, obb): + """Test the treasury auctions endpoint.""" + params = {p: v for p, v in params.items() if v} + + result = obb.fixedincome.government.treasury_auctions(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@pytest.mark.parametrize( + "params", + [ + ( + { + "date": "2023-11-16", + "cusip": None, + "security_type": "bond", + "provider": "government_us", + } + ), + ( + { + "date": "2023-12-28", + "cusip": None, + "security_type": "bill", + "provider": "government_us", + } + ), + ( + { + "date": None, + "provider": "tmx", + "govt_type": "federal", + "issue_date_min": None, + "issue_date_max": None, + "last_traded_min": None, + "maturity_date_min": None, + "maturity_date_max": None, + "use_cache": True, + } + ), + ], +) +@pytest.mark.integration +def test_fixedincome_government_treasury_prices(params, obb): + """Test the treasury prices endpoint.""" + params = {p: v for p, v in params.items() if v} + + result = obb.fixedincome.government.treasury_prices(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@pytest.mark.parametrize( + "params", + [ + ( + { + "provider": "tmx", + "issuer_name": "federal", + "issue_date_min": None, + "issue_date_max": None, + "last_traded_min": None, + "coupon_min": 3, + "coupon_max": None, + "currency": None, + "issued_amount_min": None, + "issued_amount_max": None, + "maturity_date_min": None, + "maturity_date_max": None, + "isin": None, + "lei": None, + "country": None, + "use_cache": False, + } + ) + ], +) +@pytest.mark.integration +def test_fixedincome_corporate_bond_prices(params, obb): + """Test the bond prices endpoint.""" + params = {p: v for p, v in params.items() if v} + + result = obb.fixedincome.corporate.bond_prices(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ({"date": "2023-05-01,2024-05-01", "provider": "fmp"}), + ( + { + "date": "2023-05-01", + "country": "united_kingdom", + "provider": "econdb", + "use_cache": True, + } + ), + ( + { + "provider": "ecb", + "yield_curve_type": "par_yield", + "date": None, + "rating": "aaa", + "use_cache": True, + } + ), + ( + { + "provider": "fred", + "yield_curve_type": "nominal", + "date": "2023-05-01,2024-05-01", + } + ), + ({"provider": "federal_reserve", "date": "2023-05-01,2024-05-01"}), + ], +) +@pytest.mark.integration +def test_fixedincome_government_yield_curve(params, obb): + """Test the government yield curve endpoint.""" + params = {p: v for p, v in params.items() if v} + + result = obb.fixedincome.government.yield_curve(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + { + "provider": "fred", + "category": "high_yield", + "index": "us,europe,emerging", + "index_type": "total_return", + "start_date": "2023-05-31", + "end_date": "2024-06-01", + "transform": None, + "frequency": None, + "aggregation_method": "avg", + }, + ], +) +@pytest.mark.integration +def test_fixedincome_bond_indices(params, obb): + """Test the bond indices endpoint.""" + params = {p: v for p, v in params.items() if v} + + result = obb.fixedincome.bond_indices(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + { + "provider": "fred", + "index": "usda_30y,fha_30y", + "start_date": "2023-05-31", + "end_date": "2024-06-01", + "transform": None, + "frequency": None, + "aggregation_method": "avg", + }, + ], +) +@pytest.mark.integration +def test_fixedincome_mortgage_indices(params, obb): + """Test the mortgage indices endpoint.""" + params = {p: v for p, v in params.items() if v} + + result = obb.fixedincome.mortgage_indices(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ( + { + "start_date": "2023-01-01", + "end_date": "2023-06-06", + "transform": None, + "aggregation_method": None, + "frequency": None, + "provider": "fred", + } + ), + ( + { + "start_date": "2023-01-01", + "end_date": "2023-06-06", + "provider": "federal_reserve", + } + ), + ], +) +@pytest.mark.integration +def test_fixedincome_rate_overnight_bank_funding(params, obb): + """Test the Overnight Bank Funding Rate endpoint.""" + result = obb.fixedincome.rate.overnight_bank_funding(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ( + { + "maturity": None, + "start_date": None, + "end_date": None, + "transform": None, + "aggregation_method": None, + "frequency": None, + "provider": "fred", + } + ), + ], +) +@pytest.mark.integration +def test_fixedincome_government_tips_yields(params, obb): + """Test the TIPS Yields endpoint.""" + result = obb.fixedincome.government.tips_yields(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 diff --git a/openbb_platform/extensions/fixedincome/openbb_fixedincome/__init__.py b/openbb_platform/extensions/fixedincome/openbb_fixedincome/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..93f0cdd3989dc51dee28dca84f126a01c99868aa --- /dev/null +++ b/openbb_platform/extensions/fixedincome/openbb_fixedincome/__init__.py @@ -0,0 +1 @@ +"""Fixed income router init.""" diff --git a/openbb_platform/extensions/fixedincome/openbb_fixedincome/corporate/__init__.py b/openbb_platform/extensions/fixedincome/openbb_fixedincome/corporate/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..2808111a3a24f7193bb927bbbaa1d3e0df20077e --- /dev/null +++ b/openbb_platform/extensions/fixedincome/openbb_fixedincome/corporate/__init__.py @@ -0,0 +1 @@ +"""Initialize the Fixed Income Corporate module.""" diff --git a/openbb_platform/extensions/fixedincome/openbb_fixedincome/corporate/corporate_router.py b/openbb_platform/extensions/fixedincome/openbb_fixedincome/corporate/corporate_router.py new file mode 100644 index 0000000000000000000000000000000000000000..f8a6775d057b77f19e226a8ae7e827538f19cf3e --- /dev/null +++ b/openbb_platform/extensions/fixedincome/openbb_fixedincome/corporate/corporate_router.py @@ -0,0 +1,157 @@ +"""Fixed Income Corporate Router.""" + +from openbb_core.app.deprecation import OpenBBDeprecationWarning +from openbb_core.app.model.command_context import CommandContext +from openbb_core.app.model.example import APIEx +from openbb_core.app.model.obbject import OBBject +from openbb_core.app.provider_interface import ( + ExtraParams, + ProviderChoices, + StandardParams, +) +from openbb_core.app.query import Query +from openbb_core.app.router import Router + +router = Router(prefix="/corporate") + +# pylint: disable=unused-argument + + +@router.command( + model="ICEBofA", + examples=[ + APIEx(parameters={"provider": "fred"}), + APIEx(parameters={"index_type": "yield_to_worst", "provider": "fred"}), + ], + deprecated=True, + deprecation=OpenBBDeprecationWarning( + message="This endpoint is deprecated; use `/fixedincome/bond_indices` instead.", + since=(4, 2), + expected_removal=(4, 5), + ), +) +async def ice_bofa( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """ICE BofA US Corporate Bond Indices. + + The ICE BofA US Corporate Index tracks the performance of US dollar denominated investment grade corporate debt + publicly issued in the US domestic market. Qualifying securities must have an investment grade rating (based on an + average of Moody’s, S&P and Fitch), at least 18 months to final maturity at the time of issuance, at least one year + remaining term to final maturity as of the rebalance date, a fixed coupon schedule and a minimum amount + outstanding of $250 million. The ICE BofA US Corporate Index is a component of the US Corporate Master Index. + """ + return await OBBject.from_query(Query(**locals())) + + +@router.command( + deprecated=True, + deprecation=OpenBBDeprecationWarning( + message="This endpoint is deprecated; use `/fixedincome/bond_indices` instead." + + " Set `category` to `us` and `index` to `seasoned_corporate`.", + since=(4, 2), + expected_removal=(4, 5), + ), + model="MoodyCorporateBondIndex", + examples=[ + APIEx(parameters={"provider": "fred"}), + APIEx(parameters={"index_type": "baa", "provider": "fred"}), + ], +) +async def moody( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Moody Corporate Bond Index. + + Moody's Aaa and Baa are investment bonds that acts as an index of + the performance of all bonds given an Aaa or Baa rating by Moody's Investors Service respectively. + These corporate bonds often are used in macroeconomics as an alternative to the federal ten-year + Treasury Bill as an indicator of the interest rate. + """ + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="HighQualityMarketCorporateBond", + examples=[ + APIEx(parameters={"provider": "fred"}), + APIEx(parameters={"yield_curve": "par", "provider": "fred"}), + ], +) +async def hqm( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """High Quality Market Corporate Bond. + + The HQM yield curve represents the high quality corporate bond market, i.e., + corporate bonds rated AAA, AA, or A. The HQM curve contains two regression terms. + These terms are adjustment factors that blend AAA, AA, and A bonds into a single HQM yield curve + that is the market-weighted average (MWA) quality of high quality bonds. + """ + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="SpotRate", + examples=[ + APIEx(parameters={"provider": "fred"}), + APIEx(parameters={"maturity": "10,20,30,50", "provider": "fred"}), + ], +) +async def spot_rates( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Spot Rates. + + The spot rates for any maturity is the yield on a bond that provides a single payment at that maturity. + This is a zero coupon bond. + Because each spot rate pertains to a single cashflow, it is the relevant interest rate + concept for discounting a pension liability at the same maturity. + """ + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="CommercialPaper", + examples=[ + APIEx(parameters={"provider": "fred"}), + APIEx(parameters={"category": "all", "maturity": "15d", "provider": "fred"}), + ], +) +async def commercial_paper( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Commercial Paper. + + Commercial paper (CP) consists of short-term, promissory notes issued primarily by corporations. + Maturities range up to 270 days but average about 30 days. + Many companies use CP to raise cash needed for current transactions, + and many find it to be a lower-cost alternative to bank loans. + """ + return await OBBject.from_query(Query(**locals())) + + +@router.command(model="BondPrices", examples=[APIEx(parameters={"provider": "tmx"})]) +async def bond_prices( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Corporate Bond Prices.""" + return await OBBject.from_query(Query(**locals())) diff --git a/openbb_platform/extensions/fixedincome/openbb_fixedincome/fixedincome_router.py b/openbb_platform/extensions/fixedincome/openbb_fixedincome/fixedincome_router.py new file mode 100644 index 0000000000000000000000000000000000000000..46ef7677ad04fc4dc60cd05c29776ee5d7768de7 --- /dev/null +++ b/openbb_platform/extensions/fixedincome/openbb_fixedincome/fixedincome_router.py @@ -0,0 +1,115 @@ +"""Fixed Income Router.""" + +# pylint: disable=W0613:unused-argument + +from openbb_core.app.deprecation import OpenBBDeprecationWarning +from openbb_core.app.model.command_context import CommandContext +from openbb_core.app.model.example import APIEx +from openbb_core.app.model.obbject import OBBject +from openbb_core.app.provider_interface import ( + ExtraParams, + ProviderChoices, + StandardParams, +) +from openbb_core.app.query import Query +from openbb_core.app.router import Router + +from openbb_fixedincome.corporate.corporate_router import router as corporate_router +from openbb_fixedincome.government.government_router import router as government_router +from openbb_fixedincome.rate.rate_router import router as rate_router +from openbb_fixedincome.spreads.spreads_router import router as spreads_router + +router = Router(prefix="", description="Fixed Income market data.") +router.include_router(rate_router) +router.include_router(spreads_router) +router.include_router(government_router) +router.include_router(corporate_router) + + +@router.command( + model="SOFR", + examples=[ + APIEx(parameters={"provider": "fred"}), + ], + deprecated=True, + deprecation=OpenBBDeprecationWarning( + message="This endpoint is deprecated; use `/fixedincome/rate/sofr` instead.", + since=(4, 2), + expected_removal=(4, 5), + ), +) +async def sofr( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: # type: ignore + """Secured Overnight Financing Rate. + + The Secured Overnight Financing Rate (SOFR) is a broad measure of the cost of + borrowing cash overnight collateralizing by Treasury securities. + """ + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="BondIndices", + examples=[ + APIEx( + description="The default state for FRED are series for constructing the US Corporate Bond Yield Curve.", + parameters={"provider": "fred"}, + ), + APIEx( + description="Multiple indices, from within the same 'category', can be requested.", + parameters={ + "category": "high_yield", + "index": "us,europe,emerging", + "index_type": "total_return", + "provider": "fred", + }, + ), + APIEx( + description="From FRED, there are three main categories, 'high_yield', 'us', and 'emerging_markets'." + + " Emerging markets is a broad category.", + parameters={ + "category": "emerging_markets", + "index": "corporate,private_sector,public_sector", + "provider": "fred", + }, + ), + ], +) +async def bond_indices( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: # type: ignore + """Bond Indices.""" + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="MortgageIndices", + examples=[ + APIEx( + description="The default state for FRED are the primary mortgage indices from Optimal Blue.", + parameters={"provider": "fred"}, + ), + APIEx( + description="Multiple indices can be requested.", + parameters={ + "index": "jumbo_30y,conforming_30y,conforming_15y", + "provider": "fred", + }, + ), + ], +) +async def mortgage_indices( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: # type: ignore + """Mortgage Indices.""" + return await OBBject.from_query(Query(**locals())) diff --git a/openbb_platform/extensions/fixedincome/openbb_fixedincome/fixedincome_views.py b/openbb_platform/extensions/fixedincome/openbb_fixedincome/fixedincome_views.py new file mode 100644 index 0000000000000000000000000000000000000000..a8b6cae73f26f68c8270bea8af604a755e528491 --- /dev/null +++ b/openbb_platform/extensions/fixedincome/openbb_fixedincome/fixedincome_views.py @@ -0,0 +1,222 @@ +"""Views for the Fixed Income Extension.""" + +from typing import TYPE_CHECKING, Any, Dict, Optional, Tuple + +from openbb_core.provider.abstract.data import Data + +if TYPE_CHECKING: + from openbb_charting.core.openbb_figure import ( + OpenBBFigure, + ) + + +class FixedIncomeViews: + """FixedIncome Views.""" + + @staticmethod + def fixedincome_government_yield_curve( # noqa: PLR0912 + **kwargs, + ) -> Tuple["OpenBBFigure", Dict[str, Any]]: + """Government Yield Curve Chart.""" + # pylint: disable=import-outside-toplevel + from openbb_charting.charts.helpers import ( + duration_sorter, + ) + from openbb_charting.core.chart_style import ChartStyle + from openbb_charting.core.openbb_figure import OpenBBFigure + from openbb_charting.styles.colors import LARGE_CYCLER + from openbb_core.app.utils import basemodel_to_df + from pandas import DataFrame + + data = kwargs.get("data") + df: DataFrame = DataFrame() + if data: + if isinstance(data, DataFrame) and not data.empty: # noqa: SIM108 + df = data + elif isinstance(data, (list, Data)): + df = basemodel_to_df(data, index=None) # type: ignore + else: + pass + else: + df = DataFrame([d.model_dump() for d in kwargs["obbject_item"]]) # type: ignore + + if df.empty: + raise ValueError("Error: No data to plot.") + + if "maturity" not in df.columns: + raise ValueError("Error: Maturity column not found in the data.") + + if "rate" not in df.columns: + raise ValueError("Error: Rate column not found in the data.") + + if "date" not in df.columns: + raise ValueError("Error: Date column not found in the data.") + + provider = kwargs.get("provider") + df["date"] = df["date"].astype(str) + maturities = duration_sorter(df["maturity"].unique().tolist()) + countries: list = ( + df["country"].unique().tolist() if "country" in df.columns else [] + ) + + # Use the supplied colors, if any. + colors = kwargs.get("colors", []) + if not colors: + colors = LARGE_CYCLER + color_count = 0 + + figure = OpenBBFigure().create_subplots(shared_xaxes=True) + figure.update_layout(ChartStyle().plotly_template.get("layout", {})) + text_color = "white" if ChartStyle().plt_style == "dark" else "black" + + def create_fig( + figure, dataframe, dates, color_count, country: Optional[str] = None + ): + """Create a scatter for each date in the data.""" + for date in dates: + color = colors[color_count % len(colors)] + plot_df = dataframe[dataframe["date"] == date].copy() + plot_df["rate"] = plot_df["rate"].apply(lambda x: x * 100) + plot_df = plot_df.rename(columns={"rate": "Yield"}) + plot_df = ( + plot_df.drop(columns=["date"]) + .set_index("maturity") + .filter(items=maturities, axis=0) + .reset_index() + ) + plot_df = plot_df.rename(columns={"index": "Maturity"}) + plot_df["Maturity"] = [ + (d.split("_")[1] + " " + d.split("_")[0].title()) + for d in plot_df["Maturity"] + ] + figure.add_scatter( + x=plot_df["Maturity"], + y=plot_df["Yield"], + mode="lines+markers", + name=( + f"{country.replace('_', ' ').title().replace('Ecb', 'ECB')} {date}" + if country + else date + ), + line=dict(width=3, color=color), + marker=dict(size=10, color=color), + hovertemplate=( + "Maturity: %{x}
Yield: %{y}%" + if len(dates) == 1 and not countries + else "%{fullData.name}
Maturity: %{x}
Yield: %{y}%" + ), + ) + color_count += 1 + return figure, color_count + + if countries: + for _country in countries: + _df = df[df["country"] == _country] + dates = _df.date.unique().tolist() + figure, color_count = create_fig( + figure, _df, dates, color_count, _country + ) + + else: + dates = df.date.unique().tolist() + figure, color_count = create_fig(figure, df, dates, color_count) + + extra_params = kwargs.get("extra_params", {}) + extra_params = ( + extra_params if isinstance(extra_params, dict) else extra_params.__dict__ + ) + # Set the title for the chart + country: str = "" + if provider in ("federal_reserve", "fmp"): + country = "United States" + elif provider == "ecb": + curve_type = ( + extra_params.get("yield_curve_type", "").replace("_", " ").title() + ) + grade = extra_params.get("rating", "").replace("_", " ") + grade = grade.upper() if grade == "aaa" else "All Ratings" + country = f"Euro Area ({grade}) {curve_type}" + elif provider == "fred": + curve_type = extra_params.get("yield_curve_type", "") + curve_type = ( + "Real Rates" + if curve_type == "real" + else curve_type.replace("_", " ").title() + ) + country = f"United States {curve_type}" + elif provider == "econdb": + country = ( + "" + if countries + else ( + extra_params.get("country", "") + .replace("_", " ") + .title() + .replace("Ecb", "ECB") + or "United States" + ) + ) + + country = country + " " if country else "" + title = kwargs.get("title", "") + if not title: + title = f"{country}Yield Curve" + if len(dates) == 1 and len(countries) == 1: + title = f"{country} Yield Curve - {dates[0]}" + elif countries: + title = f"Yield Curve - {', '.join(countries).replace('_', ' ').title().replace('Ecb', 'ECB')}" + + # Update the layout of the figure. + figure.update_layout( + title=dict(text=title, x=0.5, font=dict(size=20)), + plot_bgcolor=( + "rgba(0,0,0,0)" if text_color == "white" else "rgba(255,255,255,0)" + ), + paper_bgcolor=( + "rgba(0,0,0,0)" if text_color == "white" else "rgba(255,255,255,0)" + ), + xaxis=dict( + title="Maturity", + ticklen=0, + showgrid=False, + type="category", + categoryorder="array", + categoryarray=( + [ + (d.split("_")[1] + " " + d.split("_")[0].title()) + for d in maturities + ] + ), + ), + yaxis=dict( + title="Yield (%)", + ticklen=0, + showgrid=True, + gridcolor="rgba(128,128,128,0.3)", + ), + legend=dict( + orientation="v", + yanchor="top", + xanchor="right", + y=0.95, + x=0, + xref="paper", + font=dict(size=12), + bgcolor=( + "rgba(0,0,0,0)" if text_color == "white" else "rgba(255,255,255,0)" + ), + ), + margin=dict( + b=25, + t=10, + l=20, + ), + ) + + layout_kwargs = kwargs.get("layout_kwargs", {}) + if layout_kwargs: + figure.update_layout(layout_kwargs) + + content = figure.show(external=True) + + return figure, content diff --git a/openbb_platform/extensions/fixedincome/openbb_fixedincome/government/__init__.py b/openbb_platform/extensions/fixedincome/openbb_fixedincome/government/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..b7f8c4158f3c42a45029e5b8798b077cbf2d904d --- /dev/null +++ b/openbb_platform/extensions/fixedincome/openbb_fixedincome/government/__init__.py @@ -0,0 +1 @@ +"""Initialize the Fixed Income Government module.""" diff --git a/openbb_platform/extensions/fixedincome/openbb_fixedincome/government/government_router.py b/openbb_platform/extensions/fixedincome/openbb_fixedincome/government/government_router.py new file mode 100644 index 0000000000000000000000000000000000000000..d4800e0c0d3bd3084a6f4bd0be6e3cdb87d7788c --- /dev/null +++ b/openbb_platform/extensions/fixedincome/openbb_fixedincome/government/government_router.py @@ -0,0 +1,120 @@ +"""Fixed Income Government Router.""" + +from openbb_core.app.model.command_context import CommandContext +from openbb_core.app.model.example import APIEx +from openbb_core.app.model.obbject import OBBject +from openbb_core.app.provider_interface import ( + ExtraParams, + ProviderChoices, + StandardParams, +) +from openbb_core.app.query import Query +from openbb_core.app.router import Router + +router = Router(prefix="/government") + +# pylint: disable=unused-argument + + +@router.command( + model="YieldCurve", + examples=[ + APIEx(parameters={"provider": "federal_reserve"}), + APIEx(parameters={"date": "2023-05-01,2024-05-01", "provider": "fmp"}), + APIEx( + parameters={ + "date": "2023-05-01", + "country": "united_kingdom", + "provider": "econdb", + } + ), + APIEx(parameters={"provider": "ecb", "yield_curve_type": "par_yield"}), + APIEx( + parameters={ + "provider": "fred", + "yield_curve_type": "real", + "date": "2023-05-01,2024-05-01", + } + ), + ], +) +async def yield_curve( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: # type: ignore + """Get yield curve data by country and date.""" + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="TreasuryRates", + examples=[APIEx(parameters={"provider": "fmp"})], +) +async def treasury_rates( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Government Treasury Rates.""" + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="TreasuryAuctions", + examples=[ + APIEx(parameters={"provider": "government_us"}), + APIEx( + parameters={ + "security_type": "Bill", + "start_date": "2022-01-01", + "end_date": "2023-01-01", + "provider": "government_us", + } + ), + ], +) +async def treasury_auctions( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Government Treasury Auctions.""" + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="TreasuryPrices", + examples=[ + APIEx(parameters={"provider": "government_us"}), + APIEx(parameters={"date": "2019-02-05", "provider": "government_us"}), + ], +) +async def treasury_prices( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Government Treasury Prices by date.""" + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="TipsYields", + examples=[ + APIEx(parameters={"provider": "fred"}), + APIEx(parameters={"maturity": 10, "provider": "fred"}), + ], +) +async def tips_yields( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Get current Treasury inflation-protected securities yields.""" + return await OBBject.from_query(Query(**locals())) diff --git a/openbb_platform/extensions/fixedincome/openbb_fixedincome/py.typed b/openbb_platform/extensions/fixedincome/openbb_fixedincome/py.typed new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/openbb_platform/extensions/fixedincome/openbb_fixedincome/rate/__init__.py b/openbb_platform/extensions/fixedincome/openbb_fixedincome/rate/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..000b124d71acf2337e0229a77298f77f841b3c30 --- /dev/null +++ b/openbb_platform/extensions/fixedincome/openbb_fixedincome/rate/__init__.py @@ -0,0 +1 @@ +"""Initialize Fixed income rate router.""" diff --git a/openbb_platform/extensions/fixedincome/openbb_fixedincome/rate/rate_router.py b/openbb_platform/extensions/fixedincome/openbb_fixedincome/rate/rate_router.py new file mode 100644 index 0000000000000000000000000000000000000000..8da16c767d664b30360bb93b64c1de6d4331b05d --- /dev/null +++ b/openbb_platform/extensions/fixedincome/openbb_fixedincome/rate/rate_router.py @@ -0,0 +1,242 @@ +"""Fixed Income Rate Router.""" + +from openbb_core.app.model.command_context import CommandContext +from openbb_core.app.model.example import APIEx +from openbb_core.app.model.obbject import OBBject +from openbb_core.app.provider_interface import ( + ExtraParams, + ProviderChoices, + StandardParams, +) +from openbb_core.app.query import Query +from openbb_core.app.router import Router + +router = Router(prefix="/rate") + +# pylint: disable=unused-argument + + +@router.command( + model="Ameribor", + examples=[ + APIEx(parameters={"provider": "fred"}), + APIEx( + description="The change from one year ago is applied with the transform parameter.", + parameters={"maturity": "all", "transform": "pc1", "provider": "fred"}, + ), + ], +) +async def ameribor( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: # type: ignore + """AMERIBOR. + + AMERIBOR (short for the American interbank offered rate) is a benchmark interest rate that reflects the true cost of + short-term interbank borrowing. This rate is based on transactions in overnight unsecured loans conducted on the + American Financial Exchange (AFX). + """ + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="SONIA", + examples=[ + APIEx(parameters={"provider": "fred"}), + APIEx(parameters={"parameter": "total_nominal_value", "provider": "fred"}), + ], +) +async def sonia( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: # type: ignore + """Sterling Overnight Index Average. + + SONIA (Sterling Overnight Index Average) is an important interest rate benchmark. SONIA is based on actual + transactions and reflects the average of the interest rates that banks pay to borrow sterling overnight from other + financial institutions and other institutional investors. + """ + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="SOFR", + examples=[ + APIEx(parameters={"provider": "fred"}), + ], +) +async def sofr( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: # type: ignore + """Secured Overnight Financing Rate. + + The Secured Overnight Financing Rate (SOFR) is a broad measure of the cost of + borrowing cash overnight collateralizing by Treasury securities. + """ + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="IORB", + examples=[APIEx(parameters={"provider": "fred"})], +) +async def iorb( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: # type: ignore + """Interest on Reserve Balances. + + Get Interest Rate on Reserve Balances data A bank rate is the interest rate a nation's central bank charges to its + domestic banks to borrow money. The rates central banks charge are set to stabilize the economy. In the + United States, the Federal Reserve System's Board of Governors set the bank rate, also known as the discount rate. + """ + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="FederalFundsRate", + examples=[ + APIEx(parameters={"provider": "fred"}), + APIEx(parameters={"effr_only": True, "provider": "fred"}), + ], +) +async def effr( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: # type: ignore + """Fed Funds Rate. + + Get Effective Federal Funds Rate data. A bank rate is the interest rate a nation's central bank charges to its + domestic banks to borrow money. The rates central banks charge are set to stabilize the economy. + """ + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="PROJECTIONS", + examples=[ + APIEx(parameters={"provider": "fred"}), + APIEx(parameters={"long_run": True, "provider": "fred"}), + ], +) +async def effr_forecast( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: # type: ignore + """Fed Funds Rate Projections. + + The projections for the federal funds rate are the value of the midpoint of the + projected appropriate target range for the federal funds rate or the projected + appropriate target level for the federal funds rate at the end of the specified + calendar year or over the longer run. + """ + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="EuroShortTermRate", + examples=[ + APIEx(parameters={"provider": "fred"}), + APIEx(parameters={"transform": "ch1", "provider": "fred"}), + ], +) +async def estr( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: # type: ignore + """Euro Short-Term Rate. + + The euro short-term rate (€STR) reflects the wholesale euro unsecured overnight borrowing costs of banks located in + the euro area. The €STR is published on each TARGET2 business day based on transactions conducted and settled on + the previous TARGET2 business day (the reporting date “T”) with a maturity date of T+1 which are deemed to have been + executed at arm's length and thus reflect market rates in an unbiased way. + """ + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="EuropeanCentralBankInterestRates", + examples=[ + APIEx(parameters={"provider": "fred"}), + APIEx(parameters={"interest_rate_type": "refinancing", "provider": "fred"}), + ], +) +async def ecb( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """European Central Bank Interest Rates. + + The Governing Council of the ECB sets the key interest rates for the euro area: + + - The interest rate on the main refinancing operations (MRO), which provide + the bulk of liquidity to the banking system. + - The rate on the deposit facility, which banks may use to make overnight deposits with the Eurosystem. + - The rate on the marginal lending facility, which offers overnight credit to banks from the Eurosystem. + """ + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="DiscountWindowPrimaryCreditRate", + examples=[ + APIEx(parameters={"provider": "fred"}), + APIEx( + parameters={ + "start_date": "2023-02-01", + "end_date": "2023-05-01", + "provider": "fred", + } + ), + ], +) +async def dpcredit( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Discount Window Primary Credit Rate. + + A bank rate is the interest rate a nation's central bank charges to its domestic banks to borrow money. + The rates central banks charge are set to stabilize the economy. + In the United States, the Federal Reserve System's Board of Governors set the bank rate, + also known as the discount rate. + """ + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="OvernightBankFundingRate", + examples=[APIEx(parameters={"provider": "fred"})], +) +async def overnight_bank_funding( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: # type: ignore + """Overnight Bank Funding. + + For the United States, the overnight bank funding rate (OBFR) is calculated as a volume-weighted median of + overnight federal funds transactions and Eurodollar transactions reported in the + FR 2420 Report of Selected Money Market Rates. + """ + return await OBBject.from_query(Query(**locals())) diff --git a/openbb_platform/extensions/fixedincome/openbb_fixedincome/spreads/__init__.py b/openbb_platform/extensions/fixedincome/openbb_fixedincome/spreads/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..302a18e39e979c0414b867e2975e5ac8b8592be9 --- /dev/null +++ b/openbb_platform/extensions/fixedincome/openbb_fixedincome/spreads/__init__.py @@ -0,0 +1 @@ +"""Initialize the Fixed Income Spreads module.""" diff --git a/openbb_platform/extensions/fixedincome/openbb_fixedincome/spreads/spreads_router.py b/openbb_platform/extensions/fixedincome/openbb_fixedincome/spreads/spreads_router.py new file mode 100644 index 0000000000000000000000000000000000000000..1c0ee460911fd6d6e8331fc58d532334b2fde652 --- /dev/null +++ b/openbb_platform/extensions/fixedincome/openbb_fixedincome/spreads/spreads_router.py @@ -0,0 +1,86 @@ +"""Fixed Income Corporate Router.""" + +from openbb_core.app.model.command_context import CommandContext +from openbb_core.app.model.example import APIEx +from openbb_core.app.model.obbject import OBBject +from openbb_core.app.provider_interface import ( + ExtraParams, + ProviderChoices, + StandardParams, +) +from openbb_core.app.query import Query +from openbb_core.app.router import Router + +router = Router(prefix="/spreads") + +# pylint: disable=unused-argument + + +@router.command( + model="TreasuryConstantMaturity", + examples=[ + APIEx(parameters={"provider": "fred"}), + APIEx(parameters={"maturity": "2y", "provider": "fred"}), + ], +) +async def tcm( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Treasury Constant Maturity. + + Get data for 10-Year Treasury Constant Maturity Minus Selected Treasury Constant Maturity. + Constant maturity is the theoretical value of a U.S. Treasury that is based on recent values of auctioned U.S. + Treasuries. The value is obtained by the U.S. Treasury on a daily basis through interpolation of the Treasury + yield curve which, in turn, is based on closing bid-yields of actively-traded Treasury securities. + """ + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="SelectedTreasuryConstantMaturity", + examples=[ + APIEx(parameters={"provider": "fred"}), + APIEx(parameters={"maturity": "10y", "provider": "fred"}), + ], +) +async def tcm_effr( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Select Treasury Constant Maturity. + + Get data for Selected Treasury Constant Maturity Minus Federal Funds Rate + Constant maturity is the theoretical value of a U.S. Treasury that is based on recent values of auctioned U.S. + Treasuries. The value is obtained by the U.S. Treasury on a daily basis through interpolation of the Treasury + yield curve which, in turn, is based on closing bid-yields of actively-traded Treasury securities. + """ + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="SelectedTreasuryBill", + examples=[ + APIEx(parameters={"provider": "fred"}), + APIEx(parameters={"maturity": "6m", "provider": "fred"}), + ], +) +async def treasury_effr( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Select Treasury Bill. + + Get Selected Treasury Bill Minus Federal Funds Rate. + Constant maturity is the theoretical value of a U.S. Treasury that is based on recent values of + auctioned U.S. Treasuries. + The value is obtained by the U.S. Treasury on a daily basis through interpolation of the Treasury + yield curve which, in turn, is based on closing bid-yields of actively-traded Treasury securities. + """ + return await OBBject.from_query(Query(**locals())) diff --git a/openbb_platform/extensions/fixedincome/poetry.lock b/openbb_platform/extensions/fixedincome/poetry.lock new file mode 100644 index 0000000000000000000000000000000000000000..8b5cef68b5cd92a25e397a3668ab2711aa485aac --- /dev/null +++ b/openbb_platform/extensions/fixedincome/poetry.lock @@ -0,0 +1,1734 @@ +# This file is automatically @generated by Poetry 2.1.3 and should not be changed by hand. + +[[package]] +name = "aiohappyeyeballs" +version = "2.6.1" +description = "Happy Eyeballs for asyncio" +optional = false +python-versions = ">=3.9" +groups = ["main"] +files = [ + {file = "aiohappyeyeballs-2.6.1-py3-none-any.whl", hash = "sha256:f349ba8f4b75cb25c99c5c2d84e997e485204d2902a9597802b0371f09331fb8"}, + {file = "aiohappyeyeballs-2.6.1.tar.gz", hash = "sha256:c3f9d0113123803ccadfdf3f0faa505bc78e6a72d1cc4806cbd719826e943558"}, +] + +[[package]] +name = "aiohttp" +version = "3.11.18" +description = "Async 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b/openbb_platform/extensions/fixedincome/pyproject.toml new file mode 100644 index 0000000000000000000000000000000000000000..3f432a05702345f6b6163961695397de4e9dae87 --- /dev/null +++ b/openbb_platform/extensions/fixedincome/pyproject.toml @@ -0,0 +1,22 @@ +[tool.poetry] +name = "openbb-fixedincome" +version = "1.4.3" +description = "Fixed income extension for OpenBB" +authors = ["OpenBB Team "] +license = "AGPL-3.0-only" +readme = "README.md" +packages = [{ include = "openbb_fixedincome" }] + +[tool.poetry.dependencies] +python = ">=3.9.21,<3.13" +openbb-core = "^1.4.6" + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" + +[tool.poetry.plugins."openbb_core_extension"] +fixedincome = "openbb_fixedincome.fixedincome_router:router" + +[tool.poetry.plugins."openbb_charting_extension"] +fixedincome = "openbb_fixedincome.fixedincome_views:FixedIncomeViews" diff --git a/openbb_platform/extensions/fixedincome/tests/.gitkeep b/openbb_platform/extensions/fixedincome/tests/.gitkeep new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/openbb_platform/extensions/index/README.md b/openbb_platform/extensions/index/README.md new file mode 100644 index 0000000000000000000000000000000000000000..2fe9dc021a92a9b68b286c570289c8770003712f --- /dev/null +++ b/openbb_platform/extensions/index/README.md @@ -0,0 +1,13 @@ +# OpenBB Index Extension + +The Index extension provides global and european index data access for the OpenBB Platform. + +## Installation + +To install the extension, run the following command in this folder: + +```bash +pip install openbb-index +``` + +Documentation available [here](https://docs.openbb.co/platform/developer_guide/contributing). diff --git a/openbb_platform/extensions/index/integration/test_index_api.py b/openbb_platform/extensions/index/integration/test_index_api.py new file mode 100644 index 0000000000000000000000000000000000000000..32f099a7c1f434b02cecc49132ed4edef0e0ab09 --- /dev/null +++ b/openbb_platform/extensions/index/integration/test_index_api.py @@ -0,0 +1,228 @@ +"""Test the index API endpoints.""" + +import base64 + +import pytest +import requests +from extensions.tests.conftest import parametrize +from openbb_core.env import Env +from openbb_core.provider.utils.helpers import get_querystring + + +@pytest.fixture(scope="session") +def headers(): + """Get the headers for the API request.""" + userpass = f"{Env().API_USERNAME}:{Env().API_PASSWORD}" + userpass_bytes = userpass.encode("ascii") + base64_bytes = base64.b64encode(userpass_bytes) + + return {"Authorization": f"Basic {base64_bytes.decode('ascii')}"} + + +# pylint: disable=redefined-outer-name + + +@parametrize( + "params", + [ + ({"symbol": "dowjones", "provider": "fmp"}), + ({"symbol": "^TX60", "provider": "tmx", "use_cache": False}), + ({"symbol": "BUKBUS", "provider": "cboe"}), + ], +) +@pytest.mark.integration +def test_index_constituents(params, headers): + """Test the index constituents endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/index/constituents?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ( + { + "interval": "1d", + "provider": "cboe", + "symbol": "AAVE100", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + "use_cache": False, + } + ), + ( + { + "interval": "1d", + "provider": "fmp", + "symbol": "^DJI", + "start_date": "2024-01-01", + "end_date": "2024-02-05", + } + ), + ( + { + "interval": "1h", + "provider": "fmp", + "symbol": "^DJI,^NDX", + "start_date": None, + "end_date": None, + } + ), + ( + { + "interval": "1m", + "sort": "desc", + "limit": 49999, + "provider": "polygon", + "symbol": "NDX", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + } + ), + ( + { + "interval": "1d", + "sort": "desc", + "limit": 49999, + "provider": "polygon", + "symbol": "NDX", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + } + ), + ( + { + "interval": "1d", + "provider": "yfinance", + "symbol": "DJI", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + } + ), + ( + { + "provider": "intrinio", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + "symbol": "DJI", + "limit": 100, + } + ), + ], +) +@pytest.mark.integration +def test_index_price_historical(params, headers): + """Test the index historical price endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/index/price/historical?{query_str}" + result = requests.get(url, headers=headers, timeout=20) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ({"provider": "cboe", "use_cache": False}), + ({"provider": "fmp"}), + ({"provider": "yfinance"}), + ({"provider": "tmx", "use_cache": False}), + ], +) +@pytest.mark.integration +def test_index_available(params, headers): + """Test the index available endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/index/available?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ({"query": "D", "is_symbol": True, "provider": "cboe", "use_cache": False}), + ], +) +@pytest.mark.integration +def test_index_search(params, headers): + """Test the index search endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/index/search?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ({"provider": "cboe", "region": "us"}), + ({"provider": "tmx", "region": "ca", "use_cache": False}), + ], +) +@pytest.mark.integration +def test_index_snapshots(params, headers): + """Test the index snapshots endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/index/snapshots?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ( + { + "series_name": "pe_month", + "start_date": None, + "end_date": None, + "provider": "multpl", + } + ), + ], +) +@pytest.mark.integration +def test_index_sp500_multiples(params, headers): + """Test the index sp500 multiples endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/index/sp500_multiples?{query_str}" + result = requests.get(url, headers=headers, timeout=20) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ({"provider": "tmx", "symbol": "^TX60", "use_cache": False}), + ], +) +@pytest.mark.integration +def test_index_sectors(params, headers): + """Test the index sectors endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/index/sectors?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 diff --git a/openbb_platform/extensions/index/integration/test_index_python.py b/openbb_platform/extensions/index/integration/test_index_python.py new file mode 100644 index 0000000000000000000000000000000000000000..b2ccd379881588ba7576de16930e9e3310078754 --- /dev/null +++ b/openbb_platform/extensions/index/integration/test_index_python.py @@ -0,0 +1,211 @@ +"""Test economy extension.""" + +import pytest +from extensions.tests.conftest import parametrize +from openbb_core.app.model.obbject import OBBject + + +@pytest.fixture(scope="session") +def obb(pytestconfig): # pylint: disable=inconsistent-return-statements + """Fixture to setup obb.""" + + if pytestconfig.getoption("markexpr") != "not integration": + import openbb # pylint: disable=import-outside-toplevel + + return openbb.obb + + +# pylint: disable=redefined-outer-name + + +@parametrize( + "params", + [ + ({"symbol": "dowjones", "provider": "fmp"}), + ({"symbol": "BUKBUS", "provider": "cboe"}), + ({"symbol": "^TX60", "provider": "tmx", "use_cache": False}), + ], +) +@pytest.mark.integration +def test_index_constituents(params, obb): + """Test the index constituents endpoint.""" + result = obb.index.constituents(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ( + { + "interval": "1d", + "provider": "cboe", + "symbol": "AAVE100", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + "use_cache": False, + } + ), + ( + { + "interval": "1d", + "provider": "fmp", + "symbol": "^DJI", + "start_date": "2024-01-01", + "end_date": "2024-02-05", + } + ), + ( + { + "interval": "1h", + "provider": "fmp", + "symbol": "^DJI,^NDX", + "start_date": None, + "end_date": None, + } + ), + ( + { + "interval": "1m", + "sort": "desc", + "limit": 49999, + "provider": "polygon", + "symbol": "NDX", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + } + ), + ( + { + "interval": "1d", + "sort": "desc", + "limit": 49999, + "provider": "polygon", + "symbol": "NDX", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + } + ), + ( + { + "interval": "1d", + "provider": "yfinance", + "symbol": "DJI", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + } + ), + ( + { + "provider": "intrinio", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + "symbol": "DJI", + "limit": 100, + } + ), + ], +) +@pytest.mark.integration +def test_index_price_historical(params, obb): + """Test the index historical price endpoint.""" + result = obb.index.price.historical(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ({}), + ({"provider": "cboe", "use_cache": False}), + ({"provider": "fmp"}), + ({"provider": "yfinance"}), + ({"provider": "tmx", "use_cache": False}), + ], +) +@pytest.mark.integration +def test_index_available(params, obb): + """Test the index available endpoint.""" + result = obb.index.available(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ( + { + "query": "D", + "is_symbol": True, + "provider": "cboe", + "use_cache": False, + } + ), + ], +) +@pytest.mark.integration +def test_index_search(params, obb): + """Test the index search endpoint.""" + result = obb.index.search(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ({"region": "us", "provider": "cboe"}), + ({"provider": "tmx", "region": "ca", "use_cache": False}), + ], +) +@pytest.mark.integration +def test_index_snapshots(params, obb): + """Test the index snapshots endpoint.""" + result = obb.index.snapshots(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ( + { + "series_name": "pe_month", + "start_date": None, + "end_date": None, + "provider": "multpl", + } + ), + ], +) +@pytest.mark.integration +def test_index_sp500_multiples(params, obb): + """Test the index sp500 multiples endpoint.""" + result = obb.index.sp500_multiples(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ({"symbol": "^TX60", "provider": "tmx"}), + ], +) +@pytest.mark.integration +def test_index_sectors(params, obb): + """Test the index sectors endpoint.""" + result = obb.index.sectors(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 diff --git a/openbb_platform/extensions/index/openbb_index/__init__.py b/openbb_platform/extensions/index/openbb_index/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..fa35046741664007a38082b10e940c4b98f7f395 --- /dev/null +++ b/openbb_platform/extensions/index/openbb_index/__init__.py @@ -0,0 +1 @@ +"""Index Extension.""" diff --git a/openbb_platform/extensions/index/openbb_index/index_router.py b/openbb_platform/extensions/index/openbb_index/index_router.py new file mode 100644 index 0000000000000000000000000000000000000000..2e38f4a92b938a7bf6f1ee789e364aff35b99c34 --- /dev/null +++ b/openbb_platform/extensions/index/openbb_index/index_router.py @@ -0,0 +1,121 @@ +"""Index Router.""" + +from openbb_core.app.model.command_context import CommandContext +from openbb_core.app.model.example import APIEx +from openbb_core.app.model.obbject import OBBject +from openbb_core.app.provider_interface import ( + ExtraParams, + ProviderChoices, + StandardParams, +) +from openbb_core.app.query import Query +from openbb_core.app.router import Router + +from openbb_index.price.price_router import router as price_router + +router = Router(prefix="", description="Indices data.") +router.include_router(price_router) + +# pylint: disable=unused-argument + + +@router.command( + model="IndexConstituents", + examples=[ + APIEx(parameters={"symbol": "dowjones", "provider": "fmp"}), + APIEx( + description="Providers other than FMP will use the ticker symbol.", + parameters={"symbol": "BEP50P", "provider": "cboe"}, + ), + ], +) +async def constituents( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Get Index Constituents.""" + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="IndexSnapshots", + examples=[ + APIEx(parameters={"provider": "tmx"}), + APIEx(parameters={"region": "us", "provider": "cboe"}), + ], +) +async def snapshots( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Index Snapshots. Current levels for all indices from a provider, grouped by `region`.""" + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="AvailableIndices", + examples=[ + APIEx(parameters={"provider": "fmp"}), + APIEx(parameters={"provider": "yfinance"}), + ], +) +async def available( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """All indices available from a given provider.""" + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="IndexSearch", + examples=[ + APIEx(parameters={"provider": "cboe"}), + APIEx(parameters={"query": "SPX", "provider": "cboe"}), + ], +) +async def search( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Filter indices for rows containing the query.""" + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="SP500Multiples", + examples=[ + APIEx(parameters={"provider": "multpl"}), + APIEx(parameters={"series_name": "shiller_pe_year", "provider": "multpl"}), + ], +) +async def sp500_multiples( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Get historical S&P 500 multiples and Shiller PE ratios.""" + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="IndexSectors", + examples=[APIEx(parameters={"symbol": "^TX60", "provider": "tmx"})], +) +async def sectors( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Get Index Sectors. Sector weighting of an index.""" + return await OBBject.from_query(Query(**locals())) diff --git a/openbb_platform/extensions/index/openbb_index/index_views.py b/openbb_platform/extensions/index/openbb_index/index_views.py new file mode 100644 index 0000000000000000000000000000000000000000..2fefafc34d557e56f96fa84e118b2b0caff3b85d --- /dev/null +++ b/openbb_platform/extensions/index/openbb_index/index_views.py @@ -0,0 +1,22 @@ +"""Views for the index Extension.""" + +from typing import TYPE_CHECKING, Any, Dict, Tuple + +if TYPE_CHECKING: + from openbb_charting.core.openbb_figure import ( + OpenBBFigure, + ) + + +class IndexViews: + """Index Views.""" + + @staticmethod + def index_price_historical( # noqa: PLR0912 + **kwargs, + ) -> Tuple["OpenBBFigure", Dict[str, Any]]: + """Index Price Historical Chart.""" + # pylint: disable=import-outside-toplevel + from openbb_charting.charts.price_historical import price_historical + + return price_historical(**kwargs) diff --git a/openbb_platform/extensions/index/openbb_index/price/__init__.py b/openbb_platform/extensions/index/openbb_index/price/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..8246f259eea045aed82c619f8afb43e877712723 --- /dev/null +++ b/openbb_platform/extensions/index/openbb_index/price/__init__.py @@ -0,0 +1 @@ +"""Index Price.""" diff --git a/openbb_platform/extensions/index/openbb_index/price/price_router.py b/openbb_platform/extensions/index/openbb_index/price/price_router.py new file mode 100644 index 0000000000000000000000000000000000000000..883427d5f4cc698010db5014197543cfa085c167 --- /dev/null +++ b/openbb_platform/extensions/index/openbb_index/price/price_router.py @@ -0,0 +1,36 @@ +"""Price Router.""" + +from openbb_core.app.model.command_context import CommandContext +from openbb_core.app.model.example import APIEx +from openbb_core.app.model.obbject import OBBject +from openbb_core.app.provider_interface import ( + ExtraParams, + ProviderChoices, + StandardParams, +) +from openbb_core.app.query import Query +from openbb_core.app.router import Router + +router = Router(prefix="/price") + +# pylint: disable=unused-argument + + +@router.command( + model="IndexHistorical", + examples=[ + APIEx(parameters={"symbol": "^GSPC", "provider": "fmp"}), + APIEx( + description="Not all providers have the same symbols.", + parameters={"symbol": "SPX", "provider": "intrinio"}, + ), + ], +) +async def historical( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Historical Index Levels.""" + return await OBBject.from_query(Query(**locals())) diff --git a/openbb_platform/extensions/index/openbb_index/py.typed b/openbb_platform/extensions/index/openbb_index/py.typed new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/openbb_platform/extensions/index/poetry.lock 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b/openbb_platform/extensions/index/pyproject.toml new file mode 100644 index 0000000000000000000000000000000000000000..ea97c77265e0b45086bb14800cdc2538d041f0cd --- /dev/null +++ b/openbb_platform/extensions/index/pyproject.toml @@ -0,0 +1,22 @@ +[tool.poetry] +name = "openbb-index" +version = "1.4.1" +description = "Index extension for OpenBB" +authors = ["OpenBB Team "] +license = "AGPL-3.0-only" +readme = "README.md" +packages = [{ include = "openbb_index" }] + +[tool.poetry.dependencies] +python = ">=3.9.21,<3.13" +openbb-core = "^1.4.6" + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" + +[tool.poetry.plugins."openbb_core_extension"] +index = "openbb_index.index_router:router" + +[tool.poetry.plugins."openbb_charting_extension"] +index = "openbb_index.index_views:IndexViews" diff --git a/openbb_platform/extensions/index/tests/.gitkeep b/openbb_platform/extensions/index/tests/.gitkeep new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/openbb_platform/extensions/news/README.md b/openbb_platform/extensions/news/README.md new file mode 100644 index 0000000000000000000000000000000000000000..04aa2fc64601c1c80bcdf9d5833fd319e7a0a3c6 --- /dev/null +++ b/openbb_platform/extensions/news/README.md @@ -0,0 +1,13 @@ +# OpenBB News Extension + +This extension provides news for the OpenBB Platform. + +## Installation + +To install the extension, run the following command in this folder: + +```bash +pip install openbb-news +``` + +Documentation available [here](https://docs.openbb.co/platform/developer_guide/contributing). diff --git a/openbb_platform/extensions/news/integration/test_news_api.py b/openbb_platform/extensions/news/integration/test_news_api.py new file mode 100644 index 0000000000000000000000000000000000000000..f3a7a45379439b44f3dfc6fd3e3232ed20048af1 --- /dev/null +++ b/openbb_platform/extensions/news/integration/test_news_api.py @@ -0,0 +1,217 @@ +"""Test News API.""" + +import base64 + +import pytest +import requests +from extensions.tests.conftest import parametrize +from openbb_core.env import Env +from openbb_core.provider.utils.helpers import get_querystring + + +@pytest.fixture(scope="session") +def headers(): + """Generate headers for API requests with basic authentication.""" + userpass = f"{Env().API_USERNAME}:{Env().API_PASSWORD}" + userpass_bytes = userpass.encode("ascii") + base64_bytes = base64.b64encode(userpass_bytes) + + return {"Authorization": f"Basic {base64_bytes.decode('ascii')}"} + + +# pylint: disable=redefined-outer-name + + +@parametrize( + "params", + [ + ( + { + "display": "full", + "date": None, + "start_date": None, + "end_date": None, + "updated_since": None, + "published_since": None, + "sort": "created", + "order": "desc", + "isin": None, + "cusip": None, + "channels": "General", + "topics": "earnings", + "authors": None, + "content_types": "headline", + "provider": "benzinga", + "limit": 20, + } + ), + ( + { + "provider": "fmp", + "limit": 30, + "start_date": None, + "end_date": None, + } + ), + ( + { + "provider": "intrinio", + "limit": 20, + "start_date": None, + "end_date": None, + "source": "yahoo", + "topic": None, + "is_spam": False, + "sentiment": None, + "language": None, + "word_count_greater_than": None, + "word_count_less_than": None, + "business_relevance_greater_than": None, + "business_relevance_less_than": None, + } + ), + ( + { + "provider": "biztoc", + "source": None, + "term": "microsoft", + "start_date": None, + "end_date": None, + } + ), + ( + { + "provider": "tiingo", + "limit": 30, + "source": "bloomberg.com", + "start_date": None, + "end_date": None, + "offset": 0, + } + ), + ], +) +@pytest.mark.integration +def test_news_world(params, headers): + """Test retrieval of world news with various parameters.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/news/world?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ( + { + "symbol": "AAPL", + "limit": 20, + "provider": "benzinga", + "date": "2023-01-01", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + } + ), + ( + { + "display": "full", + "date": None, + "start_date": "2023-06-01", + "end_date": "2023-06-06", + "updated_since": 1, + "published_since": 1, + "sort": "created", + "order": "asc", + "isin": None, + "cusip": None, + "channels": None, + "topics": "AAPL", + "authors": None, + "content_types": "headline", + "provider": "benzinga", + "symbol": "AAPL,MSFT", + "limit": 20, + } + ), + ( + { + "order": "desc", + "provider": "polygon", + "symbol": "AAPL", + "limit": 20, + "start_date": "2024-01-10", + "end_date": "2024-01-10", + } + ), + ( + { + "provider": "fmp", + "symbol": "AAPL", + "limit": 20, + "page": 1, + "start_date": None, + "end_date": None, + } + ), + ( + { + "provider": "yfinance", + "symbol": "AAPL", + "limit": 20, + "start_date": None, + "end_date": None, + } + ), + ( + { + "provider": "intrinio", + "symbol": "AAPL", + "limit": 20, + "start_date": "2024-01-02", + "end_date": "2024-01-03", + "source": "yahoo", + "topic": None, + "is_spam": False, + "sentiment": None, + "language": None, + "word_count_greater_than": None, + "word_count_less_than": None, + "business_relevance_greater_than": None, + "business_relevance_less_than": None, + } + ), + ( + { + "provider": "tiingo", + "symbol": "AAPL,MSFT", + "limit": 20, + "source": "bloomberg.com", + "start_date": None, + "end_date": None, + "offset": None, + } + ), + ( + { + "provider": "tmx", + "symbol": "RBC", + "limit": 20, + "page": 1, + } + ), + ], +) +@pytest.mark.integration +def test_news_company(params, headers): + """Test retrieval of company-specific news with various parameters.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/news/company?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 diff --git a/openbb_platform/extensions/news/integration/test_news_python.py b/openbb_platform/extensions/news/integration/test_news_python.py new file mode 100644 index 0000000000000000000000000000000000000000..4a80fa42aa90b7cfed0dafcadb9cbe59305481b7 --- /dev/null +++ b/openbb_platform/extensions/news/integration/test_news_python.py @@ -0,0 +1,198 @@ +"""Test news extension.""" + +import pytest +from extensions.tests.conftest import parametrize +from openbb_core.app.model.obbject import OBBject + + +@pytest.fixture(scope="session") +def obb(pytestconfig): # pylint: disable=inconsistent-return-statements + """Fixture to setup obb.""" + + if pytestconfig.getoption("markexpr") != "not integration": + import openbb # pylint: disable=import-outside-toplevel + + return openbb.obb + + +# pylint: disable=redefined-outer-name + + +@parametrize( + "params", + [ + ( + { + "display": "full", + "date": None, + "start_date": "2023-05-01", + "end_date": "2023-05-31", + "updated_since": None, + "published_since": None, + "sort": "created", + "order": "asc", + "isin": None, + "cusip": None, + "channels": "General", + "topics": "car", + "authors": None, + "content_types": "Car", + "provider": "benzinga", + "limit": 20, + } + ), + ( + { + "provider": "fmp", + "limit": 20, + "start_date": None, + "end_date": None, + } + ), + ( + { + "provider": "intrinio", + "limit": 20, + "start_date": "2024-01-02", + "end_date": "2024-01-03", + "source": "yahoo", + "topic": None, + "is_spam": False, + "sentiment": None, + "language": None, + "word_count_greater_than": None, + "word_count_less_than": None, + "business_relevance_greater_than": None, + "business_relevance_less_than": None, + } + ), + ( + { + "provider": "biztoc", + "source": None, + "term": "microsoft", + "start_date": None, + "end_date": None, + } + ), + ( + { + "provider": "tiingo", + "limit": 30, + "source": "bloomberg.com", + "start_date": None, + "end_date": None, + "offset": 0, + } + ), + ], +) +@pytest.mark.integration +def test_news_world(params, obb): + """Test the news world endpoint.""" + result = obb.news.world(**params) + assert result + assert isinstance(result, OBBject) + + +@parametrize( + "params", + [ + ( + { + "display": "full", + "date": None, + "start_date": None, + "end_date": None, + "updated_since": None, + "published_since": None, + "sort": "created", + "order": "desc", + "isin": None, + "cusip": None, + "channels": "General", + "topics": "earnings", + "authors": None, + "content_types": "headline", + "provider": "benzinga", + "symbol": "AAPL,MSFT", + "limit": 20, + } + ), + ( + { + "order": "desc", + "provider": "polygon", + "symbol": "AAPL", + "limit": 20, + "start_date": "2024-01-10", + "end_date": "2024-01-10", + } + ), + ( + { + "provider": "fmp", + "symbol": "AAPL", + "limit": 20, + "page": 1, + "start_date": None, + "end_date": None, + } + ), + ( + { + "provider": "yfinance", + "symbol": "AAPL", + "limit": 20, + "start_date": None, + "end_date": None, + } + ), + ( + { + "provider": "intrinio", + "symbol": "AAPL", + "limit": 20, + "start_date": "2024-01-02", + "end_date": "2024-01-03", + "source": "yahoo", + "topic": None, + "is_spam": False, + "sentiment": None, + "language": None, + "word_count_greater_than": None, + "word_count_less_than": None, + "business_relevance_greater_than": None, + "business_relevance_less_than": None, + } + ), + ( + { + "provider": "tiingo", + "symbol": "AAPL", + "limit": 20, + "source": "bloomberg.com", + "start_date": None, + "end_date": None, + "offset": None, + } + ), + ( + { + "provider": "tmx", + "symbol": "RBC", + "limit": 20, + "page": 1, + } + ), + ], +) +@pytest.mark.integration +def test_news_company(params, obb): + """Test the news company endpoint.""" + params = {p: v for p, v in params.items() if v} + + result = obb.news.company(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 diff --git a/openbb_platform/extensions/news/openbb_news/__init__.py b/openbb_platform/extensions/news/openbb_news/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..5d81800260529a87a4f16e2ac58a359087b25950 --- /dev/null +++ b/openbb_platform/extensions/news/openbb_news/__init__.py @@ -0,0 +1 @@ +"""OpenBB News extension.""" diff --git a/openbb_platform/extensions/news/openbb_news/news_router.py b/openbb_platform/extensions/news/openbb_news/news_router.py new file mode 100644 index 0000000000000000000000000000000000000000..bad1a7f21c4703a271fab484bbdaf2a3db0a8254 --- /dev/null +++ b/openbb_platform/extensions/news/openbb_news/news_router.py @@ -0,0 +1,102 @@ +# pylint: disable=import-outside-toplevel, W0613:unused-argument +"""News Router.""" + +from openbb_core.app.model.command_context import CommandContext +from openbb_core.app.model.example import APIEx +from openbb_core.app.model.obbject import OBBject +from openbb_core.app.provider_interface import ( + ExtraParams, + ProviderChoices, + StandardParams, +) +from openbb_core.app.query import Query +from openbb_core.app.router import Router + +router = Router(prefix="", description="Financial market news data.") + + +@router.command( + model="WorldNews", + examples=[ + APIEx(parameters={"provider": "fmp"}), + APIEx(parameters={"limit": 100, "provider": "intrinio"}), + APIEx( + description="Get news on the specified dates.", + parameters={ + "start_date": "2024-02-01", + "end_date": "2024-02-07", + "provider": "intrinio", + }, + ), + APIEx( + description="Display the headlines of the news.", + parameters={"display": "headline", "provider": "benzinga"}, + ), + APIEx( + description="Get news by topics.", + parameters={"topics": "finance", "provider": "benzinga"}, + ), + APIEx( + description="Get news by source using 'tingo' as provider.", + parameters={"provider": "tiingo", "source": "bloomberg"}, + ), + APIEx( + description="Filter aticles by term using 'biztoc' as provider.", + parameters={"provider": "biztoc", "term": "apple"}, + ), + ], +) +async def world( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """World News. Global news data.""" + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="CompanyNews", + examples=[ + APIEx(parameters={"provider": "benzinga"}), + APIEx(parameters={"limit": 100, "provider": "benzinga"}), + APIEx( + description="Get news on the specified dates.", + parameters={ + "symbol": "AAPL", + "start_date": "2024-02-01", + "end_date": "2024-02-07", + "provider": "intrinio", + }, + ), + APIEx( + description="Display the headlines of the news.", + parameters={ + "symbol": "AAPL", + "display": "headline", + "provider": "benzinga", + }, + ), + APIEx( + description="Get news for multiple symbols.", + parameters={"symbol": "aapl,tsla", "provider": "fmp"}, + ), + APIEx( + description="Get news company's ISIN.", + parameters={ + "symbol": "NVDA", + "isin": "US0378331005", + "provider": "benzinga", + }, + ), + ], +) +async def company( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Company News. Get news for one or more companies.""" + return await OBBject.from_query(Query(**locals())) diff --git a/openbb_platform/extensions/news/openbb_news/py.typed b/openbb_platform/extensions/news/openbb_news/py.typed new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/openbb_platform/extensions/news/poetry.lock b/openbb_platform/extensions/news/poetry.lock new file mode 100644 index 0000000000000000000000000000000000000000..8b5cef68b5cd92a25e397a3668ab2711aa485aac --- /dev/null +++ b/openbb_platform/extensions/news/poetry.lock @@ -0,0 +1,1734 @@ +# This file is automatically @generated by Poetry 2.1.3 and should not be changed by hand. + +[[package]] +name = "aiohappyeyeballs" +version = "2.6.1" +description = "Happy Eyeballs for asyncio" +optional = false +python-versions = ">=3.9" +groups = ["main"] +files = [ + {file = "aiohappyeyeballs-2.6.1-py3-none-any.whl", hash = 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b/openbb_platform/extensions/news/pyproject.toml new file mode 100644 index 0000000000000000000000000000000000000000..8451c727f58806a9aa3785e90294d9e7c45a1178 --- /dev/null +++ b/openbb_platform/extensions/news/pyproject.toml @@ -0,0 +1,19 @@ +[tool.poetry] +name = "openbb-news" +version = "1.4.1" +description = "News extension for OpenBB" +authors = ["OpenBB Team "] +license = "AGPL-3.0-only" +readme = "README.md" +packages = [{ include = "openbb_news" }] + +[tool.poetry.dependencies] +python = ">=3.9.21,<3.13" +openbb-core = "^1.4.6" + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" + +[tool.poetry.plugins."openbb_core_extension"] +news = "openbb_news.news_router:router" diff --git a/openbb_platform/extensions/news/tests/.gitkeep b/openbb_platform/extensions/news/tests/.gitkeep new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/openbb_platform/extensions/platform_api/README.md b/openbb_platform/extensions/platform_api/README.md new file mode 100644 index 0000000000000000000000000000000000000000..18a3a9665478bc6284b98f1c1128ef0889b9b587 --- /dev/null +++ b/openbb_platform/extensions/platform_api/README.md @@ -0,0 +1,610 @@ +# OpenBB Platform API Launcher + +This package is responsible for launching and configuring an OpenBB Platform environment, or FastAPI instance, to use as an OpenBB Workspace [custom backend](https://docs.openbb.co/terminal/custom-backend). + +## Installation + +This package is included when you run [`pip install openbb`](https://docs.openbb.co/platform/installation); however, it also works as a standalone package +for creating new backends that are not part of the OpenBB GitHub [repository](https://github.com/OpenBB-finance/OpenBB/). + +To install as a standalone, use a Python environment between versions 3.9 and 3.12, inclusively. + +```sh +pip install openbb-platform-api +``` + +## Usage + +See the [keyword arguments](#keyword-arguments) section for parameters and descriptions. + +### Launch OpenBB Platform + +To start the OpenBB Platform API, open a terminal, activate the environment where it is installed, and then enter: + +``` +openbb-api +``` + +This will launch a Fast API instance, via `uvicorn`, at `http://127.0.0.1:6900` + +Uvicorn can be configured by adding keyword arguments, see the section [below](#keyword-arguments) + +### Launch Custom App + +To run your application as an OpenBB Workspace custom backend, add the path to the Python file with the FastAPI instance to the launch command. + +```sh +openbb-api --app /Users/some_user/path/to/main.py +``` + +#### Arbitrary Instance Name + +Define the FastAPI instance as an arbitrary name with the `--name` argument. + +```sh +openbb-api --app some_file.py --name my_app +``` + +#### Factory Flag + +If the FastAPI instance is served via a factory function, set the `--factory` flag. + +```sh +openbb-api --app some_file.py:main --factory +``` + +## Keyword Arguments + +The behavior of the script can be configured with the use of arguments and keyword arguments. + +Launcher specific arguments: + + --app Absolute path to the Python file with the target FastAPI instance. Default is the installed OpenBB Platform API. + --name Name of the FastAPI instance in the app file. Default is 'app'. + --factory Flag to indicate if the app name is a factory function. Default is 'false'. + --editable Flag to make widgets.json an editable file that can be modified during runtime. Default is 'false'. + --build If the file already exists, changes prompt action to overwrite/append/ignore. Only valid when --editable true. + --no-build Do not build the widgets.json file. Use this flag to load an existing widgets.json file without checking for updates. + --login Login to the OpenBB Platform. + --exclude JSON encoded list of API paths to exclude from widgets.json. Disable entire routes with '*' - e.g. '["/api/v1/*"]'. + --no-filter Do not filter out widgets in widget_settings.json file. + --widgets-json Absolute/relative path to use as the widgets.json file. Default is ~/envs/{env}/assets/widgets.json, when --editable is 'true'. + --apps-json Absolute/relative path to use as the apps.json file. Default is ~/OpenBBUserData/workspace_apps.json. + --agents-json Absolute/relative path to use as the agents.json file. Including this will add the /agents endpoint to the API. + + +All other arguments will be passed to uvicorn. Here are the most common ones: + + --host TEXT Host IP address or hostname. + [default: 127.0.0.1] + --port INTEGER Port number. + [default: 6900] + --ssl-keyfile TEXT SSL key file. + --ssl-certfile TEXT SSL certificate file. + --ssl-keyfile-password TEXT SSL keyfile password. + --ssl-version INTEGER SSL version to use. + (see stdlib ssl module's) + [default: 17] + --ssl-cert-reqs INTEGER Whether client certificate is required. + (see stdlib ssl module's) + [default: 0] + --ssl-ca-certs TEXT CA certificates file. + --ssl-ciphers TEXT Ciphers to use. + (see stdlib ssl module's) + [default: TLSv1] + +Run `uvicorn --help` to get the full list of arguments. + +### API Over HTTPS + +To run the API over the HTTPS protocol, you must first create a self-signed certificate and the associated key. After activating the environment, you can generate the files by entering this to the command line: + +```sh +openssl req -x509 -days 3650 -out localhost.crt -keyout localhost.key -newkey rsa:4096 -nodes -sha256 -subj '/CN=localhost' -extensions EXT -config <( \ + printf "[dn]\nCN=localhost\n[req]\ndistinguished_name = dn\n[EXT]\nsubjectAltName=DNS:localhost\nkeyUsage=digitalSignature\nextendedKeyUsage=serverAuth") +``` + +Two files will be created, in the current working directory, that are passed as keyword arguments to the `openbb-api` entry point. + +```sh +openbb-api --ssl_keyfile localhost.key --ssl_certfile localhost.crt +``` + +**Note**: Adjust the command to include the full path to the file if the current working directory is not where they are located. + +The certificate - `localhost.crt` - will need to be added to system's trust store. The process for this will depend on the operating system and the user account privilege. + +A quick solution is to visit the server's URL, show the details of the warning, and choose to continue anyways. + +Contact the system administrator if you are using a work device and require additional permissions to complete the configuration. + +![This Connection Is Not Private](https://in.norton.com/content/dam/blogs/images/norton/am/this_connection_not_is_private.png) + + +## Example Application + +Examples below will assume this code block is at the start of the file. + +```python +from fastapi import FastAPI + +app = FastAPI() +``` + +### Markdown Widget + +This script will create a "markdown" widget with the returned text. + +```python +@app.get("/hello") +async def hello() -> str: + """Widget Description Generated By Docstring""" + return "Hello, from OpenBB!" +``` + +### Table Widget + +Create a table widget by returning data shaped as a list of dictionaries (records) + +```python +@app.get("/hello") +async def hello() -> list: + """Widget Description Generated By Docstring""" + return [{"Column 1": "Hello", "Column 2": "from OpenBB!"}] +``` + +### Metric Widget + +This widget displays a label, value, and optional delta. + +To create a metric widget, import the custom response model below and define it as a return type. + +```python +from openbb_platform_api.response_models import MetricResponseModel + +@app.get("/hello_metric") +async def hello_metric() -> MetricResponseModel: + """Widget description created by docstring.""" + return MetricResponseModel(label="Good Vibes Score", value=100, delta="1%") +``` + +This type of widget can be created as an array of MetricResponseModels. Adjust the response to be a `list[MetricRespnoseModel]` + +### Query Parameters + +Function arguments will populate as widget parameters. + +```python +from typing import Literal, Optional + +@app.get("/hello") +async def hello(param1: Optional[str] = None, param2: Literal["Choice 1", "Choice 2"] = None, param3: bool = False) -> str: + """Widget Description Generated By Docstring""" + if not param1 and not param2 and not param3: + return "Enter a parameter or make a choice!" + if param3: + return f"Param3 enabled!" + if param2: + return f"You selected: {param2}" + if param1: + return f"You entered: {param1}" + + return "Nothing to return!" +``` + +### Easy Date Picker + +Name the parameter "date", or include "_date" in the name, and type it as a string. + +Additionally, a parameter type of `datetime.date` will work. + +```python +import datetime + +@app.get("/hello_date") +async def hello_date(date: str) -> list: + """Widget description created by docstring.""" + # Workspace returns the date as YYYY-MM-DD + return [{"Hello": "Row 1!"}, {"Hello": "Row 2!"}] + + +@app.get("/hello_date_range") +async def hello_date_range(start: datetime.date, end: datetime.date) -> list: + """Widget description created by docstring.""" + # Workspace returns the date as YYYY-MM-DD + return [{"Hello": "Row 1!"}, {"Hello": "Row 2!"}] +``` + +This demonstrates how to define any of the basic widget parameter types, in a no-frills way. If you just need something that works, it's an easy starting point. + +```python +@app.get("/hello_params") +async def hello_params( + required_param: datetime.date, + param_1: str = "Default", + param_2: int = 0, + param_3: float = None, + param_4: Literal["Choice 1", "Choice 2", "Choice 3"] = "Choice 1", + param_5: bool = True, +) -> list: + """Widget description created by docstring.""" + # Handle the "choices" parameter inside the function to convert the displayed label to the desired one. + choices_dict = {"Choice 1": "do_one", "Choice 2": "do_two", "Choice 3": "do_three"} + choice = choices_dict.get(param_4, None) + + # Do something with the parameters and return the result of work. + return [{"Hello": "Row 1!"}, {"Hello": "Row 2!"}] +``` + +### Annotated Query Params + +Adding helpful placeholder text and tooltips to parameters requires annotating them. This will also help code editors and improve the API documentation. + +Additional settings, compatible with `widgets.json`, are defined in the `json_schema_extra` dictionary, under a key, `x-widget_config` + +```python +from typing import Annotated +from fastapi import Query +``` + +The pattern for annotating a query parameter is: + +```python +my_param: Annotated[str, Query(title="My Title", description="My custom hovertext with detailed information")] = None +``` + +```python +@app.get("/hello_annotated_params") +async def hello_annotated_params( + required_param: Annotated[ + datetime.date, Query(description="The date is required.", title="Required Date") + ], + not_required_param: Annotated[ + Literal["Choice 1", "Choice 2", "Choice 3"], + Query( + description="Choose from a list of possible choices. The default is, 'Choice 1'", + title="Selector", + json_schema_extra={"x-widget_config": {"multiSelect": True}} # This lets you select multiple items from dropdown choices. + ), + ] = "Choice 1", +) -> list: + """Widget description created by docstring.""" + + # Do something with the parameters and return the result of work. + return [{"Hello": "Row 1!"}, {"Hello": "Row 2!"}] +``` + +### Annotated Table Fields + +The procedure for annotating the output is similar to the query parameters, and involves defining a response model. + +A response model is a Data model of Fields. Create one by defining a new class that inherits from "Data", and then define each column as a "Field". + +```python +from openbb_platform_api.response_models import Data +from pydantic import Field +``` + +Optional values should be defined, as `Optional[{type}]`, with a default value of `None`. + +```python +class MyData(Data): + """This is a custom Data model.""" + + # Add fields to the model. + column_1: datetime.date = Field( + description="The date column is a mandatory field.", + title="Some Date", + ) + column_2: Optional[str] = Field( + default=None, + description="This is an optional string column.", + title="Some String", + ) + column_3: int = Field( + default=-1, + description="This is an integer column.", + title="Some Integer", + ) + column_4: float = Field( + default=10.25, + description="This is a float column.", + title="Some Float", + ) + column_5: float = Field( + default=10.25, + description="This is a percent column.", + title="Some Percent", + json_schema_extra={"x-widget_config": {"formatterFn": "percent"}}, + ) + column_6: float = Field( + default=0.1025, + description="This is a normalized percent value adjusted for presentation.", + title="Some Normalized Percent", + json_schema_extra={ + "x-widget_config": { + "formatterFn": "normalizedPercent", + "renderFn": "greenRed", + } + }, + ) + + +@app.get("/hello_data") +async def hello_data() -> list[MyData]: + """Widget description created by docstring.""" + # Do something with the parameters and return the result of work. + return [MyData(column_1=datetime.date.today(), column_2="Hello!")] +``` + + +### PDF Widget + +To create a PDF widget, import the custom response model below and define it as a return type. + +The model handles conversion of the document, from a bytes object, to a base64 encoded string. + + +```python +from openbb_platform_api.response_models import PdfResponseModel + +@app.get("/open_pdf") +async def open_pdf( + url: Annotated[ + str, + Query( + description="URL, or local path, to the PDF document.", + title="URL or Path", + ), + ], + filename: Annotated[ + Optional[str], + Query( + description="Filename to associate with the PDF internally.", + title="Fiilename", + ), + ] = "", + user_agent: Annotated[ + Optional[str], + Query(description="A specific User-Agent string for the request.", title="User-Agent"), + ] = None, +) -> PdfResponseModel: + """Open a PDF document from a URL, or local file path.""" + # pylint: disable=import-outside-toplevel + from pathlib import Path # noqa + from openbb_core.provider.utils.errors import OpenBBError + from openbb_core.provider.utils.helpers import get_requests_session + + if "://" not in url: + file_path = Path(url) + if not file_path.is_file(): + raise OpenBBError(f"The file - {url} - does not exist.") + with open(file_path, "rb") as file: + pdf = file.read() + else: + session = get_requests_session(headers={"User-Agent": user_agent}) + response = session.get(url) + if response.status_code != 200: + raise OpenBBError( + f"Failed to open PDF from URL -> Code: {response.status_code} -> {response.reason}" + ) + + pdf = response.content + + return PdfResponseModel( + filename = filename, + content = pdf, + ) +``` + +### Custom Plotly Chart + +To define a chart widget, update the widget "type" and return the content from the `Figure.to_plotly_json()` method. + + +```python +@app.get( + "/hello_chart", + openapi_extra={"widget_config": {"type": "chart"}}, +) +async def hello_chart() -> dict: + """Widget description created by docstring.""" + from plotly.graph_objs import Bar, Layout, Figure + + fig = Figure( + data=[Bar(x=["A", "B", "C"], y=[1, 2, 3])], + layout=Layout(title="Hello Chart!"), + ) + + return fig.to_plotly_json() +``` + +### Form Submit Widget + +When submitted, Workspace makes a POST request to the endpoint. + +If the POST function returns a 200 status code, the widget associated with the GET function is refreshed. + +The results of the GET function does not have to correspond with the parameters and results of the POST function. + +For example, the response to submitting a form can be a Markdown widget with a custom message. + +The entry in `widgets.json` will be automatically created if the conditions below are met: + +- GET and POST methods must share the same API route. +- POST method takes 1 positional argument, a sub-class of Pydantic BaseModel. + - Create a model, like annotated table fields, defining all inputs to the form. + +#### Example + +The code below creates a widget with a form as the input, and an output table of all submitted forms, as processed through the `IntakeForm` model. + +```python +import uuid +from datetime import date as dateType +from typing import Literal, Union + +# from fastapi import FastAPI +from openbb_platform_api.query_models import FormData +from openbb_platform_api.response_models import Data +from pydantic import ConfigDict, Field + +# app = FastAPI() + +AccountTypes = Literal["General Fund", "Separately Managed", "Private Equity", "Family Office"] + +class GeneralIntake(FormData): + """Submit a form via POST request.""" + + date_created: dateType = Field( + title="Created On", default_factory=dateType.today + ) + first_name: str = Field(title="First Name") + last_name: str = Field(title="Last Name") + email: str = Field(title="Contact Email") + dob: dateType = Field( + title="Date Of Birth", + ) + account_types: Union[AccountTypes, list[AccountTypes]] = Field( + title="Type Of Account", + json_schema_extra={ + "x-widget_config": {"multiSelect": True}, + }, + ) + submit: bool = Field( + default=True, + title="Submit", + type="button", # This creates a button, when pressed the parameter is sent as True + ) + + +class IntakeForm(Data): + """Submission Records.""" + + model_config = ConfigDict(extra="ignore") + + contacted: bool = Field( + title="Contacted", + default=False, + ) + date_created: dateType = Field( + title="Created On", + ) + first_name: str = Field(title="First Name") + last_name: str = Field(title="Last Name") + email: str = Field(title="Contact Email") + dob: dateType = Field( + title="Date Of Birth", + ) + account_types: Union[AccountTypes, list[AccountTypes]] = Field( + title="Account Interest", + ) + unique_id: uuid.UUID = Field( + title="Unique ID", + default_factory=uuid.uuid4, + ) + + +INTAKE_FORMS: list[IntakeForm] = [] + + +@app.post("/general_intake") +async def general_intake_post(data: GeneralIntake) -> bool: + global INTAKE_FORMS + try: + INTAKE_FORMS.append(IntakeForm(**data.model_dump())) + return True + except Exception as e: + raise e from e + + +@app.get("/general_intake") +async def general_intake() -> list[IntakeForm]: + return INTAKE_FORMS +``` + +Screenshot 2025-03-09 at 9 51 47 PM + +## Widget Config + +Any value from the [`widgets.json`](https://docs.openbb.co/terminal/custom-backend/widgets-json-reference) structure can be passed into the `@app` decorator by including an `openapi_extra` dictionary with the key, `"widget_config"`. + +Configurations for `widgets.json` supplied here will override any of the automatically generated content. If the key does not exist, it will be created. + +When inserting/updating an entry in a `Params` or `ColumnsDefs` array, the matching identifier is "paramName" and "field", respectively. + +```python +@app.get( + "/hello_data", + openapi_extra={ + "widget_config": { + "data": { + "table": { + "columnsDefs": [ + { + "field": "column_1", + "headerName": "My Column", + "headerTooltip": "This hovertext wins!", + } + ] + } + } + } + }, +) +async def hello_data() -> list[MyData]: + """Widget description created by docstring.""" + # Do something with the parameters and return the result of work. + return [MyData(column_1=datetime.date.today(), column_2="Hello!")] +``` + +## Location of `widgets.json` + +When `--editable` is not flagged, the file remains in memory until the server is stopped. It is regenerated every run. + +The file can be served at any time by visiting the URL (host address will vary): + +```sh +http://127.0.0.1:6900/widgets.json +``` + +When launched as `openbb-api --editable`, a file will be stored to disk. By default, that location is: + +```sh +/Path/to/environments/envs/obb/assets/widgets.json +``` + +The file can be manually edited and served without the build process by passing `--editable --no-build` to the API launch script. + +```sh +openbb-api --editable --no-build +``` + +If you would like to construct this file manually, create the file and define the path as an argument. + +```sh +openbb-api --widgets-json /Users/some_user/path/to/widgets.json +``` + + +### Location of `workspace_apps.json` + +By default, the location is: + +> ~/OpenBBUserData/workspace_apps.json + +This can be changed by adding the path as an argument. + +```sh +openbb-api --apps-json /Users/some_user/path/to/workspace_apps.json +``` + +The OpenBB Workspace allows you to export the current dashboard layout - when it is a custom backend - as a template. + +To export the layout, right-click on the dashboard and select, "Export apps.json". + +A JSON dictionary will be exported. Insert the contents of the export into "~/OpenBBUserData/workspace_apps.json" by pasting between the JSON list markers, [ ]. + +If there are more than one, add a comma between each dictionary entry. + +See the page [here](https://docs.openbb.co/workspace/apps#creating-your-own-app) for details on custom backend apps. diff --git a/openbb_platform/extensions/platform_api/integration/.gitkeep b/openbb_platform/extensions/platform_api/integration/.gitkeep new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/openbb_platform/extensions/platform_api/openbb_platform_api/__init__.py b/openbb_platform/extensions/platform_api/openbb_platform_api/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..2055db3048be0acb24d50ae4c55ecdd54ed5340f --- /dev/null +++ b/openbb_platform/extensions/platform_api/openbb_platform_api/__init__.py @@ -0,0 +1 @@ +"""OpenBB Platform API Meta Package.""" diff --git a/openbb_platform/extensions/platform_api/openbb_platform_api/assets/default_apps.json b/openbb_platform/extensions/platform_api/openbb_platform_api/assets/default_apps.json new file mode 100644 index 0000000000000000000000000000000000000000..cd7b2d270cad3bbc485278c5f2f262949361b87f --- /dev/null +++ b/openbb_platform/extensions/platform_api/openbb_platform_api/assets/default_apps.json @@ -0,0 +1,546 @@ +[ + { + "name": "FOMC Documents", + "img": "https://upload.wikimedia.org/wikipedia/commons/thumb/7/71/Flag_of_the_United_States_Federal_Reserve.svg/640px-Flag_of_the_United_States_Federal_Reserve.svg.png", + "img_dark": "", + "img_light": "", + "description": "Read current and historical FOMC documents, beginning 1959.", + "allowCustomization": true, + "tabs": { + "overview": { + "id": "overview", + "name": "overview", + "layout": [ + { + "i": "economy_fomc_documents_federal_reserve_obb", + "x": 0, + "y": 0, + "w": 40, + "h": 42, + "state": { + "params": { + "year": 2025 + } + } + } + ] + } + }, + "groups": [] + }, + { + "name": "Example FRED App", + "img": "https://tvblog-static.tradingview.com/uploads/2021/07/fred-preview.png", + "description": "FRED (Federal Reserve Economic Data) offers U.S. and global economic data, including GDP, inflation (CPI, PCE), unemployment, and consumer spending. It tracks interest rates, money supply (M1, M2), stock indices, bond yields, exchange rates, housing prices (Case-Shiller), mortgage rates, and trade balances. Industry data covers manufacturing, energy, and real estate.", + "authentication": "Get your FRED API KEY at https://fred.stlouisfed.org/docs/api/api_key.html", + "allowCustomization": true, + "tabs": { + "Search": { + "id": "Search", + "name": "Search", + "layout": [ + { + "i": "economy_fred_search_fred_obb", + "x": 0, + "y": 2, + "w": 40, + "h": 15, + "state": { + "params": { + "query": "pce", + "tag_names": "inflation;pce" + }, + "chartView": { + "enabled": false, + "chartType": "line" + }, + "columnState": { + "default_": { + "columnPinning": { + "leftColIds": [ + "series_id", + "title" + ], + "rightColIds": [] + } + } + } + } + }, + { + "i": "economy_fred_series_fred_obb", + "x": 0, + "y": 17, + "w": 40, + "h": 10, + "state": { + "params": { + "symbol": "RPI,PCE,PCENOW", + "transform": "pc1", + "start_date": "2010-01-01", + "frequency": "q" + }, + "chartView": { + "enabled": false, + "chartType": "line" + } + } + } + ] + }, + "ReleaseTables": { + "id": "ReleaseTables", + "name": "Release Tables", + "layout": [ + { + "i": "economy_fred_release_table_fred_obb", + "x": 0, + "y": 10, + "w": 40, + "h": 12, + "state": { + "params": { + "release_id": "52" + }, + "chartView": { + "enabled": false, + "chartType": "line" + }, + "columnState": { + "default_": { + "columnPinning": { + "leftColIds": [ + "name" + ], + "rightColIds": [] + } + } + } + } + }, + { + "i": "economy_fred_search_fred_obb", + "x": 0, + "y": 2, + "w": 40, + "h": 8, + "state": { + "params": { + "search_type": "release" + }, + "chartView": { + "enabled": false, + "chartType": "line" + }, + "columnState": { + "default_": { + "columnVisibility": { + "hiddenColIds": [ + "observation_start", + "observation_end", + "frequency", + "units", + "seasonal_adjustment", + "seasonal_adjustment_short", + "realtime_start", + "realtime_end", + "notes" + ] + }, + "focusedCell": { + "colId": "series_id", + "rowIndex": 4, + "rowPinned": null + }, + "scroll": { + "top": 0, + "left": 12938.6357421875 + } + } + } + } + }, + { + "i": "economy_fred_release_table_fred_obb", + "x": 0, + "y": 22, + "w": 38, + "h": 13, + "state": { + "params": { + "release_id": "27", + "element_id": "1240127" + }, + "chartView": { + "enabled": false, + "chartType": "line" + }, + "columnState": { + "default_": { + "columnPinning": { + "leftColIds": [ + "date", + "symbol", + "name", + "value" + ], + "rightColIds": [] + } + } + } + } + } + ] + }, + "BondIndices": { + "id": "BondIndices", + "name": "Bond Indices", + "layout": [ + { + "i": "fixedincome_bond_indices_fred_obb", + "x": 0, + "y": 0, + "w": 40, + "h": 25, + "state": { + "chartView": { + "enabled": false, + "chartType": "line" + } + } + } + ] + }, + "PCE": { + "id": "PCE", + "name": "PCE", + "layout": [ + { + "i": "economy_pce_fred_obb", + "x": 0, + "y": 0, + "w": 40, + "h": 25, + "state": { + "chartView": { + "enabled": false, + "chartType": "line" + } + } + } + ] + }, + "cpi": { + "id": "cpi", + "name": "CPI", + "layout": [ + { + "i": "economy_cpi_fred_obb", + "x": 0, + "y": 2, + "w": 28, + "h": 18, + "state": { + "params": { + "country": [ + "australia", + "denmark" + ], + "frequency": "quarter", + "start_date": "2010-01-01" + }, + "chartView": { + "enabled": false, + "chartType": "line" + }, + "columnState": { + "default_": { + "sort": { + "sortModel": [ + { + "colId": "date", + "sort": "desc" + } + ] + }, + "columnPinning": { + "leftColIds": [ + "date" + ], + "rightColIds": [] + } + } + } + } + } + ] + }, + "NonFarmPayroll": { + "id": "NonFarmPayroll", + "name": "Nonfarm Payrolls", + "layout": [ + { + "i": "economy_survey_nonfarm_payrolls_fred_obb", + "x": 0, + "y": 0, + "w": 40, + "h": 25, + "state": { + "chartView": { + "enabled": false, + "chartType": "line" + } + } + } + ] + }, + "RetailPrices": { + "id": "RetailPrices", + "name": "Retail Prices", + "layout": [ + { + "i": "economy_retail_prices_fred_obb", + "x": 0, + "y": 0, + "w": 40, + "h": 25, + "state": { + "chartView": { + "enabled": false, + "chartType": "line" + } + } + } + ] + }, + "EconomicConditions": { + "id": "EconomicConditions", + "name": "Chicago Economic Conditions", + "layout": [ + { + "i": "economy_survey_economic_conditions_chicago_fred_obb", + "x": 0, + "y": 0, + "w": 40, + "h": 25, + "state": { + "chartView": { + "enabled": false, + "chartType": "line" + } + } + } + ] + }, + "EmpireState": { + "id": "EmpireState", + "name": "Empire State Manufacturing", + "layout": [ + { + "i": "economy_survey_manufacturing_outlook_ny_fred_obb", + "x": 0, + "y": 0, + "w": 40, + "h": 25, + "state": { + "chartView": { + "enabled": false, + "chartType": "line" + } + } + } + ] + }, + "ManufacturingOutlook": { + "id": "ManufacturingOutlook", + "name": "Texas Manufacturing Outlook", + "layout": [ + { + "i": "economy_survey_manufacturing_outlook_texas_fred_obb", + "x": 0, + "y": 0, + "w": 40, + "h": 25, + "state": { + "chartView": { + "enabled": false, + "chartType": "line" + } + } + } + ] + }, + "SLOOS": { + "id": "SLOOS", + "name": "SLOOS", + "layout": [ + { + "i": "economy_survey_sloos_fred_obb", + "x": 0, + "y": 0, + "w": 40, + "h": 25, + "state": { + "chartView": { + "enabled": false, + "chartType": "line" + } + } + } + ] + } + }, + "groups": [] + }, + { + "name": "Example BLS App", + "img": "https://tigadvisors.com/wp-content/uploads/2022/01/BLS.jpg", + "description": "The Bureau of Labor Statistics (BLS) provides U.S. economic data on employment, unemployment, wages, and productivity. Key datasets include the Consumer Price Index (CPI) for inflation, Producer Price Index (PPI) for wholesale prices, and Employment Situation Report for job market trends. It tracks wages (CES, QCEW), job openings (JOLTS), productivity (BLS Productivity), and workplace injuries (SOII). Industry and regional data are also available.", + "authentication": "Get your BLS API KEY at https://www.bls.gov/developers/home.htm", + "allowCustomization": true, + "tabs": { + "Search": { + "id": "Search", + "name": "Search", + "layout": [ + { + "i": "economy_survey_bls_search_bls_obb", + "x": 0, + "y": 0, + "w": 40, + "h": 8, + "state": { + "params": { + "category": "nfp", + "include_extras": true + }, + "chartView": { + "enabled": false, + "chartType": "line" + }, + "columnState": { + "default_": { + "columnPinning": { + "leftColIds": [ + "symbol", + "title" + ], + "rightColIds": [] + } + } + } + } + }, + { + "i": "economy_survey_bls_series_bls_obb", + "x": 0, + "y": 8, + "w": 40, + "h": 18, + "state": { + "params": { + "symbol": "CES0000000001", + "start_date": "2016-01-01" + }, + "chartView": { + "enabled": false, + "chartType": "line" + }, + "columnState": { + "default_": { + "columnPinning": { + "leftColIds": [ + "date", + "symbol", + "title" + ], + "rightColIds": [] + } + } + } + } + } + ] + } + }, + "groups": [] + }, + { + "name": "Example IMF App", + "img": "https://www.imf.org/-/media/Images/IMF/About/Factsheets/2022/Hero/imf-logo.ashx", + "description": "Global economic and financial data, including GDP, inflation, trade, and fiscal balances (WEO), financial stability risks (GFSR), and public debt (GFS, QPSD). It tracks exchange rates, interest rates, and money supply (IFS), balance of payments and trade flows (BOPS, DOTS), and banking sector health (FSIs).", + "allowCustomization": true, + "tabs": { + "DirectionOfTrade": { + "id": "DirectionOfTrade", + "name": "Direction Of Trade", + "layout": [ + { + "i": "economy_direction_of_trade_imf_obb", + "x": 0, + "y": 2, + "w": 40, + "h": 25, + "state": { + "params": { + "country": "us", + "counterpart": "canada,mexico", + "direction": "all", + "start_date": "2010-01-01" + }, + "chartView": { + "enabled": false, + "chartType": "line" + }, + "columnState": { + "default_": { + "columnPinning": { + "leftColIds": [ + "date", + "symbol", + "title" + ], + "rightColIds": [] + } + } + } + } + } + ] + }, + "Indicators": { + "id": "Indicators", + "name": "Indicators", + "layout": [ + { + "i": "economy_available_indicators_imf_obb", + "x": 0, + "y": 2, + "w": 40, + "h": 10, + "state": { + "chartView": { + "enabled": false, + "chartType": "line" + } + } + }, + { + "i": "economy_indicators_imf_obb", + "x": 0, + "y": 12, + "w": 40, + "h": 15, + "state": { + "chartView": { + "enabled": false, + "chartType": "line" + } + } + } + ] + } + }, + "groups": [] + } +] diff --git a/openbb_platform/extensions/platform_api/openbb_platform_api/assets/landing_page.html b/openbb_platform/extensions/platform_api/openbb_platform_api/assets/landing_page.html new file mode 100644 index 0000000000000000000000000000000000000000..3054244a08e45168e51c2d0685b3c4d49b3bac54 --- /dev/null +++ b/openbb_platform/extensions/platform_api/openbb_platform_api/assets/landing_page.html @@ -0,0 +1,116 @@ + + + + + + + + OpenBB Platform + + + + + +
+ +
+ +
+
+
+

+ OpenBB Platform +

+

+ The first open source financial platform. Access equity, options, crypto, forex, macro economy, fixed income, and more through a unified platform with powerful extensions. +

+
+ + Documentation + +
+ +
+
+
+ + +
+
+
+

Connect to the OpenBB Workspace

+

+ Connect your OpenBB Platform backend with OpenBB Workspace to access all your data in an enterprise UI built for AI workflows. +

+
+ OpenBB Platform +
+
+
+

Steps to Integrate with OpenBB Workspace

+ +
    +
  1. Sign in to OpenBB Workspace
    2. +
  2. Navigate to the "Apps" tab
    3. +
  3. Click on "Connect backend"
    4. +
  4. Fill in the form with: +
      +
    • Name: OpenBB Platform
      • +
    • URL: http://127.0.0.1:6900
      • +
    +
    5. +
  5. Click on "Test" - You should see "Test successful" with the number of apps found
    6. +
  6. Click on "Add" to complete the integration
    7. +
+ +
+ Integration Guide Screenshot +
+ +
+

+ For more detailed information about integrating OpenBB Platform with OpenBB Workspace, including advanced configuration options and troubleshooting, visit our + + comprehensive integration guide + . +

+
+
+
+
+
+
+
+ + +
+
+
+
+ © 2025 OpenBB. All rights reserved. +
+
+ + + + + + + + + +
+
+
+
+
+ + + \ No newline at end of file diff --git a/openbb_platform/extensions/platform_api/openbb_platform_api/main.py b/openbb_platform/extensions/platform_api/openbb_platform_api/main.py new file mode 100644 index 0000000000000000000000000000000000000000..bc44261a1ccb01ba70d68804a556939c652bd351 --- /dev/null +++ b/openbb_platform/extensions/platform_api/openbb_platform_api/main.py @@ -0,0 +1,274 @@ +"""OpenBB Platform API. + +Launch script and widgets builder for the OpenBB Workspace Custom Backend. +""" + +import json +import logging +import os +import sys +from pathlib import Path + +import uvicorn +from fastapi.responses import HTMLResponse, JSONResponse +from openbb_core.api.rest_api import app +from openbb_core.app.service.system_service import SystemService +from openbb_core.env import Env + +from .utils.api import check_port, get_user_settings, get_widgets_json, parse_args + +logger = logging.getLogger("openbb_platform_api") +logger.setLevel(logging.INFO) +handler = logging.StreamHandler() +handler.setLevel(logging.INFO) +formatter = logging.Formatter("\n%(message)s\n") +handler.setFormatter(formatter) +logger.addHandler(handler) +logger.setLevel(logging.INFO) + + +FIRST_RUN = True + +# Adds the OpenBB Environment variables to the script process. +Env() + +HOME = os.environ.get("HOME") or os.environ.get("USERPROFILE") + +if not HOME: + raise ValueError("HOME or USERPROFILE environment variable not set.") + +CURRENT_USER_SETTINGS = os.path.join(HOME, ".openbb_platform", "user_settings.json") +USER_SETTINGS_COPY = os.path.join(HOME, ".openbb_platform", "user_settings_backup.json") + +# Widget filtering is optional and can be used to exclude widgets from the widgets.json file +# You can generate this filter on OpenBB Hub: https://my.openbb.co/app/platform/widgets +# Alternatively, you can supply a JSON-encoded list of API paths to ignore. +WIDGET_SETTINGS = os.path.join(HOME, ".openbb_platform", "widget_settings.json") + +kwargs = parse_args() + +_app = kwargs.pop("app", None) + +if _app: + app = _app + + +# These are handled for backwards compatibility, and in ./utils/api::parse_args. +# It should be handled by this point in the code execution, but in case the key +# still exists for some reason, we will pop it so it doesn't get passed to uvicorn.run +# It would be reasonable to remove special handling by V1.3 +WIDGETS_PATH = kwargs.pop("widgets-json", None) or kwargs.pop("widgets-path", None) +APPS_PATH = kwargs.pop("apps-json", None) or kwargs.pop("templates-path", None) + +EDITABLE = kwargs.pop("editable", None) is True or WIDGETS_PATH is not None +DEFAULT_APPS_PATH = ( + Path(__file__).absolute().parent.joinpath("assets").joinpath("default_apps.json") +) +AGENTS_PATH = kwargs.pop("agents-json", None) +build = kwargs.pop("build", True) +build = False if kwargs.pop("no-build", None) else build +login = kwargs.pop("login", False) +dont_filter = kwargs.pop("no-filter", False) +widget_exclude_filter: list = kwargs.pop("exclude", []) + +uvicorn_settings = ( + SystemService().system_settings.python_settings.model_dump().get("uvicorn", {}) +) + +for key, value in uvicorn_settings.items(): + if key not in kwargs and key != "app" and value is not None: + kwargs[key] = value + +if not dont_filter and os.path.exists(WIDGET_SETTINGS): + with open(WIDGET_SETTINGS) as widget_settings_file: + try: + widget_exclude_filter_json = json.load(widget_settings_file).get( + "exclude", [] + ) + if isinstance(widget_exclude_filter_json, list): + widget_exclude_filter.extend(widget_exclude_filter_json) + except json.JSONDecodeError as e: + logger.info("Error loading widget filter settings -> %s", e) + + +openapi = app.openapi() + +# We don't need the current settings, +# but we need to call the function to update, login, and/or identify the settings file. +current_settings = get_user_settings(login, CURRENT_USER_SETTINGS, USER_SETTINGS_COPY) +widgets_json = get_widgets_json( + build, openapi, widget_exclude_filter, EDITABLE, WIDGETS_PATH +) + +# A template file will be served from the OpenBBUserDataDirectory, if it exists. +# If it doesn't exist, an empty list will be returned, and an empty file will be created. +APPS_PATH = ( + APPS_PATH + if APPS_PATH + else ( + current_settings.get("preferences", {}).get( + "data_directory", HOME + "/OpenBBUserData" + ) + + "/workspace_apps.json" + ) +) + + +@app.get("/") +async def root(): + """Serve the landing page HTML content.""" + html_path = Path(__file__).parent / "assets" / "landing_page.html" + with open(html_path) as f: + html_content = f.read() + return HTMLResponse(content=html_content) + + +@app.get("/widgets.json") +async def get_widgets(): + """Widgets configuration file for the OpenBB Workspace.""" + # This allows us to serve an edited widgets.json file without reloading the server. + global FIRST_RUN # noqa PLW0603 # pylint: disable=global-statement + if FIRST_RUN is True: + FIRST_RUN = False + return JSONResponse(content=widgets_json) + if EDITABLE: + return JSONResponse( + content=get_widgets_json( + False, openapi, widget_exclude_filter, EDITABLE, WIDGETS_PATH + ) + ) + return JSONResponse(content=widgets_json) + + +# If a custom implementation, you might want to override. +@app.get("/apps.json") +async def get_apps_json(): + """Get the apps.json file.""" + new_templates: list = [] + default_templates: list = [] + widgets = await get_widgets() + + if not os.path.exists(APPS_PATH): + apps_dir = os.path.dirname(APPS_PATH) + if not os.path.exists(apps_dir): + os.makedirs(apps_dir, exist_ok=True) + # Write an empty file for the user to add exported apps from Workspace to. + with open(APPS_PATH, "w", encoding="utf-8") as templates_file: + templates_file.write(json.dumps([])) + + if os.path.exists(DEFAULT_APPS_PATH): + with open(DEFAULT_APPS_PATH) as f: + default_templates = json.load(f) + + if os.path.exists(APPS_PATH): + with open(APPS_PATH) as templates_file: + templates = json.load(templates_file) + + if isinstance(templates, dict): + templates = [templates] + + templates.extend(default_templates) + + for template in templates: + if _id := template.get("id"): + if _id in widgets and template not in new_templates: + new_templates.append(template) + continue + elif template.get("layout") or template.get("tabs"): + if _tabs := template.get("tabs"): + for v in _tabs.values(): + if v.get("layout", []) and all( + item.get("i") in widgets_json for item in v.get("layout") + ): + new_templates.append(template) + break + elif ( + template.get("layout") + and all( + item.get("i") in widgets_json for item in template["layout"] + ) + and template not in new_templates + ): + new_templates.append(template) + + if new_templates: + return JSONResponse(content=new_templates) + + return JSONResponse(content=[]) + + +if AGENTS_PATH: + + @app.get("/agents.json") + async def get_agents(): + """Get the agents.json file.""" + if os.path.exists(AGENTS_PATH): + with open(AGENTS_PATH) as f: + agents = json.load(f) + return JSONResponse(content=agents) + return JSONResponse(content=[]) + + +def launch_api(**_kwargs): # noqa PRL0912 + """Main function.""" + host = _kwargs.pop("host", os.getenv("OPENBB_API_HOST", "127.0.0.1")) + if not host: + logger.info( + "OPENBB_API_HOST is set incorrectly. It should be an IP address or hostname." + ) + host = input("Enter the host IP address or hostname: ") + if not host: + host = "127.0.0.1" + + port = _kwargs.pop("port", os.getenv("OPENBB_API_PORT", "6900")) + + try: + port = int(port) + except ValueError: + logger.info("OPENBB_API_PORT is set incorrectly. It should be an port number.") + port = input("Enter the port number: ") + try: + port = int(port) + except ValueError: + logger.info("Invalid port number. Defaulting to 6900.") + port = 6900 + if port < 1025: + port = 6900 + logger.info("Invalid port number, must be above 1024. Defaulting to 6900.") + + free_port = check_port(host, port) + + if free_port != port: + logger.info("Port %d is already in use. Using port %d.", port, free_port) + port = free_port + + if "use_colors" not in _kwargs: + _kwargs["use_colors"] = "win" not in sys.platform or os.name != "nt" + + try: + package_name = __package__ + _msg = ( + "\nTo access this data from OpenBB Workspace, use the link displayed after the application startup completes." + "\nChrome is the recommended browser. Other browsers may conflict or require additional configuration." + f"\n{f'Documentation is available at {app.docs_url}.' if app.docs_url else ''}" + ) + logger.info(_msg) + uvicorn.run(f"{package_name}.main:app", host=host, port=port, **_kwargs) + finally: + # If user_settings_copy.json exists, then restore the original settings. + if os.path.exists(USER_SETTINGS_COPY): + logger.info("Restoring the original settings.") + os.replace(USER_SETTINGS_COPY, CURRENT_USER_SETTINGS) + + +def main(): + """Launch the API.""" + launch_api(**kwargs) + + +if __name__ == "__main__": + + try: + main() + except KeyboardInterrupt: + sys.exit(0) diff --git a/openbb_platform/extensions/platform_api/openbb_platform_api/query_models.py b/openbb_platform/extensions/platform_api/openbb_platform_api/query_models.py new file mode 100644 index 0000000000000000000000000000000000000000..e2a041b63530cd5ee24cf10380ee279ea20571e6 --- /dev/null +++ b/openbb_platform/extensions/platform_api/openbb_platform_api/query_models.py @@ -0,0 +1,15 @@ +"""OpenBB Workspace Query Models.""" + +from openbb_core.provider.abstract.data import Data +from pydantic import AliasGenerator, ConfigDict +from pydantic.alias_generators import to_snake + + +class FormData(Data): + """Submit a form via POST request.""" + + model_config = ConfigDict( + extra="allow", + alias_generator=AliasGenerator(to_snake), + title="Submit Form", + ) diff --git a/openbb_platform/extensions/platform_api/openbb_platform_api/response_models.py b/openbb_platform/extensions/platform_api/openbb_platform_api/response_models.py new file mode 100644 index 0000000000000000000000000000000000000000..db3b1e390ea2d572095d98890e95cfc6cb25d6ef --- /dev/null +++ b/openbb_platform/extensions/platform_api/openbb_platform_api/response_models.py @@ -0,0 +1,149 @@ +"""OpenBB Workspace Response Models.""" + +from typing import Optional, Union + +from openbb_core.provider.abstract.data import Data +from pydantic import ConfigDict, Field, model_validator + + +class MetricResponseModel(Data): + """ + Metric Widget Response Model. + + Supply a label, value, and optional delta. + + Fields + ------ + label : str + The label to display in the metric widget. + value : int, float, or str + The value to display in the metric widget. + delta : int, float, or str + The, optional, delta value to display in the metric widget. + + Returns + ------- + object + Object with the label, value, and optional delta value. + """ + + model_config = ConfigDict( + extra="ignore", + json_schema_extra={ + "title": "Metric Widget Response Model", + "x-widget_config": { + "$.type": "metric", + "$.category": "Metric", + "$.searchCategory": "Metric", + }, + }, + ) + + label: str = Field( + description="The label to display in the metric widget.", + json_schema_extra={"x-widget_config": {"exclude": True}}, + ) + value: Union[int, float, str] = Field( + description="The value to display in the metric widget.", + json_schema_extra={"x-widget_config": {"exclude": True}}, + ) + delta: Optional[Union[int, float, str]] = Field( + default=None, + description="The delta value to display in the metric widget.", + json_schema_extra={"x-widget_config": {"exclude": True}}, + ) + + +class PdfResponseModel(Data): + """ + PDF Widget Response Model. + + Supply the url or content, and an optional filename. + + Fields + ------ + filename : str + The filename of the PDF content. + content : bytes + The PDF content to display in the PDF widget. + url : str + The URL reference to the PDF + + Returns + ------- + object + Object with the PDF content serialized as a Base64 encoded string. + + Raises + ------ + ValueError + If neither 'content' or 'url_reference' is provided, or an invalid URL reference is provided. + """ + + model_config = ConfigDict( + extra="ignore", + json_schema_extra={ + "x-widget_config": { + "$.type": "pdf", + "$.refetchInterval": False, + "$.category": "File", + "$.subCategory": "PDF", + "$.searchCategory": "File", + } + }, + ) + + filename: Optional[str] = Field( + default="", + description="The filename of the PDF content.", + json_schema_extra={"x-widget_config": {"exclude": True}}, + ) + content: Optional[Union[str, bytes]] = Field( + default=None, + description="The PDF content to display in the PDF widget.", + json_schema_extra={"x-widget_config": {"exclude": True}}, + ) + url: Optional[str] = Field( + default=None, + description="The URL reference to the PDF content.", + json_schema_extra={"x-widget_config": {"exclude": True}}, + ) + data_format: Optional[dict] = Field( + default=None, + description="Leave this field empty. This is populated by the model_validator.", + json_schema_extra={"x-widget_config": {"exclude": True}}, + ) + + @model_validator(mode="after") + @classmethod + def validate_model(cls, values) -> "PdfResponseModel": + """Validate the PDF content.""" + # pylint: disable=import-outside-toplevel + import base64 # noqa + from io import BytesIO + + content = getattr(values, "content", None) + file_reference = getattr(values, "url", None) + filename = getattr(values, "filename", "") + + if not content and not file_reference: + raise ValueError("Either 'content' or 'url' must be provided.") + + if file_reference and "://" not in file_reference: + raise ValueError("Invalid URL reference provided") + + if content: + pdf = ( + base64.b64encode(BytesIO(content).getvalue()).decode("utf-8") + if isinstance(content, bytes) + else content + ) + + values.content = pdf + if file_reference: + values.url = file_reference + elif hasattr(values, "url"): + del values.url + values.data_format = {"data_type": "pdf", "filename": filename} + + return values diff --git a/openbb_platform/extensions/platform_api/openbb_platform_api/utils/__init__.py b/openbb_platform/extensions/platform_api/openbb_platform_api/utils/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..aa3b0ef651fa8c7c94ef92fa3a295c515f19cf73 --- /dev/null +++ b/openbb_platform/extensions/platform_api/openbb_platform_api/utils/__init__.py @@ -0,0 +1 @@ +"""OpenBB Platform API Utils.""" diff --git a/openbb_platform/extensions/platform_api/openbb_platform_api/utils/api.py b/openbb_platform/extensions/platform_api/openbb_platform_api/utils/api.py new file mode 100644 index 0000000000000000000000000000000000000000..7aa44a5c61230a026fcfa17039dc734f9535a295 --- /dev/null +++ b/openbb_platform/extensions/platform_api/openbb_platform_api/utils/api.py @@ -0,0 +1,474 @@ +"""API Utils.""" + +import json +import os +import socket +import sys +from pathlib import Path +from typing import Optional + +from deepdiff import DeepDiff + +from .widgets import build_json + +LAUNCH_SCRIPT_DESCRIPTION = """ +Serve the OpenBB Platform API. + + +Launcher specific arguments: + + --app Absolute path to the Python file with the target FastAPI instance. Default is the installed OpenBB Platform API. + --name Name of the FastAPI instance in the app file. Default is 'app'. + --factory Flag to indicate if the app name is a factory function. Default is 'false'. + --editable Flag to make widgets.json an editable file that can be modified during runtime. Default is 'false'. + --build If the file already exists, changes prompt action to overwrite/append/ignore. Only valid when --editable true. + --no-build Do not build the widgets.json file. Use this flag to load an existing widgets.json file without checking for updates. + --login Login to the OpenBB Platform. + --exclude JSON encoded list of API paths to exclude from widgets.json. Disable entire routes with '*' - e.g. '["/api/v1/*"]'. + --no-filter Do not filter out widgets in widget_settings.json file. + --widgets-json Absolute/relative path to use as the widgets.json file. Default is ~/envs/{env}/assets/widgets.json, when --editable is 'true'. + --apps-json Absolute/relative path to use as the apps.json file. Default is ~/OpenBBUserData/workspace_apps.json. + --agents-json Absolute/relative path to use as the agents.json file. Including this will add the /agents endpoint to the API. + + +The FastAPI app instance can be imported to another script, modified, and launched by using the --app argument. + +If the path to the app file is not absolute, it will be resolved relative to the current working directory. + +Imported with: + +>>> from openbb_platform_api.main import app +>>> +>>> @app.get() +>>> async def hello(input: str = "Hello") -> str: +>>> '''Widget description created by doctring.''' +>>> return f"You entered: {input}" + +Launched with: + +>>> openbb-api --app /path/to/some_file.py + +The app instance name can be defined by either the --name argument, or by referencing the module name, for example: + +>>> openbb-api --app some_file.py:main --factory + +A name must be set when using the factory flag. + +All other arguments will be passed to uvicorn. Here are the most common ones: + + --host TEXT Host IP address or hostname. + [default: 127.0.0.1] + --port INTEGER Port number. + [default: 6900] + --ssl-keyfile TEXT SSL key file. + --ssl-certfile TEXT SSL certificate file. + --ssl-keyfile-password TEXT SSL keyfile password. + --ssl-version INTEGER SSL version to use. + (see stdlib ssl module's) + [default: 17] + --ssl-cert-reqs INTEGER Whether client certificate is required. + (see stdlib ssl module's) + [default: 0] + --ssl-ca-certs TEXT CA certificates file. + --ssl-ciphers TEXT Ciphers to use. + (see stdlib ssl module's) + [default: TLSv1] + +Run `uvicorn --help` to get the full list of arguments. +""" # noqa: E501 + + +def check_port(host, port): + """Check if the port number is free.""" + port = int(port) + not_free = True + while not_free: + with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as sock: + res = sock.connect_ex((host, port)) + if res != 0: + not_free = False + else: + port += 1 + return port + + +def get_user_settings( + _login: bool, current_user_settings: str, user_settings_copy: str +): + """Login to the OpenBB Platform.""" + # pylint: disable=import-outside-toplevel + import getpass + + if Path(current_user_settings).exists(): + with open(current_user_settings, encoding="utf-8") as f: + _current_settings = json.load(f) + else: + _current_settings = { + "credentials": {}, + "preferences": {}, + "defaults": {"commands": {}}, + } + if (isinstance(_login, str) and _login.lower() == "false") or not _login: + return _current_settings + + pat = getpass.getpass( + "\n\nEnter your personal access token (PAT) to authorize the API and update your local settings." + + "\nSkip to use a pre-configured 'user_settings.json' file." + + "\nPress Enter to skip or copy (entered values are not displayed on screen) your PAT to the command line: " + ) + + if pat: + from openbb_core.app.service.hub_service import HubService + + hub_credentials: dict = {} + hub_preferences: dict = {} + hub_defaults: dict = {} + try: + Hub = HubService() + _ = Hub.connect(pat=pat) + hub_settings = Hub.pull() + hub_credentials = json.loads( + hub_settings.credentials.model_dump_json() # pylint: disable=no-member + ) + hub_preferences = json.loads( + hub_settings.preferences.model_dump_json() # pylint: disable=no-member + ) + hub_defaults = json.loads( + hub_settings.defaults.model_dump_json() # pylint: disable=no-member + ) + except Exception as e: # pylint: disable=broad-exception-caught + print( # noqa: T201 + f"\n\nError connecting with Hub:\n{e}\n\nUsing the local settings.\n" + ) + + if hub_credentials: + # Prompt the user to ask if they want to persist the new settings + persist_input = ( + input( + "\n\nDo you want to persist the new settings?" + + " Not recommended for public machines. (yes/no): " + ) + .strip() + .lower() + ) + + if persist_input in ["yes", "y"]: + PERSIST = True + elif persist_input in ["no", "n"]: + PERSIST = False + else: + print( # noqa: T201 + "\n\nInvalid input. Defaulting to not persisting the new settings." + ) + PERSIST = False + + # Save the current settings to restore at the end of the session. + if PERSIST is False: + with open(user_settings_copy, "w", encoding="utf-8") as f: + json.dump(_current_settings, f, indent=4) + + new_settings = _current_settings.copy() + new_settings.setdefault("credentials", {}) + new_settings.setdefault("preferences", {}) + new_settings.setdefault("defaults", {"commands": {}}) + + # Update the current settings with the new settings + if hub_credentials: + for k, v in hub_credentials.items(): + if v: + new_settings["credentials"][k] = v.strip('"').strip("'") + + if hub_preferences: + for k, v in hub_preferences.items(): + if v: + new_settings["preferences"][k] = v + + if hub_defaults: + for k, v in hub_defaults.items(): + if k == "commands": + for key, value in hub_defaults["commands"].items(): + if value: + new_settings["defaults"]["commands"][key] = value + elif v: + new_settings["defaults"][k] = v + else: + continue + + # Write the new settings to the user_settings.json file + with open(current_user_settings, "w", encoding="utf-8") as f: + json.dump(new_settings, f, indent=4) + + _current_settings = new_settings + + return _current_settings + + +def get_widgets_json( + _build: bool, + _openapi, + widget_exclude_filter: list, + editable: bool = False, + widgets_path: Optional[str] = None, +): + """Generate and serve the widgets.json for the OpenBB Platform API.""" + if editable is True: + if widgets_path is None: + python_path = Path(sys.executable) + parent_path = ( + python_path.parent if os.name == "nt" else python_path.parents[1] + ) + widgets_json_path = parent_path.joinpath("assets", "widgets.json").resolve() + else: + widgets_json_path = Path(widgets_path).absolute().resolve() + + json_exists = widgets_json_path.exists() + + if not json_exists: + widgets_json_path.parent.mkdir(parents=True, exist_ok=True) + _build = True + json_exists = widgets_json_path.exists() + + existing_widgets_json: dict = {} + + if json_exists: + with open(widgets_json_path, encoding="utf-8") as f: + existing_widgets_json = json.load(f) + + _widgets_json = ( + existing_widgets_json + if _build is False + else build_json(_openapi, widget_exclude_filter) + ) + + if _build: + diff = DeepDiff(existing_widgets_json, _widgets_json, ignore_order=True) + merge_prompt = None + if diff and json_exists: + print("Differences found:", diff) # noqa: T201 + merge_prompt = input( + "\nDo you want to overwrite the existing widgets.json configuration?" + "\nEnter 'n' to append existing with only new entries, or 'i' to ignore all changes. (y/n/i): " + ) + if merge_prompt.lower().startswith("n"): + _widgets_json.update(existing_widgets_json) + elif merge_prompt.lower().startswith("i"): + _widgets_json = existing_widgets_json + + if merge_prompt is None or not merge_prompt.lower().startswith("i"): + try: + with open(widgets_json_path, "w", encoding="utf-8") as f: + json.dump(_widgets_json, f, ensure_ascii=False, indent=4) + except Exception as e: # pylint: disable=broad-exception-caught + print( # noqa: T201 + f"Error writing widgets.json: {e}. Loading from memory instead." + ) + _widgets_json = ( + existing_widgets_json + if existing_widgets_json + else build_json(_openapi, widget_exclude_filter) + ) + else: + _widgets_json = build_json(_openapi, widget_exclude_filter) + + return _widgets_json + + +def import_app(app_path: str, name: str = "app", factory: bool = False): + """Import the FastAPI app instance from a local file or module.""" + # pylint: disable=import-outside-toplevel + from fastapi import FastAPI # noqa + from fastapi.middleware.cors import CORSMiddleware + from importlib import import_module, util + from openbb_core.api.app_loader import AppLoader + from openbb_core.api.rest_api import system + + def _load_module_from_file_path(file_path: str): + spec_name = os.path.basename(file_path).split(".")[0] + spec = util.spec_from_file_location(spec_name, file_path) + + if spec is None: + raise RuntimeError(f"Failed to load the file specs for '{file_path}'") + + module = util.module_from_spec(spec) # type: ignore + sys.modules[spec_name] = module # type: ignore + spec.loader.exec_module(module) # type: ignore + return module + + # Case 1: Module path with colon notation (e.g., "my_app.main:app" or "main:app") + if ":" in app_path: + module_path, name = app_path.split(":") + try: # First try to import as a module + module = import_module(module_path) + except ImportError: # If module import fails, try to load as a local file + if not module_path.endswith(".py"): + module_path += ".py" + + if not str(module_path).startswith("/"): + cwd = Path.cwd() + file_path = str(cwd.joinpath(module_path).resolve()) + else: + file_path = module_path + + if not Path(file_path).exists(): + raise FileNotFoundError( # pylint: disable=raise-missing-from + f"Error: Neither module '{module_path}' could be imported nor file '{file_path}' exists" + ) + + module = _load_module_from_file_path(file_path) + + # Case 2: File path (e.g., "main.py" or "my_app/main.py") + else: + if not str(app_path).startswith("/"): + cwd = Path.cwd() + app_path = str(cwd.joinpath(app_path).resolve()) + + if not Path(app_path).exists(): + raise FileNotFoundError(f"Error: The app file '{app_path}' does not exist") + + module = _load_module_from_file_path(app_path) + + if not hasattr(module, name): + raise AttributeError( + f"Error: The app file '{app_path}' does not contain an '{name}' instance" + ) + + app_or_factory = getattr(module, name) + + # Here we use the same approach as uvicorn to handle factory functions. + # This prevents us from relying on explicit type annotations. + # See: https://github.com/encode/uvicorn/blob/master/uvicorn/config.py + try: + app = app_or_factory() + if not factory: + print( # noqa: T201 + "\n\n[WARNING] " + "App factory detected. Using it, but please consider setting the --factory flag explicitly.\n" + ) + except TypeError: + if factory: + raise TypeError( # pylint: disable=raise-missing-from + f"Error: The {name} instance in '{app_path}' appears not to be a callable factory function" + ) + app = app_or_factory + + if not isinstance(app, FastAPI): + raise TypeError( + f"Error: The {name} instance in '{app_path}' is not an instance of FastAPI" + ) + + app.add_middleware( + CORSMiddleware, + allow_origins=system.api_settings.cors.allow_origins, + allow_methods=system.api_settings.cors.allow_methods, + allow_headers=system.api_settings.cors.allow_headers, + ) + + AppLoader.add_exception_handlers(app) + + return app + + +def parse_args(): # noqa: PLR0912 # pylint: disable=too-many-branches + """Parse the launch script command line arguments.""" + args = sys.argv[1:].copy() + cwd = Path.cwd() + _kwargs: dict = {} + for i, arg in enumerate(args): + if arg == "--help": + print(LAUNCH_SCRIPT_DESCRIPTION) # noqa: T201 + sys.exit(0) + if arg.startswith("--"): + key = arg[2:] + if key in ["no-use-colors", "use-colors"]: + _kwargs["use_colors"] = key == "use-colors" + elif i + 1 < len(args) and not args[i + 1].startswith("--"): + value = args[i + 1] + if isinstance(value, str) and value.lower() in ["false", "true"]: + _kwargs[key] = value.lower() == "true" + elif key == "exclude": + _kwargs[key] = json.loads(value) + else: + _kwargs[key] = value + else: + _kwargs[key] = True + + if _kwargs.get("app"): + _app_path = _kwargs.pop("app", None) + _name = _kwargs.pop("name", "app") + _factory = _kwargs.pop("factory", False) + + if ":" in _app_path: + _app_instance_name = _app_path.split(":")[-1] + _name = _app_instance_name if _app_instance_name else _name + + if _factory and not _name: + raise ValueError( + "Error: The factory function name must be provided to the --name parameter when the factory flag is set." + ) + _kwargs["app"] = import_app(_app_path, _name, _factory) + + if isinstance(_kwargs.get("exclude"), str): + _kwargs["exclude"] = [_kwargs["exclude"]] + + if _kwargs.get("agents-json") or _kwargs.get("copilots-path"): + _agents_path = _kwargs.pop("agents-json", None) or _kwargs.pop( + "copilots-path", None + ) + + if not str(_agents_path).endswith(".json"): + _agents_path = ( + f"{_agents_path}{'' if _agents_path.endswith('/') else '/'}agents.json" + ) + + if str(_agents_path).startswith("./"): + _agents_path = str(cwd.joinpath(_agents_path).resolve()) + + _kwargs["agents-json"] = _agents_path + + if _kwargs.get("widgets-json") or _kwargs.get("widgets-path"): + _widgets_path = _kwargs.pop("widgets-json", None) or _kwargs.pop( + "widgets-path", None + ) + + # If it's a file (endswith .json), use as is; else treat as directory and append widgets.json + if str(_widgets_path).endswith(".json"): + widgets_file_path = _widgets_path + else: + widgets_file_path = f"{_widgets_path}{'' if str(_widgets_path).endswith('/') else '/'}widgets.json" + + # Resolve relative paths to absolute + if str(widgets_file_path).startswith("./"): + widgets_file_path = str(cwd.joinpath(widgets_file_path).resolve()) + + _kwargs["widgets-json"] = widgets_file_path + + if _kwargs.get("widgets-json"): + _kwargs["editable"] = True + # If the file already exists, we assume that it is already built. + if os.path.exists(_kwargs["widgets-json"]): + _kwargs["no-build"] = True + + # Handle apps-json and templates-path in the same way as widgets-path + if _kwargs.get("apps-json") or _kwargs.get("templates-path"): + _apps_path = _kwargs.pop("apps-json", None) or _kwargs.pop( + "templates-path", None + ) + + # If it's a file (endswith .json), use as is; else treat as directory and append apps.json + if str(_apps_path).endswith(".json"): + apps_file_path = _apps_path + else: + # Check if "workspace_apps.json" exists in the given path + possible_workspace_file = f"{_apps_path}{'' if str(_apps_path).endswith('/') else '/'}workspace_apps.json" + if os.path.isfile(possible_workspace_file): + apps_file_path = possible_workspace_file + else: + apps_file_path = f"{_apps_path}{'' if str(_apps_path).endswith('/') else '/'}apps.json" + + # Resolve relative paths to absolute + if str(apps_file_path).startswith("./"): + apps_file_path = str(cwd.joinpath(apps_file_path).resolve()) + + _kwargs["apps-json"] = apps_file_path + + return _kwargs diff --git a/openbb_platform/extensions/platform_api/openbb_platform_api/utils/openapi.py b/openbb_platform/extensions/platform_api/openbb_platform_api/utils/openapi.py new file mode 100644 index 0000000000000000000000000000000000000000..ca0093ab8820c1dbb7a053763a90055fb5c4a609 --- /dev/null +++ b/openbb_platform/extensions/platform_api/openbb_platform_api/utils/openapi.py @@ -0,0 +1,986 @@ +"""OpenAPI parsing Utils.""" + +from typing import Optional + +from openbb_core.provider.utils.helpers import to_snake_case + +TO_CAPS_STRINGS = [ + "Pe", + "Peg", + "Sloos", + "Eps", + "Ebit", + "Ebitda", + "Otc", + "Cpi", + "Pce", + "Gdp", + "Lbma", + "Ipo", + "Nbbo", + "Ameribor", + "Sonia", + "Effr", + "Sofr", + "Iorb", + "Estr", + "Ecb", + "Dpcredit", + "Tcm", + "Us", + "Ice", + "Bofa", + "Hqm", + "Sp500", + "Sec", + "Cftc", + "Cot", + "Etf", + "Eu", + "Tips", + "Rss", + "Sic", + "Cik", + "Bls", + "Fred", + "Cusip", + "Ttm", + "Id", + "Ytd", + "Yoy", + "Dte", + "Url", + "Sedol", + "Isin", + "Figi", + "Cusip", + "Pdf", + "Otm", + "Atm", + "Itm", + "Fomc", +] + + +def extract_providers(params: list[dict]) -> list[str]: + """ + Extract provider options from parameters. + + Parameters + ---------- + params : List[Dict] + List of parameter dictionaries. + + Returns + ------- + List[str] + List of provider options. + """ + provider_params = [p for p in params if p["name"] == "provider"] + if provider_params: + if provider_params[0].get("schema", {}).get("enum"): + return provider_params[0]["schema"]["enum"] + if provider_params[0].get("schema", {}).get("default"): + return [str(provider_params[0]["schema"]["default"])] + return [] + + +def set_parameter_type(p: dict, p_schema: dict): + """ + Determine and set the type for the parameter. + + Parameters + ---------- + p : Dict + Processed parameter dictionary. + p_schema : Dict + Schema dictionary for the parameter. + """ + p_type = p_schema.get("type") if not p.get("type") else p.get("type") + + if p_type == "string": + p["type"] = "text" + + if p_type in ("float", "integer") or ( + not isinstance(p["value"], bool) and isinstance(p["value"], (int, float)) + ): + p["type"] = "number" + + if ( + p_type == "boolean" + or p_schema.get("type") == "boolean" + or ("anyOf" in p_schema and p_schema["anyOf"][0].get("type") == "boolean") + ): + p["type"] = "boolean" + + if p["parameter_name"] == "date" or "_date" in p["parameter_name"]: + p["type"] = "date" + + if "timeframe" in p["parameter_name"]: + p["type"] = "text" + + if p["parameter_name"] == "limit": + p["type"] = "number" + + if p.get("type") in ("array", "list") or isinstance(p.get("type"), (list, dict)): + p["type"] = "text" + + return p + + +def set_parameter_options( # noqa: PLR0912 # pylint: disable=too-many-branches + p: dict, p_schema: dict, providers: list[str] +) -> dict: + """ + Set options for the parameter based on the schema. + + Parameters + ---------- + p : Dict + Processed parameter dictionary. + p_schema : Dict + Schema dictionary for the parameter. + providers : List[str] + List of provider options. + + Returns + ------- + Dict + Updated parameter dictionary with options. + """ + choices: dict[str, list[dict[str, str]]] = ( + p.get("options", {}) + if p.get("options") + else p_schema.get("options", {}) if p_schema.get("options") else {} + ) + widget_configs: dict[str, dict] = {} + multiple_items_allowed_dict: dict = {} + is_provider_specific = False + available_providers: set = set() + unique_general_choices: list = [] + provider: str = "" + + # Extract provider from title if present + title_providers = [] + if ( + p_schema.get("title") + and p_schema["title"] != p.get("parameter_name") + and p_schema.get("title", "").islower() + ): + # Handle comma-separated providers in title field + for title_name in p_schema["title"].lower().split(","): + if title_name in [ + prov.lower() for prov in providers + ]: # Only actual providers + title_providers.append(title_name) + is_provider_specific = True + available_providers.add(title_name) + + # Handle provider-specific choices + for provider in providers: + if provider in p_schema or (len(providers) == 1): + is_provider_specific = True + provider_choices: list = [] + if provider not in available_providers: + available_providers.add(provider) + if provider in p_schema: + provider_choices = p_schema[provider].get("choices", []) + if widget_def := p_schema[provider].get("x-widget_config"): + widget_configs[provider] = widget_def + elif len(providers) == 1 and "enum" in p_schema: + provider_choices = p_schema["enum"] + p_schema.pop("enum") + + if provider_choices: + choices[provider] = [ + {"label": str(c), "value": c} for c in provider_choices + ] + if provider in p_schema and p_schema[provider].get( + "multiple_items_allowed", False + ): + multiple_items_allowed_dict[provider] = True + + # Handle title provider choices if present + if title_providers and "anyOf" in p_schema: + # If we have multiple providers in title and multiple enum lists in anyOf + # try to match them in order + if ( + len(title_providers) > 1 + and len([s for s in p_schema["anyOf"] if "enum" in s]) > 1 + ): + for i, provider in enumerate(title_providers): + # Only process if this provider doesn't already have choices + if provider not in choices or not choices[provider]: + # Try to match enum at the same position as the provider in the title + enum_index = min(i, len(p_schema["anyOf"]) - 1) + if "enum" in p_schema["anyOf"][enum_index]: + provider_choices = p_schema["anyOf"][enum_index]["enum"] + choices[provider] = [ + {"label": str(c), "value": c} + for c in provider_choices + if c not in ["null", None] + ] + else: + # Existing code for single provider or multiple providers with one enum + all_provider_choices = [] + for sub_schema in p_schema["anyOf"]: + if "enum" in sub_schema: + all_provider_choices.extend(sub_schema["enum"]) + + if all_provider_choices: + for provider in title_providers: + if provider not in choices or not choices[provider]: + choices[provider] = [ + {"label": str(c), "value": c} + for c in all_provider_choices + if c not in ["null", None] + ] + + # Check title for provider-specific information from description + if p_schema.get("description") and "(provider:" in p_schema["description"]: + desc_provider = ( + p_schema["description"].split("(provider:")[1].strip().rstrip(")") + ) + if desc_provider and desc_provider not in available_providers: + available_providers.add(desc_provider) + is_provider_specific = True + + # Handle general choices + general_choices: list = [] + if "enum" in p_schema: + general_choices.extend( + [ + {"label": str(c), "value": c} + for c in p_schema["enum"] + if c not in ["null", None] + ] + ) + elif "anyOf" in p_schema and not title_providers: + for sub_schema in p_schema["anyOf"]: + if "enum" in sub_schema: + general_choices.extend( + [ + {"label": str(c), "value": c} + for c in sub_schema["enum"] + if c not in ["null", None] + ] + ) + + if general_choices: + # Remove duplicates by converting list of dicts to a set of tuples and back to list of dicts + unique_general_choices = sorted( + [dict(t) for t in {tuple(d.items()) for d in general_choices}], + key=lambda x: x["label"], + ) + if not is_provider_specific: + if len(providers) == 1: + choices[providers[0]] = unique_general_choices + multiple_items_allowed_dict[providers[0]] = p_schema.get( + "multiple_items_allowed", False + ) + else: + choices["other"] = unique_general_choices + multiple_items_allowed_dict["other"] = p_schema.get( + "multiple_items_allowed", False + ) + + # Use general choices as fallback for providers without specific options + for provider in available_providers: + if provider not in choices: + if "anyOf" in p_schema and p_schema["anyOf"]: + fallback_choices = p_schema["anyOf"][0].get("enum", []) + choices[provider] = [ + {"label": str(c), "value": c} + for c in fallback_choices + if c not in ["null", None] + ] + else: + choices[provider] = unique_general_choices + + if provider in p_schema and p_schema[provider].get("x-widget_config"): + widget_configs[provider] = p_schema[provider].get("x-widget_config") + + p["multiple_items_allowed"] = multiple_items_allowed_dict + + if choices: + filtered_choices = { + provider: choice for provider, choice in choices.items() if choice + } + p["options"] = ( + filtered_choices if filtered_choices else {provider: []} if provider else [] + ) + + if is_provider_specific and len(available_providers) > 1: + p["available_providers"] = list(available_providers) + p["x-widget_config"] = widget_configs + + else: + p["x-widget_config"] = ( + widget_configs.get(provider, {}) if provider else widget_configs + ) + + return p + + +def process_parameter(param: dict, providers: list[str]) -> dict: + """Process a single parameter and return the processed dictionary.""" + p: dict = {} + schema = param.get("schema", {}) + + param_name = param["name"] + p["parameter_name"] = param_name + p["label"] = ( + param_name.replace("_", " ").replace("fixedincome", "fixed income").title() + ) + + if not p.get("label") or p.get("label") == "": + p["label"] = schema.get("title") or param.get("title") + + p["description"] = ( + (param.get("description", param_name).split(" (provider:")[0].strip()) + .split("Multiple comma separated items allowed")[0] + .strip() + if param.get("description") + else (schema.get("description") or p.get("label")) + ) + p["optional"] = param.get("required", False) is False + + # Set type first so we can use it for value determination + p["type"] = param.get("type", "text") + + # Get default value from schema if present + default_value = param.get("default") + + if default_value is None: + default_value = schema.get("default") + + p["value"] = default_value if default_value is not None else param.get("value") + + # Special handling for provider parameter + if param_name == "provider": + p["type"] = "text" + p["label"] = "Provider" + p["description"] = "Source of the data." + p["show"] = False + p["available_providers"] = providers + p["value"] = None + return p + + multiple_items_allowed_dict: dict = {} + for _provider in providers: + if param.get("schema", {}).get(_provider, {}).get( + "multiple_items_allowed" + ) and param["schema"][_provider].get("multiple_items_allowed"): + multiple_items_allowed_dict[_provider] = True + + p["multiple_items_allowed"] = multiple_items_allowed_dict + + # Safe check for description + if ( + p.get("description") + and "Multiple comma separated items allowed" in p["description"] + ): + p["description"] = ( + p["description"].split("Multiple comma separated items allowed")[0].strip() + ) + + if x_widget_config := param.get( + "x-widget_config", param.get("schema", {}).get("x-widget_config", {}) + ): + p["x-widget_config"] = x_widget_config + + p_schema = param.get("schema", {}) or param + + # Initialize provider specificity tracking + provider_specific = False + available_providers_list = ( + [] + ) # Start with empty list - only add providers that match + + # Extract providers from title + if p_schema.get("title"): + # Handle comma-separated list of providers in the title + if "," in p_schema["title"]: + title_providers = [p.strip().lower() for p in p_schema["title"].split(",")] + available_providers_list.extend(title_providers) + provider_specific = True + elif p_schema["title"].lower() in [p.lower() for p in providers]: + # Single provider in title + available_providers_list.append(p_schema["title"].lower()) + provider_specific = True + + # Extract providers from description + description = param.get("description", "") + if description and "(provider:" in description: + desc_parts = description.split("(provider:") + for part in desc_parts[1:]: # Skip the first part (before any provider mention) + desc_provider_text = part.split(")")[0].strip() + # Handle multiple providers separated by commas in description + for dp in desc_provider_text.split(","): + desc_provider = dp.strip().lower() + if desc_provider and desc_provider not in available_providers_list: + available_providers_list.append(desc_provider) + provider_specific = True + + # Process options and types + p = set_parameter_options(p, p_schema, providers) + p = set_parameter_type(p, p_schema) + + # Ensure options has the expected format: {"provider": []} rather than just [] + if "options" not in p: + p["options"] = {} if providers else [] + if providers: + for provider in providers: + p["options"][provider] = [] + + # Handle widget config + if _widget_config := p_schema.get("x-widget_config", {}): + for provider in providers: + if provider in _widget_config: + _widget_config = _widget_config[provider] + break + p.update(_widget_config) + + # Check if this parameter is provider-specific and filter appropriately + if provider_specific and available_providers_list: + # ONLY include providers that are actually in the provided providers list + valid_provider_list = [ + p + for p in available_providers_list + if p.lower() in [prov.lower() for prov in providers] + ] + + if valid_provider_list: + p["available_providers"] = valid_provider_list + + # Check if any of our current providers match the validated available providers list + valid_for_current_providers = any( + current_provider.lower() + in [valid_p.lower() for valid_p in valid_provider_list] + for current_provider in providers + ) + + # If parameter is provider-specific but not valid for any of our current providers, skip it + if not valid_for_current_providers: + return {} + + return p + + +def get_query_schema_for_widget( + openapi_json: dict, command_route: str +) -> tuple[list[dict], bool]: + """ + Extract the query schema for a widget. + + Parameters + ---------- + openapi_json : dict + The OpenAPI specification as a dictionary. + command_route : str + The route of the command in the OpenAPI specification. + + Returns + ------- + Tuple[List[Dict], bool] + A tuple containing the list of processed parameters and a boolean indicating if a chart is present. + """ + has_chart = False + command = openapi_json["paths"][command_route] + command = command.get("get", {}) + params = command.get("parameters", []) + route_params: list[dict] = [] + providers: list[str] = extract_providers(params) + + if not providers: + providers = ["custom"] + + for param in params: + if param["name"] in ["sort", "order"]: + continue + if param["name"] == "chart": + has_chart = True + continue + + p = process_parameter(param, providers) + if "show" not in p: + p["show"] = True + + if not p.get("exclude") and not p.get("x-widget_config", {}).get("exclude"): + route_params.append(p) + + return route_params, has_chart + + +def get_data_schema_for_widget(openapi_json, operation_id, route: Optional[str] = None): + """ + Get the data schema for a widget based on its operationId. + + Args: + openapi (dict): The OpenAPI specification as a dictionary. + operation_id (str): The operationId of the widget. + + Returns: + dict: The schema dictionary for the widget's data. + """ + # Find the route and method for the given operationId + + if not route: + for path, methods in openapi_json["paths"].items(): + for _method, details in methods.items(): + if details.get("operationId") == operation_id: + route = path + break + + _route = openapi_json["paths"].get(route, {}).get("get", {}) + + if ( + schema := _route.get("responses", {}) + .get("200", {}) + .get("content", {}) + .get("application/json", {}) + .get("schema", {}) + ): + # Get the reference to the schema from the successful response + + if "items" in schema: + response_ref = schema["items"].get("$ref") + else: + response_ref = schema.get("$ref") or _route["responses"]["200"]["content"][ + "application/json" + ].get("schema") + + if isinstance(response_ref, dict) and "type" in response_ref: + response_ref = response_ref["type"] + + if response_ref and isinstance(response_ref, str): + # Extract the schema name from the reference + schema_name = response_ref.split("/")[-1] + # Fetch and return the schema from components + if schema_name and schema_name in openapi_json.get("components", {}).get( + "schemas", {} + ): + props = openapi_json["components"]["schemas"][schema_name].get( + "properties", {} + ) + if props and "results" in props: + return props["results"] + + return openapi_json["components"]["schemas"].get(schema_name, schema_name) + # Return None if the schema is not found + return None + + +def data_schema_to_columns_defs( # noqa: PLR0912 # pylint: disable=too-many-branches + openapi_json, + operation_id, + provider, + route: Optional[str] = None, + get_widget_config: bool = False, +): + """Convert data schema to column definitions for the widget.""" + # Initialize an empty list to hold the schema references + schema_refs: list = [] + + result_schema_ref = get_data_schema_for_widget(openapi_json, operation_id, route) + + # Check if 'anyOf' is in the result_schema_ref and handle the nested structure + if result_schema_ref and "anyOf" in result_schema_ref: + for item in result_schema_ref["anyOf"]: + # When there are multiple providers a 'oneOf' is used + if "items" in item and "oneOf" in item["items"]: + # Extract the $ref values + schema_refs.extend( + [ + oneOf_item["$ref"].split("/")[-1] + for oneOf_item in item["items"]["oneOf"] + if "$ref" in oneOf_item + ] + ) + # When there's only one model there is no oneOf + elif "items" in item and "$ref" in item["items"]: + schema_refs.append(item["items"]["$ref"].split("/")[-1]) + elif "$ref" in item: + schema_refs.append(item["$ref"].split("/")[-1]) + elif "oneOf" in item: + for ref in item.get("oneOf", []): + maybe_ref = ref.get("$ref").split("/")[-1] + if maybe_ref.lower().startswith(provider): + schema_refs.append(maybe_ref) + break + + # Fetch the schemas using the extracted references + schemas = [ + openapi_json["components"]["schemas"][ref] + for ref in schema_refs + if ref and ref in openapi_json["components"]["schemas"] + ] + + if not schemas and result_schema_ref and "properties" in result_schema_ref: + schemas.append(result_schema_ref) + + # Proceed with finding common keys and generating column definitions + if not schemas: + return [] + + target_schema: dict = {} + + if len(schemas) == 1: + target_schema = schemas[0] + else: + for schema in schemas: + if ( + schema.get("description", "") + .lower() + .startswith(provider.lower().replace("tradingeconomics", "te")) + ) or ( + schema.get("description", "").lower().startswith("us government") + or (schema.get("description", "").lower().startswith(provider)) + ): + target_schema = schema + break + + if get_widget_config: + return target_schema.get("x-widget_config", {}) + + keys = list(target_schema.get("properties", {})) + + column_defs: list = [] + for key in keys: + cell_data_type = None + formatterFn = None + prop = target_schema.get("properties", {}).get(key) + # Handle prop types for both when there's a single prop type or multiple + if "items" in prop: + items = prop.get("items", {}) + items = items.get("anyOf", items) + prop["anyOf"] = items if isinstance(items, list) else [items] + types = [ + sub_prop.get("type") for sub_prop in prop["anyOf"] if "type" in sub_prop + ] + if "number" in types or "integer" in types or "float" in types: + cell_data_type = "number" + elif "string" in types and any( + sub_prop.get("format") in ["date", "date-time"] + for sub_prop in prop["anyOf"] + if "format" in sub_prop + ): + cell_data_type = "date" + else: + cell_data_type = "text" + elif "anyOf" in prop: + types = [ + sub_prop.get("type") for sub_prop in prop["anyOf"] if "type" in sub_prop + ] + if "number" in types or "integer" in types or "float" in types: + cell_data_type = "number" + elif "string" in types and any( + sub_prop.get("format") in ["date", "date-time"] + for sub_prop in prop["anyOf"] + if "format" in sub_prop + ): + cell_data_type = "date" + else: + cell_data_type = "text" + else: + prop_type = prop.get("type", None) + if prop_type in ["number", "integer", "float"]: + cell_data_type = "number" + if prop_type == "integer": + formatterFn = "int" + elif "format" in prop and prop["format"] in ["date", "date-time"]: + cell_data_type = "date" + else: + cell_data_type = "text" + + column_def: dict = {} + # OpenAPI changes some of the field names. + k = to_snake_case(key) + column_def["field"] = k + if k in [ + "symbol", + "symbol_root", + "series_id", + "date", + "published", + "fiscal_year", + "period_ending", + "period_beginning", + "order", + "name", + "title", + "cusip", + "isin", + "expiration", + "dte", + "strike", + "option_type", + ]: + column_def["pinned"] = "left" + + column_def["formatterFn"] = formatterFn + header_name = prop.get("title", key.title()) + column_def["headerName"] = " ".join( + [ + (word.upper() if word in TO_CAPS_STRINGS else word) + for word in header_name.replace("_", " ").split(" ") + ] + ) + column_def["headerTooltip"] = prop.get( + "description", prop.get("title", key.title()) + ) + column_def["cellDataType"] = cell_data_type + measurement = prop.get("x-unit_measurement") + + if measurement == "percent": + column_def["formatterFn"] = ( + "normalizedPercent" + if prop.get("x-frontend_multiply") == 100 + else "percent" + ) + column_def["renderFn"] = "greenRed" + column_def["cellDataType"] = "number" + + if k in ["cik", "isin", "figi", "cusip", "sedol", "symbol"]: + column_def["cellDataType"] = "text" + column_def["headerName"] = ( + column_def["headerName"].upper() if k != "symbol" else "Symbol" + ) + + if k in ["fiscal_year", "year", "year_born"]: + column_def["cellDataType"] = "number" + column_def["formatterFn"] = "none" + + if ( + route + and route.endswith("chains") + and column_def.get("field") + in [ + "underlying_symbol", + "contract_symbol", + "underlying_price", + "contract_symbol", + ] + ): + column_def["hide"] = True + + if column_def.get("field") in [ + "delta", + "gamma", + "theta", + "vega", + "rho", + "vega", + "charm", + "vanna", + "vomma", + ]: + column_def["formatterFn"] = "none" + if column_def["field"] in ["delta", "theta", "rho"]: + column_def["renderFn"] = "greenRed" + + if ( + route + and route.endswith("chains") + and column_def["field"] == "implied_volatility" + ): + column_def["formatterFn"] = "normalizedPercent" + + if column_def.get("field") == "change": + column_def["renderFn"] = "greenRed" + + if ( + route + and route.endswith("chains") + and column_def.get("field") + in [ + "underlying_symbol", + "contract_symbol", + "underlying_price", + "contract_symbol", + ] + ): + column_def["hide"] = True + + if column_def.get("field") in [ + "delta", + "gamma", + "theta", + "vega", + "rho", + "vega", + "charm", + "vanna", + "vomma", + ]: + column_def["formatterFn"] = "none" + if column_def["field"] in ["delta", "theta", "rho"]: + column_def["renderFn"] = "greenRed" + + if ( + route + and route.endswith("chains") + and column_def["field"] == "implied_volatility" + ): + column_def["formatterFn"] = "normalizedPercent" + + if column_def.get("field") == "change": + column_def["renderFn"] = "greenRed" + + if _widget_config := prop.get("x-widget_config", {}): + + if _widget_config.get("exclude"): + continue + + column_def.update(_widget_config) + + column_defs.append(column_def) + return column_defs + + +def post_query_schema_for_widget( + openapi_json, + operation_id, + route: Optional[str] = None, + target_schema: Optional[str] = None, +): + """ + Get the POST query schema for a widget based on its operationId. + + Args: + openapi (dict): The OpenAPI specification as a dictionary. + operation_id (str): The operationId of the widget. + route (str): The route of the widget, if any. + target_schema (str): The target schema to extract, if any. + + Returns: + list[dict]: The schema dictionary for the widget's data. + """ + + new_params: dict = {} + + def set_param(k, v): + """Set the parameter.""" + nonlocal new_params + + new_params[k] = {} + new_params[k]["name"] = k + new_params[k]["type"] = ( + "text" + if v.get("type") == "object" + else "date" if "date" in v.get("format", "") else v.get("type", "text") + ) + new_params[k]["title"] = v.get("title") + new_params[k]["description"] = v.get("description") + new_params[k]["default"] = v.get("default") + new_params[k]["x-widget_config"] = v.get("x-widget_config", {}) + choices: list = ( + [{"label": c, "value": c} for c in v.get("choices", []) if c] + if v.get("choices") + else [] + ) + + if isinstance(v, dict) and "anyOf" in v: + param_types = [] + for item in v["anyOf"]: + if "type" in item and item.get("type") != "null": + param_types.append(item["type"]) + if "enum" in item: + choices.extend({"label": c, "value": c} for c in item["enum"]) + + if param_types: + new_params[k]["type"] = ( + "number" + if "number" in param_types + or "integer" in param_types + and "string" not in param_types + and "date" not in param_types + else ( + "date" + if any( + "date" in sub_prop.get("format", "") + for sub_prop in v["anyOf"] + if isinstance(sub_prop, dict) + ) + else "text" + ) + ) + else: + new_params[k]["type"] = ( + "text" + if v.get("type") == "object" + else ( + "date" + if "date" in v.get("format", "") + else v.get("type", "text") + ) + ) + elif isinstance(v, dict) and "enum" in v: + choices.extend({"label": c, "value": c} for c in v["enum"] if c) + + if choices: + new_params[k]["options"] = {"custom": choices} + + if not route: + for path, methods in openapi_json["paths"].items(): + for _method, details in methods.items(): + if details.get("operationId") == operation_id: + route = path + break + + _route = openapi_json["paths"].get(route, {}).get("post", {}) + + if ( + schema := _route.get("requestBody", {}) + .get("content", {}) + .get("application/json", {}) + .get("schema", {}) + ): + # Get the reference to the schema for the request body. + + param_ref = ( + schema["items"].get("$ref") + if "items" in schema + else schema.get("$ref") or schema + ) + + if isinstance(param_ref, dict) and "type" in param_ref: + param_ref = param_ref["type"] + + if param_ref: + # Extract the schema name from the reference + schema_name = param_ref.split("/")[-1] + schema = openapi_json["components"]["schemas"].get(schema_name, schema_name) + props = schema.get("properties", {}) + + for k, v in props.items(): + if target_schema and target_schema != k: + continue + if nested_schema := v.get("$ref"): + nested_schema_name = nested_schema.split("/")[-1] + nested_schema = openapi_json["components"]["schemas"].get( + nested_schema_name, {} + ) + for nested_k, nested_v in nested_schema.get( + "properties", {} + ).items(): + set_param(nested_k, nested_v) + + else: + set_param(k, v) + + route_params: list[dict] = [] + providers = ["custom"] + + for new_param_values in new_params.values(): + _new_values = new_param_values.copy() + p = process_parameter(_new_values, providers) + if not p.get("exclude") and not p.get("x-widget_config", {}).get( + "exclude" + ): + route_params.append(p) + + return route_params + + # Return None if the schema is not found + return None diff --git a/openbb_platform/extensions/platform_api/openbb_platform_api/utils/widgets.py b/openbb_platform/extensions/platform_api/openbb_platform_api/utils/widgets.py new file mode 100644 index 0000000000000000000000000000000000000000..839b8325c8240331de0cf02124236cea03dbfcfc --- /dev/null +++ b/openbb_platform/extensions/platform_api/openbb_platform_api/utils/widgets.py @@ -0,0 +1,627 @@ +"""Utils for building the widgets.json file.""" + +from copy import deepcopy +from typing import Union + + +def deep_merge_configs( + base: dict, + update: dict, + match_keys: Union[str, tuple, list, None] = None, +) -> dict: + """Deep merge two nested dictionaries.""" + + if match_keys is None: + match_keys = ["paramName", "field"] + + if isinstance(match_keys, str): + match_keys = (match_keys,) + + def merge_values(base_val, update_val): + """Merge two values.""" + # Handle explicit empty values + if update_val in ([], {}, None): + return update_val + + if isinstance(update_val, dict) and isinstance(base_val, dict): + return deep_merge_configs(base_val, update_val, match_keys) + + if isinstance(update_val, list) and isinstance(base_val, list): + return merge_lists(base_val, update_val) + + return update_val + + def merge_lists(base_list: list, update_list: list) -> list: + """Merge two lists.""" + new_list: list = [] + update_items: dict = {} + + # Handle nested structures in lists + for item in update_list: + if isinstance(item, dict): + for match_key in match_keys: + if match_key in item: + update_items[item[match_key]] = item + break + elif isinstance(item, (list, dict)): + new_list.append(item) + + for base_item in base_list: + if isinstance(base_item, dict): + matched = False + for match_key in match_keys: + if match_key in base_item: + item_id = base_item[match_key] + if item_id in update_items: + merged = base_item.copy() + update_item = update_items.pop(item_id) + for k, v in update_item.items(): + merged[k] = merge_values(merged.get(k), v) + new_list.append(merged) + matched = True + break + if not matched: + new_list.append(base_item) + elif isinstance(base_item, list): + matching_update = next( + (x for x in update_list if isinstance(x, list)), None + ) + if matching_update: + new_list.append(merge_lists(base_item, matching_update)) + else: + new_list.append(base_item) + else: + new_list.append(base_item) + + new_list.extend(update_items.values()) + + return new_list + + for key, value in update.items(): + if key in base: + base[key] = merge_values(base[key], value) + else: + base[key] = value + + return base + + +def modify_query_schema(query_schema: list[dict], provider_value: str): + """Modify query_schema and the description for the current provider.""" + # pylint: disable=import-outside-toplevel + from .openapi import ( + TO_CAPS_STRINGS, + ) + + modified_query_schema: list = [] + for item in query_schema: + # copy the item + _item = deepcopy(item) + provider_value_options: dict = {} + provider_value_widget_config: dict = {} + # Exclude provider parameter. Those will be added last. + if "parameter_name" in _item and _item["parameter_name"] == "provider": + continue + + # Exclude parameters that are not available for the current provider. + if ( + "available_providers" in _item + and provider_value not in _item["available_providers"] + ): + continue + + if ( + provider_value + and isinstance(_item, dict) + and provider_value in _item.get("multiple_items_allowed", {}) + and _item.get("multiple_items_allowed", {}).get(provider_value, False) + ): + _item["description"] = ( + _item["description"] + " Multiple comma separated items allowed." + ) + _item["type"] = "text" + _item["multiSelect"] = True + + if "options" in _item and _item.get("options"): + provider_value_options = _item.pop("options", None) + if isinstance(provider_value_options, list): + provider_value_options = {provider_value: provider_value_options} + + if provider_value in provider_value_options and bool( + provider_value_options[provider_value] + ): + _item["options"] = provider_value_options[provider_value] + _item["type"] = "text" + elif len(provider_value_options) == 1 and "other" in provider_value_options: + _item["options"] = provider_value_options["other"] + _item["type"] = "text" + + _ = _item.pop("multiple_items_allowed", None) + + if "available_providers" in _item: + _item.pop("available_providers") + + _item["paramName"] = _item.pop("parameter_name", None) + + if not _item.get("label") and _item["paramName"] in [ + "url", + "cik", + "lei", + "cusip", + "isin", + "sedol", + ]: + _item["label"] = _item["paramName"].upper() + + if _label := _item.get("label"): + _item["label"] = " ".join( + [ + (word.upper() if word in TO_CAPS_STRINGS else word) + for word in _label.split() + ] + ) + + if "x-widget_config" in _item: + provider_value_widget_config[ + provider_value if provider_value else "custom" + ] = _item.pop("x-widget_config", {}) + + if ( + provider_value_widget_config + and provider_value in provider_value_widget_config + ): + + if provider_value_widget_config[provider_value].get("exclude"): + continue + + _item = deep_merge_configs( + _item, + provider_value_widget_config[provider_value], + ["paramName", "value"], + ) + + if not _item.get("label") and _item["paramName"] in [ + "url", + "cik", + "lei", + "cusip", + "isin", + "sedol", + ]: + _item["label"] = _item["paramName"].upper() + + if _label := _item.get("label"): + _item["label"] = " ".join( + [ + (word.upper() if word in TO_CAPS_STRINGS else word) + for word in _label.split() + ] + ) + + if "x-widget_config" in _item: + provider_value_widget_config = _item.pop("x-widget_config") + _item.update(provider_value_widget_config) + + if ( + provider_value_widget_config + and provider_value in provider_value_widget_config + ): + + if provider_value_widget_config[provider_value].get("exclude"): + continue + + _item = deep_merge_configs( + _item, + provider_value_widget_config[provider_value], + ["paramName", "value"], + ) + + modified_query_schema.append(_item) + + if provider_value != "custom": + modified_query_schema.append( + {"paramName": "provider", "value": provider_value, "show": False} + ) + + return modified_query_schema + + +def build_json( # noqa: PLR0912 # pylint: disable=too-many-branches, too-many-locals + openapi: dict, widget_exclude_filter: list +): + """Build the widgets.json file.""" + # pylint: disable=import-outside-toplevel + from .openapi import ( + TO_CAPS_STRINGS, + data_schema_to_columns_defs, + get_query_schema_for_widget, + post_query_schema_for_widget, + ) + + if not openapi: + return {} + + starred_list: list = [] + for item in widget_exclude_filter.copy(): + if "*" in item: + starred_list.append(item) + widget_exclude_filter.remove(item) + + widgets_json: dict = {} + routes = [ + p + for p in openapi["paths"] + if openapi["paths"].get(p, {}) and "get" in openapi["paths"][p] + ] + for route in routes: + has_form_endpoint = False + form_route: dict = {} + if "post" in openapi["paths"][route] and "get" in openapi["paths"][route]: + has_form_endpoint = True + form_route = openapi["paths"][route]["post"] + + skip = False + for starred in starred_list: + if route.startswith( + starred.replace("*", "") + .replace("[", "") + .replace("]", "") + .replace('"', "") + .replace("'", "") + ): + skip = True + break + + if skip is True: + continue + + route_api = openapi["paths"][route] + widget_id = ( + ( + route[1:].replace("/", "_") + if route[0] == "/" + else route.replace("/", "_") + ) + .replace("api_", "") + .replace("v1_", "") + ) + + if widget_id in widget_exclude_filter: + continue + + widget_config_dict = route_api.get("get", {}).get("widget_config", {}) + + # If the widget is marked as excluded, skip it. + if widget_config_dict.get("exclude") is True: + continue + + # Prepare the query schema of the widget + query_schema, has_chart = get_query_schema_for_widget(openapi, route) + response_schema = ( + route_api.get("get", {}) + .get("responses", {}) + .get("200", {}) + .get("content", {}) + .get("application/json", {}) + .get("schema", {}) + ) + # Extract providers from the query schema + providers: list = [] + for item in query_schema: + if item["parameter_name"] == "provider": + providers = item["available_providers"] + + if not providers: + providers = ["custom"] + + for provider in providers: + + columns_defs = ( + data_schema_to_columns_defs(openapi, widget_id, provider, route) + if widget_config_dict.get("type") + not in ["multi_file_viewer", "pdf", "metric"] + else [] + ) + _cats = [ + r + for r in route.split("/") + if r and r != "api" and r[0].lower() != "v" and not r[1:].isdigit() + ] + category = _cats[0].title() if _cats else "" + category = category.replace("Fixedincome", "Fixed Income") + subcat = ( + _cats[1].title().replace("_", " ") + if len(_cats) > 2 + else _cats[1].replace("_", " ").title() if len(_cats) > 1 else None + ) + name = ( + widget_id.replace("fixedincome", "fixed income") + .replace("_", " ") + .title() + .replace(category if category else "", "") + .replace(subcat if subcat else "", "") + .strip() + ) + + name = " ".join( + [ + (word.upper() if word in TO_CAPS_STRINGS else word) + for word in name.split() + ] + ) + modified_query_schema = modify_query_schema(query_schema, provider) + + if has_form_endpoint: + param_names: list = [] + var_schema: dict = {} + if ( + _schema := route_api.get("post", {}) + .get("requestBody", {}) + .get("content", {}) + .get("application/json", {}) + .get("schema", {}) + ): + schema_name = _schema.get("$ref", "").split("/")[-1] + var_schema = openapi["components"]["schemas"].get(schema_name, {}) + + if var_schema: + var_props = var_schema.get("properties", {}) + for k, v in var_props.items(): + if "$ref" in v: + param_names.append(k) + + if param_names: + for _param in param_names: + post_params = post_query_schema_for_widget( + openapi, form_route.get("operationId"), route, _param + ) + modified_post_params = modify_query_schema( + post_params, provider + ) + form_params = { + "type": "form", + "paramName": _param, + "label": "Form", + "description": "Form Data", + "endpoint": ( + route.replace("/api", "api") + if "/api" in route + else route[1:] if route[0] == "/" else route + ), + "inputParams": modified_post_params, + } + + if post_config := var_schema.get("x-widget_config", {}): + form_params = deep_merge_configs( + form_params, + post_config, + ) + + modified_query_schema.append(form_params) + else: + post_params = post_query_schema_for_widget( + openapi, form_route.get("operationId"), route + ) + modified_post_params = modify_query_schema(post_params, provider) + form_params = { + "type": "form", + "paramName": "", + "label": var_schema.get("title", "Form"), + "description": var_schema.get("description", ""), + "endpoint": ( + route.replace("/api", "api") + if "/api" in route + else route[1:] if route[0] == "/" else route + ), + "inputParams": modified_post_params, + } + + # Widget Config at the model level goes first. + if post_config := var_schema.get("x-widget_config", {}): + form_params = deep_merge_configs( + form_params, + post_config, + ) + + var_key: dict = {} + + # Then the widget config at the POST endpoint level takes priority. + if post_config := form_route.get("widget_config", {}): + for key, value in post_config.copy().items(): + if key.startswith("$."): + var_key[key] = value + + form_params = deep_merge_configs( + form_params, + { + k: v + for k, v in post_config.items() + if not k.startswith("$.") + }, + ) + + modified_query_schema.append(form_params) + + if var_key: + for key, value in var_key.items(): + if ( + key.replace("$.", "") in widget_config_dict + and "params" not in key + and "inputParams" not in key + ): + widget_config_dict.update( + {key.replace("$.", ""): value} + ) + else: + widget_config_dict[key.replace("$.", "")] = value + + provider_map = { + "tmx": "TMX", + "ecb": "ECB", + "econdb": "EconDB", + "eia": "EIA", + "fmp": "FMP", + "oecd": "OECD", + "finra": "FINRA", + "fred": "FRED", + "imf": "IMF", + "bls": "BLS", + "yfinance": "yFinance", + "sec": "SEC", + "cftc": "CFTC", + "tradingeconomics": "Trading Economics", + "wsj": "WSJ", + } + provider_name = provider_map.get( + provider.lower(), provider.replace("_", " ").title() + ) + + data_key = ( + "results" + if response_schema + and isinstance(response_schema, dict) + and "$ref" in response_schema + and "/OBBject" in response_schema.get("$ref", "") + else "" + ) + + widget_type = ( + "markdown" + if isinstance(response_schema, dict) + and response_schema.get("type") == "string" + else "table" + ) + + widget_config = { + "name": f"{name}" if name else route_api["get"].get("summary"), + "description": route_api["get"].get("description", ""), + "category": category.replace("_", " ").title(), + "type": widget_type, + "searchCategory": category.replace("_", " ").title(), + "widgetId": f"{widget_id}_{provider}_obb", + "params": modified_query_schema, + "endpoint": ( + route.replace("/api", "api") + if "/api" in route + 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(provider: fred)" + }, + { + "name": "transform", + "in": "query", + "required": false, + "schema": { + "anyOf": [ + { + "enum": [ + "chg", + "ch1", + "pch", + "pc1", + "pca", + "cch", + "cca", + "log" + ], + "type": "string" + }, + { + "type": null + } + ], + "title": "fred", + "description": "\n Transformation type\n None = No transformation\n chg = Change\n ch1 = Change from Year Ago\n pch = Percent Change\n pc1 = Percent Change from Year Ago\n pca = Compounded Annual Rate of Change\n cch = Continuously Compounded Rate of Change\n cca = Continuously Compounded Annual Rate of Change\n log = Natural Log\n (provider: fred)" + }, + "description": "\n Transformation type\n None = No transformation\n chg = Change\n ch1 = Change from Year Ago\n pch = Percent Change\n pc1 = Percent Change from Year Ago\n pca = Compounded Annual Rate of Change\n cch = Continuously Compounded Rate of Change\n cca = Continuously Compounded Annual Rate of Change\n log = Natural Log\n (provider: fred)" + } + ], + "responses": { + "200": { + "description": "Successful Response", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/OBBject_SeniorLoanOfficerSurvey" + } + } + } + }, + "404": { + "description": "Not found" + }, + "400": { + "description": "No Results Found", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/OpenBBErrorResponse" + } + } + } + }, + "500": { + "description": "Internal Error", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/OpenBBErrorResponse" + } + } + } + }, + "422": { + "description": "Validation Error", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/HTTPValidationError" + } + } + } + } + }, + "model": "SeniorLoanOfficerSurvey", + "examples": [ + { + "scope": "api", + "parameters": { + "provider": "fred" + }, + "provider": "fred" + }, + { + "scope": "api", + "parameters": { + "category": "credit_card", + "provider": "fred" + }, + "provider": "fred" + } + ] + } + }, + "/api/v1/economy/survey/university_of_michigan": { + "get": { + "tags": [ + "economy" + ], + "summary": "University Of Michigan", + "description": "Get University of Michigan Consumer Sentiment and Inflation Expectations Surveys.", + "operationId": "economy_survey_university_of_michigan", + "parameters": [ + { + "name": "provider", + "in": "query", + "required": false, + "schema": { + "enum": [ + "fred" + ], + "const": "fred", + "type": "string", + "default": "fred", + "title": "Provider" + } + }, + { + "name": "start_date", + "in": "query", + "required": false, + "schema": { + "anyOf": [ + { + "type": "string", + "format": "date" + }, + { + "type": null + } + ], + "description": "Start date of the data, in YYYY-MM-DD format.", + "title": "Start Date" + }, + "description": "Start date of the data, in YYYY-MM-DD format." + }, + { + "name": "end_date", + "in": "query", + "required": false, + "schema": { + "anyOf": [ + { + "type": "string", + "format": "date" + }, + { + "type": null + } + ], + "description": "End date of the data, in YYYY-MM-DD format.", + "title": "End Date" + }, + "description": "End date of the data, in YYYY-MM-DD format." + }, + { + "name": "frequency", + "in": "query", + "required": false, + "schema": { + "anyOf": [ + { + "enum": [ + "annual", + "quarter" + ], + "type": "string" + }, + { + "type": null + } + ], + "title": "fred", + "description": "Frequency aggregation to convert monthly data to lower frequency. None is monthly. (provider: fred)" + }, + "description": "Frequency aggregation to convert monthly data to lower frequency. None is monthly. (provider: fred)" + }, + { + "name": "aggregation_method", + "in": "query", + "required": false, + "schema": { + "anyOf": [ + { + "enum": [ + "avg", + "sum", + "eop" + ], + "type": "string" + }, + { + "type": null + } + ], + "title": "fred", + "description": "A key that indicates the aggregation method used for frequency aggregation.\n \n avg = Average\n \n sum = Sum\n \n eop = End of Period\n (provider: fred)" + }, + "description": "A key that indicates the aggregation method used for frequency aggregation.\n \n avg = Average\n \n sum = Sum\n \n eop = End of Period\n (provider: fred)" + }, + { + "name": "transform", + "in": "query", + "required": false, + "schema": { + "anyOf": [ + { + "enum": [ + "chg", + "ch1", + "pch", + "pc1", + "pca", + "cch", + "cca", + "log" + ], + "type": "string" + }, + { + "type": null + } + ], + "title": "fred", + "description": "Transformation type\n \n None = No transformation\n \n chg = Change\n \n ch1 = Change from Year Ago\n \n pch = Percent Change\n \n pc1 = Percent Change from Year Ago\n \n pca = Compounded Annual Rate of Change\n \n cch = Continuously Compounded Rate of Change\n \n cca = Continuously Compounded Annual Rate of Change\n \n log = Natural Log\n (provider: fred)" + }, + "description": "Transformation type\n \n None = No transformation\n \n chg = Change\n \n ch1 = Change from Year Ago\n \n pch = Percent Change\n \n pc1 = Percent Change from Year Ago\n \n pca = Compounded Annual Rate of Change\n \n cch = Continuously Compounded Rate of Change\n \n cca = Continuously Compounded Annual Rate of Change\n \n log = Natural Log\n (provider: fred)" + } + ], + "responses": { + "200": { + "description": "Successful Response", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/OBBject_UniversityOfMichigan" + } + } + } + }, + "404": { + "description": "Not found" + }, + "400": { + "description": "No Results Found", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/OpenBBErrorResponse" + } + } + } + }, + "500": { + "description": "Internal Error", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/OpenBBErrorResponse" + } + } + } + }, + "422": { + "description": "Validation Error", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/HTTPValidationError" + } + } + } + } + }, + "model": "UniversityOfMichigan", + "examples": [ + { + "scope": "api", + "parameters": { + "provider": "fred" + }, + "provider": "fred" + } + ] + } + }, + "/api/v1/economy/survey/economic_conditions_chicago": { + "get": { + "tags": [ + "economy" + ], + "summary": "Economic Conditions Chicago", + "description": "Get The Survey Of Economic Conditions For The Chicago Region.", + "operationId": "economy_survey_economic_conditions_chicago", + "parameters": [ + { + "name": "provider", + "in": "query", + "required": false, + "schema": { + "enum": [ + "fred" + ], + "const": "fred", + "type": "string", + "default": "fred", + "title": "Provider" + } + }, + { + "name": "start_date", + "in": "query", + "required": false, + "schema": { + "anyOf": [ + { + "type": "string", + "format": "date" + }, + { + "type": null + } + ], + "description": "Start date of the data, in YYYY-MM-DD format.", + "title": "Start Date" + }, + "description": "Start date of the data, in YYYY-MM-DD format." + }, + { + "name": "end_date", + "in": "query", + "required": false, + "schema": { + "anyOf": [ + { + "type": "string", + "format": "date" + }, + { + "type": null + } + ], + "description": "End date of the data, in YYYY-MM-DD format.", + "title": "End Date" + }, + "description": "End date of the data, in YYYY-MM-DD format." + }, + { + "name": "frequency", + "in": "query", + "required": false, + "schema": { + "anyOf": [ + { + "enum": [ + "annual", + "quarter" + ], + "type": "string" + }, + { + "type": null + } + ], + "title": "fred", + "description": "Frequency aggregation to convert monthly data to lower frequency. None is monthly. (provider: fred)" + }, + "description": "Frequency aggregation to convert monthly data to lower frequency. None is monthly. (provider: fred)" + }, + { + "name": "aggregation_method", + "in": "query", + "required": false, + "schema": { + "anyOf": [ + { + "enum": [ + "avg", + "sum", + "eop" + ], + "type": "string" + }, + { + "type": null + } + ], + "title": "fred", + "description": "A key that indicates the aggregation method used for frequency aggregation.\n \n avg = Average\n \n sum = Sum\n \n eop = End of Period\n (provider: fred)" + }, + "description": "A key that indicates the aggregation method used for frequency aggregation.\n \n avg = Average\n \n sum = Sum\n \n eop = End of Period\n (provider: fred)" + }, + { + "name": "transform", + "in": "query", + "required": false, + "schema": { + "anyOf": [ + { + "enum": [ + "chg", + "ch1", + "pch", + "pc1", + "pca", + "cch", + "cca", + "log" + ], + "type": "string" + }, + { + "type": null + } + ], + "title": "fred", + "description": "Transformation type\n \n None = No transformation\n \n chg = Change\n \n ch1 = Change from Year Ago\n \n pch = Percent Change\n \n pc1 = Percent Change from Year Ago\n \n pca = Compounded Annual Rate of Change\n \n cch = Continuously Compounded Rate of Change\n \n cca = Continuously Compounded Annual Rate of Change\n \n log = Natural Log\n (provider: fred)" + }, + "description": "Transformation type\n \n None = No transformation\n \n chg = Change\n \n ch1 = Change from Year Ago\n \n pch = Percent Change\n \n pc1 = Percent Change from Year Ago\n \n pca = Compounded Annual Rate of Change\n \n cch = Continuously Compounded Rate of Change\n \n cca = Continuously Compounded Annual Rate of Change\n \n log = Natural Log\n (provider: fred)" + } + ], + "responses": { + "200": { + "description": "Successful Response", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/OBBject_SurveyOfEconomicConditionsChicago" + } + } + } + }, + "404": { + "description": "Not found" + }, + "400": { + "description": "No Results Found", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/OpenBBErrorResponse" + } + } + } + }, + "500": { + "description": "Internal Error", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/OpenBBErrorResponse" + } + } + } + }, + "422": { + "description": "Validation Error", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/HTTPValidationError" + } + } + } + } + }, + "model": "SurveyOfEconomicConditionsChicago", + "examples": [ + { + "scope": "api", + "parameters": { + "provider": "fred" + }, + "provider": "fred" + } + ] + } + }, + "/api/v1/economy/survey/manufacturing_outlook_texas": { + "get": { + "tags": [ + "economy" + ], + "summary": "Manufacturing Outlook Texas", + "description": "Get The Manufacturing Outlook Survey For The Texas Region.", + "operationId": "economy_survey_manufacturing_outlook_texas", + "parameters": [ + { + "name": "provider", + "in": "query", + "required": false, + "schema": { + "enum": [ + "fred" + ], + "const": "fred", + "type": "string", + "default": "fred", + "title": "Provider" + } + }, + { + "name": "start_date", + "in": "query", + "required": false, + "schema": { + "anyOf": [ + { + "type": "string", + "format": "date" + }, + { + "type": null + } + ], + "description": "Start date of the data, in YYYY-MM-DD format.", + "title": "Start Date" + }, + "description": "Start date of the data, in YYYY-MM-DD format." + }, + { + "name": "end_date", + "in": "query", + "required": false, + "schema": { + "anyOf": [ + { + "type": "string", + "format": "date" + }, + { + "type": null + } + ], + "description": "End date of the data, in YYYY-MM-DD format.", + "title": "End Date" + }, + "description": "End date of the data, in YYYY-MM-DD format." + }, + { + "name": "topic", + "in": "query", + "required": false, + "schema": { + "anyOf": [ + { + "enum": [ + "business_activity", + "business_outlook", + "capex", + "prices_paid", + "production", + "inventory", + "new_orders", + "new_orders_growth", + "unfilled_orders", + "shipments", + "delivery_time", + "employment", + "wages", + "hours_worked" + ], + "type": "string" + }, + { + "type": "string" + } + ], + "title": "fred", + "description": "The topic for the survey response. Multiple comma separated items allowed. (provider: fred)", + "fred": { + "multiple_items_allowed": true, + "choices": [ + "business_activity", + "business_outlook", + "capex", + "prices_paid", + "production", + "inventory", + "new_orders", + "new_orders_growth", + "unfilled_orders", + "shipments", + "delivery_time", + "employment", + "wages", + "hours_worked" + ] + }, + "default": "new_orders_growth" + }, + "description": "The topic for the survey response. Multiple comma separated items allowed. (provider: fred)" + }, + { + "name": "frequency", + "in": "query", + "required": false, + "schema": { + "anyOf": [ + { + "enum": [ + "annual", + "quarter" + ], + "type": "string" + }, + { + "type": null + } + ], + "title": "fred", + "description": "\n Frequency aggregation to convert monthly data to lower frequency. None is monthly.\n (provider: fred)" + }, + "description": "\n Frequency aggregation to convert monthly data to lower frequency. None is monthly.\n (provider: fred)" + }, + { + "name": "aggregation_method", + "in": "query", + "required": false, + "schema": { + "anyOf": [ + { + "enum": [ + "avg", + "sum", + "eop" + ], + "type": "string" + }, + { + "type": null + } + ], + "title": "fred", + "description": "\n A key that indicates the aggregation method used for frequency aggregation.\n avg = Average\n sum = Sum\n eop = End of Period\n (provider: fred)" + }, + "description": "\n A key that indicates the aggregation method used for frequency aggregation.\n avg = Average\n sum = Sum\n eop = End of Period\n (provider: fred)" + }, + { + "name": "transform", + "in": "query", + "required": false, + "schema": { + "anyOf": [ + { + "enum": [ + "chg", + "ch1", + "pch", + "pc1", + "pca", + "cch", + "cca", + "log" + ], + "type": "string" + }, + { + "type": null + } + ], + "title": "fred", + "description": "\n Transformation type\n None = No transformation\n chg = Change\n ch1 = Change from Year Ago\n pch = Percent Change\n pc1 = Percent Change from Year Ago\n pca = Compounded Annual Rate of Change\n cch = Continuously Compounded Rate of Change\n cca = Continuously Compounded Annual Rate of Change\n log = Natural Log\n (provider: fred)" + }, + "description": "\n Transformation type\n None = No transformation\n chg = Change\n ch1 = Change from Year Ago\n pch = Percent Change\n pc1 = Percent Change from Year Ago\n pca = Compounded Annual Rate of Change\n cch = Continuously Compounded Rate of Change\n cca = Continuously Compounded Annual Rate of Change\n log = Natural Log\n (provider: fred)" + } + ], + "responses": { + "200": { + "description": "Successful Response", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/OBBject_ManufacturingOutlookTexas" + } + } + } + }, + "404": { + "description": "Not found" + }, + "400": { + "description": "No Results Found", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/OpenBBErrorResponse" + } + } + } + }, + "500": { + "description": "Internal Error", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/OpenBBErrorResponse" + } + } + } + }, + "422": { + "description": "Validation Error", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/HTTPValidationError" + } + } + } + } + }, + "model": "ManufacturingOutlookTexas", + "examples": [ + { + "scope": "api", + "parameters": { + "provider": "fred" + }, + "provider": "fred" + }, + { + "scope": "api", + "parameters": { + "topic": "business_outlook,new_orders", + "transform": "pc1", + "provider": "fred" + }, + "provider": "fred" + } + ] + } + }, + "/api/v1/economy/survey/nonfarm_payrolls": { + "get": { + "tags": [ + "economy" + ], + "summary": "Nonfarm Payrolls", + "description": "Get Nonfarm Payrolls Survey.", + "operationId": "economy_survey_nonfarm_payrolls", + "parameters": [ + { + "name": "provider", + "in": "query", + "required": false, + "schema": { + "enum": [ + "fred" + ], + "const": "fred", + "type": "string", + "default": "fred", + "title": "Provider" + } + }, + { + "name": "date", + "in": "query", + "required": false, + "schema": { + "anyOf": [ + { + "type": "string", + "format": "date" + }, + { + "type": "string" + }, + { + "type": null + } + ], + "description": "A specific date to get data for. Default is the latest report. Multiple comma separated items allowed for provider(s): fred.", + "fred": { + "multiple_items_allowed": true + }, + "title": "Date" + }, + "description": "A specific date to get data for. Default is the latest report. Multiple comma separated items allowed for provider(s): fred." + }, + { + "name": "category", + "in": "query", + "required": false, + "schema": { + "enum": [ + "employees_nsa", + "employees_sa", + "employees_production_and_nonsupervisory", + "employees_women", + "employees_women_percent", + "avg_hours", + "avg_hours_production_and_nonsupervisory", + "avg_hours_overtime", + "avg_hours_overtime_production_and_nonsupervisory", + "avg_earnings_hourly", + "avg_earnings_hourly_production_and_nonsupervisory", + "avg_earnings_weekly", + "avg_earnings_weekly_production_and_nonsupervisory", + "index_weekly_hours", + "index_weekly_hours_production_and_nonsupervisory", + "index_weekly_payrolls", + "index_weekly_payrolls_production_and_nonsupervisory" + ], + "type": "string", + "title": "fred", + "description": "The category to query. (provider: fred)", + "default": "employees_nsa" + }, + "description": "The category to query. (provider: fred)" + } + ], + "responses": { + "200": { + "description": "Successful Response", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/OBBject_NonFarmPayrolls" + } + } + } + }, + "404": { + "description": "Not found" + }, + "400": { + "description": "No Results Found", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/OpenBBErrorResponse" + } + } + } + }, + "500": { + "description": "Internal Error", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/OpenBBErrorResponse" + } + } + } + }, + "422": { + "description": "Validation Error", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/HTTPValidationError" + } + } + } + } + }, + "model": "NonFarmPayrolls", + "examples": [ + { + "scope": "api", + "parameters": { + "provider": "fred" + }, + "provider": "fred" + }, + { + "scope": "api", + "parameters": { + "category": "avg_hours", + "provider": "fred" + }, + "provider": "fred" + } + ] + } + }, + "/api/v1/economy/cpi": { + "get": { + "tags": [ + "economy" + ], + "summary": "Cpi", + "description": "Get Consumer Price Index (CPI).\n\nReturns either the rescaled index value, or a rate of change (inflation).", + "operationId": "economy_cpi", + "parameters": [ + { + "name": "provider", + "in": "query", + "required": false, + "schema": { + "enum": [ + "fred" + ], + "const": "fred", + "type": "string", + "default": "fred", + "title": "Provider" + } + }, + { + "name": "country", + "in": "query", + "required": false, + "schema": { + "type": "string", + "description": "The country to get data. Multiple comma separated items allowed for provider(s): fred.", + "fred": { + "multiple_items_allowed": true, + "choices": [ + "australia", + "austria", + "belgium", + "brazil", + "bulgaria", + "canada", + "chile", + "china", + "croatia", + "cyprus", + "czech_republic", + "denmark", + "estonia", + "euro_area", + "finland", + "france", + "germany", + "greece", + "hungary", + "iceland", + "india", + "indonesia", + "ireland", + "israel", + "italy", + "japan", + "korea", + "latvia", + "lithuania", + "luxembourg", + "malta", + "mexico", + "netherlands", + "new_zealand", + "norway", + "poland", + "portugal", + "romania", + "russian_federation", + "slovak_republic", + "slovakia", + "slovenia", + "south_africa", + "spain", + "sweden", + "switzerland", + "turkey", + "united_kingdom", + "united_states" + ] + }, + "default": "united_states", + "title": "Country" + }, + "description": "The country to get data. Multiple comma separated items allowed for provider(s): fred." + }, + { + "name": "transform", + "in": "query", + "required": false, + "schema": { + "enum": [ + "index", + "yoy", + "period" + ], + "type": "string", + "description": "Transformation of the CPI data. Period represents the change since previous. Defaults to change from one year ago (yoy).", + "default": "yoy", + "title": "Transform" + }, + "description": "Transformation of the CPI data. Period represents the change since previous. Defaults to change from one year ago (yoy)." + }, + { + "name": "frequency", + "in": "query", + "required": false, + "schema": { + "enum": [ + "annual", + "quarter", + "monthly" + ], + "type": "string", + "description": "The frequency of the data.", + "default": "monthly", + "title": "Frequency" + }, + "description": "The frequency of the data." + }, + { + "name": "harmonized", + "in": "query", + "required": false, + "schema": { + "type": "boolean", + "description": "If true, returns harmonized data.", + "default": false, + "title": "Harmonized" + }, + "description": "If true, returns harmonized data." + }, + { + "name": "start_date", + "in": "query", + "required": false, + "schema": { + "anyOf": [ + { + "type": "string", + "format": "date" + }, + { + "type": null + } + ], + "description": "Start date of the data, in YYYY-MM-DD format.", + "title": "Start Date" + }, + "description": "Start date of the data, in YYYY-MM-DD format." + }, + { + "name": "end_date", + "in": "query", + "required": false, + "schema": { + "anyOf": [ + { + "type": "string", + "format": "date" + }, + { + "type": null + } + ], + "description": "End date of the data, in YYYY-MM-DD format.", + "title": "End Date" + }, + "description": "End date of the data, in YYYY-MM-DD format." + } + ], + "responses": { + "200": { + "description": "Successful Response", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/OBBject_ConsumerPriceIndex" + } + } + } + }, + "404": { + "description": "Not found" + }, + "400": { + "description": "No Results Found", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/OpenBBErrorResponse" + } + } + } + }, + "500": { + "description": "Internal Error", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/OpenBBErrorResponse" + } + } + } + }, + "422": { + "description": "Validation Error", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/HTTPValidationError" + } + } + } + } + }, + "model": "ConsumerPriceIndex", + "examples": [ + { + "scope": "api", + "parameters": { + "country": "japan,china,turkey", + "provider": "fred" + }, + "provider": "fred" + } + ] + } + }, + "/api/v1/economy/balance_of_payments": { + "get": { + "tags": [ + "economy" + ], + "summary": "Balance Of Payments", + "description": "Balance of Payments Reports.", + "operationId": "economy_balance_of_payments", + "parameters": [ + { + "name": "provider", + "in": "query", + "required": false, + "schema": { + "enum": [ + "fred" + ], + "const": "fred", + "type": "string", + "default": "fred", + "title": "Provider" + } + }, + { + "name": "country", + "in": "query", + "required": false, + "schema": { + "enum": [ + "argentina", + "australia", + "austria", + "belgium", + "brazil", + "canada", + "chile", + "china", + "colombia", + "costa_rica", + "czechia", + "denmark", + "estonia", + "finland", + "france", + "germany", + "greece", + "hungary", + "iceland", + "india", + "indonesia", + "ireland", + "israel", + "italy", + "japan", + "korea", + "latvia", + "lithuania", + "luxembourg", + "mexico", + "netherlands", + "new_zealand", + "norway", + "poland", + "portugal", + "russia", + "saudi_arabia", + "slovak_republic", + "slovenia", + "south_africa", + "spain", + "sweden", + "switzerland", + "turkey", + "united_kingdom", + "united_states", + "g7", + "g20" + ], + "type": "string", + "title": "fred", + "description": "The country to get data. Enter as a 3-letter ISO country code, default is USA. (provider: fred)", + "default": "united_states" + }, + "description": "The country to get data. Enter as a 3-letter ISO country code, default is USA. (provider: fred)" + }, + { + "name": "start_date", + "in": "query", + "required": false, + "schema": { + "anyOf": [ + { + "type": "string", + "format": "date" + }, + { + "type": null + } + ], + "title": "fred", + "description": "Start date of the data, in YYYY-MM-DD format. (provider: fred)" + }, + "description": "Start date of the data, in YYYY-MM-DD format. (provider: fred)" + }, + { + "name": "end_date", + "in": "query", + "required": false, + "schema": { + "anyOf": [ + { + "type": "string", + "format": "date" + }, + { + "type": null + } + ], + "title": "fred", + "description": "End date of the data, in YYYY-MM-DD format. (provider: fred)" + }, + "description": "End date of the data, in YYYY-MM-DD format. (provider: fred)" + } + ], + "responses": { + "200": { + "description": "Successful Response", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/OBBject_BalanceOfPayments" + } + } + } + }, + "404": { + "description": "Not found" + }, + "400": { + "description": "No Results Found", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/OpenBBErrorResponse" + } + } + } + }, + "500": { + "description": "Internal Error", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/OpenBBErrorResponse" + } + } + } + }, + "422": { + "description": "Validation Error", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/HTTPValidationError" + } + } + } + } + }, + "model": "BalanceOfPayments", + "examples": [ + { + "scope": "api", + "parameters": { + "provider": "fred" + }, + "provider": "fred" + }, + { + "scope": "api", + "parameters": { + "provider": "fred", + "country": "brazil" + }, + "provider": "fred" + } + ] + } + }, + "/api/v1/economy/fred_search": { + "get": { + "tags": [ + "economy" + ], + "summary": "Fred Search", + "description": "Search for FRED series or economic releases by ID or string.\n\nThis does not return the observation values, only the metadata.\nUse this function to find series IDs for `fred_series()`.", + "operationId": "economy_fred_search", + "parameters": [ + { + "name": "provider", + "in": "query", + "required": false, + "schema": { + "enum": [ + "fred" + ], + "const": "fred", + "type": "string", + "default": "fred", + "title": "Provider" + } + }, + { + "name": "query", + "in": "query", + "required": false, + "schema": { + "anyOf": [ + { + "type": "string" + }, + { + "type": null + } + ], + "description": "The search word(s).", + "title": "Query" + }, + "description": "The search word(s)." + }, + { + "name": "is_release", + "in": "query", + "required": false, + "schema": { + "anyOf": [ + { + "type": "boolean" + }, + { + "type": null + } + ], + "title": "fred", + "description": "Is release? If True, other search filter variables are ignored. If no query text or release_id is supplied, this defaults to True. (provider: fred)", + "default": false + }, + "description": "Is release? If True, other search filter variables are ignored. If no query text or release_id is supplied, this defaults to True. (provider: fred)" + }, + { + "name": "release_id", + "in": "query", + "required": false, + "schema": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "integer" + }, + { + "type": null + } + ], + "title": "fred", + "description": "A specific release ID to target. (provider: fred)" + }, + "description": "A specific release ID to target. (provider: fred)" + }, + { + "name": "limit", + "in": "query", + "required": false, + "schema": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": null + } + ], + "title": "fred", + "description": "The number of data entries to return. (1-1000) (provider: fred)" + }, + "description": "The number of data entries to return. (1-1000) (provider: fred)" + }, + { + "name": "offset", + "in": "query", + "required": false, + "schema": { + "anyOf": [ + { + "type": "integer", + "minimum": 0 + }, + { + "type": null + } + ], + "title": "fred", + "description": "Offset the results in conjunction with limit. (provider: fred)", + "default": 0 + }, + "description": "Offset the results in conjunction with limit. (provider: fred)" + }, + { + "name": "filter_variable", + "in": "query", + "required": false, + "schema": { + "anyOf": [ + { + "enum": [ + "frequency", + "units", + "seasonal_adjustment" + ], + "type": "string" + }, + { + "type": null + } + ], + "title": "fred", + "description": "Filter by an attribute. (provider: fred)" + }, + "description": "Filter by an attribute. (provider: fred)" + }, + { + "name": "filter_value", + "in": "query", + "required": false, + "schema": { + "anyOf": [ + { + "type": "string" + }, + { + "type": null + } + ], + "title": "fred", + "description": "String value to filter the variable by. Used in conjunction with filter_variable. (provider: fred)" + }, + "description": "String value to filter the variable by. Used in conjunction with filter_variable. (provider: fred)" + }, + { + "name": "tag_names", + "in": "query", + "required": false, + "schema": { + "anyOf": [ + { + "type": "string" + }, + { + "type": null + } + ], + "title": "fred", + "description": "A semicolon delimited list of tag names that series match all of. Example: 'japan;imports' Multiple comma separated items allowed. (provider: fred)", + "fred": { + "multiple_items_allowed": true + } + }, + "description": "A semicolon delimited list of tag names that series match all of. Example: 'japan;imports' Multiple comma separated items allowed. (provider: fred)" + }, + { + "name": "exclude_tag_names", + "in": "query", + "required": false, + "schema": { + "anyOf": [ + { + "type": "string" + }, + { + "type": null + } + ], + "title": "fred", + "description": "A semicolon delimited list of tag names that series match none of. Example: 'imports;services'. Requires that variable tag_names also be set to limit the number of matching series. Multiple comma separated items allowed. (provider: fred)", + "fred": { + "multiple_items_allowed": true + } + }, + "description": "A semicolon delimited list of tag names that series match none of. Example: 'imports;services'. Requires that variable tag_names also be set to limit the number of matching series. Multiple comma separated items allowed. (provider: fred)" + }, + { + "name": "series_id", + "in": "query", + "required": false, + "schema": { + "anyOf": [ + { + "type": "string" + }, + { + "type": null + } + ], + "title": "fred", + "description": "A FRED Series ID to return series group information for. This returns the required information to query for regional data. Not all series that are in FRED have geographical data. Entering a value for series_id will override all other parameters. Multiple series_ids can be separated by commas. (provider: fred)" + }, + "description": "A FRED Series ID to return series group information for. This returns the required information to query for regional data. Not all series that are in FRED have geographical data. Entering a value for series_id will override all other parameters. Multiple series_ids can be separated by commas. (provider: fred)" + } + ], + "responses": { + "200": { + "description": "Successful Response", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/OBBject_FredSearch" + } + } + } + }, + "404": { + "description": "Not found" + }, + "400": { + "description": "No Results Found", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/OpenBBErrorResponse" + } + } + } + }, + "500": { + "description": "Internal Error", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/OpenBBErrorResponse" + } + } + } + }, + "422": { + "description": "Validation Error", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/HTTPValidationError" + } + } + } + } + }, + "model": "FredSearch", + "examples": [ + { + "scope": "api", + "parameters": { + "provider": "fred" + }, + "provider": "fred" + } + ] + } + }, + "/api/v1/economy/fred_series": { + "get": { + "tags": [ + "economy" + ], + "summary": "Fred Series", + "description": "Get data by series ID from FRED.", + "operationId": "economy_fred_series", + "parameters": [ + { + "name": "chart", + "in": "query", + "required": false, + "schema": { + "type": "boolean", + "default": false, + "title": "Chart" + } + }, + { + "name": "provider", + "in": "query", + "required": false, + "schema": { + "enum": [ + "fred" + ], + "const": "fred", + "type": "string", + "default": "fred", + "title": "Provider" + } + }, + { + "name": "symbol", + "in": "query", + "required": true, + "schema": { + "type": "string", + "description": "Symbol to get data for. Multiple comma separated items allowed for provider(s): fred.", + "fred": { + "multiple_items_allowed": true + }, + "title": "Symbol" + }, + "description": "Symbol to get data for. Multiple comma separated items allowed for provider(s): fred." + }, + { + "name": "start_date", + "in": "query", + "required": false, + "schema": { + "anyOf": [ + { + "type": "string", + "format": "date" + }, + { + "type": null + } + ], + "description": "Start date of the data, in YYYY-MM-DD format.", + "title": "Start Date" + }, + "description": "Start date of the data, in YYYY-MM-DD format." + }, + { + "name": "end_date", + "in": "query", + "required": false, + "schema": { + "anyOf": [ + { + "type": "string", + "format": "date" + }, + { + "type": null + } + ], + "description": "End date of the data, in YYYY-MM-DD format.", + "title": "End Date" + }, + "description": "End date of the data, in YYYY-MM-DD format." + }, + { + "name": "limit", + "in": "query", + "required": false, + "schema": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": null + } + ], + "description": "The number of data entries to return.", + "default": 100000, + "title": "Limit" + }, + "description": "The number of data entries to return." + }, + { + "name": "frequency", + "in": "query", + "required": false, + "schema": { + "anyOf": [ + { + "enum": [ + "a", + "q", + "m", + "w", + "d", + "wef", + "weth", + "wew", + "wetu", + "wem", + "wesu", + "wesa", + "bwew", + "bwem" + ], + "type": "string" + }, + { + "type": null + } + ], + "title": "fred", + "description": "Frequency aggregation to convert high frequency data to lower frequency.\n \n None = No change\n \n a = Annual\n \n q = Quarterly\n \n m = Monthly\n \n w = Weekly\n \n d = Daily\n \n wef = Weekly, Ending Friday\n \n weth = Weekly, Ending Thursday\n \n wew = Weekly, Ending Wednesday\n \n wetu = Weekly, Ending Tuesday\n \n wem = Weekly, Ending Monday\n \n wesu = Weekly, Ending Sunday\n \n wesa = Weekly, Ending Saturday\n \n bwew = Biweekly, Ending Wednesday\n \n bwem = Biweekly, Ending Monday\n (provider: fred)" + }, + "description": "Frequency aggregation to convert high frequency data to lower frequency.\n \n None = No change\n \n a = Annual\n \n q = Quarterly\n \n m = Monthly\n \n w = Weekly\n \n d = Daily\n \n wef = Weekly, Ending Friday\n \n weth = Weekly, Ending Thursday\n \n wew = Weekly, Ending Wednesday\n \n wetu = Weekly, Ending Tuesday\n \n wem = Weekly, Ending Monday\n \n wesu = Weekly, Ending Sunday\n \n wesa = Weekly, Ending Saturday\n \n bwew = Biweekly, Ending Wednesday\n \n bwem = Biweekly, Ending Monday\n (provider: fred)" + }, + { + "name": "aggregation_method", + "in": "query", + "required": false, + "schema": { + "anyOf": [ + { + "enum": [ + "avg", + "sum", + "eop" + ], + "type": "string" + }, + { + "type": null + } + ], + "title": "fred", + "description": "A key that indicates the aggregation method used for frequency aggregation.\n This parameter has no affect if the frequency parameter is not set.\n \n avg = Average\n \n sum = Sum\n \n eop = End of Period\n (provider: fred)", + "default": "eop" + }, + "description": "A key that indicates the aggregation method used for frequency aggregation.\n This parameter has no affect if the frequency parameter is not set.\n \n avg = Average\n \n sum = Sum\n \n eop = End of Period\n (provider: fred)" + }, + { + "name": "transform", + "in": "query", + "required": false, + "schema": { + "anyOf": [ + { + "enum": [ + "chg", + "ch1", + "pch", + "pc1", + "pca", + "cch", + "cca", + "log" + ], + "type": "string" + }, + { + "type": null + } + ], + "title": "fred", + "description": "Transformation type\n \n None = No transformation\n \n chg = Change\n \n ch1 = Change from Year Ago\n \n pch = Percent Change\n \n pc1 = Percent Change from Year Ago\n \n pca = Compounded Annual Rate of Change\n \n cch = Continuously Compounded Rate of Change\n \n cca = Continuously Compounded Annual Rate of Change\n \n log = Natural Log\n (provider: fred)" + }, + "description": "Transformation type\n \n None = No transformation\n \n chg = Change\n \n ch1 = Change from Year Ago\n \n pch = Percent Change\n \n pc1 = Percent Change from Year Ago\n \n pca = Compounded Annual Rate of Change\n \n cch = Continuously Compounded Rate of Change\n \n cca = Continuously Compounded Annual Rate of Change\n \n log = Natural Log\n (provider: fred)" + } + ], + "responses": { + "200": { + "description": "Successful Response", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/OBBject_FredSeries" + } + } + } + }, + "404": { + "description": "Not found" + }, + "400": { + "description": "No Results Found", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/OpenBBErrorResponse" + } + } + } + }, + "500": { + "description": "Internal Error", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/OpenBBErrorResponse" + } + } + } + }, + "422": { + "description": "Validation Error", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/HTTPValidationError" + } + } + } + } + }, + "model": "FredSeries", + "examples": [ + { + "scope": "api", + "parameters": { + "symbol": "NFCI", + "provider": "fred" + }, + "provider": "fred" + }, + { + "scope": "api", + "description": "Multiple series can be passed in as a list.", + "parameters": { + "symbol": "NFCI,STLFSI4", + "provider": "fred" + }, + "provider": "fred" + }, + { + "scope": "api", + "description": "Use the `transform` parameter to transform the data as change, log, or percent change.", + "parameters": { + "symbol": "CBBTCUSD", + "transform": "pc1", + "provider": "fred" + }, + "provider": "fred" + } + ] + } + }, + "/api/v1/economy/fred_release_table": { + "get": { + "tags": [ + "economy" + ], + "summary": "Fred Release Table", + "description": "Get economic release data by ID and/or element from FRED.", + "operationId": "economy_fred_release_table", + "parameters": [ + { + "name": "provider", + "in": "query", + "required": false, + "schema": { + "enum": [ + "fred" + ], + "const": "fred", + "type": "string", + "default": "fred", + "title": "Provider" + } + }, + { + "name": "release_id", + "in": "query", + "required": true, + "schema": { + "type": "string", + "description": "The ID of the release. Use `fred_search` to find releases.", + "title": "Release Id" + }, + "description": "The ID of the release. Use `fred_search` to find releases." + }, + { + "name": "element_id", + "in": "query", + "required": false, + "schema": { + "anyOf": [ + { + "type": "string" + }, + { + "type": null + } + ], + "description": "The element ID of a specific table in the release.", + "title": "Element ID" + }, + "description": "The element ID of a specific table in the release." + }, + { + "name": "date", + "in": "query", + "required": false, + "schema": { + "anyOf": [ + { + "type": "string", + "format": "date" + }, + { + "type": "string" + }, + { + "type": null + } + ], + "description": "A specific date to get data for. Multiple comma separated items allowed for provider(s): fred.", + "fred": { + "multiple_items_allowed": true + }, + "title": "Date" + }, + "description": "A specific date to get data for. Multiple comma separated items allowed for provider(s): fred." + } + ], + "responses": { + "200": { + "description": "Successful Response", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/OBBject_FredReleaseTable" + } + } + } + }, + "404": { + "description": "Not found" + }, + "400": { + "description": "No Results Found", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/OpenBBErrorResponse" + } + } + } + }, + "500": { + "description": "Internal Error", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/OpenBBErrorResponse" + } + } + } + }, + "422": { + "description": "Validation Error", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/HTTPValidationError" + } + } + } + } + }, + "model": "FredReleaseTable", + "examples": [ + { + "scope": "api", + "description": "Get the top-level elements of a release by not supplying an element ID.", + "parameters": { + "release_id": "50", + "provider": "fred" + }, + "provider": "fred" + }, + { + "scope": "api", + "description": "Drill down on a specific section of the release.", + "parameters": { + "release_id": "50", + "element_id": "4880", + "provider": "fred" + }, + "provider": "fred" + }, + { + "scope": "api", + "description": "Drill down on a specific table of the release.", + "parameters": { + "release_id": "50", + "element_id": "4881", + "provider": "fred" + }, + "provider": "fred" + } + ] + } + }, + "/api/v1/economy/fred_regional": { + "get": { + "tags": [ + "economy" + ], + "summary": "Fred Regional", + "description": "Query the Geo Fred API for regional economic data by series group.\n\nThe series group ID is found by using `fred_search` and the `series_id` parameter.", + "operationId": "economy_fred_regional", + "parameters": [ + { + "name": "provider", + "in": "query", + "required": false, + "schema": { + "enum": [ + "fred" + ], + "const": "fred", + "type": "string", + "default": "fred", + "title": "Provider" + } + }, + { + "name": "symbol", + "in": "query", + "required": true, + "schema": { + "type": "string", + "description": "Symbol to get data for.", + "title": "Symbol" + }, + "description": "Symbol to get data for." + }, + { + "name": "start_date", + "in": "query", + "required": false, + "schema": { + "anyOf": [ + { + "type": "string", + "format": "date" + }, + { + "type": null + } + ], + "description": "Start date of the data, in YYYY-MM-DD format.", + "title": "Start Date" + }, + "description": "Start date of the data, in YYYY-MM-DD format." + }, + { + "name": "end_date", + "in": "query", + "required": false, + "schema": { + "anyOf": [ + { + "type": "string", + "format": "date" + }, + { + "type": null + } + ], + "description": "End date of the data, in YYYY-MM-DD format.", + "title": "End Date" + }, + "description": "End date of the data, in YYYY-MM-DD format." + }, + { + "name": "limit", + "in": "query", + "required": false, + "schema": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": null + } + ], + "description": "The number of data entries to return.", + "default": 100000, + "title": "Limit" + }, + "description": "The number of data entries to return." + }, + { + "name": "is_series_group", + "in": "query", + "required": false, + "schema": { + "type": "boolean", + "title": "fred", + "description": "When True, the symbol provided is for a series_group, else it is for a series ID. (provider: fred)", + "default": false + }, + "description": "When True, the symbol provided is for a series_group, else it is for a series ID. (provider: fred)" + }, + { + "name": "region_type", + "in": "query", + "required": false, + "schema": { + "anyOf": [ + { + "enum": [ + "bea", + "msa", + "frb", + "necta", + "state", + "country", + "county", + "censusregion" + ], + "type": "string" + }, + { + "type": null + } + ], + "title": "fred", + "description": "The type of regional data. Parameter is only valid when `is_series_group` is True. (provider: fred)" + }, + "description": "The type of regional data. Parameter is only valid when `is_series_group` is True. (provider: fred)" + }, + { + "name": "season", + "in": "query", + "required": false, + "schema": { + "enum": [ + "sa", + "nsa", + "ssa" + ], + "type": "string", + "title": "fred", + "description": "The seasonal adjustments to the data. Parameter is only valid when `is_series_group` is True. (provider: fred)", + "default": "nsa" + }, + "description": "The seasonal adjustments to the data. Parameter is only valid when `is_series_group` is True. (provider: fred)" + }, + { + "name": "units", + "in": "query", + "required": false, + "schema": { + "anyOf": [ + { + "type": "string" + }, + { + "type": null + } + ], + "title": "fred", + "description": "The units of the data. This should match the units returned from searching by series ID. An incorrect field will not necessarily return an error. Parameter is only valid when `is_series_group` is True. (provider: fred)" + }, + "description": "The units of the data. This should match the units returned from searching by series ID. An incorrect field will not necessarily return an error. Parameter is only valid when `is_series_group` is True. (provider: fred)" + }, + { + "name": "frequency", + "in": "query", + "required": false, + "schema": { + "anyOf": [ + { + "enum": [ + "a", + "q", + "m", + "w", + "d", + "wef", + "weth", + "wew", + "wetu", + "wem", + "wesu", + "wesa", + "bwew", + "bwem" + ], + "type": "string" + }, + { + "type": null + } + ], + "title": "fred", + "description": "Frequency aggregation to convert high frequency data to lower frequency.\n \n None = No change\n \n a = Annual\n \n q = Quarterly\n \n m = Monthly\n \n w = Weekly\n \n d = Daily\n \n wef = Weekly, Ending Friday\n \n weth = Weekly, Ending Thursday\n \n wew = Weekly, Ending Wednesday\n \n wetu = Weekly, Ending Tuesday\n \n wem = Weekly, Ending Monday\n \n wesu = Weekly, Ending Sunday\n \n wesa = Weekly, Ending Saturday\n \n bwew = Biweekly, Ending Wednesday\n \n bwem = Biweekly, Ending Monday\n (provider: fred)" + }, + "description": "Frequency aggregation to convert high frequency data to lower frequency.\n \n None = No change\n \n a = Annual\n \n q = Quarterly\n \n m = Monthly\n \n w = Weekly\n \n d = Daily\n \n wef = Weekly, Ending Friday\n \n weth = Weekly, Ending Thursday\n \n wew = Weekly, Ending Wednesday\n \n wetu = Weekly, Ending Tuesday\n \n wem = Weekly, Ending Monday\n \n wesu = Weekly, Ending Sunday\n \n wesa = Weekly, Ending Saturday\n \n bwew = Biweekly, Ending Wednesday\n \n bwem = Biweekly, Ending Monday\n (provider: fred)" + }, + { + "name": "aggregation_method", + "in": "query", + "required": false, + "schema": { + "anyOf": [ + { + "enum": [ + "avg", + "sum", + "eop" + ], + "type": "string" + }, + { + "type": null + } + ], + "title": "fred", + "description": "A key that indicates the aggregation method used for frequency aggregation.\n This parameter has no affect if the frequency parameter is not set.\n \n avg = Average\n \n sum = Sum\n \n eop = End of Period\n (provider: fred)", + "default": "eop" + }, + "description": "A key that indicates the aggregation method used for frequency aggregation.\n This parameter has no affect if the frequency parameter is not set.\n \n avg = Average\n \n sum = Sum\n \n eop = End of Period\n (provider: fred)" + }, + { + "name": "transform", + "in": "query", + "required": false, + "schema": { + "anyOf": [ + { + "enum": [ + "chg", + "ch1", + "pch", + "pc1", + "pca", + "cch", + "cca", + "log" + ], + "type": "string" + }, + { + "type": null + } + ], + "title": "fred", + "description": "Transformation type\n \n None = No transformation\n \n chg = Change\n \n ch1 = Change from Year Ago\n \n pch = Percent Change\n \n pc1 = Percent Change from Year Ago\n \n pca = Compounded Annual Rate of Change\n \n cch = Continuously Compounded Rate of Change\n \n cca = Continuously Compounded Annual Rate of Change\n \n log = Natural Log\n (provider: fred)" + }, + "description": "Transformation type\n \n None = No transformation\n \n chg = Change\n \n ch1 = Change from Year Ago\n \n pch = Percent Change\n \n pc1 = Percent Change from Year Ago\n \n pca = Compounded Annual Rate of Change\n \n cch = Continuously Compounded Rate of Change\n \n cca = Continuously Compounded Annual Rate of Change\n \n log = Natural Log\n (provider: fred)" + } + ], + "responses": { + "200": { + "description": "Successful Response", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/OBBject_FredRegional" + } + } + } + }, + "404": { + "description": "Not found" + }, + "400": { + "description": "No Results Found", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/OpenBBErrorResponse" + } + } + } + }, + "500": { + "description": "Internal Error", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/OpenBBErrorResponse" + } + } + } + }, + "422": { + "description": "Validation Error", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/HTTPValidationError" + } + } + } + } + }, + "model": "FredRegional", + "examples": [ + { + "scope": "api", + "parameters": { + "symbol": "NYICLAIMS", + "provider": "fred" + }, + "provider": "fred" + }, + { + "scope": "api", + "description": "With a date, time series data is returned.", + "parameters": { + "symbol": "NYICLAIMS", + "start_date": "2021-01-01", + "end_date": "2021-12-31", + "limit": 10, + "provider": "fred" + }, + "provider": "fred" + } + ] + } + }, + "/api/v1/economy/retail_prices": { + "get": { + "tags": [ + "economy" + ], + "summary": "Retail Prices", + "description": "Get retail prices for common items.", + "operationId": "economy_retail_prices", + "parameters": [ + { + "name": "provider", + "in": "query", + "required": false, + "schema": { + "enum": [ + "fred" + ], + "const": "fred", + "type": "string", + "default": "fred", + "title": "Provider" + } + }, + { + "name": "item", + "in": "query", + "required": false, + "schema": { + "anyOf": [ + { + "type": "string" + }, + { + "type": null + } + ], + "description": "The item or basket of items to query.", + "title": "Item" + }, + "description": "The item or basket of items to query." + }, + { + "name": "country", + "in": "query", + "required": false, + "schema": { + "type": "string", + "description": "The country to get data.", + "default": "united_states", + "title": "Country" + }, + "description": "The country to get data." + }, + { + "name": "start_date", + "in": "query", + "required": false, + "schema": { + "anyOf": [ + { + "type": "string", + "format": "date" + }, + { + "type": null + } + ], + "description": "Start date of the data, in YYYY-MM-DD format.", + "title": "Start Date" + }, + "description": "Start date of the data, in YYYY-MM-DD format." + }, + { + "name": "end_date", + "in": "query", + "required": false, + "schema": { + "anyOf": [ + { + "type": "string", + "format": "date" + }, + { + "type": null + } + ], + "description": "End date of the data, in YYYY-MM-DD format.", + "title": "End Date" + }, + "description": "End date of the data, in YYYY-MM-DD format." + }, + { + "name": "region", + "in": "query", + "required": false, + "schema": { + "enum": [ + "all_city", + "northeast", + "midwest", + "south", + "west" + ], + "type": "string", + "title": "fred", + "description": "The region to get average price levels for. (provider: fred)", + "default": "all_city" + }, + "description": "The region to get average price levels for. (provider: fred)" + }, + { + "name": "frequency", + "in": "query", + "required": false, + "schema": { + "enum": [ + "annual", + "quarter", + "monthly" + ], + "type": "string", + "title": "fred", + "description": "The frequency of the data. (provider: fred)", + "default": "monthly" + }, + "description": "The frequency of the data. (provider: fred)" + }, + { + "name": "transform", + "in": "query", + "required": false, + "schema": { + "anyOf": [ + { + "enum": [ + "chg", + "ch1", + "pch", + "pc1", + "pca", + "cch", + "cca", + "log" + ], + "type": "string" + }, + { + "type": null + } + ], + "title": "fred", + "description": "\n Transformation type\n None = No transformation\n chg = Change\n ch1 = Change from Year Ago\n pch = Percent Change\n pc1 = Percent Change from Year Ago\n pca = Compounded Annual Rate of Change\n cch = Continuously Compounded Rate of Change\n cca = Continuously Compounded Annual Rate of Change\n log = Natural Log\n (provider: fred)" + }, + "description": "\n Transformation type\n None = No transformation\n chg = Change\n ch1 = Change from Year Ago\n pch = Percent Change\n pc1 = Percent Change from Year Ago\n pca = Compounded Annual Rate of Change\n cch = Continuously Compounded Rate of Change\n cca = Continuously Compounded Annual Rate of Change\n log = Natural Log\n (provider: fred)" + } + ], + "responses": { + "200": { + "description": "Successful Response", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/OBBject_RetailPrices" + } + } + } + }, + "404": { + "description": "Not found" + }, + "400": { + "description": "No Results Found", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/OpenBBErrorResponse" + } + } + } + }, + "500": { + "description": "Internal Error", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/OpenBBErrorResponse" + } + } + } + }, + "422": { + "description": "Validation Error", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/HTTPValidationError" + } + } + } + } + }, + "model": "RetailPrices", + "examples": [ + { + "scope": "api", + "parameters": { + "provider": "fred" + }, + "provider": "fred" + }, + { + "scope": "api", + "description": "The price of eggs in the northeast census region.", + "parameters": { + "item": "eggs", + "region": "northeast", + "provider": "fred" + }, + "provider": "fred" + }, + { + "scope": "api", + "description": "The percentage change in price, from one-year ago, of various meats, US City Average.", + "parameters": { + "item": "meats", + "transform": "pc1", + "provider": "fred" + }, + "provider": "fred" + } + ] + } + }, + "/api/v1/economy/pce": { + "get": { + "tags": [ + "economy" + ], + "summary": "Pce", + "description": "Get Personal Consumption Expenditures (PCE) reports.", + "operationId": "economy_pce", + "parameters": [ + { + "name": "provider", + "in": "query", + "required": false, + "schema": { + "enum": [ + "fred" + ], + "const": "fred", + "type": "string", + "default": "fred", + "title": "Provider" + } + }, + { + "name": "date", + "in": "query", + "required": false, + "schema": { + "anyOf": [ + { + "type": "string", + "format": "date" + }, + { + "type": "string" + }, + { + "type": null + } + ], + "description": "A specific date to get data for. Default is the latest report. Multiple comma separated items allowed for provider(s): fred.", + "fred": { + "multiple_items_allowed": true + }, + "title": "Date" + }, + "description": "A specific date to get data for. Default is the latest report. Multiple comma separated items allowed for provider(s): fred." + }, + { + "name": "category", + "in": "query", + "required": false, + "schema": { + "enum": [ + "personal_income", + "wages_by_industry", + "real_pce_percent_change", + "real_pce_quantity_index", + "pce_price_index", + "pce_dollars", + "real_pce_chained_dollars", + "pce_price_percent_change" + ], + "type": "string", + "title": "fred", + "description": "The category to query. (provider: fred)", + "default": "personal_income" + }, + "description": "The category to query. (provider: fred)" + } + ], + "responses": { + "200": { + "description": "Successful Response", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/OBBject_PersonalConsumptionExpenditures" + } + } + } + }, + "404": { + "description": "Not found" + }, + "400": { + "description": "No Results Found", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/OpenBBErrorResponse" + } + } + } + }, + "500": { + "description": "Internal Error", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/OpenBBErrorResponse" + } + } + } + }, + "422": { + "description": "Validation Error", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/HTTPValidationError" + } + } + } + } + }, + "model": "PersonalConsumptionExpenditures", + "examples": [ + { + "scope": "api", + "parameters": { + "provider": "fred" + }, + "provider": "fred" + }, + { + "scope": "api", + "description": "Get reports for multiple dates, entered as a comma-separated string.", + "parameters": { + "provider": "fred", + "date": "2024-05-01,2024-04-01,2023-05-01", + "category": "pce_price_index" + }, + "provider": "fred" + } + ] + } + }, + "/api/v1/regulators/sec/cik_map": { + "get": { + "tags": [ + "regulators" + ], + "summary": "Cik Map", + "description": "Map a ticker symbol to a CIK number.", + "operationId": "regulators_sec_cik_map", + "parameters": [ + { + "name": "provider", + "in": "query", + "required": false, + "schema": { + "enum": [ + "sec" + ], + "const": "sec", + "type": "string", + "default": "sec", + "title": "Provider" + } + }, + { + "name": "symbol", + "in": "query", + "required": true, + "schema": { + "type": "string", + "description": "Symbol to get data for.", + "title": "Symbol" + }, + "description": "Symbol to get data for." + }, + { + "name": "use_cache", + "in": "query", + "required": false, + "schema": { + "anyOf": [ + { + "type": "boolean" + }, + { + "type": null + } + ], + "title": "sec", + "description": "Whether or not to use cache for the request, default is True. (provider: sec)", + "default": true + }, + "description": "Whether or not to use cache for the request, default is True. (provider: sec)" + } + ], + "responses": { + "200": { + "description": "Successful Response", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/OBBject_CikMap" + } + } + } + }, + "404": { + "description": "Not found" + }, + "400": { + "description": "No Results Found", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/OpenBBErrorResponse" + } + } + } + }, + "500": { + "description": "Internal Error", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/OpenBBErrorResponse" + } + } + } + }, + "422": { + "description": "Validation Error", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/HTTPValidationError" + } + } + } + } + }, + "model": "CikMap", + "examples": [ + { + "scope": "api", + "parameters": { + "symbol": "MSFT", + "provider": "sec" + }, + "provider": "sec" + } + ] + } + }, + "/api/v1/regulators/sec/institutions_search": { + "get": { + "tags": [ + "regulators" + ], + "summary": "Institutions Search", + "description": "Search SEC-regulated institutions by name and return a list of results with CIK numbers.", + "operationId": "regulators_sec_institutions_search", + "parameters": [ + { + "name": "provider", + "in": "query", + "required": false, + "schema": { + "enum": [ + "sec" + ], + "const": "sec", + "type": "string", + "default": "sec", + "title": "Provider" + } + }, + { + "name": "query", + "in": "query", + "required": false, + "schema": { + "type": "string", + "description": "Search query.", + "default": "", + "title": "Query" + }, + "description": "Search query." + }, + { + "name": "use_cache", + "in": "query", + "required": false, + "schema": { + "anyOf": [ + { + "type": "boolean" + }, + { + "type": null + } + ], + "description": "Whether or not to use cache.", + "default": true, + "title": "Use Cache" + }, + "description": "Whether or not to use cache." + } + ], + "responses": { + "200": { + "description": "Successful Response", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/OBBject_InstitutionsSearch" + } + } + } + }, + "404": { + "description": "Not found" + }, + "400": { + "description": "No Results Found", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/OpenBBErrorResponse" + } + } + } + }, + "500": { + "description": "Internal Error", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/OpenBBErrorResponse" + } + } + } + }, + "422": { + "description": "Validation Error", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/HTTPValidationError" + } + } + } + } + }, + "model": "InstitutionsSearch", + "examples": [ + { + "scope": "api", + "parameters": { + "provider": "sec" + }, + "provider": "sec" + }, + { + "scope": "api", + "parameters": { + "query": "blackstone real estate", + "provider": "sec" + }, + "provider": "sec" + } + ] + } + }, + "/api/v1/regulators/sec/schema_files": { + "get": { + "tags": [ + "regulators" + ], + "summary": "Schema Files", + "description": "Use tool for navigating the directory of SEC XML schema files by year.", + "operationId": "regulators_sec_schema_files", + "parameters": [ + { + "name": "provider", + "in": "query", + "required": false, + "schema": { + "enum": [ + "sec" + ], + "const": "sec", + "type": "string", + "default": "sec", + "title": "Provider" + } + }, + { + "name": "query", + "in": "query", + "required": false, + "schema": { + "type": "string", + "description": "Search query.", + "default": "", + "title": "Query" + }, + "description": "Search query." + }, + { + "name": "use_cache", + "in": "query", + "required": false, + "schema": { + "anyOf": [ + { + "type": "boolean" + }, + { + "type": null + } + ], + "description": "Whether or not to use cache.", + "default": true, + "title": "Use Cache" + }, + "description": "Whether or not to use cache." + }, + { + "name": "url", + "in": "query", + "required": false, + "schema": { + "anyOf": [ + { + "type": "string" + }, + { + "type": null + } + ], + "title": "sec", + "description": "Enter an optional URL path to fetch the next level. (provider: sec)" + }, + "description": "Enter an optional URL path to fetch the next level. (provider: sec)" + } + ], + "responses": { + "200": { + "description": "Successful Response", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/OBBject_SchemaFiles" + } + } + } + }, + "404": { + "description": "Not found" + }, + "400": { + "description": "No Results Found", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/OpenBBErrorResponse" + } + } + } + }, + "500": { + "description": "Internal Error", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/OpenBBErrorResponse" + } + } + } + }, + "422": { + "description": "Validation Error", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/HTTPValidationError" + } + } + } + } + }, + "model": "SchemaFiles", + "examples": [ + { + "scope": "api", + "parameters": { + "provider": "sec" + }, + "provider": "sec" + }, + { + "scope": "python", + "description": "Get a list of schema files.", + "code": [ + "data = obb.regulators.sec.schema_files().results", + "data.files[0]", + "'https://xbrl.fasb.org/us-gaap/'", + "# The directory structure can be navigated by constructing a URL from the 'results' list.", + "url = data.files[0]+data.files[-1]", + "# The URL base will always be the 0 position in the list, feed the URL back in as a parameter.", + "obb.regulators.sec.schema_files(url=url).results.files", + "['https://xbrl.fasb.org/us-gaap/2024/'", + "'USGAAP2024FileList.xml'", + "'dis/'", + "'dqcrules/'", + "'ebp/'", + "'elts/'", + "'entire/'", + "'meta/'", + "'stm/'", + "'us-gaap-2024.zip']" + ] + } + ] + } + }, + "/api/v1/regulators/sec/symbol_map": { + "get": { + "tags": [ + "regulators" + ], + "summary": "Symbol Map", + "description": "Map a CIK number to a ticker symbol, leading 0s can be omitted or included.", + "operationId": "regulators_sec_symbol_map", + "parameters": [ + { + "name": "provider", + "in": "query", + "required": false, + "schema": { + "enum": [ + "sec" + ], + "const": "sec", + "type": "string", + "default": "sec", + "title": "Provider" + } + }, + { + "name": "query", + "in": "query", + "required": true, + "schema": { + "type": "string", + "description": "Search query.", + "title": "Query" + }, + "description": "Search query." + }, + { + "name": "use_cache", + "in": "query", + "required": false, + "schema": { + "anyOf": [ + { + "type": "boolean" + }, + { + "type": null + } + ], + "description": "Whether or not to use cache. If True, cache will store for seven days.", + "default": true, + "title": "Use Cache" + }, + "description": "Whether or not to use cache. If True, cache will store for seven days." + } + ], + "responses": { + "200": { + "description": "Successful Response", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/OBBject_SymbolMap" + } + } + } + }, + "404": { + "description": "Not found" + }, + "400": { + "description": "No Results Found", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/OpenBBErrorResponse" + } + } + } + }, + "500": { + "description": "Internal Error", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/OpenBBErrorResponse" + } + } + } + }, + "422": { + "description": "Validation Error", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/HTTPValidationError" + } + } + } + } + }, + "model": "SymbolMap", + "examples": [ + { + "scope": "api", + "parameters": { + "query": "0000789019", + "provider": "sec" + }, + "provider": "sec" + } + ] + } + }, + "/api/v1/regulators/sec/rss_litigation": { + "get": { + "tags": [ + "regulators" + ], + "summary": "Rss Litigation", + "description": "Get the RSS feed that provides links to litigation releases concerning civil lawsuits brought by the Commission in federal court.", + "operationId": "regulators_sec_rss_litigation", + "parameters": [ + { + "name": "provider", + "in": "query", + "required": false, + "schema": { + "enum": [ + "sec" + ], + "const": "sec", + "type": "string", + "default": "sec", + "title": "Provider" + } + } + ], + "responses": { + "200": { + "description": "Successful Response", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/OBBject_RssLitigation" + } + } + } + }, + "404": { + "description": "Not found" + }, + "400": { + "description": "No Results Found", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/OpenBBErrorResponse" + } + } + } + }, + "500": { + "description": "Internal Error", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/OpenBBErrorResponse" + } + } + } + }, + "422": { + "description": "Validation Error", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/HTTPValidationError" + } + } + } + } + }, + "model": "RssLitigation", + "examples": [ + { + "scope": "api", + "parameters": { + "provider": "sec" + }, + "provider": "sec" + } + ] + } + }, + "/api/v1/regulators/sec/sic_search": { + "get": { + "tags": [ + "regulators" + ], + "summary": "Sic Search", + "description": "Search for Industry Titles, Reporting Office, and SIC Codes. An empty query string returns all results.", + "operationId": "regulators_sec_sic_search", + "parameters": [ + { + "name": "provider", + "in": "query", + "required": false, + "schema": { + "enum": [ + "sec" + ], + "const": "sec", + "type": "string", + "default": "sec", + "title": "Provider" + } + }, + { + "name": "query", + "in": "query", + "required": false, + "schema": { + "type": "string", + "description": "Search query.", + "default": "", + "title": "Query" + }, + "description": "Search query." + }, + { + "name": "use_cache", + "in": "query", + "required": false, + "schema": { + "anyOf": [ + { + "type": "boolean" + }, + { + "type": null + } + ], + "description": "Whether or not to use cache.", + "default": true, + "title": "Use Cache" + }, + "description": "Whether or not to use cache." + } + ], + "responses": { + "200": { + "description": "Successful Response", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/OBBject_SicSearch" + } + } + } + }, + "404": { + "description": "Not found" + }, + "400": { + "description": "No Results Found", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/OpenBBErrorResponse" + } + } + } + }, + "500": { + "description": "Internal Error", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/OpenBBErrorResponse" + } + } + } + }, + "422": { + "description": "Validation Error", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/HTTPValidationError" + } + } + } + } + }, + "model": "SicSearch", + "examples": [ + { + "scope": "api", + "parameters": { + "provider": "sec" + }, + "provider": "sec" + }, + { + "scope": "api", + "parameters": { + "query": "real estate investment trusts", + "provider": "sec" + }, + "provider": "sec" + } + ] + } + }, + "/form_submit": { + "post": { + "summary": "Form Submit", + "operationId": "form_submit_form_submit_post", + "requestBody": { + "required": true, + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/Form" + } + } + } + }, + "responses": { + "200": { + "description": "Successful Response", + "content": { + "application/json": { + "schema": { + "type": "boolean", + "title": "Response Form Submit Form Submit Post" + } + } + } + }, + "422": { + "description": "Validation Error", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/HTTPValidationError" + } + } + } + } + } + }, + "get": { + "summary": "All Forms", + "operationId": "all_forms_form_submit_get", + "parameters": [ + { + "name": "some_param", + "in": "query", + "required": false, + "schema": { + "type": "string", + "default": "", + "title": "Some Param" + } + } + ], + "responses": { + "200": { + "description": "Successful Response", + "content": { + "application/json": { + "schema": { + "type": "array", + "items": { + + }, + "title": "Response All Forms Form Submit Get" + } + } + } + }, + "422": { + "description": "Validation Error", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/HTTPValidationError" + } + } + } + } + } + } + } + }, + "components": { + "schemas": { + "Chart": { + "properties": { + "content": { + "anyOf": [ + { + "type": "object" + }, + { + "type": null + } + ], + "title": "Content", + "description": "Raw textual representation of the chart." + }, + "format": { + "anyOf": [ + { + "type": "string" + }, + { + "type": null + } + ], + "title": "Format", + "description": "Complementary attribute to the `content` attribute. It specifies the format of the chart." + }, + "fig": { + "anyOf": [ + { + + }, + { + "type": null + } + ], + "title": "Fig", + "description": "The figure object.", + "exclude_from_api": true + } + }, + "type": "object", + "title": "Chart", + "description": "Model for Chart." + }, + "FREDConsumerPriceIndexData": { + "properties": { + "date": { + "type": "string", + "format": "date", + "title": "Date", + "description": "The date of the data." + }, + "country": { + "type": "string", + "title": "Country" + }, + "value": { + "type": "number", + "title": "Value", + "description": "CPI index value or period change." + } + }, + "additionalProperties": true, + "type": "object", + "required": [ + "date", + "country", + "value" + ], + "title": "FREDConsumerPriceIndexData", + "description": "FRED Consumer Price Index Data." + }, + "FredBalanceOfPaymentsData": { + "properties": { + "period": { + "type": "string", + "format": "date", + "title": "Period", + "description": "The date representing the beginning of the reporting period." + }, + "balance_percent_of_gdp": { + "anyOf": [ + { + "type": "number" + }, + { + "type": null + } + ], + "title": "Balance Percent Of Gdp", + "description": "Current Account Balance as Percent of GDP", + "x-frontend_multiply": 100, + "x-unit_measurement": "percent" + }, + "balance_total": { + "anyOf": [ + { + "type": "number" + }, + { + "type": null + } + ], + "title": "Balance Total", + "description": "Current Account Total Balance (USD)" + }, + "balance_total_services": { + "anyOf": [ + { + "type": "number" + }, + { + "type": null + } + ], + "title": "Balance Total Services", + "description": "Current Account Total Services Balance (USD)" + }, + "balance_total_secondary_income": { + "anyOf": [ + { + "type": "number" + }, + { + "type": null + } + ], + "title": "Balance Total Secondary Income", + "description": "Current Account Total Secondary Income Balance (USD)" + }, + "balance_total_goods": { + "anyOf": [ + { + "type": "number" + }, + { + "type": null + } + ], + "title": "Balance Total Goods", + "description": "Current Account Total Goods Balance (USD)" + }, + "balance_total_primary_income": { + "anyOf": [ + { + "type": "number" + }, + { + "type": null + } + ], + "title": "Balance Total Primary Income", + "description": "Current Account Total Primary Income Balance (USD)" + }, + "credits_services_percent_of_goods_and_services": { + "anyOf": [ + { + "type": "number" + }, + { + "type": null + } + ], + "title": "Credits Services Percent Of Goods And Services", + "description": "Current Account Credits Services as Percent of Goods and Services", + "x-frontend_multiply": 100, + "x-unit_measurement": "percent" + }, + "credits_services_percent_of_current_account": { + "anyOf": [ + { + "type": "number" + }, + { + "type": null + } + ], + "title": "Credits Services Percent Of Current Account", + "description": "Current Account Credits Services as Percent of Current Account", + "x-frontend_multiply": 100, + "x-unit_measurement": "percent" + }, + "credits_total_services": { + "anyOf": [ + { + "type": "number" + }, + { + "type": null + } + ], + "title": "Credits Total Services", + "description": "Current Account Credits Total Services (USD)" + }, + "credits_total_goods": { + "anyOf": [ + { + "type": "number" + }, + { + "type": null + } + ], + "title": "Credits Total Goods", + "description": "Current Account Credits Total Goods (USD)" + }, + "credits_total_primary_income": { + "anyOf": [ + { + "type": "number" + }, + { + "type": null + } + ], + "title": "Credits Total Primary Income", + "description": "Current Account Credits Total Primary Income (USD)" + }, + "credits_total_secondary_income": { + "anyOf": [ + { + "type": "number" + }, + { + "type": null + } + ], + "title": "Credits Total Secondary Income", + "description": "Current Account Credits Total Secondary Income (USD)" + }, + "credits_total": { + "anyOf": [ + { + "type": "number" + }, + { + "type": null + } + ], + "title": "Credits Total", + "description": "Current Account Credits Total (USD)" + }, + "debits_services_percent_of_goods_and_services": { + "anyOf": [ + { + "type": "number" + }, + { + "type": null + } + ], + "title": "Debits Services Percent Of Goods And Services", + "description": "Current Account Debits Services as Percent of Goods and Services", + "x-frontend_multiply": 100, + "x-unit_measurement": "percent" + }, + "debits_services_percent_of_current_account": { + "anyOf": [ + { + "type": "number" + }, + { + "type": null + } + ], + "title": "Debits Services Percent Of Current Account", + "description": "Current Account Debits Services as Percent of Current Account", + "x-frontend_multiply": 100, + "x-unit_measurement": "percent" + }, + "debits_total_services": { + "anyOf": [ + { + "type": "number" + }, + { + "type": null + } + ], + "title": "Debits Total Services", + "description": "Current Account Debits Total Services (USD)" + }, + "debits_total_goods": { + "anyOf": [ + { + "type": "number" + }, + { + "type": null + } + ], + "title": "Debits Total Goods", + "description": "Current Account Debits Total Goods (USD)" + }, + "debits_total_primary_income": { + "anyOf": [ + { + "type": "number" + }, + { + "type": null + } + ], + "title": "Debits Total Primary Income", + "description": "Current Account Debits Total Primary Income (USD)" + }, + "debits_total": { + "anyOf": [ + { + "type": "number" + }, + { + "type": null + } + ], + "title": "Debits Total", + "description": "Current Account Debits Total (USD)" + }, + "debits_total_secondary_income": { + "anyOf": [ + { + "type": "number" + }, + { + "type": null + } + ], + "title": "Debits Total Secondary Income", + "description": "Current Account Debits Total Secondary Income (USD)" + } + }, + "additionalProperties": true, + "type": "object", + "title": "FredBalanceOfPaymentsData", + "description": "FRED Balance Of Payments Data." + }, + "FredManufacturingOutlookTexasData": { + "properties": { + "date": { + "type": "string", + "format": "date", + "title": "Date", + "description": "The date of the data." + }, + "topic": { + "anyOf": [ + { + "type": "string" + }, + { + "type": null + } + ], + "title": "Topic", + "description": "Topic of the survey response." + }, + "diffusion_index": { + "anyOf": [ + { + "type": "number" + }, + { + "type": null + } + ], + "title": "Diffusion Index", + "description": "Diffusion Index." + }, + "percent_reporting_increase": { + "anyOf": [ + { + "type": "number" + }, + { + "type": null + } + ], + "title": "Percent Reporting Increase", + "description": "Percent of respondents reporting an increase over the last month.", + "x-frontend_multiply": 100, + "x-unit_measurement": "percent" + }, + "percent_reporting_decrease": { + "anyOf": [ + { + "type": "number" + }, + { + "type": null + } + ], + "title": "Percent Reporting Decrease", + "description": "Percent of respondents reporting a decrease over the last month.", + "x-frontend_multiply": 100, + "x-unit_measurement": "percent" + }, + "percent_reporting_no_change": { + "anyOf": [ + { + "type": "number" + }, + { + "type": null + } + ], + "title": "Percent Reporting No Change", + "description": "Percent of respondents reporting no change over the last month.", + "x-frontend_multiply": 100, + "x-unit_measurement": "percent" + } + }, + "additionalProperties": true, + "type": "object", + "required": [ + "date" + ], + "title": "FredManufacturingOutlookTexasData", + "description": "FRED Manufacturing Outlook - Texas - Data." + }, + "FredNonFarmPayrollsData": { + "properties": { + "date": { + "type": "string", + "format": "date", + "title": "Date", + "description": "The date of the data." + }, + "symbol": { + "type": "string", + "title": "Symbol", + "description": "Symbol representing the entity requested in the data." + }, + "value": { + "type": "number", + "title": "Value", + "description": "" + }, + "name": { + "type": "string", + "title": "Name", + "description": "The name of the series." + }, + "element_id": { + "type": "string", + "title": "Element Id", + "description": "The element id in the parent/child relationship." + }, + "parent_id": { + "type": "string", + "title": "Parent Id", + "description": "The parent id in the parent/child relationship." + }, + "children": { + "anyOf": [ + { + "type": "string" + }, + { + "type": null + } + ], + "title": "Children", + "description": "The element_id of each child, as a comma-separated string." + }, + "level": { + "type": "integer", + "title": "Level", + "description": "The indentation level of the element." + } + }, + "additionalProperties": true, + "type": "object", + "required": [ + "date", + "symbol", + "value", + "name", + "element_id", + "parent_id", + "level" + ], + "title": "FredNonFarmPayrollsData", + "description": "FRED NonFarm Payrolls Data." + }, + "FredPersonalConsumptionExpendituresData": { + "properties": { + "date": { + "type": "string", + "format": "date", + "title": "Date", + "description": "The date of the data." + }, + "symbol": { + "type": "string", + "title": "Symbol", + "description": "Symbol representing the entity requested in the data." + }, + "value": { + "type": "number", + "title": "Value", + "description": "" + }, + "name": { + "type": "string", + "title": "Name", + "description": "The name of the series." + }, + "element_id": { + "type": "string", + "title": "Element Id", + "description": "The element id in the parent/child relationship." + }, + "parent_id": { + "type": "string", + "title": "Parent Id", + "description": "The parent id in the parent/child relationship." + }, + "children": { + "anyOf": [ + { + "type": "string" + }, + { + "type": null + } + ], + "title": "Children", + "description": "The element_id of each child, as a comma-separated string." + }, + "level": { + "type": "integer", + "title": "Level", + "description": "The indentation level of the element." + }, + "line": { + "type": "integer", + "title": "Line", + "description": "The line number of the series in the table." + } + }, + "additionalProperties": true, + "type": "object", + "required": [ + "date", + "symbol", + "value", + "name", + "element_id", + "parent_id", + "level", + "line" + ], + "title": "FredPersonalConsumptionExpendituresData", + "description": "FRED Personal Consumption Expenditures Data." + }, + "FredRegionalData": { + "properties": { + "date": { + "type": "string", + "format": "date", + "title": "Date", + "description": "The date of the data." + }, + "region": { + "type": "string", + "title": "Region", + "description": "The name of the region." + }, + "code": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "integer" + } + ], + "title": "Code", + "description": "The code of the region." + }, + "value": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "number" + }, + { + "type": null + } + ], + "title": "Value", + "description": "The obersvation value. The units are defined in the search results by series ID." + }, + "series_id": { + "type": "string", + "title": "Series Id", + "description": "The individual series ID for the region." + } + }, + "additionalProperties": true, + "type": "object", + "required": [ + "date", + "region", + "code", + "series_id" + ], + "title": "FredRegionalData", + "description": "FRED Regional Data." + }, + "FredReleaseTableData": { + "properties": { + "date": { + "anyOf": [ + { + "type": "string", + "format": "date" + }, + { + "type": null + } + ], + "title": "Date", + "description": "The date of the data." + }, + "level": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": null + } + ], + "title": "Level", + "description": "The indentation level of the element." + }, + "element_type": { + "anyOf": [ + { + "type": "string" + }, + { + "type": null + } + ], + "title": "Element Type", + "description": "The type of the element." + }, + "line": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": null + } + ], + "title": "Line", + "description": "The line number of the element." + }, + "element_id": { + "anyOf": [ + { + "type": "string" + }, + { + "type": null + } + ], + "title": "Element Id", + "description": "The element id in the parent/child relationship." + }, + "parent_id": { + "anyOf": [ + { + "type": "string" + }, + { + "type": null + } + ], + "title": "Parent Id", + "description": "The parent id in the parent/child relationship." + }, + "children": { + "anyOf": [ + { + "type": "string" + }, + { + "type": null + } + ], + "title": "Children", + "description": "The element_id of each child, as a comma-separated string." + }, + "symbol": { + "anyOf": [ + { + "type": "string" + }, + { + "type": null + } + ], + "title": "Symbol", + "description": "Symbol representing the entity requested in the data." + }, + "name": { + "anyOf": [ + { + "type": "string" + }, + { + "type": null + } + ], + "title": "Name", + "description": "The name of the series." + }, + "value": { + "anyOf": [ + { + "type": "number" + }, + { + "type": null + } + ], + "title": "Value", + "description": "The reported value of the series." + } + }, + "additionalProperties": true, + "type": "object", + "title": "FredReleaseTableData", + "description": "FRED Release Table Data." + }, + "FredRetailPricesData": { + "properties": { + "date": { + "anyOf": [ + { + "type": "string", + "format": "date" + }, + { + "type": null + } + ], + "title": "Date", + "description": "The date of the data." + }, + "symbol": { + "anyOf": [ + { + "type": "string" + }, + { + "type": null + } + ], + "title": "Symbol", + "description": "Symbol representing the entity requested in the data." + }, + "country": { + "anyOf": [ + { + "type": "string" + }, + { + "type": null + } + ], + "title": "Country", + "description": "" + }, + "description": { + "type": "string", + "title": "Description", + "description": "Description of the item." + }, + "value": { + "anyOf": [ + { + "type": "number" + }, + { + "type": null + } + ], + "title": "Value", + "description": "Price, or change in price, per unit." + } + }, + "additionalProperties": true, + "type": "object", + "title": "FredRetailPricesData", + "description": "FRED Retail Prices Data." + }, + "FredSearchData": { + "properties": { + "release_id": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "integer" + }, + { + "type": null + } + ], + "title": "Release Id", + "description": "The release ID for queries." + }, + "series_id": { + "anyOf": [ + { + "type": "string" + }, + { + "type": null + } + ], + "title": "Series Id", + "description": "The series ID for the item in the release." + }, + "name": { + "anyOf": [ + { + "type": "string" + }, + { + "type": null + } + ], + "title": "Name", + "description": "The name of the release." + }, + "title": { + "anyOf": [ + { + "type": "string" + }, + { + "type": null + } + ], + "title": "Title", + "description": "The title of the series." + }, + "observation_start": { + "anyOf": [ + { + "type": "string", + "format": "date" + }, + { + "type": null + } + ], + "title": "Observation Start", + "description": "The date of the first observation in the series." + }, + "observation_end": { + "anyOf": [ + { + "type": "string", + "format": "date" + }, + { + "type": null + } + ], + "title": "Observation End", + "description": "The date of the last observation in the series." + }, + "frequency": { + "anyOf": [ + { + "type": "string" + }, + { + "type": null + } + ], + "title": "Frequency", + "description": "The frequency of the data." + }, + "frequency_short": { + "anyOf": [ + { + "type": "string" + }, + { + "type": null + } + ], + "title": "Frequency Short", + "description": "Short form of the data frequency." + }, + "units": { + "anyOf": [ + { + "type": "string" + }, + { + "type": null + } + ], + "title": "Units", + "description": "The units of the data." + }, + "units_short": { + "anyOf": [ + { + "type": "string" + }, + { + "type": null + } + ], + "title": "Units Short", + "description": "Short form of the data units." + }, + "seasonal_adjustment": { + "anyOf": [ + { + "type": "string" + }, + { + "type": null + } + ], + "title": "Seasonal Adjustment", + "description": "The seasonal adjustment of the data." + }, + "seasonal_adjustment_short": { + "anyOf": [ + { + "type": "string" + }, + { + "type": null + } + ], + "title": "Seasonal Adjustment Short", + "description": "Short form of the data seasonal adjustment." + }, + "last_updated": { + "anyOf": [ + { + "type": "string", + "format": "date-time" + }, + { + "type": null + } + ], + "title": "Last Updated", + "description": "The datetime of the last update to the data." + }, + "notes": { + "anyOf": [ + { + "type": "string" + }, + { + "type": null + } + ], + "title": "Notes", + "description": "Description of the release." + }, + "press_release": { + "anyOf": [ + { + "type": "boolean" + }, + { + "type": null + } + ], + "title": "Press Release", + "description": "If the release is a press release." + }, + "url": { + "anyOf": [ + { + "type": "string" + }, + { + "type": null + } + ], + "title": "Url", + "description": "URL to the release." + }, + "popularity": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": null + } + ], + "title": "Popularity", + "description": "Popularity of the series" + }, + "group_popularity": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": null + } + ], + "title": "Group Popularity", + "description": "Group popularity of the release" + }, + "region_type": { + "anyOf": [ + { + "type": "string" + }, + { + "type": null + } + ], + "title": "Region Type", + "description": "The region type of the series." + }, + "series_group": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "integer" + }, + { + "type": null + } + ], + "title": "Series Group", + "description": "The series group ID of the series. This value is used to query for regional data." + } + }, + "additionalProperties": true, + "type": "object", + "title": "FredSearchData", + "description": "FRED Search Data." + }, + "FredSeniorLoanOfficerSurveyData": { + "properties": { + "date": { + "type": "string", + "format": "date", + "title": "Date", + "description": "The date of the data." + }, + "symbol": { + "anyOf": [ + { + "type": "string" + }, + { + "type": null + } + ], + "title": "Symbol", + "description": "Symbol representing the entity requested in the data." + }, + "value": { + "type": "number", + "title": "Value", + "description": "Survey value." + }, + "title": { + "anyOf": [ + { + "type": "string" + }, + { + "type": null + } + ], + "title": "Title", + "description": "Survey title." + } + }, + "additionalProperties": true, + "type": "object", + "required": [ + "date", + "value", + "title" + ], + "title": "FredSeniorLoanOfficerSurveyData", + "description": "FRED Senior Loan Officer Opinion Survey Data." + }, + "FredSeriesData": { + "properties": { + "date": { + "type": "string", + "format": "date", + "title": "Date", + "description": "The date of the data." + } + }, + "additionalProperties": true, + "type": "object", + "required": [ + "date" + ], + "title": "FredSeriesData", + "description": "FRED Series Data." + }, + "FredSurveyOfEconomicConditionsChicagoData": { + "properties": { + "date": { + "type": "string", + "format": "date", + "title": "Date", + "description": "The date of the data." + }, + "activity_index": { + "anyOf": [ + { + "type": "number" + }, + { + "type": null + } + ], + "title": "Activity Index", + "description": "Activity Index." + }, + "one_year_outlook": { + "anyOf": [ + { + "type": "number" + }, + { + "type": null + } + ], + "title": "One Year Outlook", + "description": "One Year Outlook Index." + }, + "manufacturing_activity": { + "anyOf": [ + { + "type": "number" + }, + { + "type": null + } + ], + "title": "Manufacturing Activity", + "description": "Manufacturing Activity Index." + }, + "non_manufacturing_activity": { + "anyOf": [ + { + "type": "number" + }, + { + "type": null + } + ], + "title": "Non Manufacturing Activity", + "description": "Non-Manufacturing Activity Index." + }, + "capital_expenditures_expectations": { + "anyOf": [ + { + "type": "number" + }, + { + "type": null + } + ], + "title": "Capital Expenditures Expectations", + "description": "Capital Expenditures Expectations Index." + }, + "hiring_expectations": { + "anyOf": [ + { + "type": "number" + }, + { + "type": null + } + ], + "title": "Hiring Expectations", + "description": "Hiring Expectations Index." + }, + "current_hiring": { + "anyOf": [ + { + "type": "number" + }, + { + "type": null + } + ], + "title": "Current Hiring", + "description": "Current Hiring Index." + }, + "labor_costs": { + "anyOf": [ + { + "type": "number" + }, + { + "type": null + } + ], + "title": "Labor Costs", + "description": "Labor Costs Index." + }, + "non_labor_costs": { + "anyOf": [ + { + "type": "number" + }, + { + "type": null + } + ], + "title": "Non Labor Costs", + "description": "Non-Labor Costs Index." + } + }, + "additionalProperties": true, + "type": "object", + "required": [ + "date" + ], + "title": "FredSurveyOfEconomicConditionsChicagoData", + "description": "FRED Survey Of Economic Conditions - Chicago - Data." + }, + "FredUofMichiganData": { + "properties": { + "date": { + "type": "string", + "format": "date", + "title": "Date", + "description": "The date of the data." + }, + "consumer_sentiment": { + "anyOf": [ + { + "type": "number" + }, + { + "type": null + } + ], + "title": "Consumer Sentiment", + "description": "Index of the results of the University of Michigan's monthly Survey of Consumers, which is used to estimate future spending and saving. (1966:Q1=100)." + }, + "inflation_expectation": { + "anyOf": [ + { + "type": "number" + }, + { + "type": null + } + ], + "title": "Inflation Expectation", + "description": "Median expected price change next 12 months, Surveys of Consumers.", + "x-frontend_multiply": 100, + "x-unit_measurement": "percent" + } + }, + "additionalProperties": true, + "type": "object", + "required": [ + "date" + ], + "title": "FredUofMichiganData", + "description": "FRED University of Michigan Survey Data." + }, + "HTTPValidationError": { + "properties": { + "detail": { + "items": { + "$ref": "#/components/schemas/ValidationError" + }, + "type": "array", + "title": "Detail" + } + }, + "type": "object", + "title": "HTTPValidationError" + }, + "OBBject_BalanceOfPayments": { + "properties": { + "_id": { + "type": "string", + "title": " Id" + }, + "results": { + "anyOf": [ + { + "items": { + "$ref": "#/components/schemas/FredBalanceOfPaymentsData" + }, + "type": "array" + }, + { + "type": null + } + ], + "title": "Results", + "description": "Serializable results." + }, + "provider": { + "anyOf": [ + { + "type": "string" + }, + { + "type": null + } + ], + "title": "Provider", + "description": "Provider name." + }, + "warnings": { + "anyOf": [ + { + "items": { + "$ref": "#/components/schemas/Warning_" + }, + "type": "array" + }, + { + "type": null + } + ], + "title": "Warnings", + "description": "List of warnings." + }, + "chart": { + "anyOf": [ + { + "$ref": "#/components/schemas/Chart" + }, + { + "type": null + } + ], + "description": "Chart object." + }, + "extra": { + "type": "object", + "title": "Extra", + "description": "Extra info." + } + }, + "type": "object", + "title": "OBBject_BalanceOfPayments", + "description": "OBBject with results of type BalanceOfPayments" + }, + "OBBject_CikMap": { + "properties": { + "_id": { + "type": "string", + "title": " Id" + }, + "results": { + "anyOf": [ + { + "$ref": "#/components/schemas/SecCikMapData" + }, + { + "type": null + } + ], + "description": "Serializable results." + }, + "provider": { + "anyOf": [ + { + "type": "string" + }, + { + "type": null + } + ], + "title": "Provider", + "description": "Provider name." + }, + "warnings": { + "anyOf": [ + { + "items": { + "$ref": "#/components/schemas/Warning_" + }, + "type": "array" + }, + { + "type": null + } + ], + "title": "Warnings", + "description": "List of warnings." + }, + "chart": { + "anyOf": [ + { + "$ref": "#/components/schemas/Chart" + }, + { + "type": null + } + ], + "description": "Chart object." + }, + "extra": { + "type": "object", + "title": "Extra", + "description": "Extra info." + } + }, + "type": "object", + "title": "OBBject_CikMap", + "description": "OBBject with results of type CikMap" + }, + "OBBject_ConsumerPriceIndex": { + "properties": { + "_id": { + "type": "string", + "title": " Id" + }, + "results": { + "anyOf": [ + { + "items": { + "$ref": "#/components/schemas/FREDConsumerPriceIndexData" + }, + "type": "array" + }, + { + "type": null + } + ], + "title": "Results", + "description": "Serializable results." + }, + "provider": { + "anyOf": [ + { + "type": "string" + }, + { + "type": null + } + ], + "title": "Provider", + "description": "Provider name." + }, + "warnings": { + "anyOf": [ + { + "items": { + "$ref": "#/components/schemas/Warning_" + }, + "type": "array" + }, + { + "type": null + } + ], + "title": "Warnings", + "description": "List of warnings." + }, + "chart": { + "anyOf": [ + { + "$ref": "#/components/schemas/Chart" + }, + { + "type": null + } + ], + "description": "Chart object." + }, + "extra": { + "type": "object", + "title": "Extra", + "description": "Extra info." + } + }, + "type": "object", + "title": "OBBject_ConsumerPriceIndex", + "description": "OBBject with results of type ConsumerPriceIndex" + }, + "OBBject_FredRegional": { + "properties": { + "_id": { + "type": "string", + "title": " Id" + }, + "results": { + "anyOf": [ + { + "items": { + "$ref": "#/components/schemas/FredRegionalData" + }, + "type": "array" + }, + { + "type": null + } + ], + "title": "Results", + "description": "Serializable results." + }, + "provider": { + "anyOf": [ + { + "type": "string" + }, + { + "type": null + } + ], + "title": "Provider", + "description": "Provider name." + }, + "warnings": { + "anyOf": [ + { + "items": { + "$ref": "#/components/schemas/Warning_" + }, + "type": "array" + }, + { + "type": null + } + ], + "title": "Warnings", + "description": "List of warnings." + }, + "chart": { + "anyOf": [ + { + "$ref": "#/components/schemas/Chart" + }, + { + "type": null + } + ], + "description": "Chart object." + }, + "extra": { + "type": "object", + "title": "Extra", + "description": "Extra info." + } + }, + "type": "object", + "title": "OBBject_FredRegional", + "description": "OBBject with results of type FredRegional" + }, + "OBBject_FredReleaseTable": { + "properties": { + "_id": { + "type": "string", + "title": " Id" + }, + "results": { + "anyOf": [ + { + "items": { + "$ref": "#/components/schemas/FredReleaseTableData" + }, + "type": "array" + }, + { + "type": null + } + ], + "title": "Results", + "description": "Serializable results." + }, + "provider": { + "anyOf": [ + { + "type": "string" + }, + { + "type": null + } + ], + "title": "Provider", + "description": "Provider name." + }, + "warnings": { + "anyOf": [ + { + "items": { + "$ref": "#/components/schemas/Warning_" + }, + "type": "array" + }, + { + "type": null + } + ], + "title": "Warnings", + "description": "List of warnings." + }, + "chart": { + "anyOf": [ + { + "$ref": "#/components/schemas/Chart" + }, + { + "type": null + } + ], + "description": "Chart object." + }, + "extra": { + "type": "object", + "title": "Extra", + "description": "Extra info." + } + }, + "type": "object", + "title": "OBBject_FredReleaseTable", + "description": "OBBject with results of type FredReleaseTable" + }, + "OBBject_FredSearch": { + "properties": { + "_id": { + "type": "string", + "title": " Id" + }, + "results": { + "anyOf": [ + { + "items": { + "$ref": "#/components/schemas/FredSearchData" + }, + "type": "array" + }, + { + "type": null + } + ], + "title": "Results", + "description": "Serializable results." + }, + "provider": { + "anyOf": [ + { + "type": "string" + }, + { + "type": null + } + ], + "title": "Provider", + "description": "Provider name." + }, + "warnings": { + "anyOf": [ + { + "items": { + "$ref": "#/components/schemas/Warning_" + }, + "type": "array" + }, + { + "type": null + } + ], + "title": "Warnings", + "description": "List of warnings." + }, + "chart": { + "anyOf": [ + { + "$ref": "#/components/schemas/Chart" + }, + { + "type": null + } + ], + "description": "Chart object." + }, + "extra": { + "type": "object", + "title": "Extra", + "description": "Extra info." + } + }, + "type": "object", + "title": "OBBject_FredSearch", + "description": "OBBject with results of type FredSearch" + }, + "OBBject_FredSeries": { + "properties": { + "_id": { + "type": "string", + "title": " Id" + }, + "results": { + "anyOf": [ + { + "items": { + "$ref": "#/components/schemas/FredSeriesData" + }, + "type": "array" + }, + { + "type": null + } + ], + "title": "Results", + "description": "Serializable results." + }, + "provider": { + "anyOf": [ + { + "type": "string" + }, + { + "type": null + } + ], + "title": "Provider", + "description": "Provider name." + }, + "warnings": { + "anyOf": [ + { + "items": { + "$ref": "#/components/schemas/Warning_" + }, + "type": "array" + }, + { + "type": null + } + ], + "title": "Warnings", + "description": "List of warnings." + }, + "chart": { + "anyOf": [ + { + "$ref": "#/components/schemas/Chart" + }, + { + "type": null + } + ], + "description": "Chart object." + }, + "extra": { + "type": "object", + "title": "Extra", + "description": "Extra info." + } + }, + "type": "object", + "title": "OBBject_FredSeries", + "description": "OBBject with results of type FredSeries" + }, + "OBBject_InstitutionsSearch": { + "properties": { + "_id": { + "type": "string", + "title": " Id" + }, + "results": { + "anyOf": [ + { + "items": { + "$ref": "#/components/schemas/SecInstitutionsSearchData" + }, + "type": "array" + }, + { + "type": null + } + ], + "title": "Results", + "description": "Serializable results." + }, + "provider": { + "anyOf": [ + { + "type": "string" + }, + { + "type": null + } + ], + "title": "Provider", + "description": "Provider name." + }, + "warnings": { + "anyOf": [ + { + "items": { + "$ref": "#/components/schemas/Warning_" + }, + "type": "array" + }, + { + "type": null + } + ], + "title": "Warnings", + "description": "List of warnings." + }, + "chart": { + "anyOf": [ + { + "$ref": "#/components/schemas/Chart" + }, + { + "type": null + } + ], + "description": "Chart object." + }, + "extra": { + "type": "object", + "title": "Extra", + "description": "Extra info." + } + }, + "type": "object", + "title": "OBBject_InstitutionsSearch", + "description": "OBBject with results of type InstitutionsSearch" + }, + "OBBject_ManufacturingOutlookTexas": { + "properties": { + "_id": { + "type": "string", + "title": " Id" + }, + "results": { + "anyOf": [ + { + "items": { + "$ref": "#/components/schemas/FredManufacturingOutlookTexasData" + }, + "type": "array" + }, + { + "type": null + } + ], + "title": "Results", + "description": "Serializable results." + }, + "provider": { + "anyOf": [ + { + "type": "string" + }, + { + "type": null + } + ], + "title": "Provider", + "description": "Provider name." + }, + "warnings": { + "anyOf": [ + { + "items": { + "$ref": "#/components/schemas/Warning_" + }, + "type": "array" + }, + { + "type": null + } + ], + "title": "Warnings", + "description": "List of warnings." + }, + "chart": { + "anyOf": [ + { + "$ref": "#/components/schemas/Chart" + }, + { + "type": null + } + ], + "description": "Chart object." + }, + "extra": { + "type": "object", + "title": "Extra", + "description": "Extra info." + } + }, + "type": "object", + "title": "OBBject_ManufacturingOutlookTexas", + "description": "OBBject with results of type ManufacturingOutlookTexas" + }, + "OBBject_NonFarmPayrolls": { + "properties": { + "_id": { + "type": "string", + "title": " Id" + }, + "results": { + "anyOf": [ + { + "items": { + "$ref": "#/components/schemas/FredNonFarmPayrollsData" + }, + "type": "array" + }, + { + "type": null + } + ], + "title": "Results", + "description": "Serializable results." + }, + "provider": { + "anyOf": [ + { + "type": "string" + }, + { + "type": null + } + ], + "title": "Provider", + "description": "Provider name." + }, + "warnings": { + "anyOf": [ + { + "items": { + "$ref": "#/components/schemas/Warning_" + }, + "type": "array" + }, + { + "type": null + } + ], + "title": "Warnings", + "description": "List of warnings." + }, + "chart": { + "anyOf": [ + { + "$ref": "#/components/schemas/Chart" + }, + { + "type": null + } + ], + "description": "Chart object." + }, + "extra": { + "type": "object", + "title": "Extra", + "description": "Extra info." + } + }, + "type": "object", + "title": "OBBject_NonFarmPayrolls", + "description": "OBBject with results of type NonFarmPayrolls" + }, + "OBBject_PersonalConsumptionExpenditures": { + "properties": { + "_id": { + "type": "string", + "title": " Id" + }, + "results": { + "anyOf": [ + { + "items": { + "$ref": "#/components/schemas/FredPersonalConsumptionExpendituresData" + }, + "type": "array" + }, + { + "type": null + } + ], + "title": "Results", + "description": "Serializable results." + }, + "provider": { + "anyOf": [ + { + "type": "string" + }, + { + "type": null + } + ], + "title": "Provider", + "description": "Provider name." + }, + "warnings": { + "anyOf": [ + { + "items": { + "$ref": "#/components/schemas/Warning_" + }, + "type": "array" + }, + { + "type": null + } + ], + "title": "Warnings", + "description": "List of warnings." + }, + "chart": { + "anyOf": [ + { + "$ref": "#/components/schemas/Chart" + }, + { + "type": null + } + ], + "description": "Chart object." + }, + "extra": { + "type": "object", + "title": "Extra", + "description": "Extra info." + } + }, + "type": "object", + "title": "OBBject_PersonalConsumptionExpenditures", + "description": "OBBject with results of type PersonalConsumptionExpenditures" + }, + "OBBject_RetailPrices": { + "properties": { + "_id": { + "type": "string", + "title": " Id" + }, + "results": { + "anyOf": [ + { + "items": { + "$ref": "#/components/schemas/FredRetailPricesData" + }, + "type": "array" + }, + { + "type": null + } + ], + "title": "Results", + "description": "Serializable results." + }, + "provider": { + "anyOf": [ + { + "type": "string" + }, + { + "type": null + } + ], + "title": "Provider", + "description": "Provider name." + }, + "warnings": { + "anyOf": [ + { + "items": { + "$ref": "#/components/schemas/Warning_" + }, + "type": "array" + }, + { + "type": null + } + ], + "title": "Warnings", + "description": "List of warnings." + }, + "chart": { + "anyOf": [ + { + "$ref": "#/components/schemas/Chart" + }, + { + "type": null + } + ], + "description": "Chart object." + }, + "extra": { + "type": "object", + "title": "Extra", + "description": "Extra info." + } + }, + "type": "object", + "title": "OBBject_RetailPrices", + "description": "OBBject with results of type RetailPrices" + }, + "OBBject_RssLitigation": { + "properties": { + "_id": { + "type": "string", + "title": " Id" + }, + "results": { + "anyOf": [ + { + "items": { + "$ref": "#/components/schemas/SecRssLitigationData" + }, + "type": "array" + }, + { + "type": null + } + ], + "title": "Results", + "description": "Serializable results." + }, + "provider": { + "anyOf": [ + { + "type": "string" + }, + { + "type": null + } + ], + "title": "Provider", + "description": "Provider name." + }, + "warnings": { + "anyOf": [ + { + "items": { + "$ref": "#/components/schemas/Warning_" + }, + "type": "array" + }, + { + "type": null + } + ], + "title": "Warnings", + "description": "List of warnings." + }, + "chart": { + "anyOf": [ + { + "$ref": "#/components/schemas/Chart" + }, + { + "type": null + } + ], + "description": "Chart object." + }, + "extra": { + "type": "object", + "title": "Extra", + "description": "Extra info." + } + }, + "type": "object", + "title": "OBBject_RssLitigation", + "description": "OBBject with results of type RssLitigation" + }, + "OBBject_SchemaFiles": { + "properties": { + "_id": { + "type": "string", + "title": " Id" + }, + "results": { + "anyOf": [ + { + "$ref": "#/components/schemas/SecSchemaFilesData" + }, + { + "type": null + } + ], + "description": "Serializable results." + }, + "provider": { + "anyOf": [ + { + "type": "string" + }, + { + "type": null + } + ], + "title": "Provider", + "description": "Provider name." + }, + "warnings": { + "anyOf": [ + { + "items": { + "$ref": "#/components/schemas/Warning_" + }, + "type": "array" + }, + { + "type": null + } + ], + "title": "Warnings", + "description": "List of warnings." + }, + "chart": { + "anyOf": [ + { + "$ref": "#/components/schemas/Chart" + }, + { + "type": null + } + ], + "description": "Chart object." + }, + "extra": { + "type": "object", + "title": "Extra", + "description": "Extra info." + } + }, + "type": "object", + "title": "OBBject_SchemaFiles", + "description": "OBBject with results of type SchemaFiles" + }, + "OBBject_SeniorLoanOfficerSurvey": { + "properties": { + "_id": { + "type": "string", + "title": " Id" + }, + "results": { + "anyOf": [ + { + "items": { + "$ref": "#/components/schemas/FredSeniorLoanOfficerSurveyData" + }, + "type": "array" + }, + { + "type": null + } + ], + "title": "Results", + "description": "Serializable results." + }, + "provider": { + "anyOf": [ + { + "type": "string" + }, + { + "type": null + } + ], + "title": "Provider", + "description": "Provider name." + }, + "warnings": { + "anyOf": [ + { + "items": { + "$ref": "#/components/schemas/Warning_" + }, + "type": "array" + }, + { + "type": null + } + ], + "title": "Warnings", + "description": "List of warnings." + }, + "chart": { + "anyOf": [ + { + "$ref": "#/components/schemas/Chart" + }, + { + "type": null + } + ], + "description": "Chart object." + }, + "extra": { + "type": "object", + "title": "Extra", + "description": "Extra info." + } + }, + "type": "object", + "title": "OBBject_SeniorLoanOfficerSurvey", + "description": "OBBject with results of type SeniorLoanOfficerSurvey" + }, + "OBBject_SicSearch": { + "properties": { + "_id": { + "type": "string", + "title": " Id" + }, + "results": { + "anyOf": [ + { + "items": { + "$ref": "#/components/schemas/SecSicSearchData" + }, + "type": "array" + }, + { + "type": null + } + ], + "title": "Results", + "description": "Serializable results." + }, + "provider": { + "anyOf": [ + { + "type": "string" + }, + { + "type": null + } + ], + "title": "Provider", + "description": "Provider name." + }, + "warnings": { + "anyOf": [ + { + "items": { + "$ref": "#/components/schemas/Warning_" + }, + "type": "array" + }, + { + "type": null + } + ], + "title": "Warnings", + "description": "List of warnings." + }, + "chart": { + "anyOf": [ + { + "$ref": "#/components/schemas/Chart" + }, + { + "type": null + } + ], + "description": "Chart object." + }, + "extra": { + "type": "object", + "title": "Extra", + "description": "Extra info." + } + }, + "type": "object", + "title": "OBBject_SicSearch", + "description": "OBBject with results of type SicSearch" + }, + "OBBject_SurveyOfEconomicConditionsChicago": { + "properties": { + "_id": { + "type": "string", + "title": " Id" + }, + "results": { + "anyOf": [ + { + "items": { + "$ref": "#/components/schemas/FredSurveyOfEconomicConditionsChicagoData" + }, + "type": "array" + }, + { + "type": null + } + ], + "title": "Results", + "description": "Serializable results." + }, + "provider": { + "anyOf": [ + { + "type": "string" + }, + { + "type": null + } + ], + "title": "Provider", + "description": "Provider name." + }, + "warnings": { + "anyOf": [ + { + "items": { + "$ref": "#/components/schemas/Warning_" + }, + "type": "array" + }, + { + "type": null + } + ], + "title": "Warnings", + "description": "List of warnings." + }, + "chart": { + "anyOf": [ + { + "$ref": "#/components/schemas/Chart" + }, + { + "type": null + } + ], + "description": "Chart object." + }, + "extra": { + "type": "object", + "title": "Extra", + "description": "Extra info." + } + }, + "type": "object", + "title": "OBBject_SurveyOfEconomicConditionsChicago", + "description": "OBBject with results of type SurveyOfEconomicConditionsChicago" + }, + "OBBject_SymbolMap": { + "properties": { + "_id": { + "type": "string", + "title": " Id" + }, + "results": { + "anyOf": [ + { + "$ref": "#/components/schemas/SecSymbolMapData" + }, + { + "type": null + } + ], + "description": "Serializable results." + }, + "provider": { + "anyOf": [ + { + "type": "string" + }, + { + "type": null + } + ], + "title": "Provider", + "description": "Provider name." + }, + "warnings": { + "anyOf": [ + { + "items": { + "$ref": "#/components/schemas/Warning_" + }, + "type": "array" + }, + { + "type": null + } + ], + "title": "Warnings", + "description": "List of warnings." + }, + "chart": { + "anyOf": [ + { + "$ref": "#/components/schemas/Chart" + }, + { + "type": null + } + ], + "description": "Chart object." + }, + "extra": { + "type": "object", + "title": "Extra", + "description": "Extra info." + } + }, + "type": "object", + "title": "OBBject_SymbolMap", + "description": "OBBject with results of type SymbolMap" + }, + "OBBject_UniversityOfMichigan": { + "properties": { + "_id": { + "type": "string", + "title": " Id" + }, + "results": { + "anyOf": [ + { + "items": { + "$ref": "#/components/schemas/FredUofMichiganData" + }, + "type": "array" + }, + { + "type": null + } + ], + "title": "Results", + "description": "Serializable results." + }, + "provider": { + "anyOf": [ + { + "type": "string" + }, + { + "type": null + } + ], + "title": "Provider", + "description": "Provider name." + }, + "warnings": { + "anyOf": [ + { + "items": { + "$ref": "#/components/schemas/Warning_" + }, + "type": "array" + }, + { + "type": null + } + ], + "title": "Warnings", + "description": "List of warnings." + }, + "chart": { + "anyOf": [ + { + "$ref": "#/components/schemas/Chart" + }, + { + "type": null + } + ], + "description": "Chart object." + }, + "extra": { + "type": "object", + "title": "Extra", + "description": "Extra info." + } + }, + "type": "object", + "title": "OBBject_UniversityOfMichigan", + "description": "OBBject with results of type UniversityOfMichigan" + }, + "OpenBBErrorResponse": { + "properties": { + "detail": { + "type": "string", + "title": "Detail" + }, + "error_kind": { + "type": "string", + "title": "Error Kind" + } + }, + "type": "object", + "required": [ + "detail", + "error_kind" + ], + "title": "OpenBBErrorResponse", + "description": "OpenBB Error Response." + }, + "SecCikMapData": { + "properties": { + "cik": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "integer" + }, + { + "type": null + } + ], + "title": "Cik", + "description": "Central Index Key (CIK) for the requested entity." + } + }, + "additionalProperties": true, + "type": "object", + "title": "SecCikMapData", + "description": "SEC CIK Mapping Data." + }, + "SecInstitutionsSearchData": { + "properties": { + "Institution": { + "anyOf": [ + { + "type": "string" + }, + { + "type": null + } + ], + "title": "Institution", + "description": "The name of the institution." + }, + "CIK Number": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "integer" + }, + { + "type": null + } + ], + "title": "Cik Number", + "description": "Central Index Key (CIK)" + } + }, + "additionalProperties": true, + "type": "object", + "title": "SecInstitutionsSearchData", + "description": "SEC Institutions Search Data." + }, + "SecRssLitigationData": { + "properties": { + "date": { + "type": "string", + "format": "date-time", + "title": "Date", + "description": "The date of publication." + }, + "title": { + "type": "string", + "title": "Title", + "description": "The title of the release." + }, + "summary": { + "type": "string", + "title": "Summary", + "description": "Short summary of the release." + }, + "id": { + "type": "string", + "title": "Id", + "description": "The identifier associated with the release." + }, + "link": { + "type": "string", + "title": "Link", + "description": "URL to the release." + } + }, + "additionalProperties": true, + "type": "object", + "required": [ + "date", + "title", + "summary", + "id", + "link" + ], + "title": "SecRssLitigationData", + "description": "SEC Litigation RSS Feed Data." + }, + "SecSchemaFilesData": { + "properties": { + "files": { + "items": { + "type": "string" + }, + "type": "array", + "title": "Files", + "description": "Dictionary of URLs to SEC Schema Files" + } + }, + "additionalProperties": true, + "type": "object", + "required": [ + "files" + ], + "title": "SecSchemaFilesData", + "description": "SEC Schema Files List Data." + }, + "SecSicSearchData": { + "properties": { + "SIC Code": { + "type": "integer", + "title": "Sic Code", + "description": "Sector Industrial Code (SIC)" + }, + "Industry Title": { + "type": "string", + "title": "Industry Title", + "description": "Industry title." + }, + "Office": { + "type": "string", + "title": "Office", + "description": "Reporting office within the Corporate Finance Office" + } + }, + "additionalProperties": true, + "type": "object", + "required": [ + "SIC Code", + "Industry Title", + "Office" + ], + "title": "SecSicSearchData", + "description": "SEC Standard Industrial Classification Code (SIC) Data." + }, + "SecSymbolMapData": { + "properties": { + "symbol": { + "type": "string", + "title": "Symbol", + "description": "Symbol representing the entity requested in the data." + } + }, + "additionalProperties": true, + "type": "object", + "required": [ + "symbol" + ], + "title": "SecSymbolMapData", + "description": "SEC symbol map Data." + }, + "Form": { + "properties": { + "date": { + "type": "string", + "format": "date", + "title": "Record Opened Date" + }, + "record_id": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "number" + } + ], + "title": "Record ID" + }, + "first_name": { + "anyOf": [ + { + "type": "string" + }, + { + "type": null + } + ], + "title": "First Name" + }, + "last_name": { + "anyOf": [ + { + "type": "string" + }, + { + "type": null + } + ], + "title": "Last Name" + }, + "symptoms": { + "anyOf": [ + { + "type": "string" + }, + { + "items": { + "type": "string" + }, + "type": "array" + } + ], + "title": "Symptoms", + "choices": [ + "dizzy", + "hungry", + "sleepy" + ], + "x-widget_config": { + "multiSelect": true + } + }, + "diagnosis": { + "anyOf": [ + { + "type": "string" + }, + { + "type": null + } + ], + "title": "Diagnosis" + }, + "add_record": { + "type": "button", + "title": "Add Record", + "default": true + } + }, + "additionalProperties": true, + "type": "object", + "title": "Submit Form", + "description": "Submit a form via POST request." + }, + "ValidationError": { + "properties": { + "loc": { + "items": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "integer" + } + ] + }, + "type": "array", + "title": "Location" + }, + "msg": { + "type": "string", + "title": "Message" + }, + "type": { + "type": "string", + "title": "Error Type" + } + }, + "type": "object", + "required": [ + "loc", + "msg", + "type" + ], + "title": "ValidationError" + }, + "Warning_": { + "properties": { + "category": { + "type": "string", + "title": "Category" + }, + "message": { + "type": "string", + "title": "Message" + } + }, + "type": "object", + "required": [ + "category", + "message" + ], + "title": "Warning_", + "description": "Model for Warning." + } + } + }, + "tags": [ + { + "name": "economy", + "description": "Economic data." + }, + { + "name": "regulators", + "description": "Financial market regulators data." + } + ] +} diff --git a/openbb_platform/extensions/platform_api/tests/mock_widgets.json b/openbb_platform/extensions/platform_api/tests/mock_widgets.json new file mode 100644 index 0000000000000000000000000000000000000000..72496d8a69383dcaf36da34f8f76b9b6127bca7f --- /dev/null +++ b/openbb_platform/extensions/platform_api/tests/mock_widgets.json @@ -0,0 +1,3748 @@ +{ + "economy_survey_sloos_fred_obb": { + "name": "SLOOS", + "description": "Get Senior Loan Officers Opinion Survey.", + "category": "Economy", + "type": "table", + "searchCategory": "Economy", + "widgetId": "economy_survey_sloos_fred_obb", + "params": [ + { + "label": "Start Date", + "description": "Start date of the data, in YYYY-MM-DD format.", + "optional": true, + "type": "date", + "value": null, + "show": true, + "paramName": "start_date" + }, + { + "label": "End Date", + "description": "End date of the data, in YYYY-MM-DD format.", + "optional": true, + "type": "date", + "value": null, + "show": true, + "paramName": "end_date" + }, + { + "label": "Category", + "description": "Category of survey response.", + "optional": true, + "type": "text", + "value": "spreads", + "show": true, + "options": [ + { + "label": "spreads", + "value": "spreads" + }, + { + "label": "consumer", + "value": "consumer" + }, + { + "label": "auto", + "value": "auto" + }, + { + "label": "credit_card", + "value": "credit_card" + }, + { + "label": "firms", + "value": "firms" + }, + { + "label": "mortgage", + "value": "mortgage" + }, + { + "label": "commercial_real_estate", + "value": "commercial_real_estate" + }, + { + "label": "standards", + "value": "standards" + }, + { + "label": "demand", + "value": "demand" + }, + { + "label": "foreign_banks", + "value": "foreign_banks" + } + ], + "paramName": "category" + }, + { + "label": "Transform", + "description": "Transformation type\n None = No transformation\n chg = Change\n ch1 = Change from Year Ago\n pch = Percent Change\n pc1 = Percent Change from Year Ago\n pca = Compounded Annual Rate of Change\n cch = Continuously Compounded Rate of Change\n cca = Continuously Compounded Annual Rate of Change\n log = Natural Log", + "optional": true, + "type": "text", + "value": null, + "show": true, + "options": [ + { + "label": "chg", + "value": "chg" + }, + { + "label": "ch1", + "value": "ch1" + }, + { + "label": "pch", + "value": "pch" + }, + { + "label": "pc1", + "value": "pc1" + }, + { + "label": "pca", + "value": "pca" + }, + { + "label": "cch", + "value": "cch" + }, + { + "label": "cca", + "value": "cca" + }, + { + "label": "log", + "value": "log" + } + ], + "paramName": "transform" + }, + { + "paramName": "provider", + "value": "fred", + "show": false + } + ], + "endpoint": "api/v1/economy/survey/sloos", + "runButton": false, + "gridData": { + "w": 40, + "h": 15 + }, + "data": { + "dataKey": "results", + "table": { + "showAll": true, + "columnsDefs": [ + { + "field": "date", + "pinned": "left", + "formatterFn": null, + "headerName": "Date", + "headerTooltip": "The date of the data.", + "cellDataType": "date" + }, + { + "field": "symbol", + "pinned": "left", + "formatterFn": null, + "headerName": "Symbol", + "headerTooltip": "Symbol representing the entity requested in the data.", + "cellDataType": "text" + }, + { + "field": "value", + "formatterFn": null, + "headerName": "Value", + "headerTooltip": "Survey value.", + "cellDataType": "number" + }, + { + "field": "title", + "pinned": "left", + "formatterFn": null, + "headerName": "Title", + "headerTooltip": "Survey title.", + "cellDataType": "text" + } + ] + } + }, + "source": [ + "FRED" + ], + "subCategory": "Survey" + }, + "economy_survey_university_of_michigan_fred_obb": { + "name": "University Of Michigan", + "description": "Get University of Michigan Consumer Sentiment and Inflation Expectations Surveys.", + "category": "Economy", + "type": "table", + "searchCategory": "Economy", + "widgetId": "economy_survey_university_of_michigan_fred_obb", + "params": [ + { + "label": "Start Date", + "description": "Start date of the data, in YYYY-MM-DD format.", + "optional": true, + "type": "date", + "value": null, + "show": true, + "paramName": "start_date" + }, + { + "label": "End Date", + "description": "End date of the data, in YYYY-MM-DD format.", + "optional": true, + "type": "date", + "value": null, + "show": true, + "paramName": "end_date" + }, + { + "label": "Frequency", + "description": "Frequency aggregation to convert monthly data to lower frequency. None is monthly.", + "optional": true, + "type": "text", + "value": null, + "show": true, + "options": [ + { + "label": "annual", + "value": "annual" + }, + { + "label": "quarter", + "value": "quarter" + } + ], + "paramName": "frequency" + }, + { + "label": "Aggregation Method", + "description": "A key that indicates the aggregation method used for frequency aggregation.\n \n avg = Average\n \n sum = Sum\n \n eop = End of Period", + "optional": true, + "type": "text", + "value": null, + "show": true, + "options": [ + { + "label": "avg", + "value": "avg" + }, + { + "label": "sum", + "value": "sum" + }, + { + "label": "eop", + "value": "eop" + } + ], + "paramName": "aggregation_method" + }, + { + "label": "Transform", + "description": "Transformation type\n \n None = No transformation\n \n chg = Change\n \n ch1 = Change from Year Ago\n \n pch = Percent Change\n \n pc1 = Percent Change from Year Ago\n \n pca = Compounded Annual Rate of Change\n \n cch = Continuously Compounded Rate of Change\n \n cca = Continuously Compounded Annual Rate of Change\n \n log = Natural Log", + "optional": true, + "type": "text", + "value": null, + "show": true, + "options": [ + { + "label": "chg", + "value": "chg" + }, + { + "label": "ch1", + "value": "ch1" + }, + { + "label": "pch", + "value": "pch" + }, + { + "label": "pc1", + "value": "pc1" + }, + { + "label": "pca", + "value": "pca" + }, + { + "label": "cch", + "value": "cch" + }, + { + "label": "cca", + "value": "cca" + }, + { + "label": "log", + "value": "log" + } + ], + "paramName": "transform" + }, + { + "paramName": "provider", + "value": "fred", + "show": false + } + ], + "endpoint": "api/v1/economy/survey/university_of_michigan", + "runButton": false, + "gridData": { + "w": 40, + "h": 15 + }, + "data": { + "dataKey": "results", + "table": { + "showAll": true, + "columnsDefs": [ + { + "field": "date", + "pinned": "left", + "formatterFn": null, + "headerName": "Date", + "headerTooltip": "The date of the data.", + "cellDataType": "date" + }, + { + "field": "consumer_sentiment", + "formatterFn": null, + "headerName": "Consumer Sentiment", + "headerTooltip": "Index of the results of the University of Michigan's monthly Survey of Consumers, which is used to estimate future spending and saving. (1966:Q1=100).", + "cellDataType": "number" + }, + { + "field": "inflation_expectation", + "formatterFn": "normalizedPercent", + "headerName": "Inflation Expectation", + "headerTooltip": "Median expected price change next 12 months, Surveys of Consumers.", + "cellDataType": "number", + "renderFn": "greenRed" + } + ] + } + }, + "source": [ + "FRED" + ], + "subCategory": "Survey" + }, + "economy_survey_economic_conditions_chicago_fred_obb": { + "name": "Economic Conditions Chicago", + "description": "Get The Survey Of Economic Conditions For The Chicago Region.", + "category": "Economy", + "type": "table", + "searchCategory": "Economy", + "widgetId": "economy_survey_economic_conditions_chicago_fred_obb", + "params": [ + { + "label": "Start Date", + "description": "Start date of the data, in YYYY-MM-DD format.", + "optional": true, + "type": "date", + "value": null, + "show": true, + "paramName": "start_date" + }, + { + "label": "End Date", + "description": "End date of the data, in YYYY-MM-DD format.", + "optional": true, + "type": "date", + "value": null, + "show": true, + "paramName": "end_date" + }, + { + "label": "Frequency", + "description": "Frequency aggregation to convert monthly data to lower frequency. None is monthly.", + "optional": true, + "type": "text", + "value": null, + "show": true, + "options": [ + { + "label": "annual", + "value": "annual" + }, + { + "label": "quarter", + "value": "quarter" + } + ], + "paramName": "frequency" + }, + { + "label": "Aggregation Method", + "description": "A key that indicates the aggregation method used for frequency aggregation.\n \n avg = Average\n \n sum = Sum\n \n eop = End of Period", + "optional": true, + "type": "text", + "value": null, + "show": true, + "options": [ + { + "label": "avg", + "value": "avg" + }, + { + "label": "sum", + "value": "sum" + }, + { + "label": "eop", + "value": "eop" + } + ], + "paramName": "aggregation_method" + }, + { + "label": "Transform", + "description": "Transformation type\n \n None = No transformation\n \n chg = Change\n \n ch1 = Change from Year Ago\n \n pch = Percent Change\n \n pc1 = Percent Change from Year Ago\n \n pca = Compounded Annual Rate of Change\n \n cch = Continuously Compounded Rate of Change\n \n cca = Continuously Compounded Annual Rate of Change\n \n log = Natural Log", + "optional": true, + "type": "text", + "value": null, + "show": true, + "options": [ + { + "label": "chg", + "value": "chg" + }, + { + "label": "ch1", + "value": "ch1" + }, + { + "label": "pch", + "value": "pch" + }, + { + "label": "pc1", + "value": "pc1" + }, + { + "label": "pca", + "value": "pca" + }, + { + "label": "cch", + "value": "cch" + }, + { + "label": "cca", + "value": "cca" + }, + { + "label": "log", + "value": "log" + } + ], + "paramName": "transform" + }, + { + "paramName": "provider", + "value": "fred", + "show": false + } + ], + "endpoint": "api/v1/economy/survey/economic_conditions_chicago", + "runButton": false, + "gridData": { + "w": 40, + "h": 15 + }, + "data": { + "dataKey": "results", + "table": { + "showAll": true, + "columnsDefs": [ + { + "field": "date", + "pinned": "left", + "formatterFn": null, + "headerName": "Date", + "headerTooltip": "The date of the data.", + "cellDataType": "date" + }, + { + "field": "activity_index", + "formatterFn": null, + "headerName": "Activity Index", + "headerTooltip": "Activity Index.", + "cellDataType": "number" + }, + { + "field": "one_year_outlook", + "formatterFn": null, + "headerName": "One Year Outlook", + "headerTooltip": "One Year Outlook Index.", + "cellDataType": "number" + }, + { + "field": "manufacturing_activity", + "formatterFn": null, + "headerName": "Manufacturing Activity", + "headerTooltip": "Manufacturing Activity Index.", + "cellDataType": "number" + }, + { + "field": "non_manufacturing_activity", + "formatterFn": null, + "headerName": "Non Manufacturing Activity", + "headerTooltip": "Non-Manufacturing Activity Index.", + "cellDataType": "number" + }, + { + "field": "capital_expenditures_expectations", + "formatterFn": null, + "headerName": "Capital Expenditures Expectations", + "headerTooltip": "Capital Expenditures Expectations Index.", + "cellDataType": "number" + }, + { + "field": "hiring_expectations", + "formatterFn": null, + "headerName": "Hiring Expectations", + "headerTooltip": "Hiring Expectations Index.", + "cellDataType": "number" + }, + { + "field": "current_hiring", + "formatterFn": null, + "headerName": "Current Hiring", + "headerTooltip": "Current Hiring Index.", + "cellDataType": "number" + }, + { + "field": "labor_costs", + "formatterFn": null, + "headerName": "Labor Costs", + "headerTooltip": "Labor Costs Index.", + "cellDataType": "number" + }, + { + "field": "non_labor_costs", + "formatterFn": null, + "headerName": "Non Labor Costs", + "headerTooltip": "Non-Labor Costs Index.", + "cellDataType": "number" + } + ] + } + }, + "source": [ + "FRED" + ], + "subCategory": "Survey" + }, + "economy_survey_manufacturing_outlook_texas_fred_obb": { + "name": "Manufacturing Outlook Texas", + "description": "Get The Manufacturing Outlook Survey For The Texas Region.", + "category": "Economy", + "type": "table", + "searchCategory": "Economy", + "widgetId": "economy_survey_manufacturing_outlook_texas_fred_obb", + "params": [ + { + "label": "Start Date", + "description": "Start date of the data, in YYYY-MM-DD format.", + "optional": true, + "type": "date", + "value": null, + "show": true, + "paramName": "start_date" + }, + { + "label": "End Date", + "description": "End date of the data, in YYYY-MM-DD format.", + "optional": true, + "type": "date", + "value": null, + "show": true, + "paramName": "end_date" + }, + { + "label": "Topic", + "description": "The topic for the survey response. Multiple comma separated items allowed.", + "optional": true, + "type": "text", + "value": "new_orders_growth", + "show": true, + "multiSelect": true, + "options": [ + { + "label": "business_activity", + "value": "business_activity" + }, + { + "label": "business_outlook", + "value": "business_outlook" + }, + { + "label": "capex", + "value": "capex" + }, + { + "label": "prices_paid", + "value": "prices_paid" + }, + { + "label": "production", + "value": "production" + }, + { + "label": "inventory", + "value": "inventory" + }, + { + "label": "new_orders", + "value": "new_orders" + }, + { + "label": "new_orders_growth", + "value": "new_orders_growth" + }, + { + "label": "unfilled_orders", + "value": "unfilled_orders" + }, + { + "label": "shipments", + "value": "shipments" + }, + { + "label": "delivery_time", + "value": "delivery_time" + }, + { + "label": "employment", + "value": "employment" + }, + { + "label": "wages", + "value": "wages" + }, + { + "label": "hours_worked", + "value": "hours_worked" + } + ], + "paramName": "topic" + }, + { + "label": "Frequency", + "description": "Frequency aggregation to convert monthly data to lower frequency. None is monthly.", + "optional": true, + "type": "text", + "value": null, + "show": true, + "options": [ + { + "label": "annual", + "value": "annual" + }, + { + "label": "quarter", + "value": "quarter" + } + ], + "paramName": "frequency" + }, + { + "label": "Aggregation Method", + "description": "A key that indicates the aggregation method used for frequency aggregation.\n avg = Average\n sum = Sum\n eop = End of Period", + "optional": true, + "type": "text", + "value": null, + "show": true, + "options": [ + { + "label": "avg", + "value": "avg" + }, + { + "label": "sum", + "value": "sum" + }, + { + "label": "eop", + "value": "eop" + } + ], + "paramName": "aggregation_method" + }, + { + "label": "Transform", + "description": "Transformation type\n None = No transformation\n chg = Change\n ch1 = Change from Year Ago\n pch = Percent Change\n pc1 = Percent Change from Year Ago\n pca = Compounded Annual Rate of Change\n cch = Continuously Compounded Rate of Change\n cca = Continuously Compounded Annual Rate of Change\n log = Natural Log", + "optional": true, + "type": "text", + "value": null, + "show": true, + "options": [ + { + "label": "chg", + "value": "chg" + }, + { + "label": "ch1", + "value": "ch1" + }, + { + "label": "pch", + "value": "pch" + }, + { + "label": "pc1", + "value": "pc1" + }, + { + "label": "pca", + "value": "pca" + }, + { + "label": "cch", + "value": "cch" + }, + { + "label": "cca", + "value": "cca" + }, + { + "label": "log", + "value": "log" + } + ], + "paramName": "transform" + }, + { + "paramName": "provider", + "value": "fred", + "show": false + } + ], + "endpoint": "api/v1/economy/survey/manufacturing_outlook_texas", + "runButton": false, + "gridData": { + "w": 40, + "h": 15 + }, + "data": { + "dataKey": "results", + "table": { + "showAll": true, + "columnsDefs": [ + { + "field": "date", + "pinned": "left", + "formatterFn": null, + "headerName": "Date", + "headerTooltip": "The date of the data.", + "cellDataType": "date" + }, + { + "field": "topic", + "formatterFn": null, + "headerName": "Topic", + "headerTooltip": "Topic of the survey response.", + "cellDataType": "text" + }, + { + "field": "diffusion_index", + "formatterFn": null, + "headerName": "Diffusion Index", + "headerTooltip": "Diffusion Index.", + "cellDataType": "number" + }, + { + "field": "percent_reporting_increase", + "formatterFn": "normalizedPercent", + "headerName": "Percent Reporting Increase", + "headerTooltip": "Percent of respondents reporting an increase over the last month.", + "cellDataType": "number", + "renderFn": "greenRed" + }, + { + "field": "percent_reporting_decrease", + "formatterFn": "normalizedPercent", + "headerName": "Percent Reporting Decrease", + "headerTooltip": "Percent of respondents reporting a decrease over the last month.", + "cellDataType": "number", + "renderFn": "greenRed" + }, + { + "field": "percent_reporting_no_change", + "formatterFn": "normalizedPercent", + "headerName": "Percent Reporting No Change", + "headerTooltip": "Percent of respondents reporting no change over the last month.", + "cellDataType": "number", + "renderFn": "greenRed" + } + ] + } + }, + "source": [ + "FRED" + ], + "subCategory": "Survey" + }, + "economy_survey_nonfarm_payrolls_fred_obb": { + "name": "Nonfarm Payrolls", + "description": "Get Nonfarm Payrolls Survey.", + "category": "Economy", + "type": "table", + "searchCategory": "Economy", + "widgetId": "economy_survey_nonfarm_payrolls_fred_obb", + "params": [ + { + "label": "Date", + "description": "A specific date to get data for. Default is the latest report. Multiple comma separated items allowed.", + "optional": true, + "type": "text", + "value": null, + "show": true, + "multiSelect": true, + "paramName": "date" + }, + { + "label": "Category", + "description": "The category to query.", + "optional": true, + "type": "text", + "value": "employees_nsa", + "show": true, + "options": [ + { + "label": "employees_nsa", + "value": "employees_nsa" + }, + { + "label": "employees_sa", + "value": "employees_sa" + }, + { + "label": "employees_production_and_nonsupervisory", + "value": "employees_production_and_nonsupervisory" + }, + { + "label": "employees_women", + "value": "employees_women" + }, + { + "label": "employees_women_percent", + "value": "employees_women_percent" + }, + { + "label": "avg_hours", + "value": "avg_hours" + }, + { + "label": "avg_hours_production_and_nonsupervisory", + "value": "avg_hours_production_and_nonsupervisory" + }, + { + "label": "avg_hours_overtime", + "value": "avg_hours_overtime" + }, + { + "label": "avg_hours_overtime_production_and_nonsupervisory", + "value": "avg_hours_overtime_production_and_nonsupervisory" + }, + { + "label": "avg_earnings_hourly", + "value": "avg_earnings_hourly" + }, + { + "label": "avg_earnings_hourly_production_and_nonsupervisory", + "value": "avg_earnings_hourly_production_and_nonsupervisory" + }, + { + "label": "avg_earnings_weekly", + "value": "avg_earnings_weekly" + }, + { + "label": "avg_earnings_weekly_production_and_nonsupervisory", + "value": "avg_earnings_weekly_production_and_nonsupervisory" + }, + { + "label": "index_weekly_hours", + "value": "index_weekly_hours" + }, + { + "label": "index_weekly_hours_production_and_nonsupervisory", + "value": "index_weekly_hours_production_and_nonsupervisory" + }, + { + "label": "index_weekly_payrolls", + "value": "index_weekly_payrolls" + }, + { + "label": "index_weekly_payrolls_production_and_nonsupervisory", + "value": "index_weekly_payrolls_production_and_nonsupervisory" + } + ], + "paramName": "category" + }, + { + "paramName": "provider", + "value": "fred", + "show": false + } + ], + "endpoint": "api/v1/economy/survey/nonfarm_payrolls", + "runButton": false, + "gridData": { + "w": 40, + "h": 15 + }, + "data": { + "dataKey": "results", + "table": { + "showAll": true, + "columnsDefs": [ + { + "field": "date", + "pinned": "left", + "formatterFn": null, + "headerName": "Date", + "headerTooltip": "The date of the data.", + "cellDataType": "date" + }, + { + "field": "symbol", + "pinned": "left", + "formatterFn": null, + "headerName": "Symbol", + "headerTooltip": "Symbol representing the entity requested in the data.", + "cellDataType": "text" + }, + { + "field": "value", + "formatterFn": null, + "headerName": "Value", + "headerTooltip": "", + "cellDataType": "number" + }, + { + "field": "name", + "pinned": "left", + "formatterFn": null, + "headerName": "Name", + "headerTooltip": "The name of the series.", + "cellDataType": "text" + }, + { + "field": "element_id", + "formatterFn": null, + "headerName": "Element ID", + "headerTooltip": "The element id in the parent/child relationship.", + "cellDataType": "text" + }, + { + "field": "parent_id", + "formatterFn": null, + "headerName": "Parent ID", + "headerTooltip": "The parent id in the parent/child relationship.", + "cellDataType": "text" + }, + { + "field": "children", + "formatterFn": null, + "headerName": "Children", + "headerTooltip": "The element_id of each child, as a comma-separated string.", + "cellDataType": "text" + }, + { + "field": "level", + "formatterFn": "int", + "headerName": "Level", + "headerTooltip": "The indentation level of the element.", + "cellDataType": "number" + } + ] + } + }, + "source": [ + "FRED" + ], + "subCategory": "Survey" + }, + "economy_cpi_fred_obb": { + "name": "Cpi", + "description": "Get Consumer Price Index (CPI).\n\nReturns either the rescaled index value, or a rate of change (inflation).", + "category": "Economy", + "type": "table", + "searchCategory": "Economy", + "widgetId": "economy_cpi_fred_obb", + "params": [ + { + "label": "Country", + "description": "The country to get data. Multiple comma separated items allowed.", + "optional": true, + "type": "text", + "value": "united_states", + "show": true, + "multiSelect": true, + "options": [ + { + "label": "australia", + "value": "australia" + }, + { + "label": "austria", + "value": "austria" + }, + { + "label": "belgium", + "value": "belgium" + }, + { + "label": "brazil", + "value": "brazil" + }, + { + "label": "bulgaria", + "value": "bulgaria" + }, + { + "label": "canada", + "value": "canada" + }, + { + "label": "chile", + "value": "chile" + }, + { + "label": "china", + "value": "china" + }, + { + "label": "croatia", + "value": "croatia" + }, + { + "label": "cyprus", + "value": "cyprus" + }, + { + "label": "czech_republic", + "value": "czech_republic" + }, + { + "label": "denmark", + "value": "denmark" + }, + { + "label": "estonia", + "value": "estonia" + }, + { + "label": "euro_area", + "value": "euro_area" + }, + { + "label": "finland", + "value": "finland" + }, + { + "label": "france", + "value": "france" + }, + { + "label": "germany", + "value": "germany" + }, + { + "label": "greece", + "value": "greece" + }, + { + "label": "hungary", + "value": "hungary" + }, + { + "label": "iceland", + "value": "iceland" + }, + { + "label": "india", + "value": "india" + }, + { + "label": "indonesia", + "value": "indonesia" + }, + { + "label": "ireland", + "value": "ireland" + }, + { + "label": "israel", + "value": "israel" + }, + { + "label": "italy", + "value": "italy" + }, + { + "label": "japan", + "value": "japan" + }, + { + "label": "korea", + "value": "korea" + }, + { + "label": "latvia", + "value": "latvia" + }, + { + "label": "lithuania", + "value": "lithuania" + }, + { + "label": "luxembourg", + "value": "luxembourg" + }, + { + "label": "malta", + "value": "malta" + }, + { + "label": "mexico", + "value": "mexico" + }, + { + "label": "netherlands", + "value": "netherlands" + }, + { + "label": "new_zealand", + "value": "new_zealand" + }, + { + "label": "norway", + "value": "norway" + }, + { + "label": "poland", + "value": "poland" + }, + { + "label": "portugal", + "value": "portugal" + }, + { + "label": "romania", + "value": "romania" + }, + { + "label": "russian_federation", + "value": "russian_federation" + }, + { + "label": "slovak_republic", + "value": "slovak_republic" + }, + { + "label": "slovakia", + "value": "slovakia" + }, + { + "label": "slovenia", + "value": "slovenia" + }, + { + "label": "south_africa", + "value": "south_africa" + }, + { + "label": "spain", + "value": "spain" + }, + { + "label": "sweden", + "value": "sweden" + }, + { + "label": "switzerland", + "value": "switzerland" + }, + { + "label": "turkey", + "value": "turkey" + }, + { + "label": "united_kingdom", + "value": "united_kingdom" + }, + { + "label": "united_states", + "value": "united_states" + } + ], + "paramName": "country" + }, + { + "label": "Transform", + "description": "Transformation of the CPI data. Period represents the change since previous. Defaults to change from one year ago (yoy).", + "optional": true, + "type": "text", + "value": "yoy", + "show": true, + "options": [ + { + "label": "index", + "value": "index" + }, + { + "label": "yoy", + "value": "yoy" + }, + { + "label": "period", + "value": "period" + } + ], + "paramName": "transform" + }, + { + "label": "Frequency", + "description": "The frequency of the data.", + "optional": true, + "type": "text", + "value": "monthly", + "show": true, + "options": [ + { + "label": "annual", + "value": "annual" + }, + { + "label": "quarter", + "value": "quarter" + }, + { + "label": "monthly", + "value": "monthly" + } + ], + "paramName": "frequency" + }, + { + "label": "Harmonized", + "description": "If true, returns harmonized data.", + "optional": true, + "type": "boolean", + "value": false, + "show": true, + "paramName": "harmonized" + }, + { + "label": "Start Date", + "description": "Start date of the data, in YYYY-MM-DD format.", + "optional": true, + "type": "date", + "value": null, + "show": true, + "paramName": "start_date" + }, + { + "label": "End Date", + "description": "End date of the data, in YYYY-MM-DD format.", + "optional": true, + "type": "date", + "value": null, + "show": true, + "paramName": "end_date" + }, + { + "paramName": "provider", + "value": "fred", + "show": false + } + ], + "endpoint": "api/v1/economy/cpi", + "runButton": false, + "gridData": { + "w": 40, + "h": 15 + }, + "data": { + "dataKey": "results", + "table": { + "showAll": true, + "columnsDefs": [ + { + "field": "date", + "pinned": "left", + "formatterFn": null, + "headerName": "Date", + "headerTooltip": "The date of the data.", + "cellDataType": "date" + }, + { + "field": "country", + "formatterFn": null, + "headerName": "Country", + "headerTooltip": "Country", + "cellDataType": "text" + }, + { + "field": "value", + "formatterFn": null, + "headerName": "Value", + "headerTooltip": "CPI index value or period change.", + "cellDataType": "number" + } + ] + } + }, + "source": [ + "FRED" + ], + "subCategory": "CPI" + }, + "economy_balance_of_payments_fred_obb": { + "name": "Balance Of Payments", + "description": "Balance of Payments Reports.", + "category": "Economy", + "type": "table", + "searchCategory": "Economy", + "widgetId": "economy_balance_of_payments_fred_obb", + "params": [ + { + "label": "Country", + "description": "The country to get data. Enter as a 3-letter ISO country code, default is USA.", + "optional": true, + "type": "text", + "value": "united_states", + "show": true, + "options": [ + { + "label": "argentina", + "value": "argentina" + }, + { + "label": "australia", + "value": "australia" + }, + { + "label": "austria", + "value": "austria" + }, + { + "label": "belgium", + "value": "belgium" + }, + { + "label": "brazil", + "value": "brazil" + }, + { + "label": "canada", + "value": "canada" + }, + { + "label": "chile", + "value": "chile" + }, + { + "label": "china", + "value": "china" + }, + { + "label": "colombia", + "value": "colombia" + }, + { + "label": "costa_rica", + "value": "costa_rica" + }, + { + "label": "czechia", + "value": "czechia" + }, + { + "label": "denmark", + "value": "denmark" + }, + { + "label": "estonia", + "value": "estonia" + }, + { + "label": "finland", + "value": "finland" + }, + { + "label": "france", + "value": "france" + }, + { + "label": "germany", + "value": "germany" + }, + { + "label": "greece", + "value": "greece" + }, + { + "label": "hungary", + "value": "hungary" + }, + { + "label": "iceland", + "value": "iceland" + }, + { + "label": "india", + "value": "india" + }, + { + "label": "indonesia", + "value": "indonesia" + }, + { + "label": "ireland", + "value": "ireland" + }, + { + "label": "israel", + "value": "israel" + }, + { + "label": "italy", + "value": "italy" + }, + { + "label": "japan", + "value": "japan" + }, + { + "label": "korea", + "value": "korea" + }, + { + "label": "latvia", + "value": "latvia" + }, + { + "label": "lithuania", + "value": "lithuania" + }, + { + "label": "luxembourg", + "value": "luxembourg" + }, + { + "label": "mexico", + "value": "mexico" + }, + { + "label": "netherlands", + "value": "netherlands" + }, + { + "label": "new_zealand", + "value": "new_zealand" + }, + { + "label": "norway", + "value": "norway" + }, + { + "label": "poland", + "value": "poland" + }, + { + "label": "portugal", + "value": "portugal" + }, + { + "label": "russia", + "value": "russia" + }, + { + "label": "saudi_arabia", + "value": "saudi_arabia" + }, + { + "label": "slovak_republic", + "value": "slovak_republic" + }, + { + "label": "slovenia", + "value": "slovenia" + }, + { + "label": "south_africa", + "value": "south_africa" + }, + { + "label": "spain", + "value": "spain" + }, + { + "label": "sweden", + "value": "sweden" + }, + { + "label": "switzerland", + "value": "switzerland" + }, + { + "label": "turkey", + "value": "turkey" + }, + { + "label": "united_kingdom", + "value": "united_kingdom" + }, + { + "label": "united_states", + "value": "united_states" + }, + { + "label": "g7", + "value": "g7" + }, + { + "label": "g20", + "value": "g20" + } + ], + "paramName": "country" + }, + { + "label": "Start Date", + "description": "Start date of the data, in YYYY-MM-DD format.", + "optional": true, + "type": "date", + "value": null, + "show": true, + "paramName": "start_date" + }, + { + "label": "End Date", + "description": "End date of the data, in YYYY-MM-DD format.", + "optional": true, + "type": "date", + "value": null, + "show": true, + "paramName": "end_date" + }, + { + "paramName": "provider", + "value": "fred", + "show": false + } + ], + "endpoint": "api/v1/economy/balance_of_payments", + "runButton": false, + "gridData": { + "w": 40, + "h": 15 + }, + "data": { + "dataKey": "results", + "table": { + "showAll": true, + "columnsDefs": [ + { + "field": "period", + "formatterFn": null, + "headerName": "Period", + "headerTooltip": "The date representing the beginning of the reporting period.", + "cellDataType": "date" + }, + { + "field": "balance_percent_of_gdp", + "formatterFn": "normalizedPercent", + "headerName": "Balance Percent Of GDP", + "headerTooltip": "Current Account Balance as Percent of GDP", + "cellDataType": "number", + "renderFn": "greenRed" + }, + { + "field": "balance_total", + "formatterFn": null, + "headerName": "Balance Total", + "headerTooltip": "Current Account Total Balance (USD)", + "cellDataType": "number" + }, + { + "field": "balance_total_services", + "formatterFn": null, + "headerName": "Balance Total Services", + "headerTooltip": "Current Account Total Services Balance (USD)", + "cellDataType": "number" + }, + { + "field": "balance_total_secondary_income", + "formatterFn": null, + "headerName": "Balance Total Secondary Income", + "headerTooltip": "Current Account Total Secondary Income Balance (USD)", + "cellDataType": "number" + }, + { + "field": "balance_total_goods", + "formatterFn": null, + "headerName": "Balance Total Goods", + "headerTooltip": "Current Account Total Goods Balance (USD)", + "cellDataType": "number" + }, + { + "field": "balance_total_primary_income", + "formatterFn": null, + "headerName": "Balance Total Primary Income", + "headerTooltip": "Current Account Total Primary Income Balance (USD)", + "cellDataType": "number" + }, + { + "field": "credits_services_percent_of_goods_and_services", + "formatterFn": "normalizedPercent", + "headerName": "Credits Services Percent Of Goods And Services", + "headerTooltip": "Current Account Credits Services as Percent of Goods and Services", + "cellDataType": "number", + "renderFn": "greenRed" + }, + { + "field": "credits_services_percent_of_current_account", + "formatterFn": "normalizedPercent", + "headerName": "Credits Services Percent Of Current Account", + "headerTooltip": "Current Account Credits Services as Percent of Current Account", + "cellDataType": "number", + "renderFn": "greenRed" + }, + { + "field": "credits_total_services", + "formatterFn": null, + "headerName": "Credits Total Services", + "headerTooltip": "Current Account Credits Total Services (USD)", + "cellDataType": "number" + }, + { + "field": "credits_total_goods", + "formatterFn": null, + "headerName": "Credits Total Goods", + "headerTooltip": "Current Account Credits Total Goods (USD)", + "cellDataType": "number" + }, + { + "field": "credits_total_primary_income", + "formatterFn": null, + "headerName": "Credits Total Primary Income", + "headerTooltip": "Current Account Credits Total Primary Income (USD)", + "cellDataType": "number" + }, + { + "field": "credits_total_secondary_income", + "formatterFn": null, + "headerName": "Credits Total Secondary Income", + "headerTooltip": "Current Account Credits Total Secondary Income (USD)", + "cellDataType": "number" + }, + { + "field": "credits_total", + "formatterFn": null, + "headerName": "Credits Total", + "headerTooltip": "Current Account Credits Total (USD)", + "cellDataType": "number" + }, + { + "field": "debits_services_percent_of_goods_and_services", + "formatterFn": "normalizedPercent", + "headerName": "Debits Services Percent Of Goods And Services", + "headerTooltip": "Current Account Debits Services as Percent of Goods and Services", + "cellDataType": "number", + "renderFn": "greenRed" + }, + { + "field": "debits_services_percent_of_current_account", + "formatterFn": "normalizedPercent", + "headerName": "Debits Services Percent Of Current Account", + "headerTooltip": "Current Account Debits Services as Percent of Current Account", + "cellDataType": "number", + "renderFn": "greenRed" + }, + { + "field": "debits_total_services", + "formatterFn": null, + "headerName": "Debits Total Services", + "headerTooltip": "Current Account Debits Total Services (USD)", + "cellDataType": "number" + }, + { + "field": "debits_total_goods", + "formatterFn": null, + "headerName": "Debits Total Goods", + "headerTooltip": "Current Account Debits Total Goods (USD)", + "cellDataType": "number" + }, + { + "field": "debits_total_primary_income", + "formatterFn": null, + "headerName": "Debits Total Primary Income", + "headerTooltip": "Current Account Debits Total Primary Income (USD)", + "cellDataType": "number" + }, + { + "field": "debits_total", + "formatterFn": null, + "headerName": "Debits Total", + "headerTooltip": "Current Account Debits Total (USD)", + "cellDataType": "number" + }, + { + "field": "debits_total_secondary_income", + "formatterFn": null, + "headerName": "Debits Total Secondary Income", + "headerTooltip": "Current Account Debits Total Secondary Income (USD)", + "cellDataType": "number" + } + ] + } + }, + "source": [ + "FRED" + ], + "subCategory": "Balance Of Payments" + }, + "economy_fred_search_fred_obb": { + "name": "Fred Search", + "description": "Search for FRED series or economic releases by ID or string.\n\nThis does not return the observation values, only the metadata.\nUse this function to find series IDs for `fred_series()`.", + "category": "Economy", + "type": "table", + "searchCategory": "Economy", + "widgetId": "economy_fred_search_fred_obb", + "params": [ + { + "label": "Query", + "description": "The search word(s).", + "optional": true, + "type": "text", + "value": null, + "show": true, + "paramName": "query" + }, + { + "label": "Is Release", + "description": "Is release? If True, other search filter variables are ignored. If no query text or release_id is supplied, this defaults to True.", + "optional": true, + "type": "boolean", + "value": false, + "show": true, + "paramName": "is_release" + }, + { + "label": "Release ID", + "description": "A specific release ID to target.", + "optional": true, + "type": "text", + "value": null, + "show": true, + "paramName": "release_id" + }, + { + "label": "Limit", + "description": "The number of data entries to return. (1-1000)", + "optional": true, + "type": "number", + "value": null, + "show": true, + "paramName": "limit" + }, + { + "label": "Offset", + "description": "Offset the results in conjunction with limit.", + "optional": true, + "type": "number", + "value": 0, + "show": true, + "paramName": "offset" + }, + { + "label": "Filter Variable", + "description": "Filter by an attribute.", + "optional": true, + "type": "text", + "value": null, + "show": true, + "options": [ + { + "label": "frequency", + "value": "frequency" + }, + { + "label": "units", + "value": "units" + }, + { + "label": "seasonal_adjustment", + "value": "seasonal_adjustment" + } + ], + "paramName": "filter_variable" + }, + { + "label": "Filter Value", + "description": "String value to filter the variable by. Used in conjunction with filter_variable.", + "optional": true, + "type": "text", + "value": null, + "show": true, + "paramName": "filter_value" + }, + { + "label": "Tag Names", + "description": "A semicolon delimited list of tag names that series match all of. Example: 'japan;imports' Multiple comma separated items allowed.", + "optional": true, + "type": "text", + "value": null, + "show": true, + "multiSelect": true, + "paramName": "tag_names" + }, + { + "label": "Exclude Tag Names", + "description": "A semicolon delimited list of tag names that series match none of. Example: 'imports;services'. Requires that variable tag_names also be set to limit the number of matching series. Multiple comma separated items allowed.", + "optional": true, + "type": "text", + "value": null, + "show": true, + "multiSelect": true, + "paramName": "exclude_tag_names" + }, + { + "label": "Series ID", + "description": "A FRED Series ID to return series group information for. This returns the required information to query for regional data. Not all series that are in FRED have geographical data. Entering a value for series_id will override all other parameters. Multiple series_ids can be separated by commas.", + "optional": true, + "type": "text", + "value": null, + "show": true, + "paramName": "series_id" + }, + { + "paramName": "provider", + "value": "fred", + "show": false + } + ], + "endpoint": "api/v1/economy/fred_search", + "runButton": false, + "gridData": { + "w": 40, + "h": 15 + }, + "data": { + "dataKey": "results", + "table": { + "showAll": true, + "columnsDefs": [ + { + "field": "release_id", + "formatterFn": null, + "headerName": "Release ID", + "headerTooltip": "The release ID for queries.", + "cellDataType": "number" + }, + { + "field": "series_id", + "pinned": "left", + "formatterFn": null, + "headerName": "Series ID", + "headerTooltip": "The series ID for the item in the release.", + "cellDataType": "text" + }, + { + "field": "name", + "pinned": "left", + "formatterFn": null, + "headerName": "Name", + "headerTooltip": "The name of the release.", + "cellDataType": "text" + }, + { + "field": "title", + "pinned": "left", + "formatterFn": null, + "headerName": "Title", + "headerTooltip": "The title of the series.", + "cellDataType": "text" + }, + { + "field": "observation_start", + "formatterFn": null, + "headerName": "Observation Start", + "headerTooltip": "The date of the first observation in the series.", + "cellDataType": "date" + }, + { + "field": "observation_end", + "formatterFn": null, + "headerName": "Observation End", + "headerTooltip": "The date of the last observation in the series.", + "cellDataType": "date" + }, + { + "field": "frequency", + "formatterFn": null, + "headerName": "Frequency", + "headerTooltip": "The frequency of the data.", + "cellDataType": "text" + }, + { + "field": "frequency_short", + "formatterFn": null, + "headerName": "Frequency Short", + "headerTooltip": "Short form of the data frequency.", + "cellDataType": "text" + }, + { + "field": "units", + "formatterFn": null, + "headerName": "Units", + "headerTooltip": "The units of the data.", + "cellDataType": "text" + }, + { + "field": "units_short", + "formatterFn": null, + "headerName": "Units Short", + "headerTooltip": "Short form of the data units.", + "cellDataType": "text" + }, + { + "field": "seasonal_adjustment", + "formatterFn": null, + "headerName": "Seasonal Adjustment", + "headerTooltip": "The seasonal adjustment of the data.", + "cellDataType": "text" + }, + { + "field": "seasonal_adjustment_short", + "formatterFn": null, + "headerName": "Seasonal Adjustment Short", + "headerTooltip": "Short form of the data seasonal adjustment.", + "cellDataType": "text" + }, + { + "field": "last_updated", + "formatterFn": null, + "headerName": "Last Updated", + "headerTooltip": "The datetime of the last update to the data.", + "cellDataType": "date" + }, + { + "field": "notes", + "formatterFn": null, + "headerName": "Notes", + "headerTooltip": "Description of the release.", + "cellDataType": "text" + }, + { + "field": "press_release", + "formatterFn": null, + "headerName": "Press Release", + "headerTooltip": "If the release is a press release.", + "cellDataType": "text" + }, + { + "field": "url", + "formatterFn": null, + "headerName": "URL", + "headerTooltip": "URL to the release.", + "cellDataType": "text" + }, + { + "field": "popularity", + "formatterFn": null, + "headerName": "Popularity", + "headerTooltip": "Popularity of the series", + "cellDataType": "number" + }, + { + "field": "group_popularity", + "formatterFn": null, + "headerName": "Group Popularity", + "headerTooltip": "Group popularity of the release", + "cellDataType": "number" + }, + { + "field": "region_type", + "formatterFn": null, + "headerName": "Region Type", + "headerTooltip": "The region type of the series.", + "cellDataType": "text" + }, + { + "field": "series_group", + "formatterFn": null, + "headerName": "Series Group", + "headerTooltip": "The series group ID of the series. This value is used to query for regional data.", + "cellDataType": "number" + } + ] + } + }, + "source": [ + "FRED" + ], + "subCategory": "FRED Search" + }, + "economy_fred_series_fred_obb": { + "name": "Fred Series", + "description": "Get data by series ID from FRED.", + "category": "Economy", + "type": "table", + "searchCategory": "Economy", + "widgetId": "economy_fred_series_fred_obb", + "params": [ + { + "label": "Symbol", + "description": "Symbol to get data for. Multiple comma separated items allowed.", + "optional": false, + "type": "text", + "value": null, + "show": true, + "multiSelect": true, + "paramName": "symbol" + }, + { + "label": "Start Date", + "description": "Start date of the data, in YYYY-MM-DD format.", + "optional": true, + "type": "date", + "value": null, + "show": true, + "paramName": "start_date" + }, + { + "label": "End Date", + "description": "End date of the data, in YYYY-MM-DD format.", + "optional": true, + "type": "date", + "value": null, + "show": true, + "paramName": "end_date" + }, + { + "label": "Limit", + "description": "The number of data entries to return.", + "optional": true, + "type": "number", + "value": 100000, + "show": true, + "paramName": "limit" + }, + { + "label": "Frequency", + "description": "Frequency aggregation to convert high frequency data to lower frequency.\n \n None = No change\n \n a = Annual\n \n q = Quarterly\n \n m = Monthly\n \n w = Weekly\n \n d = Daily\n \n wef = Weekly, Ending Friday\n \n weth = Weekly, Ending Thursday\n \n wew = Weekly, Ending Wednesday\n \n wetu = Weekly, Ending Tuesday\n \n wem = Weekly, Ending Monday\n \n wesu = Weekly, Ending Sunday\n \n wesa = Weekly, Ending Saturday\n \n bwew = Biweekly, Ending Wednesday\n \n bwem = Biweekly, Ending Monday", + "optional": true, + "type": "text", + "value": null, + "show": true, + "options": [ + { + "label": "a", + "value": "a" + }, + { + "label": "q", + "value": "q" + }, + { + "label": "m", + "value": "m" + }, + { + "label": "w", + "value": "w" + }, + { + "label": "d", + "value": "d" + }, + { + "label": "wef", + "value": "wef" + }, + { + "label": "weth", + "value": "weth" + }, + { + "label": "wew", + "value": "wew" + }, + { + "label": "wetu", + "value": "wetu" + }, + { + "label": "wem", + "value": "wem" + }, + { + "label": "wesu", + "value": "wesu" + }, + { + "label": "wesa", + "value": "wesa" + }, + { + "label": "bwew", + "value": "bwew" + }, + { + "label": "bwem", + "value": "bwem" + } + ], + "paramName": "frequency" + }, + { + "label": "Aggregation Method", + "description": "A key that indicates the aggregation method used for frequency aggregation.\n This parameter has no affect if the frequency parameter is not set.\n \n avg = Average\n \n sum = Sum\n \n eop = End of Period", + "optional": true, + "type": "text", + "value": "eop", + "show": true, + "options": [ + { + "label": "avg", + "value": "avg" + }, + { + "label": "sum", + "value": "sum" + }, + { + "label": "eop", + "value": "eop" + } + ], + "paramName": "aggregation_method" + }, + { + "label": "Transform", + "description": "Transformation type\n \n None = No transformation\n \n chg = Change\n \n ch1 = Change from Year Ago\n \n pch = Percent Change\n \n pc1 = Percent Change from Year Ago\n \n pca = Compounded Annual Rate of Change\n \n cch = Continuously Compounded Rate of Change\n \n cca = Continuously Compounded Annual Rate of Change\n \n log = Natural Log", + "optional": true, + "type": "text", + "value": null, + "show": true, + "options": [ + { + "label": "chg", + "value": "chg" + }, + { + "label": "ch1", + "value": "ch1" + }, + { + "label": "pch", + "value": "pch" + }, + { + "label": "pc1", + "value": "pc1" + }, + { + "label": "pca", + "value": "pca" + }, + { + "label": "cch", + "value": "cch" + }, + { + "label": "cca", + "value": "cca" + }, + { + "label": "log", + "value": "log" + } + ], + "paramName": "transform" + }, + { + "paramName": "provider", + "value": "fred", + "show": false + } + ], + "endpoint": "api/v1/economy/fred_series", + "runButton": false, + "gridData": { + "w": 40, + "h": 15 + }, + "data": { + "dataKey": "results", + "table": { + "showAll": true, + "columnsDefs": [ + { + "field": "date", + "pinned": "left", + "formatterFn": null, + "headerName": "Date", + "headerTooltip": "The date of the data.", + "cellDataType": "date" + } + ] + } + }, + "source": [ + "FRED" + ], + "subCategory": "FRED Series" + }, + "economy_fred_series_fred_obb_chart": { + "name": "Fred Series (Chart)", + "description": "Get data by series ID from FRED.", + "category": "Economy", + "type": "chart", + "searchCategory": "chart", + "widgetId": "economy_fred_series_fred_obb_chart", + "params": [ + { + "label": "Symbol", + "description": "Symbol to get data for. Multiple comma separated items allowed.", + "optional": false, + "type": "text", + "value": null, + "show": true, + "multiSelect": true, + "paramName": "symbol" + }, + { + "label": "Start Date", + "description": "Start date of the data, in YYYY-MM-DD format.", + "optional": true, + "type": "date", + "value": null, + "show": true, + "paramName": "start_date" + }, + { + "label": "End Date", + "description": "End date of the data, in YYYY-MM-DD format.", + "optional": true, + "type": "date", + "value": null, + "show": true, + "paramName": "end_date" + }, + { + "label": "Limit", + "description": "The number of data entries to return.", + "optional": true, + "type": "number", + "value": 100000, + "show": true, + "paramName": "limit" + }, + { + "label": "Frequency", + "description": "Frequency aggregation to convert high frequency data to lower frequency.\n \n None = No change\n \n a = Annual\n \n q = Quarterly\n \n m = Monthly\n \n w = Weekly\n \n d = Daily\n \n wef = Weekly, Ending Friday\n \n weth = Weekly, Ending Thursday\n \n wew = Weekly, Ending Wednesday\n \n wetu = Weekly, Ending Tuesday\n \n wem = Weekly, Ending Monday\n \n wesu = Weekly, Ending Sunday\n \n wesa = Weekly, Ending Saturday\n \n bwew = Biweekly, Ending Wednesday\n \n bwem = Biweekly, Ending Monday", + "optional": true, + "type": "text", + "value": null, + "show": true, + "options": [ + { + "label": "a", + "value": "a" + }, + { + "label": "q", + "value": "q" + }, + { + "label": "m", + "value": "m" + }, + { + "label": "w", + "value": "w" + }, + { + "label": "d", + "value": "d" + }, + { + "label": "wef", + "value": "wef" + }, + { + "label": "weth", + "value": "weth" + }, + { + "label": "wew", + "value": "wew" + }, + { + "label": "wetu", + "value": "wetu" + }, + { + "label": "wem", + "value": "wem" + }, + { + "label": "wesu", + "value": "wesu" + }, + { + "label": "wesa", + "value": "wesa" + }, + { + "label": "bwew", + "value": "bwew" + }, + { + "label": "bwem", + "value": "bwem" + } + ], + "paramName": "frequency" + }, + { + "label": "Aggregation Method", + "description": "A key that indicates the aggregation method used for frequency aggregation.\n This parameter has no affect if the frequency parameter is not set.\n \n avg = Average\n \n sum = Sum\n \n eop = End of Period", + "optional": true, + "type": "text", + "value": "eop", + "show": true, + "options": [ + { + "label": "avg", + "value": "avg" + }, + { + "label": "sum", + "value": "sum" + }, + { + "label": "eop", + "value": "eop" + } + ], + "paramName": "aggregation_method" + }, + { + "label": "Transform", + "description": "Transformation type\n \n None = No transformation\n \n chg = Change\n \n ch1 = Change from Year Ago\n \n pch = Percent Change\n \n pc1 = Percent Change from Year Ago\n \n pca = Compounded Annual Rate of Change\n \n cch = Continuously Compounded Rate of Change\n \n cca = Continuously Compounded Annual Rate of Change\n \n log = Natural Log", + "optional": true, + "type": "text", + "value": null, + "show": true, + "options": [ + { + "label": "chg", + "value": "chg" + }, + { + "label": "ch1", + "value": "ch1" + }, + { + "label": "pch", + "value": "pch" + }, + { + "label": "pc1", + "value": "pc1" + }, + { + "label": "pca", + "value": "pca" + }, + { + "label": "cch", + "value": "cch" + }, + { + "label": "cca", + "value": "cca" + }, + { + "label": "log", + "value": "log" + } + ], + "paramName": "transform" + }, + { + "paramName": "provider", + "value": "fred", + "show": false + }, + { + "paramName": "chart", + "label": "Chart", + "description": "Returns chart", + "optional": true, + "value": true, + "type": "boolean", + "show": false + } + ], + "endpoint": "api/v1/economy/fred_series", + "runButton": false, + "gridData": { + "w": 40, + "h": 20 + }, + "data": { + "dataKey": "chart.content", + "table": { + "showAll": true, + "columnsDefs": [ + { + "field": "date", + "pinned": "left", + "formatterFn": null, + "headerName": "Date", + "headerTooltip": "The date of the data.", + "cellDataType": "date" + } + ] + } + }, + "source": [ + "FRED" + ], + "subCategory": "FRED Series", + "defaultViz": "chart" + }, + "economy_fred_release_table_fred_obb": { + "name": "Fred Release Table", + "description": "Get economic release data by ID and/or element from FRED.", + "category": "Economy", + "type": "table", + "searchCategory": "Economy", + "widgetId": "economy_fred_release_table_fred_obb", + "params": [ + { + "label": "Release ID", + "description": "The ID of the release. Use `fred_search` to find releases.", + "optional": false, + "type": "text", + "value": null, + "show": true, + "paramName": "release_id" + }, + { + "label": "Element ID", + "description": "The element ID of a specific table in the release.", + "optional": true, + "type": "text", + "value": null, + "show": true, + "paramName": "element_id" + }, + { + "label": "Date", + "description": "A specific date to get data for. Multiple comma separated items allowed.", + "optional": true, + "type": "text", + "value": null, + "show": true, + "multiSelect": true, + "paramName": "date" + }, + { + "paramName": "provider", + "value": "fred", + "show": false + } + ], + "endpoint": "api/v1/economy/fred_release_table", + "runButton": false, + "gridData": { + "w": 40, + "h": 15 + }, + "data": { + "dataKey": "results", + "table": { + "showAll": true, + "columnsDefs": [ + { + "field": "date", + "pinned": "left", + "formatterFn": null, + "headerName": "Date", + "headerTooltip": "The date of the data.", + "cellDataType": "date" + }, + { + "field": "level", + "formatterFn": null, + "headerName": "Level", + "headerTooltip": "The indentation level of the element.", + "cellDataType": "number" + }, + { + "field": "element_type", + "formatterFn": null, + "headerName": "Element Type", + "headerTooltip": "The type of the element.", + "cellDataType": "text" + }, + { + "field": "line", + "formatterFn": null, + "headerName": "Line", + "headerTooltip": "The line number of the element.", + "cellDataType": "number" + }, + { + "field": "element_id", + "formatterFn": null, + "headerName": "Element ID", + "headerTooltip": "The element id in the parent/child relationship.", + "cellDataType": "text" + }, + { + "field": "parent_id", + "formatterFn": null, + "headerName": "Parent ID", + "headerTooltip": "The parent id in the parent/child relationship.", + "cellDataType": "text" + }, + { + "field": "children", + "formatterFn": null, + "headerName": "Children", + "headerTooltip": "The element_id of each child, as a comma-separated string.", + "cellDataType": "text" + }, + { + "field": "symbol", + "pinned": "left", + "formatterFn": null, + "headerName": "Symbol", + "headerTooltip": "Symbol representing the entity requested in the data.", + "cellDataType": "text" + }, + { + "field": "name", + "pinned": "left", + "formatterFn": null, + "headerName": "Name", + "headerTooltip": "The name of the series.", + "cellDataType": "text" + }, + { + "field": "value", + "formatterFn": null, + "headerName": "Value", + "headerTooltip": "The reported value of the series.", + "cellDataType": "number" + } + ] + } + }, + "source": [ + "FRED" + ], + "subCategory": "FRED Release Table" + }, + "economy_fred_regional_fred_obb": { + "name": "Fred Regional", + "description": "Query the Geo Fred API for regional economic data by series group.\n\nThe series group ID is found by using `fred_search` and the `series_id` parameter.", + "category": "Economy", + "type": "table", + "searchCategory": "Economy", + "widgetId": "economy_fred_regional_fred_obb", + "params": [ + { + "label": "Symbol", + "description": "Symbol to get data for.", + "optional": false, + "type": "text", + "value": null, + "show": true, + "paramName": "symbol" + }, + { + "label": "Start Date", + "description": "Start date of the data, in YYYY-MM-DD format.", + "optional": true, + "type": "date", + "value": null, + "show": true, + "paramName": "start_date" + }, + { + "label": "End Date", + "description": "End date of the data, in YYYY-MM-DD format.", + "optional": true, + "type": "date", + "value": null, + "show": true, + "paramName": "end_date" + }, + { + "label": "Limit", + "description": "The number of data entries to return.", + "optional": true, + "type": "number", + "value": 100000, + "show": true, + "paramName": "limit" + }, + { + "label": "Is Series Group", + "description": "When True, the symbol provided is for a series_group, else it is for a series ID.", + "optional": true, + "type": "boolean", + "value": false, + "show": true, + "paramName": "is_series_group" + }, + { + "label": "Region Type", + "description": "The type of regional data. Parameter is only valid when `is_series_group` is True.", + "optional": true, + "type": "text", + "value": null, + "show": true, + "options": [ + { + "label": "bea", + "value": "bea" + }, + { + "label": "msa", + "value": "msa" + }, + { + "label": "frb", + "value": "frb" + }, + { + "label": "necta", + "value": "necta" + }, + { + "label": "state", + "value": "state" + }, + { + "label": "country", + "value": "country" + }, + { + "label": "county", + "value": "county" + }, + { + "label": "censusregion", + "value": "censusregion" + } + ], + "paramName": "region_type" + }, + { + "label": "Season", + "description": "The seasonal adjustments to the data. Parameter is only valid when `is_series_group` is True.", + "optional": true, + "type": "text", + "value": "nsa", + "show": true, + "options": [ + { + "label": "sa", + "value": "sa" + }, + { + "label": "nsa", + "value": "nsa" + }, + { + "label": "ssa", + "value": "ssa" + } + ], + "paramName": "season" + }, + { + "label": "Units", + "description": "The units of the data. This should match the units returned from searching by series ID. An incorrect field will not necessarily return an error. Parameter is only valid when `is_series_group` is True.", + "optional": true, + "type": "text", + "value": null, + "show": true, + "paramName": "units" + }, + { + "label": "Frequency", + "description": "Frequency aggregation to convert high frequency data to lower frequency.\n \n None = No change\n \n a = Annual\n \n q = Quarterly\n \n m = Monthly\n \n w = Weekly\n \n d = Daily\n \n wef = Weekly, Ending Friday\n \n weth = Weekly, Ending Thursday\n \n wew = Weekly, Ending Wednesday\n \n wetu = Weekly, Ending Tuesday\n \n wem = Weekly, Ending Monday\n \n wesu = Weekly, Ending Sunday\n \n wesa = Weekly, Ending Saturday\n \n bwew = Biweekly, Ending Wednesday\n \n bwem = Biweekly, Ending Monday", + "optional": true, + "type": "text", + "value": null, + "show": true, + "options": [ + { + "label": "a", + "value": "a" + }, + { + "label": "q", + "value": "q" + }, + { + "label": "m", + "value": "m" + }, + { + "label": "w", + "value": "w" + }, + { + "label": "d", + "value": "d" + }, + { + "label": "wef", + "value": "wef" + }, + { + "label": "weth", + "value": "weth" + }, + { + "label": "wew", + "value": "wew" + }, + { + "label": "wetu", + "value": "wetu" + }, + { + "label": "wem", + "value": "wem" + }, + { + "label": "wesu", + "value": "wesu" + }, + { + "label": "wesa", + "value": "wesa" + }, + { + "label": "bwew", + "value": "bwew" + }, + { + "label": "bwem", + "value": "bwem" + } + ], + "paramName": "frequency" + }, + { + "label": "Aggregation Method", + "description": "A key that indicates the aggregation method used for frequency aggregation.\n This parameter has no affect if the frequency parameter is not set.\n \n avg = Average\n \n sum = Sum\n \n eop = End of Period", + "optional": true, + "type": "text", + "value": "eop", + "show": true, + "options": [ + { + "label": "avg", + "value": "avg" + }, + { + "label": "sum", + "value": "sum" + }, + { + "label": "eop", + "value": "eop" + } + ], + "paramName": "aggregation_method" + }, + { + "label": "Transform", + "description": "Transformation type\n \n None = No transformation\n \n chg = Change\n \n ch1 = Change from Year Ago\n \n pch = Percent Change\n \n pc1 = Percent Change from Year Ago\n \n pca = Compounded Annual Rate of Change\n \n cch = Continuously Compounded Rate of Change\n \n cca = Continuously Compounded Annual Rate of Change\n \n log = Natural Log", + "optional": true, + "type": "text", + "value": null, + "show": true, + "options": [ + { + "label": "chg", + "value": "chg" + }, + { + "label": "ch1", + "value": "ch1" + }, + { + "label": "pch", + "value": "pch" + }, + { + "label": "pc1", + "value": "pc1" + }, + { + "label": "pca", + "value": "pca" + }, + { + "label": "cch", + "value": "cch" + }, + { + "label": "cca", + "value": "cca" + }, + { + "label": "log", + "value": "log" + } + ], + "paramName": "transform" + }, + { + "paramName": "provider", + "value": "fred", + "show": false + } + ], + "endpoint": "api/v1/economy/fred_regional", + "runButton": false, + "gridData": { + "w": 40, + "h": 15 + }, + "data": { + "dataKey": "results", + "table": { + "showAll": true, + "columnsDefs": [ + { + "field": "date", + "pinned": "left", + "formatterFn": null, + "headerName": "Date", + "headerTooltip": "The date of the data.", + "cellDataType": "date" + }, + { + "field": "region", + "formatterFn": null, + "headerName": "Region", + "headerTooltip": "The name of the region.", + "cellDataType": "text" + }, + { + "field": "code", + "formatterFn": null, + "headerName": "Code", + "headerTooltip": "The code of the region.", + "cellDataType": "number" + }, + { + "field": "value", + "formatterFn": null, + "headerName": "Value", + "headerTooltip": "The obersvation value. The units are defined in the search results by series ID.", + "cellDataType": "number" + }, + { + "field": "series_id", + "pinned": "left", + "formatterFn": null, + "headerName": "Series ID", + "headerTooltip": "The individual series ID for the region.", + "cellDataType": "text" + } + ] + } + }, + "source": [ + "FRED" + ], + "subCategory": "FRED Regional" + }, + "economy_retail_prices_fred_obb": { + "name": "Retail Prices", + "description": "Get retail prices for common items.", + "category": "Economy", + "type": "table", + "searchCategory": "Economy", + "widgetId": "economy_retail_prices_fred_obb", + "params": [ + { + "label": "Item", + "description": "The item or basket of items to query.", + "optional": true, + "type": "text", + "value": null, + "show": true, + "paramName": "item" + }, + { + "label": "Country", + "description": "The country to get data.", + "optional": true, + "type": "text", + "value": "united_states", + "show": true, + "paramName": "country" + }, + { + "label": "Start Date", + "description": "Start date of the data, in YYYY-MM-DD format.", + "optional": true, + "type": "date", + "value": null, + "show": true, + "paramName": "start_date" + }, + { + "label": "End Date", + "description": "End date of the data, in YYYY-MM-DD format.", + "optional": true, + "type": "date", + "value": null, + "show": true, + "paramName": "end_date" + }, + { + "label": "Region", + "description": "The region to get average price levels for.", + "optional": true, + "type": "text", + "value": "all_city", + "show": true, + "options": [ + { + "label": "all_city", + "value": "all_city" + }, + { + "label": "northeast", + "value": "northeast" + }, + { + "label": "midwest", + "value": "midwest" + }, + { + "label": "south", + "value": "south" + }, + { + "label": "west", + "value": "west" + } + ], + "paramName": "region" + }, + { + "label": "Frequency", + "description": "The frequency of the data.", + "optional": true, + "type": "text", + "value": "monthly", + "show": true, + "options": [ + { + "label": "annual", + "value": "annual" + }, + { + "label": "quarter", + "value": "quarter" + }, + { + "label": "monthly", + "value": "monthly" + } + ], + "paramName": "frequency" + }, + { + "label": "Transform", + "description": "Transformation type\n None = No transformation\n chg = Change\n ch1 = Change from Year Ago\n pch = Percent Change\n pc1 = Percent Change from Year Ago\n pca = Compounded Annual Rate of Change\n cch = Continuously Compounded Rate of Change\n cca = Continuously Compounded Annual Rate of Change\n log = Natural Log", + "optional": true, + "type": "text", + "value": null, + "show": true, + "options": [ + { + "label": "chg", + "value": "chg" + }, + { + "label": "ch1", + "value": "ch1" + }, + { + "label": "pch", + "value": "pch" + }, + { + "label": "pc1", + "value": "pc1" + }, + { + "label": "pca", + "value": "pca" + }, + { + "label": "cch", + "value": "cch" + }, + { + "label": "cca", + "value": "cca" + }, + { + "label": "log", + "value": "log" + } + ], + "paramName": "transform" + }, + { + "paramName": "provider", + "value": "fred", + "show": false + } + ], + "endpoint": "api/v1/economy/retail_prices", + "runButton": false, + "gridData": { + "w": 40, + "h": 15 + }, + "data": { + "dataKey": "results", + "table": { + "showAll": true, + "columnsDefs": [ + { + "field": "date", + "pinned": "left", + "formatterFn": null, + "headerName": "Date", + "headerTooltip": "The date of the data.", + "cellDataType": "date" + }, + { + "field": "symbol", + "pinned": "left", + "formatterFn": null, + "headerName": "Symbol", + "headerTooltip": "Symbol representing the entity requested in the data.", + "cellDataType": "text" + }, + { + "field": "country", + "formatterFn": null, + "headerName": "Country", + "headerTooltip": "", + "cellDataType": "text" + }, + { + "field": "description", + "formatterFn": null, + "headerName": "Description", + "headerTooltip": "Description of the item.", + "cellDataType": "text" + }, + { + "field": "value", + "formatterFn": null, + "headerName": "Value", + "headerTooltip": "Price, or change in price, per unit.", + "cellDataType": "number" + } + ] + } + }, + "source": [ + "FRED" + ], + "subCategory": "Retail Prices" + }, + "economy_pce_fred_obb": { + "name": "Pce", + "description": "Get Personal Consumption Expenditures (PCE) reports.", + "category": "Economy", + "type": "table", + "searchCategory": "Economy", + "widgetId": "economy_pce_fred_obb", + "params": [ + { + "label": "Date", + "description": "A specific date to get data for. Default is the latest report. Multiple comma separated items allowed.", + "optional": true, + "type": "text", + "value": null, + "show": true, + "multiSelect": true, + "paramName": "date" + }, + { + "label": "Category", + "description": "The category to query.", + "optional": true, + "type": "text", + "value": "personal_income", + "show": true, + "options": [ + { + "label": "personal_income", + "value": "personal_income" + }, + { + "label": "wages_by_industry", + "value": "wages_by_industry" + }, + { + "label": "real_pce_percent_change", + "value": "real_pce_percent_change" + }, + { + "label": "real_pce_quantity_index", + "value": "real_pce_quantity_index" + }, + { + "label": "pce_price_index", + "value": "pce_price_index" + }, + { + "label": "pce_dollars", + "value": "pce_dollars" + }, + { + "label": "real_pce_chained_dollars", + "value": "real_pce_chained_dollars" + }, + { + "label": "pce_price_percent_change", + "value": "pce_price_percent_change" + } + ], + "paramName": "category" + }, + { + "paramName": "provider", + "value": "fred", + "show": false + } + ], + "endpoint": "api/v1/economy/pce", + "runButton": false, + "gridData": { + "w": 40, + "h": 15 + }, + "data": { + "dataKey": "results", + "table": { + "showAll": true, + "columnsDefs": [ + { + "field": "date", + "pinned": "left", + "formatterFn": null, + "headerName": "Date", + "headerTooltip": "The date of the data.", + "cellDataType": "date" + }, + { + "field": "symbol", + "pinned": "left", + "formatterFn": null, + "headerName": "Symbol", + "headerTooltip": "Symbol representing the entity requested in the data.", + "cellDataType": "text" + }, + { + "field": "value", + "formatterFn": null, + "headerName": "Value", + "headerTooltip": "", + "cellDataType": "number" + }, + { + "field": "name", + "pinned": "left", + "formatterFn": null, + "headerName": "Name", + "headerTooltip": "The name of the series.", + "cellDataType": "text" + }, + { + "field": "element_id", + "formatterFn": null, + "headerName": "Element ID", + "headerTooltip": "The element id in the parent/child relationship.", + "cellDataType": "text" + }, + { + "field": "parent_id", + "formatterFn": null, + "headerName": "Parent ID", + "headerTooltip": "The parent id in the parent/child relationship.", + "cellDataType": "text" + }, + { + "field": "children", + "formatterFn": null, + "headerName": "Children", + "headerTooltip": "The element_id of each child, as a comma-separated string.", + "cellDataType": "text" + }, + { + "field": "level", + "formatterFn": "int", + "headerName": "Level", + "headerTooltip": "The indentation level of the element.", + "cellDataType": "number" + }, + { + "field": "line", + "formatterFn": "int", + "headerName": "Line", + "headerTooltip": "The line number of the series in the table.", + "cellDataType": "number" + } + ] + } + }, + "source": [ + "FRED" + ], + "subCategory": "PCE" + }, + "regulators_sec_cik_map_sec_obb": { + "name": "CIK Map", + "description": "Map a ticker symbol to a CIK number.", + "category": "Regulators", + "type": "table", + "searchCategory": "Regulators", + "widgetId": "regulators_sec_cik_map_sec_obb", + "params": [ + { + "label": "Symbol", + "description": "Symbol to get data for.", + "optional": false, + "type": "text", + "value": null, + "show": true, + "paramName": "symbol" + }, + { + "label": "Use Cache", + "description": "Whether or not to use cache for the request, default is True.", + "optional": true, + "type": "boolean", + "value": true, + "show": true, + "paramName": "use_cache" + }, + { + "paramName": "provider", + "value": "sec", + "show": false + } + ], + "endpoint": "api/v1/regulators/sec/cik_map", + "runButton": false, + "gridData": { + "w": 40, + "h": 15 + }, + "data": { + "dataKey": "results", + "table": { + "showAll": true, + "columnsDefs": [ + { + "field": "cik", + "formatterFn": null, + "headerName": "CIK", + "headerTooltip": "Central Index Key (CIK) for the requested entity.", + "cellDataType": "text" + } + ] + } + }, + "source": [ + "SEC" + ], + "subCategory": "SEC" + }, + "regulators_sec_institutions_search_sec_obb": { + "name": "Institutions Search", + "description": "Search SEC-regulated institutions by name and return a list of results with CIK numbers.", + "category": "Regulators", + "type": "table", + "searchCategory": "Regulators", + "widgetId": "regulators_sec_institutions_search_sec_obb", + "params": [ + { + "label": "Query", + "description": "Search query.", + "optional": true, + "type": "text", + "value": "", + "show": true, + "paramName": "query" + }, + { + "label": "Use Cache", + "description": "Whether or not to use cache.", + "optional": true, + "type": "boolean", + "value": true, + "show": true, + "paramName": "use_cache" + }, + { + "paramName": "provider", + "value": "sec", + "show": false + } + ], + "endpoint": "api/v1/regulators/sec/institutions_search", + "runButton": false, + "gridData": { + "w": 40, + "h": 15 + }, + "data": { + "dataKey": "results", + "table": { + "showAll": true, + "columnsDefs": [ + { + "field": "institution", + "formatterFn": null, + "headerName": "Institution", + "headerTooltip": "The name of the institution.", + "cellDataType": "text" + }, + { + "field": "cik_number", + "formatterFn": null, + "headerName": "CIK Number", + "headerTooltip": "Central Index Key (CIK)", + "cellDataType": "number" + } + ] + } + }, + "source": [ + "SEC" + ], + "subCategory": "SEC" + }, + "regulators_sec_schema_files_sec_obb": { + "name": "Schema Files", + "description": "Use tool for navigating the directory of SEC XML schema files by year.", + "category": "Regulators", + "type": "table", + "searchCategory": "Regulators", + "widgetId": "regulators_sec_schema_files_sec_obb", + "params": [ + { + "label": "Query", + "description": "Search query.", + "optional": true, + "type": "text", + "value": "", + "show": true, + "paramName": "query" + }, + { + "label": "Use Cache", + "description": "Whether or not to use cache.", + "optional": true, + "type": "boolean", + "value": true, + "show": true, + "paramName": "use_cache" + }, + { + "label": "URL", + "description": "Enter an optional URL path to fetch the next level.", + "optional": true, + "type": "text", + "value": null, + "show": true, + "paramName": "url" + }, + { + "paramName": "provider", + "value": "sec", + "show": false + } + ], + "endpoint": "api/v1/regulators/sec/schema_files", + "runButton": false, + "gridData": { + "w": 40, + "h": 15 + }, + "data": { + "dataKey": "results", + "table": { + "showAll": true, + "columnsDefs": [ + { + "field": "files", + "formatterFn": null, + "headerName": "Files", + "headerTooltip": "Dictionary of URLs to SEC Schema Files", + "cellDataType": "text" + } + ] + } + }, + "source": [ + "SEC" + ], + "subCategory": "SEC" + }, + "regulators_sec_symbol_map_sec_obb": { + "name": "Symbol Map", + "description": "Map a CIK number to a ticker symbol, leading 0s can be omitted or included.", + "category": "Regulators", + "type": "table", + "searchCategory": "Regulators", + "widgetId": "regulators_sec_symbol_map_sec_obb", + "params": [ + { + "label": "Query", + "description": "Search query.", + "optional": false, + "type": "text", + "value": null, + "show": true, + "paramName": "query" + }, + { + "label": "Use Cache", + "description": "Whether or not to use cache. If True, cache will store for seven days.", + "optional": true, + "type": "boolean", + "value": true, + "show": true, + "paramName": "use_cache" + }, + { + "paramName": "provider", + "value": "sec", + "show": false + } + ], + "endpoint": "api/v1/regulators/sec/symbol_map", + "runButton": false, + "gridData": { + "w": 40, + "h": 15 + }, + "data": { + "dataKey": "results", + "table": { + "showAll": true, + "columnsDefs": [ + { + "field": "symbol", + "pinned": "left", + "formatterFn": null, + "headerName": "Symbol", + "headerTooltip": "Symbol representing the entity requested in the data.", + "cellDataType": "text" + } + ] + } + }, + "source": [ + "SEC" + ], + "subCategory": "SEC" + }, + "regulators_sec_rss_litigation_sec_obb": { + "name": "RSS Litigation", + "description": "Get the RSS feed that provides links to litigation releases concerning civil lawsuits brought by the Commission in federal court.", + "category": "Regulators", + "type": "table", + "searchCategory": "Regulators", + "widgetId": "regulators_sec_rss_litigation_sec_obb", + "params": [ + { + "paramName": "provider", + "value": "sec", + "show": false + } + ], + "endpoint": "api/v1/regulators/sec/rss_litigation", + "runButton": false, + "gridData": { + "w": 40, + "h": 15 + }, + "data": { + "dataKey": "results", + "table": { + "showAll": true, + "columnsDefs": [ + { + "field": "date", + "pinned": "left", + "formatterFn": null, + "headerName": "Date", + "headerTooltip": "The date of publication.", + "cellDataType": "date" + }, + { + "field": "title", + "pinned": "left", + "formatterFn": null, + "headerName": "Title", + "headerTooltip": "The title of the release.", + "cellDataType": "text" + }, + { + "field": "summary", + "formatterFn": null, + "headerName": "Summary", + "headerTooltip": "Short summary of the release.", + "cellDataType": "text" + }, + { + "field": "id", + "formatterFn": null, + "headerName": "ID", + "headerTooltip": "The identifier associated with the release.", + "cellDataType": "text" + }, + { + "field": "link", + "formatterFn": null, + "headerName": "Link", + "headerTooltip": "URL to the release.", + "cellDataType": "text" + } + ] + } + }, + "source": [ + "SEC" + ], + "subCategory": "SEC" + }, + "regulators_sec_sic_search_sec_obb": { + "name": "SIC Search", + "description": "Search for Industry Titles, Reporting Office, and SIC Codes. An empty query string returns all results.", + "category": "Regulators", + "type": "table", + "searchCategory": "Regulators", + "widgetId": "regulators_sec_sic_search_sec_obb", + "params": [ + { + "label": "Query", + "description": "Search query.", + "optional": true, + "type": "text", + "value": "", + "show": true, + "paramName": "query" + }, + { + "label": "Use Cache", + "description": "Whether or not to use cache.", + "optional": true, + "type": "boolean", + "value": true, + "show": true, + "paramName": "use_cache" + }, + { + "paramName": "provider", + "value": "sec", + "show": false + } + ], + "endpoint": "api/v1/regulators/sec/sic_search", + "runButton": false, + "gridData": { + "w": 40, + "h": 15 + }, + "data": { + "dataKey": "results", + "table": { + "showAll": true, + "columnsDefs": [ + { + "field": "sic_code", + "formatterFn": "int", + "headerName": "SIC Code", + "headerTooltip": "Sector Industrial Code (SIC)", + "cellDataType": "number" + }, + { + "field": "industry_title", + "formatterFn": null, + "headerName": "Industry Title", + "headerTooltip": "Industry title.", + "cellDataType": "text" + }, + { + "field": "office", + "formatterFn": null, + "headerName": "Office", + "headerTooltip": "Reporting office within the Corporate Finance Office", + "cellDataType": "text" + } + ] + } + }, + "source": [ + "SEC" + ], + "subCategory": "SEC" + }, + "form_submit_custom_obb": { + "name": "Form Submit", + "description": "", + "category": "Form Submit", + "type": "table", + "searchCategory": "Form Submit", + "widgetId": "form_submit_custom_obb", + "params": [ + { + "label": "Some Param", + "description": "Some Param", + "optional": true, + "type": "text", + "value": "", + "show": true, + "paramName": "some_param" + }, + { + "type": "form", + "paramName": "", + "label": "Submit Form", + "description": "Submit a form via POST request.", + "endpoint": "form_submit", + "inputParams": [ + { + "label": "Date", + "description": "Date", + "optional": true, + "type": "date", + "value": null, + "paramName": "date" + }, + { + "label": "Record ID", + "description": "Record Id", + "optional": true, + "type": "number", + "value": null, + "paramName": "record_id" + }, + { + "label": "First Name", + "description": "First Name", + "optional": true, + "type": "text", + "value": null, + "paramName": "first_name" + }, + { + "label": "Last Name", + "description": "Last Name", + "optional": true, + "type": "text", + "value": null, + "paramName": "last_name" + }, + { + "label": "Symptoms", + "description": "Symptoms", + "optional": true, + "type": "text", + "value": null, + "multiSelect": true, + "options": [ + { + "label": "dizzy", + "value": "dizzy" + }, + { + "label": "hungry", + "value": "hungry" + }, + { + "label": "sleepy", + "value": "sleepy" + } + ], + "paramName": "symptoms" + }, + { + "label": "Diagnosis", + "description": "Diagnosis", + "optional": true, + "type": "text", + "value": null, + "paramName": "diagnosis" + }, + { + "label": "Add Record", + "description": "Add Record", + "optional": true, + "type": "button", + "value": true, + "paramName": "add_record" + } + ] + } + ], + "endpoint": "form_submit", + "runButton": false, + "gridData": { + "w": 40, + "h": 15 + }, + "data": { + "dataKey": "", + "table": { + "showAll": true + } + }, + "source": [ + "Custom" + ] + } +} \ No newline at end of file diff --git a/openbb_platform/extensions/platform_api/tests/test_api.py b/openbb_platform/extensions/platform_api/tests/test_api.py new file mode 100644 index 0000000000000000000000000000000000000000..45348748956314f68ec3fc1499ab152f98a5cc13 --- /dev/null +++ b/openbb_platform/extensions/platform_api/tests/test_api.py @@ -0,0 +1,182 @@ +from unittest.mock import MagicMock, mock_open, patch + +import pytest +from openbb_platform_api.utils.api import ( + check_port, + get_user_settings, + get_widgets_json, + import_app, + parse_args, +) + + +# Mock environment variables +@pytest.fixture(autouse=True) +def mock_env_vars(monkeypatch): + monkeypatch.setenv("HOME", "/mock/home") + monkeypatch.setenv("USERPROFILE", "/mock/home") + + +@pytest.mark.parametrize("port_input", [6900, "6900", 6901, "6901"]) +def test_check_port(port_input): + with patch("socket.socket") as mock_socket: + mock_sock_instance = MagicMock() + mock_socket.return_value.__enter__.return_value = mock_sock_instance + + # Mock connect_ex to return 0 for port 6900 (indicating it's in use) + mock_sock_instance.connect_ex.side_effect = lambda addr: ( + 0 if addr[1] == 6900 else 1 + ) + + port = check_port("127.0.0.1", port_input) + assert port == 6901 # The next available port should be 6901 + + +def test_get_user_settings_no_login(): + with patch( + "builtins.open", + mock_open( + read_data='{"credentials": {}, "preferences": {}, "defaults": {"commands": {}}}' + ), + ): + settings = get_user_settings( + _login=False, current_user_settings="", user_settings_copy="" + ) + assert settings == { + "credentials": {}, + "preferences": {}, + "defaults": {"commands": {}}, + } + + +def test_get_widgets_json_no_build(): + with ( + patch("builtins.open", mock_open(read_data="{}")), + patch("os.path.exists", return_value=True), + ): + widgets_json = get_widgets_json( + _build=False, _openapi={}, widget_exclude_filter=[] + ) + assert widgets_json == {} + + +def test_parse_args(): + with patch("sys.argv", ["script.py", "--help"]): + with pytest.raises(SystemExit) as e: + parse_args() + assert e.type == SystemExit + assert e.value.code == 0 + + with patch("sys.argv", ["script.py", "--key", "value"]): + args = parse_args() + assert args == {"key": "value"} + + with patch("sys.argv", ["script.py", "--flag"]): + args = parse_args() + assert args == {"flag": True} + + +def test_import_module_app(): + # pylint: disable=import-outside-toplevel + from fastapi import FastAPI as RealFastAPI + + class MockFastAPI(RealFastAPI): + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + self.add_middleware = MagicMock() + + with ( + patch("importlib.import_module") as mock_import, + patch("fastapi.FastAPI", new=MockFastAPI), + patch("openbb_core.app.service.system_service.SystemService") as mock_system, + patch("openbb_core.app.service.user_service.UserService.read_from_file"), + patch("openbb_core.app.model.credentials.CredentialsLoader.load"), + patch("openbb_core.api.app_loader.AppLoader.add_routers"), + ): + + # Mock system settings + mock_system.return_value.system_settings.cors.allow_origins = ["*"] + mock_system.return_value.system_settings.cors.allow_methods = ["*"] + mock_system.return_value.system_settings.cors.allow_headers = ["*"] + + # Rest of test setup... + mock_module = MagicMock() + mock_module.app = MockFastAPI() + mock_import.return_value = mock_module + + result = import_app("my_module:app", "app", False) + assert isinstance(result, MockFastAPI) + + +@pytest.mark.skip("Not working yet. Asking for a fix. Mocking is messed up.") +def test_import_file_app(): + # pylint: disable=import-outside-toplevel + from fastapi import FastAPI as RealFastAPI + + class MockFastAPI(RealFastAPI): + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + self.add_middleware = MagicMock() + + with ( + patch("importlib.import_module") as mock_import, + patch("pathlib.Path.exists", return_value=True), + patch("fastapi.FastAPI", new=MockFastAPI), + patch("openbb_core.app.service.user_service.UserService.read_from_file"), + patch( + "openbb_core.app.model.credentials.CredentialsLoader.load", + return_value=MagicMock(), + ), + patch("openbb_core.app.service.system_service.SystemService") as mock_system, + patch("openbb_core.api.app_loader.AppLoader.add_routers"), + ): + # Configure system settings mock + mock_system.return_value.system_settings.cors.allow_origins = ["*"] + mock_system.return_value.system_settings.cors.allow_methods = ["*"] + mock_system.return_value.system_settings.cors.allow_headers = ["*"] + + mock_import.side_effect = ImportError + mock_module = MagicMock() + mock_module.app = MockFastAPI() + + result = import_app("main.py", "app", False) + assert isinstance(result, MockFastAPI) + + +def test_import_factory_app(): + # pylint: disable=import-outside-toplevel + from fastapi import FastAPI as RealFastAPI + + class MockFastAPI(RealFastAPI): + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + self.add_middleware = MagicMock() + + with ( + patch("importlib.import_module") as mock_import, + patch("fastapi.FastAPI", new=MockFastAPI), + patch("openbb_core.app.service.system_service.SystemService") as mock_system, + patch("openbb_core.app.service.user_service.UserService.read_from_file"), + patch( + "openbb_core.app.model.credentials.CredentialsLoader.load", + return_value=MagicMock(), + ), + patch("openbb_core.api.app_loader.AppLoader.add_routers"), + ): + # Configure system settings mock + mock_system.return_value.system_settings.cors.allow_origins = ["*"] + mock_system.return_value.system_settings.cors.allow_methods = ["*"] + mock_system.return_value.system_settings.cors.allow_headers = ["*"] + + mock_module = MagicMock() + factory = MagicMock(return_value=MockFastAPI()) + mock_module.factory_func = factory + mock_import.return_value = mock_module + + result = import_app("main:factory_func", "factory_func", True) + factory.assert_called_once() + assert isinstance(result, MockFastAPI) + + +if __name__ == "__main__": + pytest.main() diff --git a/openbb_platform/extensions/platform_api/tests/test_openapi_utils.py b/openbb_platform/extensions/platform_api/tests/test_openapi_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..de5a63095b7a75e9dc030442de7f6f741ade7342 --- /dev/null +++ b/openbb_platform/extensions/platform_api/tests/test_openapi_utils.py @@ -0,0 +1,387 @@ +import json +from pathlib import Path + +import pytest +from openbb_platform_api.utils.openapi import ( + data_schema_to_columns_defs, + get_data_schema_for_widget, + get_query_schema_for_widget, + post_query_schema_for_widget, +) + +# pylint: disable=redefined-outer-name + + +# Load the mock OpenAPI JSON +@pytest.fixture(scope="module") +def mock_openapi_json(): + mock_openapi_path = Path(__file__).parent / "mock_openapi.json" + with open(mock_openapi_path) as file: + return json.load(file) + + +@pytest.mark.parametrize( + "path, params_number, query_schema, expected_has_chart", + [ + ( + "/api/v1/economy/balance_of_payments", + 4, + [ + { + "parameter_name": "provider", + "label": "Provider", + "description": "Source of the data.", + "optional": True, + "type": "text", + "value": None, + "show": False, + "available_providers": ["fred"], + }, + { + "parameter_name": "country", + "label": "Country", + "description": "The country to get data. Enter as a 3-letter ISO country code, default is USA.", + "optional": True, + "type": "text", + "value": "united_states", + "multiple_items_allowed": {}, + "options": { + "fred": [ + {"label": "argentina", "value": "argentina"}, + {"label": "australia", "value": "australia"}, + {"label": "austria", "value": "austria"}, + {"label": "belgium", "value": "belgium"}, + {"label": "brazil", "value": "brazil"}, + {"label": "canada", "value": "canada"}, + {"label": "chile", "value": "chile"}, + {"label": "china", "value": "china"}, + {"label": "colombia", "value": "colombia"}, + {"label": "costa_rica", "value": "costa_rica"}, + {"label": "czechia", "value": "czechia"}, + {"label": "denmark", "value": "denmark"}, + {"label": "estonia", "value": "estonia"}, + {"label": "finland", "value": "finland"}, + {"label": "france", "value": "france"}, + {"label": "germany", "value": "germany"}, + {"label": "greece", "value": "greece"}, + {"label": "hungary", "value": "hungary"}, + {"label": "iceland", "value": "iceland"}, + {"label": "india", "value": "india"}, + {"label": "indonesia", "value": "indonesia"}, + {"label": "ireland", "value": "ireland"}, + {"label": "israel", "value": "israel"}, + {"label": "italy", "value": "italy"}, + {"label": "japan", "value": "japan"}, + {"label": "korea", "value": "korea"}, + {"label": "latvia", "value": "latvia"}, + {"label": "lithuania", "value": "lithuania"}, + {"label": "luxembourg", "value": "luxembourg"}, + {"label": "mexico", "value": "mexico"}, + {"label": "netherlands", "value": "netherlands"}, + {"label": "new_zealand", "value": "new_zealand"}, + {"label": "norway", "value": "norway"}, + {"label": "poland", "value": "poland"}, + {"label": "portugal", "value": "portugal"}, + {"label": "russia", "value": "russia"}, + {"label": "saudi_arabia", "value": "saudi_arabia"}, + {"label": "slovak_republic", "value": "slovak_republic"}, + {"label": "slovenia", "value": "slovenia"}, + {"label": "south_africa", "value": "south_africa"}, + {"label": "spain", "value": "spain"}, + {"label": "sweden", "value": "sweden"}, + {"label": "switzerland", "value": "switzerland"}, + {"label": "turkey", "value": "turkey"}, + {"label": "united_kingdom", "value": "united_kingdom"}, + {"label": "united_states", "value": "united_states"}, + {"label": "g7", "value": "g7"}, + {"label": "g20", "value": "g20"}, + ] + }, + "x-widget_config": {}, + "available_providers": ["fred"], + "show": True, + "x-widget_config": {}, + }, + { + "parameter_name": "start_date", + "label": "Start Date", + "description": "Start date of the data, in YYYY-MM-DD format.", + "optional": True, + "type": "date", + "value": None, + "multiple_items_allowed": {}, + "options": {"fred": []}, + "x-widget_config": {}, + "available_providers": ["fred"], + "show": True, + "x-widget_config": {}, + }, + { + "parameter_name": "end_date", + "label": "End Date", + "description": "End date of the data, in YYYY-MM-DD format.", + "optional": True, + "type": "date", + "value": None, + "multiple_items_allowed": {}, + "options": {"fred": []}, + "x-widget_config": {}, + "available_providers": ["fred"], + "show": True, + "x-widget_config": {}, + }, + ], + False, + ), + ( + "/api/v1/economy/fred_series", + 8, + [ + { + "parameter_name": "provider", + "label": "Provider", + "description": "Source of the data.", + "optional": True, + "type": "text", + "value": None, + "show": False, + "available_providers": ["fred"], + }, + { + "parameter_name": "symbol", + "label": "Symbol", + "description": "Symbol to get data for.", + "optional": False, + "type": "text", + "value": None, + "multiple_items_allowed": {"fred": True}, + "options": {"fred": []}, + "x-widget_config": {}, + "show": True, + }, + { + "parameter_name": "start_date", + "label": "Start Date", + "description": "Start date of the data, in YYYY-MM-DD format.", + "optional": True, + "type": "date", + "value": None, + "multiple_items_allowed": {}, + "options": {"fred": []}, + "x-widget_config": {}, + "show": True, + "x-widget_config": {}, + }, + { + "parameter_name": "end_date", + "label": "End Date", + "description": "End date of the data, in YYYY-MM-DD format.", + "optional": True, + "type": "date", + "value": None, + "multiple_items_allowed": {}, + "options": {"fred": []}, + "x-widget_config": {}, + "show": True, + "x-widget_config": {}, + }, + { + "parameter_name": "limit", + "label": "Limit", + "description": "The number of data entries to return.", + "optional": True, + "type": "number", + "value": 100000, + "multiple_items_allowed": {}, + "options": {"fred": []}, + "x-widget_config": {}, + "show": True, + "x-widget_config": {}, + }, + { + "parameter_name": "frequency", + "label": "Frequency", + "description": "Frequency aggregation to convert high frequency data to lower frequency.\n \n None = No change\n \n a = Annual\n \n q = Quarterly\n \n m = Monthly\n \n w = Weekly\n \n d = Daily\n \n wef = Weekly, Ending Friday\n \n weth = Weekly, Ending Thursday\n \n wew = Weekly, Ending Wednesday\n \n wetu = Weekly, Ending Tuesday\n \n wem = Weekly, Ending Monday\n \n wesu = Weekly, Ending Sunday\n \n wesa = Weekly, Ending Saturday\n \n bwew = Biweekly, Ending Wednesday\n \n bwem = Biweekly, Ending Monday", + "optional": True, + "type": "text", + "value": None, + "multiple_items_allowed": {}, + "options": { + "fred": [ + {"label": "a", "value": "a"}, + {"label": "q", "value": "q"}, + {"label": "m", "value": "m"}, + {"label": "w", "value": "w"}, + {"label": "d", "value": "d"}, + {"label": "wef", "value": "wef"}, + {"label": "weth", "value": "weth"}, + {"label": "wew", "value": "wew"}, + {"label": "wetu", "value": "wetu"}, + {"label": "wem", "value": "wem"}, + {"label": "wesu", "value": "wesu"}, + {"label": "wesa", "value": "wesa"}, + {"label": "bwew", "value": "bwew"}, + {"label": "bwem", "value": "bwem"}, + ] + }, + "x-widget_config": {}, + "available_providers": ["fred"], + "show": True, + "x-widget_config": {}, + }, + { + "parameter_name": "aggregation_method", + "label": "Aggregation Method", + "description": "A key that indicates the aggregation method used for frequency aggregation.\n This parameter has no affect if the frequency parameter is not set.\n \n avg = Average\n \n sum = Sum\n \n eop = End of Period", + "optional": True, + "type": "text", + "value": "eop", + "multiple_items_allowed": {}, + "options": { + "fred": [ + {"label": "avg", "value": "avg"}, + {"label": "sum", "value": "sum"}, + {"label": "eop", "value": "eop"}, + ] + }, + "x-widget_config": {}, + "available_providers": ["fred"], + "show": True, + "x-widget_config": {}, + }, + { + "parameter_name": "transform", + "label": "Transform", + "description": "Transformation type\n \n None = No transformation\n \n chg = Change\n \n ch1 = Change from Year Ago\n \n pch = Percent Change\n \n pc1 = Percent Change from Year Ago\n \n pca = Compounded Annual Rate of Change\n \n cch = Continuously Compounded Rate of Change\n \n cca = Continuously Compounded Annual Rate of Change\n \n log = Natural Log", + "optional": True, + "type": "text", + "value": None, + "multiple_items_allowed": {}, + "options": { + "fred": [ + {"label": "chg", "value": "chg"}, + {"label": "ch1", "value": "ch1"}, + {"label": "pch", "value": "pch"}, + {"label": "pc1", "value": "pc1"}, + {"label": "pca", "value": "pca"}, + {"label": "cch", "value": "cch"}, + {"label": "cca", "value": "cca"}, + {"label": "log", "value": "log"}, + ] + }, + "x-widget_config": {}, + "available_providers": ["fred"], + "show": True, + "x-widget_config": {}, + }, + ], + True, + ), + ( + "/api/v1/regulators/sec/schema_files", + 4, + [ + { + "parameter_name": "provider", + "label": "Provider", + "description": "Source of the data.", + "optional": True, + "type": "text", + "value": None, + "show": False, + "available_providers": ["sec"], + }, + { + "parameter_name": "query", + "label": "Query", + "description": "Search query.", + "optional": True, + "type": "text", + "value": "", + "multiple_items_allowed": {}, + "options": {"sec": []}, + "x-widget_config": {}, + "show": True, + "x-widget_config": {}, + }, + { + "parameter_name": "use_cache", + "label": "Use Cache", + "description": "Whether or not to use cache.", + "optional": True, + "type": "boolean", + "value": True, + "multiple_items_allowed": {}, + "options": {"sec": []}, + "x-widget_config": {}, + "show": True, + }, + { + "parameter_name": "url", + "label": "Url", + "description": "Enter an optional URL path to fetch the next level.", + "optional": True, + "type": "text", + "value": None, + "multiple_items_allowed": {}, + "options": {"sec": []}, + "x-widget_config": {}, + "available_providers": ["sec"], + "x-widget_config": {}, + "show": True, + }, + ], + False, + ), + ], +) +def test_get_query_schema_for_widget( + mock_openapi_json, path, params_number, query_schema, expected_has_chart +): + route_params, has_chart = get_query_schema_for_widget(mock_openapi_json, path) + assert len(route_params) == params_number + assert route_params == query_schema + assert has_chart == expected_has_chart + + +@pytest.mark.parametrize( + "openapi_operation_id", + [ + "economy_survey_sloos", + "economy_survey_university_of_michigan", + "economy_balance_of_payments", + ], +) +def test_get_data_schema_for_widget(mock_openapi_json, openapi_operation_id): + schema = get_data_schema_for_widget(mock_openapi_json, openapi_operation_id) + assert schema is not None + + +@pytest.mark.parametrize( + "openapi_operation_id", + [ + "form_submit_form_submit_post", + ], +) +def test_post_query_schema_for_widget(mock_openapi_json, openapi_operation_id): + schema = post_query_schema_for_widget(mock_openapi_json, openapi_operation_id) + assert schema + + +@pytest.mark.parametrize( + "openapi_operation_id", + [ + "economy_survey_sloos", + "economy_survey_university_of_michigan", + "economy_balance_of_payments", + ], +) +def test_data_schema_to_columns_defs(mock_openapi_json, openapi_operation_id): + column_defs = data_schema_to_columns_defs( + mock_openapi_json, openapi_operation_id, provider="fred" + ) + assert len(column_defs) > 1 # There should be at least two columns + + +if __name__ == "__main__": + pytest.main() diff --git a/openbb_platform/extensions/platform_api/tests/test_widgets_utils.py b/openbb_platform/extensions/platform_api/tests/test_widgets_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..fa771813968af29868f5b55de650ba1f6f2a3a18 --- /dev/null +++ b/openbb_platform/extensions/platform_api/tests/test_widgets_utils.py @@ -0,0 +1,456 @@ +import json +from pathlib import Path + +import pytest +from openbb_platform_api.utils.widgets import build_json, modify_query_schema + +# pylint: disable=redefined-outer-name + + +# Load the mock OpenAPI JSON +@pytest.fixture(scope="module") +def mock_openapi_json(): + mock_openapi_path = Path(__file__).parent / "mock_openapi.json" + with open(mock_openapi_path) as file: + return json.load(file) + + +# Load the mock widgets JSON +@pytest.fixture(scope="module") +def mock_widgets_json(): + mock_widgets_path = Path(__file__).parent / "mock_widgets.json" + with open(mock_widgets_path) as file: + return json.load(file) + + +@pytest.mark.parametrize( + "query_schema, provider_value, expected_result", + [ + ( + [ + { + "parameter_name": "provider", + "label": "Provider", + "description": "Source of the data.", + "optional": True, + "type": "text", + "available_providers": ["sec"], + "show": True, + }, + { + "parameter_name": "query", + "label": "Query", + "description": "Search query.", + "optional": True, + "value": "", + "options": {"sec": []}, + "multiple_items_allowed": {}, + "available_providers": ["sec"], + "type": "text", + "show": True, + }, + { + "parameter_name": "use_cache", + "label": "Use Cache", + "description": "Whether or not to use cache.", + "optional": True, + "value": True, + "options": {"sec": []}, + "multiple_items_allowed": {}, + "available_providers": ["sec"], + "type": "boolean", + "show": True, + }, + { + "parameter_name": "url", + "label": "Url", + "description": "Enter an optional URL path to fetch the next level.", + "optional": True, + "value": None, + "options": {"sec": []}, + "multiple_items_allowed": {}, + "available_providers": ["sec"], + "show": True, + }, + ], + "sec", + [ + { + "label": "Query", + "description": "Search query.", + "optional": True, + "value": "", + "type": "text", + "show": True, + "paramName": "query", + }, + { + "label": "Use Cache", + "description": "Whether or not to use cache.", + "optional": True, + "value": True, + "type": "boolean", + "show": True, + "paramName": "use_cache", + }, + { + "label": "URL", + "description": "Enter an optional URL path to fetch the next level.", + "optional": True, + "value": None, + "show": True, + "paramName": "url", + }, + {"paramName": "provider", "value": "sec", "show": False}, + ], + ), + ( + [ + { + "parameter_name": "provider", + "label": "Provider", + "description": "Source of the data.", + "optional": True, + "type": "text", + "available_providers": ["fred"], + "show": True, + }, + { + "parameter_name": "symbol", + "label": "Symbol", + "description": "Symbol to get data for.", + "optional": False, + "type": "text", + "multiple_items_allowed": {"fred": True}, + "show": True, + }, + { + "parameter_name": "start_date", + "label": "Start Date", + "description": "Start date of the data, in YYYY-MM-DD format.", + "optional": True, + "value": None, + "options": {"fred": []}, + "multiple_items_allowed": {}, + "available_providers": ["fred"], + "type": "date", + "show": True, + }, + { + "parameter_name": "end_date", + "label": "End Date", + "description": "End date of the data, in YYYY-MM-DD format.", + "optional": True, + "value": None, + "options": {"fred": []}, + "multiple_items_allowed": {}, + "available_providers": ["fred"], + "type": "date", + "show": True, + }, + { + "parameter_name": "limit", + "label": "Limit", + "description": "The number of data entries to return.", + "optional": True, + "value": 100000, + "options": {"fred": []}, + "multiple_items_allowed": {}, + "available_providers": ["fred"], + "type": "number", + "show": True, + }, + { + "parameter_name": "frequency", + "label": "Frequency", + "description": "Frequency aggregation to convert high frequency data to lower frequency." + + "\n " + + "\n None = No change" + + "\n " + + "\n a = Annual" + + "\n " + + "\n q = Quarterly" + + "\n " + + "\n m = Monthly" + + "\n " + + "\n w = Weekly" + + "\n " + + "\n d = Daily" + + "\n " + + "\n wef = Weekly, Ending Friday" + + "\n " + + "\n weth = Weekly, Ending Thursday" + + "\n " + + "\n wew = Weekly, Ending Wednesday" + + "\n " + + "\n wetu = Weekly, Ending Tuesday" + + "\n " + + "\n wem = Weekly, Ending Monday" + + "\n " + + "\n wesu = Weekly, Ending Sunday" + + "\n " + + "\n wesa = Weekly, Ending Saturday" + + "\n " + + "\n bwew = Biweekly, Ending Wednesday" + + "\n " + + "\n bwem = Biweekly, Ending Monday", + "optional": True, + "value": None, + "options": { + "fred": [ + {"label": "a", "value": "a"}, + {"label": "q", "value": "q"}, + {"label": "m", "value": "m"}, + {"label": "w", "value": "w"}, + {"label": "d", "value": "d"}, + {"label": "wef", "value": "wef"}, + {"label": "weth", "value": "weth"}, + {"label": "wew", "value": "wew"}, + {"label": "wetu", "value": "wetu"}, + {"label": "wem", "value": "wem"}, + {"label": "wesu", "value": "wesu"}, + {"label": "wesa", "value": "wesa"}, + {"label": "bwew", "value": "bwew"}, + {"label": "bwem", "value": "bwem"}, + ] + }, + "multiple_items_allowed": {}, + "available_providers": ["fred"], + "show": True, + }, + { + "parameter_name": "aggregation_method", + "label": "Aggregation Method", + "description": "A key that indicates the aggregation method used for frequency aggregation." + + "\n This parameter has no affect if the frequency parameter is not set." + + "\n " + + "\n avg = Average" + + "\n " + + "\n sum = Sum" + + "\n " + + "\n eop = End of Period", + "optional": True, + "value": "eop", + "options": { + "fred": [ + {"label": "avg", "value": "avg"}, + {"label": "sum", "value": "sum"}, + {"label": "eop", "value": "eop"}, + ] + }, + "multiple_items_allowed": {}, + "available_providers": ["fred"], + "show": True, + }, + { + "parameter_name": "transform", + "label": "Transform", + "description": "Transformation type" + + "\n " + + "\n None = No transformation" + + "\n " + + "\n chg = Change" + + "\n " + + "\n ch1 = Change from Year Ago" + + "\n " + + "\n pch = Percent Change" + + "\n " + + "\n pc1 = Percent Change from Year Ago" + + "\n " + + "\n pca = Compounded Annual Rate of Change" + + "\n " + + "\n cch = Continuously Compounded Rate of Change" + + "\n " + + "\n cca = Continuously Compounded Annual Rate of Change" + + "\n " + + "\n log = Natural Log", + "optional": True, + "value": None, + "options": { + "fred": [ + {"label": "chg", "value": "chg"}, + {"label": "ch1", "value": "ch1"}, + {"label": "pch", "value": "pch"}, + {"label": "pc1", "value": "pc1"}, + {"label": "pca", "value": "pca"}, + {"label": "cch", "value": "cch"}, + {"label": "cca", "value": "cca"}, + {"label": "log", "value": "log"}, + ] + }, + "multiple_items_allowed": {}, + "available_providers": ["fred"], + "show": True, + }, + ], + "fred", + [ + { + "label": "Symbol", + "description": "Symbol to get data for. Multiple comma separated items allowed.", + "optional": False, + "type": "text", + "show": True, + "multiSelect": True, + "paramName": "symbol", + }, + { + "label": "Start Date", + "description": "Start date of the data, in YYYY-MM-DD format.", + "optional": True, + "value": None, + "type": "date", + "show": True, + "paramName": "start_date", + }, + { + "label": "End Date", + "description": "End date of the data, in YYYY-MM-DD format.", + "optional": True, + "value": None, + "type": "date", + "show": True, + "paramName": "end_date", + }, + { + "label": "Limit", + "description": "The number of data entries to return.", + "optional": True, + "value": 100000, + "type": "number", + "show": True, + "paramName": "limit", + }, + { + "label": "Frequency", + "description": "Frequency aggregation to convert high frequency data to lower frequency." + + "\n " + + "\n None = No change" + + "\n " + + "\n a = Annual" + + "\n " + + "\n q = Quarterly" + + "\n " + + "\n m = Monthly" + + "\n " + + "\n w = Weekly" + + "\n " + + "\n d = Daily" + + "\n " + + "\n wef = Weekly, Ending Friday" + + "\n " + + "\n weth = Weekly, Ending Thursday" + + "\n " + + "\n wew = Weekly, Ending Wednesday" + + "\n " + + "\n wetu = Weekly, Ending Tuesday" + + "\n " + + "\n wem = Weekly, Ending Monday" + + "\n " + + "\n wesu = Weekly, Ending Sunday" + + "\n " + + "\n wesa = Weekly, Ending Saturday" + + "\n " + + "\n bwew = Biweekly, Ending Wednesday" + + "\n " + + "\n bwem = Biweekly, Ending Monday", + "optional": True, + "value": None, + "show": True, + "options": [ + {"label": "a", "value": "a"}, + {"label": "q", "value": "q"}, + {"label": "m", "value": "m"}, + {"label": "w", "value": "w"}, + {"label": "d", "value": "d"}, + {"label": "wef", "value": "wef"}, + {"label": "weth", "value": "weth"}, + {"label": "wew", "value": "wew"}, + {"label": "wetu", "value": "wetu"}, + {"label": "wem", "value": "wem"}, + {"label": "wesu", "value": "wesu"}, + {"label": "wesa", "value": "wesa"}, + {"label": "bwew", "value": "bwew"}, + {"label": "bwem", "value": "bwem"}, + ], + "type": "text", + "paramName": "frequency", + }, + { + "label": "Aggregation Method", + "description": "A key that indicates the aggregation method used for frequency aggregation." + + "\n This parameter has no affect if the frequency parameter is not set." + + "\n " + + "\n avg = Average" + + "\n " + + "\n sum = Sum" + + "\n " + + "\n eop = End of Period", + "optional": True, + "value": "eop", + "show": True, + "options": [ + {"label": "avg", "value": "avg"}, + {"label": "sum", "value": "sum"}, + {"label": "eop", "value": "eop"}, + ], + "type": "text", + "paramName": "aggregation_method", + }, + { + "label": "Transform", + "description": "Transformation type" + + "\n " + + "\n None = No transformation" + + "\n " + + "\n chg = Change" + + "\n " + + "\n ch1 = Change from Year Ago" + + "\n " + + "\n pch = Percent Change" + + "\n " + + "\n pc1 = Percent Change from Year Ago" + + "\n " + + "\n pca = Compounded Annual Rate of Change" + + "\n " + + "\n cch = Continuously Compounded Rate of Change" + + "\n " + + "\n cca = Continuously Compounded Annual Rate of Change" + + "\n " + + "\n log = Natural Log", + "optional": True, + "value": None, + "show": True, + "options": [ + {"label": "chg", "value": "chg"}, + {"label": "ch1", "value": "ch1"}, + {"label": "pch", "value": "pch"}, + {"label": "pc1", "value": "pc1"}, + {"label": "pca", "value": "pca"}, + {"label": "cch", "value": "cch"}, + {"label": "cca", "value": "cca"}, + {"label": "log", "value": "log"}, + ], + "type": "text", + "paramName": "transform", + }, + {"paramName": "provider", "value": "fred", "show": False}, + ], + ), + ], +) +def test_modify_query_schema(query_schema, provider_value, expected_result): + result = modify_query_schema(query_schema, provider_value) + assert result == expected_result + + +def test_build_json(mock_openapi_json, mock_widgets_json): + result = build_json(openapi=mock_openapi_json, widget_exclude_filter=[]) + assert ( + len(result) == len(mock_openapi_json["paths"]) + 1 + ) # +1 for the duplicate path with a chart. + assert result == mock_widgets_json + + +if __name__ == "__main__": + pytest.main() diff --git a/openbb_platform/extensions/quantitative/README.md b/openbb_platform/extensions/quantitative/README.md new file mode 100644 index 0000000000000000000000000000000000000000..6ccff09995cffb2425b91b1ccd6f3652939948c5 --- /dev/null +++ b/openbb_platform/extensions/quantitative/README.md @@ -0,0 +1,17 @@ +# OpenBB QA Extension + +This extension provides Quantitative Analysis (QA) tools for the OpenBB Platform. + +Features of the QA extension include various statistical tools and models. + +This extension works nicely with a companion `openbb-charting` extension + +## Installation + +To install the extension, run the following command in this folder: + +```bash +pip install openbb-quantitative +``` + +Documentation available [here](https://docs.openbb.co/platform/developer_guide/contributing). diff --git a/openbb_platform/extensions/quantitative/integration/test_quantitative_api.py b/openbb_platform/extensions/quantitative/integration/test_quantitative_api.py new file mode 100644 index 0000000000000000000000000000000000000000..ce16394488bc2663ca365fb7abf6f7d9e7d3ec6f --- /dev/null +++ b/openbb_platform/extensions/quantitative/integration/test_quantitative_api.py @@ -0,0 +1,565 @@ +"""Integration tests for the quantitative extension.""" + +import base64 +import json +import random +from typing import Literal + +import pytest +import requests +from extensions.tests.conftest import parametrize +from openbb_core.env import Env +from openbb_core.provider.utils.helpers import get_querystring + +# pylint:disable=redefined-outer-name + +data: dict = {} + + +def get_headers(): + """Get the headers for the API request.""" + if "headers" in data: + return data["headers"] + + userpass = f"{Env().API_USERNAME}:{Env().API_PASSWORD}" + userpass_bytes = userpass.encode("ascii") + base64_bytes = base64.b64encode(userpass_bytes) + + data["headers"] = {"Authorization": f"Basic {base64_bytes.decode('ascii')}"} + return data["headers"] + + +def request_data( + menu: str, symbol: str, provider: str, start_date: str = "", end_date: str = "" +): + """Randomly pick a symbol and a provider and get data from the selected menu.""" + url = f"http://0.0.0.0:8000/api/v1/{menu}/price/historical?symbol={symbol}&provider={provider}&start_date={start_date}&end_date={end_date}" # pylint: disable=line-too-long # noqa: E501 + result = requests.get(url, headers=get_headers(), timeout=10) + return result.json()["results"] + + +def get_stocks_data(): + """Get stocks data.""" + if "stocks_data" in data: + return data["stocks_data"] + + symbol = random.choice(["AAPL", "NVDA", "MSFT", "TSLA", "AMZN", "V"]) # noqa: S311 + provider = random.choice(["fmp", "polygon", "yfinance"]) # noqa: S311 + + data["stocks_data"] = request_data( + menu="equity", + symbol=symbol, + provider=provider, + start_date="2023-01-01", + end_date="2023-12-31", + ) + return data["stocks_data"] + + +def get_crypto_data(): + """Get crypto data.""" + if "crypto_data" in data: + return data["crypto_data"] + + # TODO : add more crypto providers and symbols + symbol = random.choice(["BTCUSD"]) # noqa: S311 + provider = random.choice(["fmp"]) # noqa: S311 + + data["crypto_data"] = request_data( + menu="crypto", + symbol=symbol, + provider=provider, + start_date="2023-01-01", + end_date="2023-12-31", + ) + return data["crypto_data"] + + +def get_data(menu: Literal["equity", "crypto"]): + """Get data based on the selected menu.""" + funcs = {"equity": get_stocks_data, "crypto": get_crypto_data} + return funcs[menu]() + + +@parametrize( + "params, data_type", + [ + ({"data": "", "target": "close"}, "equity"), + ({"data": "", "target": "high"}, "crypto"), + ], +) +@pytest.mark.integration +def test_quantitative_normality(params, data_type): + """Test the normality endpoint.""" + params = {p: v for p, v in params.items() if v} + data = json.dumps(get_data(data_type)) + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/quantitative/normality?{query_str}" + result = requests.post(url, headers=get_headers(), timeout=10, data=data) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params, data_type", + [ + ({"data": "", "target": "high"}, "equity"), + ({"data": "", "target": "high"}, "crypto"), + ], +) +@pytest.mark.integration +def test_quantitative_capm(params, data_type): + """Test the CAPM endpoint.""" + params = {p: v for p, v in params.items() if v} + data = json.dumps(get_data(data_type)) + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/quantitative/capm?{query_str}" + result = requests.post(url, headers=get_headers(), timeout=10, data=data) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params, data_type", + [ + ( + { + "data": "", + "target": "close", + "threshold_start": "", + "threshold_end": "", + }, + "equity", + ), + ( + { + "data": "", + "target": "high", + "threshold_start": "0.1", + "threshold_end": "1.6", + }, + "crypto", + ), + ], +) +@pytest.mark.integration +def test_quantitative_performance_omega_ratio(params, data_type): + """Test the Omega Ratio endpoint.""" + params = {p: v for p, v in params.items() if v} + data = json.dumps(get_data(data_type)) + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/quantitative/performance/omega_ratio?{query_str}" + result = requests.post(url, headers=get_headers(), timeout=10, data=data) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params, data_type", + [ + ({"data": "", "target": "close", "window": "5", "index": "date"}, "equity"), + ({"data": "", "target": "high", "window": "10", "index": "date"}, "crypto"), + ], +) +@pytest.mark.integration +def test_quantitative_rolling_kurtosis(params, data_type): + """Test the rolling kurtosis endpoint.""" + params = {p: v for p, v in params.items() if v} + data = json.dumps(get_data(data_type)) + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/quantitative/rolling/kurtosis?{query_str}" + result = requests.post(url, headers=get_headers(), timeout=10, data=data) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params, data_type", + [ + ( + { + "data": "", + "target": "close", + "fuller_reg": "c", + "kpss_reg": "ct", + }, + "equity", + ), + ( + { + "data": "", + "target": "high", + "fuller_reg": "ct", + "kpss_reg": "c", + }, + "crypto", + ), + ], +) +@pytest.mark.integration +def test_quantitative_unitroot_test(params, data_type): + """Test the unit root test endpoint.""" + params = {p: v for p, v in params.items() if v} + data = json.dumps(get_data(data_type)) + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/quantitative/unitroot_test?{query_str}" + result = requests.post(url, headers=get_headers(), timeout=10, data=data) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params, data_type", + [ + ( + { + "data": "", + "target": "close", + "rfr": "", + "window": "100", + "index": "date", + }, + "equity", + ), + ( + { + "data": "", + "target": "high", + "rfr": "0.5", + "window": "150", + "index": "date", + }, + "crypto", + ), + ], +) +@pytest.mark.integration +def test_quantitative_performance_sharpe_ratio(params, data_type): + """Test the Sharpe Ratio endpoint.""" + params = {p: v for p, v in params.items() if v} + data = json.dumps(get_data(data_type)) + + query_str = get_querystring(params, []) + url = ( + f"http://0.0.0.0:8000/api/v1/quantitative/performance/sharpe_ratio?{query_str}" + ) + result = requests.post(url, headers=get_headers(), timeout=10, data=data) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params, data_type", + [ + ( + { + "data": "", + "target": "close", + "target_return": "", + "window": "100", + "adjusted": "", + "index": "date", + }, + "equity", + ), + ( + { + "data": "", + "target": "close", + "target_return": "0.5", + "window": "150", + "adjusted": "true", + "index": "date", + }, + "crypto", + ), + ], +) +@pytest.mark.integration +def test_quantitative_performance_sortino_ratio(params, data_type): + """Test the Sortino Ratio endpoint.""" + params = {p: v for p, v in params.items() if v} + data = json.dumps(get_data(data_type)) + + query_str = get_querystring(params, []) + url = ( + f"http://0.0.0.0:8000/api/v1/quantitative/performance/sortino_ratio?{query_str}" + ) + result = requests.post(url, headers=get_headers(), timeout=10, data=data) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params, data_type", + [ + ({"data": "", "target": "close", "window": "220", "index": "date"}, "equity"), + ], +) +@pytest.mark.integration +def test_quantitative_rolling_skew(params, data_type): + """Test the rolling skew endpoint.""" + params = {p: v for p, v in params.items() if v} + data = json.dumps(get_data(data_type)) + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/quantitative/rolling/skew?{query_str}" + result = requests.post(url, headers=get_headers(), timeout=60, data=data) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params, data_type", + [ + ({"data": "", "target": "close", "window": "220", "index": "date"}, "equity"), + ], +) +@pytest.mark.integration +def test_quantitative_rolling_variance(params, data_type): + """Test the rolling variance endpoint.""" + params = {p: v for p, v in params.items() if v} + data = json.dumps(get_data(data_type)) + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/quantitative/rolling/variance?{query_str}" + result = requests.post(url, headers=get_headers(), timeout=60, data=data) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params, data_type", + [ + ({"data": "", "target": "close", "window": "220", "index": "date"}, "equity"), + ], +) +@pytest.mark.integration +def test_quantitative_rolling_stdev(params, data_type): + """Test the rolling standard deviation endpoint.""" + params = {p: v for p, v in params.items() if v} + data = json.dumps(get_data(data_type)) + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/quantitative/rolling/stdev?{query_str}" + result = requests.post(url, headers=get_headers(), timeout=60, data=data) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params, data_type", + [ + ({"data": "", "target": "close", "window": "220", "index": "date"}, "equity"), + ], +) +@pytest.mark.integration +def test_quantitative_rolling_mean(params, data_type): + """Test the rolling mean endpoint.""" + params = {p: v for p, v in params.items() if v} + data = json.dumps(get_data(data_type)) + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/quantitative/rolling/mean?{query_str}" + result = requests.post(url, headers=get_headers(), timeout=60, data=data) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params, data_type", + [ + ( + { + "data": "", + "target": "close", + "window": "10", + "quantile_pct": "", + "index": "date", + }, + "equity", + ), + ( + { + "data": "", + "target": "high", + "window": "50", + "quantile_pct": "0.6", + "index": "date", + }, + "crypto", + ), + ], +) +@pytest.mark.integration +def test_quantitative_rolling_quantile(params, data_type): + """Test the rolling quantile endpoint.""" + params = {p: v for p, v in params.items() if v} + data = json.dumps(get_data(data_type)) + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/quantitative/rolling/quantile?{query_str}" + result = requests.post(url, headers=get_headers(), timeout=10, data=data) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params, data_type", + [ + ({"data": "", "target": "close"}, "equity"), + ({"data": "", "target": "high"}, "crypto"), + ], +) +@pytest.mark.integration +def test_quantitative_summary(params, data_type): + """Test the summary endpoint.""" + params = {p: v for p, v in params.items() if v} + data = json.dumps(get_data(data_type)) + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/quantitative/summary?{query_str}" + result = requests.post(url, headers=get_headers(), timeout=10, data=data) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +############ +# quantitative/stats +############ + + +@parametrize( + "params, data_type", + [ + ({"data": "", "target": "close", "index": "date"}, "equity"), + ], +) +@pytest.mark.integration +def test_quantitative_stats_skew(params, data_type): + """Test the skew endpoint.""" + params = {p: v for p, v in params.items() if v} + data = json.dumps(get_data(data_type)) + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/quantitative/stats/skew?{query_str}" + result = requests.post(url, headers=get_headers(), timeout=60, data=data) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params, data_type", + [ + ({"data": "", "target": "close", "index": "date"}, "equity"), + ], +) +@pytest.mark.integration +def test_quantitative_stats_kurtosis(params, data_type): + """Test the kurtosis endpoint.""" + params = {p: v for p, v in params.items() if v} + data = json.dumps(get_data(data_type)) + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/quantitative/stats/kurtosis?{query_str}" + result = requests.post(url, headers=get_headers(), timeout=60, data=data) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params, data_type", + [ + ({"data": "", "target": "close", "index": "date"}, "equity"), + ], +) +@pytest.mark.integration +def test_quantitative_stats_mean(params, data_type): + """Test the mean endpoint.""" + params = {p: v for p, v in params.items() if v} + data = json.dumps(get_data(data_type)) + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/quantitative/stats/mean?{query_str}" + result = requests.post(url, headers=get_headers(), timeout=60, data=data) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params, data_type", + [ + ({"data": "", "target": "close", "index": "date"}, "equity"), + ], +) +@pytest.mark.integration +def test_quantitative_stats_stdev(params, data_type): + """Test the standard deviation endpoint.""" + params = {p: v for p, v in params.items() if v} + data = json.dumps(get_data(data_type)) + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/quantitative/stats/stdev?{query_str}" + result = requests.post(url, headers=get_headers(), timeout=60, data=data) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params, data_type", + [ + ({"data": "", "target": "close", "index": "date"}, "equity"), + ], +) +@pytest.mark.integration +def test_quantitative_stats_variance(params, data_type): + """Test the variance endpoint.""" + params = {p: v for p, v in params.items() if v} + data = json.dumps(get_data(data_type)) + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/quantitative/stats/variance?{query_str}" + result = requests.post(url, headers=get_headers(), timeout=60, data=data) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params, data_type", + [ + ( + { + "data": "", + "target": "close", + "quantile_pct": "", + "index": "date", + }, + "equity", + ), + ( + { + "data": "", + "target": "high", + "quantile_pct": "0.6", + "index": "date", + }, + "crypto", + ), + ], +) +@pytest.mark.integration +def test_quantitative_stats_quantile(params, data_type): + """Test the quantile endpoint.""" + params = {p: v for p, v in params.items() if v} + data = json.dumps(get_data(data_type)) + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/quantitative/stats/quantile?{query_str}" + result = requests.post(url, headers=get_headers(), timeout=10, data=data) + assert isinstance(result, requests.Response) + assert result.status_code == 200 diff --git a/openbb_platform/extensions/quantitative/integration/test_quantitative_python.py b/openbb_platform/extensions/quantitative/integration/test_quantitative_python.py new file mode 100644 index 0000000000000000000000000000000000000000..4a2433e8ba7e78936aa32c56a7f4bae9a02caab1 --- /dev/null +++ b/openbb_platform/extensions/quantitative/integration/test_quantitative_python.py @@ -0,0 +1,560 @@ +"""Test qa extension.""" + +import random +from typing import Literal + +import pytest +from extensions.tests.conftest import parametrize +from openbb_core.app.model.obbject import OBBject + + +# pylint:disable=inconsistent-return-statements +@pytest.fixture(scope="session") +def obb(pytestconfig): + """Fixture to setup obb.""" + if pytestconfig.getoption("markexpr") != "not integration": + import openbb # pylint:disable=import-outside-toplevel + + return openbb.obb + + +# pylint:disable=redefined-outer-name + +data: dict = {} + + +def get_stocks_data(): + """Get stocks data.""" + import openbb # pylint:disable=import-outside-toplevel + + if "stocks_data" in data: + return data["stocks_data"] + + symbol = random.choice(["AAPL", "NVDA", "MSFT", "TSLA", "AMZN", "V"]) # noqa: S311 + provider = random.choice(["fmp", "polygon", "yfinance"]) # noqa: S311 + + data["stocks_data"] = openbb.obb.equity.price.historical( + symbol=symbol, provider=provider + ).results + return data["stocks_data"] + + +def get_crypto_data(): + """Get crypto data.""" + import openbb # pylint:disable=import-outside-toplevel + + if "crypto_data" in data: + return data["crypto_data"] + + # TODO : add more crypto providers and symbols + symbol = random.choice(["BTCUSD"]) # noqa: S311 + provider = random.choice(["fmp"]) # noqa: S311 + + data["crypto_data"] = openbb.obb.crypto.price.historical( + symbol=symbol, provider=provider + ).results + return data["crypto_data"] + + +def get_data(menu: Literal["equity", "crypto"]): + """Get data.""" + funcs = {"equity": get_stocks_data, "crypto": get_crypto_data} + return funcs[menu]() + + +@parametrize( + "params, data_type", + [ + ({"data": "", "target": "close"}, "equity"), + ({"data": "", "target": "high"}, "crypto"), + ], +) +@pytest.mark.integration +def test_quantitative_normality(params, data_type, obb): + """Test normality.""" + params = {p: v for p, v in params.items() if v} + params["data"] = get_data(data_type) + + result = obb.quantitative.normality(**params) + assert result + assert isinstance(result, OBBject) + + +@parametrize( + "params, data_type", + [ + ({"data": "", "target": "close"}, "equity"), + ({"data": "", "target": "high"}, "crypto"), + ], +) +@pytest.mark.integration +def test_quantitative_capm(params, data_type, obb): + """Test capm.""" + params = {p: v for p, v in params.items() if v} + params["data"] = get_data(data_type) + + result = obb.quantitative.capm(**params) + assert result + assert isinstance(result, OBBject) + + +@parametrize( + "params, data_type", + [ + ( + { + "data": "", + "target": "close", + "threshold_start": "", + "threshold_end": "", + }, + "equity", + ), + ( + { + "data": "", + "target": "high", + "threshold_start": "0.1", + "threshold_end": "1.6", + }, + "crypto", + ), + ], +) +@pytest.mark.integration +def test_quantitative_performance_omega_ratio(params, data_type, obb): + """Test omega ratio.""" + params = {p: v for p, v in params.items() if v} + params["data"] = get_data(data_type) + + result = obb.quantitative.performance.omega_ratio(**params) + assert result + assert isinstance(result, OBBject) + + +@parametrize( + "params, data_type", + [ + ({"data": "", "target": "close", "window": "5", "index": "date"}, "equity"), + ({"data": "", "target": "high", "window": "10", "index": "date"}, "crypto"), + ], +) +@pytest.mark.integration +def test_quantitative_rolling_kurtosis(params, data_type, obb): + """Test rolling kurtosis.""" + params = {p: v for p, v in params.items() if v} + params["data"] = get_data(data_type) + + result = obb.quantitative.rolling.kurtosis(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params, data_type", + [ + ( + { + "data": "", + "target": "close", + "fuller_reg": "c", + "kpss_reg": "ct", + }, + "equity", + ), + ( + { + "data": "", + "target": "high", + "fuller_reg": "ct", + "kpss_reg": "c", + }, + "crypto", + ), + ], +) +@pytest.mark.integration +def test_quantitative_unitroot_test(params, data_type, obb): + """Test unitroot test.""" + params = {p: v for p, v in params.items() if v} + params["data"] = get_data(data_type) + + result = obb.quantitative.unitroot_test(**params) + assert result + assert isinstance(result, OBBject) + + +@parametrize( + "params, data_type", + [ + ( + { + "data": "", + "target": "close", + "rfr": "", + "window": "100", + "index": "date", + }, + "equity", + ), + ( + { + "data": "", + "target": "high", + "rfr": "0.5", + "window": "100", + "index": "date", + }, + "crypto", + ), + ], +) +@pytest.mark.integration +def test_quantitative_performance_sharpe_ratio(params, data_type, obb): + """Test sharpe ratio.""" + params = {p: v for p, v in params.items() if v} + params["data"] = get_data(data_type) + + result = obb.quantitative.performance.sharpe_ratio(**params) + assert result + assert isinstance(result, OBBject) + + +@parametrize( + "params, data_type", + [ + ( + { + "data": "", + "target": "close", + "target_return": "", + "window": "100", + "adjusted": "", + "index": "date", + }, + "equity", + ), + ( + { + "data": "", + "target": "close", + "target_return": "", + "window": "100", + "adjusted": "true", + "index": "date", + }, + "crypto", + ), + ], +) +@pytest.mark.integration +def test_quantitative_performance_sortino_ratio(params, data_type, obb): + """Test sortino ratio.""" + params = {p: v for p, v in params.items() if v} + params["data"] = get_data(data_type) + + result = obb.quantitative.performance.sortino_ratio(**params) + assert result + assert isinstance(result, OBBject) + + +@parametrize( + "params, data_type", + [ + ({"data": "", "target": "close", "window": "220", "index": "date"}, "equity"), + ], +) +@pytest.mark.integration +def test_quantitative_rolling_skew(params, data_type, obb): + """Test rolling skew.""" + params = {p: v for p, v in params.items() if v} + params["data"] = get_data(data_type) + + result = obb.quantitative.rolling.skew(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params, data_type", + [ + ( + { + "data": "", + "target": "close", + "window": "10", + "quantile_pct": "", + "index": "date", + }, + "equity", + ), + ( + { + "data": "", + "target": "high", + "window": "50", + "quantile_pct": "0.6", + "index": "date", + }, + "crypto", + ), + ], +) +@pytest.mark.integration +def test_quantitative_rolling_quantile(params, data_type, obb): + """Test rolling quantile.""" + params = {p: v for p, v in params.items() if v} + params["data"] = get_data(data_type) + + result = obb.quantitative.rolling.quantile(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params, data_type", + [ + ({"data": "", "target": "close"}, "equity"), + ({"data": "", "target": "high"}, "crypto"), + ], +) +@pytest.mark.integration +def test_quantitative_summary(params, data_type, obb): + """Test summary.""" + params = {p: v for p, v in params.items() if v} + params["data"] = get_data(data_type) + + result = obb.quantitative.summary(**params) + assert result + assert isinstance(result, OBBject) + + +@parametrize( + "params, data_type", + [ + ( + { + "data": "", + "target": "close", + "window": "10", + "index": "date", + }, + "equity", + ), + ( + { + "data": "", + "target": "high", + "window": "50", + "index": "date", + }, + "crypto", + ), + ], +) +@pytest.mark.integration +def test_quantitative_rolling_stdev(params, data_type, obb): + """Test rolling stdev.""" + params = {p: v for p, v in params.items() if v} + params["data"] = get_data(data_type) + + result = obb.quantitative.rolling.stdev(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params, data_type", + [ + ( + { + "data": "", + "target": "close", + "window": "10", + "index": "date", + }, + "equity", + ), + ( + { + "data": "", + "target": "high", + "window": "50", + "index": "date", + }, + "crypto", + ), + ], +) +@pytest.mark.integration +def test_quantitative_rolling_mean(params, data_type, obb): + """Test rolling mean.""" + params = {p: v for p, v in params.items() if v} + params["data"] = get_data(data_type) + + result = obb.quantitative.rolling.mean(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params, data_type", + [ + ( + { + "data": "", + "target": "close", + "window": "10", + "index": "date", + }, + "equity", + ), + ( + { + "data": "", + "target": "high", + "window": "50", + "index": "date", + }, + "crypto", + ), + ], +) +@pytest.mark.integration +def test_quantitative_rolling_variance(params, data_type, obb): + """Test rolling variance.""" + params = {p: v for p, v in params.items() if v} + params["data"] = get_data(data_type) + + result = obb.quantitative.rolling.variance(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params, data_type", + [ + ({"data": "", "target": "close"}, "equity"), + ], +) +@pytest.mark.integration +def test_quantitative_stats_skew(params, data_type, obb): + """Test skew.""" + params = {p: v for p, v in params.items() if v} + params["data"] = get_data(data_type) + + result = obb.quantitative.stats.skew(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params, data_type", + [ + ({"data": "", "target": "close"}, "equity"), + ], +) +@pytest.mark.integration +def test_quantitative_stats_kurtosis(params, data_type, obb): + """Test kurtosis.""" + params = {p: v for p, v in params.items() if v} + params["data"] = get_data(data_type) + + result = obb.quantitative.stats.kurtosis(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params, data_type", + [ + ({"data": "", "target": "close"}, "equity"), + ], +) +@pytest.mark.integration +def test_quantitative_stats_variance(params, data_type, obb): + """Test variance.""" + params = {p: v for p, v in params.items() if v} + params["data"] = get_data(data_type) + + result = obb.quantitative.stats.variance(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params, data_type", + [ + ({"data": "", "target": "close"}, "equity"), + ], +) +@pytest.mark.integration +def test_quantitative_stats_stdev(params, data_type, obb): + """Test stdev.""" + params = {p: v for p, v in params.items() if v} + params["data"] = get_data(data_type) + + result = obb.quantitative.stats.stdev(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params, data_type", + [ + ({"data": "", "target": "close"}, "equity"), + ], +) +@pytest.mark.integration +def test_quantitative_stats_mean(params, data_type, obb): + """Test mean.""" + params = {p: v for p, v in params.items() if v} + params["data"] = get_data(data_type) + + result = obb.quantitative.stats.mean(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params, data_type", + [ + ( + { + "data": "", + "target": "close", + "quantile_pct": "", + }, + "equity", + ), + ( + { + "data": "", + "target": "close", + "quantile_pct": "0.6", + }, + "crypto", + ), + ], +) +@pytest.mark.integration +def test_quantitative_stats_quantile(params, data_type, obb): + """Test quantile.""" + params = {p: v for p, v in params.items() if v} + params["data"] = get_data(data_type) + + result = obb.quantitative.stats.quantile(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 diff --git a/openbb_platform/extensions/quantitative/openbb_quantitative/__init__.py b/openbb_platform/extensions/quantitative/openbb_quantitative/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..857a5d5938c880ce91f274a535bbe5f1b758f8e9 --- /dev/null +++ b/openbb_platform/extensions/quantitative/openbb_quantitative/__init__.py @@ -0,0 +1 @@ +"""Quantitative analysis extension for OpenBB Platform.""" diff --git a/openbb_platform/extensions/quantitative/openbb_quantitative/helpers.py b/openbb_platform/extensions/quantitative/openbb_quantitative/helpers.py new file mode 100644 index 0000000000000000000000000000000000000000..dbd3d21c0c5828f6d7d377e49310ccbe18be5d0b --- /dev/null +++ b/openbb_platform/extensions/quantitative/openbb_quantitative/helpers.py @@ -0,0 +1,80 @@ +"""Helper functions for Quantitative Analysis.""" + +from typing import TYPE_CHECKING, Union + +if TYPE_CHECKING: + from pandas import DataFrame, Series + + +# ruff: ignore=S310 +def get_fama_raw(start_date: str, end_date: str) -> "DataFrame": + """Get base Fama French data to calculate risk. + + Returns + ------- + DataFrame + A data with fama french model information + """ + # pylint: disable=import-outside-toplevel + from io import BytesIO + from urllib.request import urlopen + from zipfile import ZipFile + + from pandas import read_csv, to_datetime, to_numeric + + with urlopen( # nosec # noqa: S310 SIM117 + "https://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/F-F_Research_Data_Factors_CSV.zip" + ) as url: + # Download Zipfile and create pandas DataFrame + with ZipFile(BytesIO(url.read())) as zipfile: + with zipfile.open("F-F_Research_Data_Factors.CSV") as zip_open: + df = read_csv( + zip_open, + header=0, + names=["Date", "MKT-RF", "SMB", "HML", "RF"], + skiprows=3, + ) + + df = df[df["Date"].apply(lambda x: len(str(x).strip()) == 6)] + df["Date"] = df["Date"].astype(str) + "01" + df["Date"] = to_datetime(df["Date"], format="%Y%m%d") + df["MKT-RF"] = to_numeric(df["MKT-RF"], downcast="float") + df["SMB"] = to_numeric(df["SMB"], downcast="float") + df["HML"] = to_numeric(df["HML"], downcast="float") + df["RF"] = to_numeric(df["RF"], downcast="float") + df["MKT-RF"] = df["MKT-RF"] / 100 + df["SMB"] = df["SMB"] / 100 + df["HML"] = df["HML"] / 100 + df["RF"] = df["RF"] / 100 + df = df.set_index("Date") + + dt_start_date = to_datetime(start_date, format="%Y-%m-%d") + if dt_start_date > df.index.max(): + raise ValueError( + f"Start date '{dt_start_date}' is after the last date available for Fama-French '{df.index[-1]}'" + ) + + df = df.loc[start_date:end_date] # type: ignore + + return df + + +def validate_window(input_data: Union["Series", "DataFrame"], window: int) -> None: + """Validate the window input. + + Parameters + ---------- + input_data : Union[Series, DataFrame] + The input data to be validated. + window : int + The window to be validated. + + Raises + ------ + ValueError + If the window is greater than the input data length. + """ + if window > len(input_data): + raise ValueError( + f"Window '{window}' is greater than the input data length '{len(input_data)}'" + ) diff --git a/openbb_platform/extensions/quantitative/openbb_quantitative/models.py b/openbb_platform/extensions/quantitative/openbb_quantitative/models.py new file mode 100644 index 0000000000000000000000000000000000000000..5082739d3159783d233cacd168273bbad0d72fc5 --- /dev/null +++ b/openbb_platform/extensions/quantitative/openbb_quantitative/models.py @@ -0,0 +1,70 @@ +"""Pydantic models for Quantitative Analysis.""" + +from pydantic import BaseModel + + +class TestModel(BaseModel): + """Base model for QA tests.""" + + statistic: float + p_value: float + + +class NormalityModel(BaseModel): + """Normality model.""" + + kurtosis: TestModel + skewness: TestModel + jarque_bera: TestModel + shapiro_wilk: TestModel + kolmogorov_smirnov: TestModel + + +class ADFTestModel(TestModel): + """Augmented Dickey-Fuller test model.""" + + nlags: int + nobs: int + icbest: float + + +class KPSSTestModel(TestModel): + """Kwiatkowski–Phillips–Schmidt–Shin test model.""" + + nlags: int + + +class UnitRootModel(BaseModel): + """Unit root model.""" + + adf: ADFTestModel + kpss: KPSSTestModel + + +class OmegaModel(BaseModel): + """Omega model.""" + + threshold: float + omega: float + + +class SummaryModel(BaseModel): + """Summary model.""" + + count: int + mean: float + std: float + var: float + min: float + max: float + p_25: float + p_50: float + p_75: float + + +class CAPMModel(BaseModel): + """CAPM model.""" + + market_risk: float + systematic_risk: float + idiosyncratic_risk: float diff --git a/openbb_platform/extensions/quantitative/openbb_quantitative/performance/performance_router.py b/openbb_platform/extensions/quantitative/openbb_quantitative/performance/performance_router.py new file mode 100644 index 0000000000000000000000000000000000000000..ca6c7a4af3fbb8e9b165f586f3ebaa28bd9f7d9d --- /dev/null +++ b/openbb_platform/extensions/quantitative/openbb_quantitative/performance/performance_router.py @@ -0,0 +1,271 @@ +"""OpenBB Performance Extension router.""" + +# pylint: disable=too-many-positional-arguments + +from typing import TYPE_CHECKING + +from openbb_core.app.model.example import APIEx, PythonEx +from openbb_core.app.model.obbject import OBBject +from openbb_core.app.router import Router +from openbb_core.provider.abstract.data import Data +from openbb_quantitative.models import ( + OmegaModel, +) +from pydantic import PositiveInt + +if TYPE_CHECKING: + from pandas import Series + +router = Router(prefix="/performance") + + +@router.command( + methods=["POST"], + examples=[ + PythonEx( + description="Get Omega Ratio.", + code=[ + 'stock_data = obb.equity.price.historical(symbol="TSLA", start_date="2023-01-01", provider="fmp").to_df()', # noqa: E501 + 'returns = stock_data["close"].pct_change().dropna()', + 'obb.quantitative.performance.omega_ratio(data=returns, target="close")', + ], + ), + APIEx( + parameters={ + "target": "close", + "data": APIEx.mock_data( + "timeseries", + sample={"date": "2023-01-01", "close": 0.05}, + ), + }, + ), + ], +) +def omega_ratio( + data: list[Data], + target: str, + threshold_start: float = 0.0, + threshold_end: float = 1.5, +) -> OBBject[list[OmegaModel]]: + """Calculate the Omega Ratio. + + The Omega Ratio is a sophisticated metric that goes beyond traditional performance measures by considering the + probability of achieving returns above a given threshold. It offers a more nuanced view of risk and reward, + focusing on the likelihood of success rather than just average outcomes. + + Parameters + ---------- + data : list[Data] + Time series data. + target : str + Target column name. + threshold_start : float, optional + Start threshold, by default 0.0 + threshold_end : float, optional + End threshold, by default 1.5 + + Returns + ------- + OBBject[list[OmegaModel]] + Omega ratios. + """ + # pylint: disable=import-outside-toplevel + from numpy import linspace, sqrt + from openbb_core.app.utils import ( + basemodel_to_df, + get_target_column, + ) + + df = basemodel_to_df(data) + series_target = get_target_column(df, target) + + epsilon = 1e-6 # to avoid division by zero + + def get_omega_ratio(df_target: "Series", threshold: float) -> float: + """Get omega ratio.""" + daily_threshold = (threshold + 1) ** sqrt(1 / 252) - 1 + excess = df_target - daily_threshold + numerator = excess[excess > 0].sum() + denominator = -excess[excess < 0].sum() + epsilon + + return numerator / denominator + + threshold = linspace(threshold_start, threshold_end, 50) + results = [] + for i in threshold: + omega_ = get_omega_ratio(series_target, i) + results.append(OmegaModel(threshold=i, omega=omega_)) + + return OBBject(results=results) + + +@router.command( + methods=["POST"], + examples=[ + PythonEx( + description="Get Rolling Sharpe Ratio.", + code=[ + 'stock_data = obb.equity.price.historical(symbol="TSLA", start_date="2023-01-01", provider="fmp").to_df()', # noqa: E501 # pylint: disable=line-too-long + 'returns = stock_data["close"].pct_change().dropna()', + 'obb.quantitative.performance.sharpe_ratio(data=returns, target="close")', + ], + ), + APIEx( + parameters={ + "target": "close", + "window": 2, + "data": APIEx.mock_data( + "timeseries", + sample={"date": "2023-01-01", "close": 0.05}, + ), + }, + ), + ], +) +def sharpe_ratio( + data: list[Data], + target: str, + rfr: float = 0.0, + window: PositiveInt = 252, + index: str = "date", +) -> OBBject[list[Data]]: + """Get Rolling Sharpe Ratio. + + This function calculates the Sharpe Ratio, a metric used to assess the return of an investment compared to its risk. + By factoring in the risk-free rate, it helps you understand how much extra return you're getting for the extra + volatility that you endure by holding a riskier asset. The Sharpe Ratio is essential for investors looking to + compare the efficiency of different investments, providing a clear picture of potential rewards in relation to their + risks over a specified period. Ideal for gauging the effectiveness of investment strategies, it offers insights into + optimizing your portfolio for maximum return on risk. + + Parameters + ---------- + data : list[Data] + Time series data. + target : str + Target column name. + rfr : float, optional + Risk-free rate, by default 0.0 + window : PositiveInt, optional + Window size, by default 252 + index : str, optional + + Returns + ------- + OBBject[list[Data]] + Sharpe ratio. + """ + # pylint: disable=import-outside-toplevel + from numpy import sqrt + from openbb_core.app.utils import ( + basemodel_to_df, + df_to_basemodel, + get_target_column, + ) + from openbb_quantitative.helpers import validate_window + + df = basemodel_to_df(data, index=index) + series_target = get_target_column(df, target) + validate_window(series_target, window) + series_target.name = f"sharpe_{window}" + returns = series_target.pct_change().dropna().rolling(window).sum() + std = series_target.rolling(window).std() / sqrt(window) + results = ((returns - rfr) / std).dropna().reset_index(drop=False) + + results = df_to_basemodel(results) + + return OBBject(results=results) + + +@router.command( + methods=["POST"], + examples=[ + PythonEx( + description="Get Rolling Sortino Ratio.", + code=[ + 'stock_data = obb.equity.price.historical(symbol="TSLA", start_date="2023-01-01", provider="fmp").to_df()', # noqa: E501 + 'returns = stock_data["close"].pct_change().dropna()', + 'obb.quantitative.performance.sortino_ratio(data=stock_data, target="close")', + 'obb.quantitative.performance.sortino_ratio(data=stock_data, target="close", target_return=0.01, window=126, adjusted=True)', # noqa: E501 pylint: disable=line-too-long + ], + ), + APIEx( + parameters={ + "target": "close", + "window": 2, + "data": APIEx.mock_data( + "timeseries", + sample={"date": "2023-01-01", "close": 0.05}, + ), + }, + ), + ], +) +def sortino_ratio( + data: list[Data], + target: str, + target_return: float = 0.0, + window: PositiveInt = 252, + adjusted: bool = False, + index: str = "date", +) -> OBBject[list[Data]]: + """Get rolling Sortino Ratio. + + The Sortino Ratio enhances the evaluation of investment returns by distinguishing harmful volatility + from total volatility. Unlike other metrics that treat all volatility as risk, this command specifically assesses + the volatility of negative returns relative to a target or desired return. + It's particularly useful for investors who are more concerned with downside risk than with overall volatility. + By calculating the Sortino Ratio, investors can better understand the risk-adjusted return of their investments, + focusing on the likelihood and impact of negative returns. + This approach offers a more nuanced tool for portfolio optimization, especially in strategies aiming + to minimize the downside. + + For method & terminology see: + http://www.redrockcapital.com/Sortino__A__Sharper__Ratio_Red_Rock_Capital.pdf + + Parameters + ---------- + data : list[Data] + Time series data. + target : str + Target column name. + target_return : float, optional + Target return, by default 0.0 + window : PositiveInt, optional + Window size, by default 252 + adjusted : bool, optional + Adjust sortino ratio to compare it to sharpe ratio, by default False + index:str + Index column for input data + Returns + ------- + OBBject[list[Data]] + Sortino ratio. + """ + # pylint: disable=import-outside-toplevel + from numpy import sqrt + from openbb_core.app.utils import ( + basemodel_to_df, + df_to_basemodel, + get_target_column, + ) + from openbb_quantitative.helpers import validate_window + + df = basemodel_to_df(data, index=index) + series_target = get_target_column(df, target) + validate_window(series_target, window) + returns = series_target.pct_change().dropna().rolling(window).sum().dropna() + downside_deviation = returns.rolling(window).apply( + lambda x: (x.values[x.values < 0]).std() / sqrt(252) * 100 + ) + results = ( + ((returns - target_return) / downside_deviation) + .dropna() + .reset_index(drop=False) + ) + + if adjusted: + results = results.applymap(lambda x: x / sqrt(2) if isinstance(x, float) else x) + results_ = df_to_basemodel(results) + + return OBBject(results=results_) diff --git a/openbb_platform/extensions/quantitative/openbb_quantitative/py.typed b/openbb_platform/extensions/quantitative/openbb_quantitative/py.typed new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/openbb_platform/extensions/quantitative/openbb_quantitative/quantitative_router.py b/openbb_platform/extensions/quantitative/openbb_quantitative/quantitative_router.py new file mode 100644 index 0000000000000000000000000000000000000000..60e56906c311ab855eadea784309b23cd58d35e5 --- /dev/null +++ b/openbb_platform/extensions/quantitative/openbb_quantitative/quantitative_router.py @@ -0,0 +1,297 @@ +"""Quantitative Analysis Router.""" + +from typing import Literal + +from openbb_core.app.model.example import APIEx, PythonEx +from openbb_core.app.model.obbject import OBBject +from openbb_core.app.router import Router +from openbb_core.provider.abstract.data import Data + +from openbb_quantitative.models import ( + ADFTestModel, + CAPMModel, + KPSSTestModel, + NormalityModel, + SummaryModel, + TestModel, + UnitRootModel, +) +from openbb_quantitative.performance.performance_router import ( + router as performance_router, +) +from openbb_quantitative.rolling.rolling_router import router as rolling_router +from openbb_quantitative.stats.stats_router import router as stats_router + +router = Router(prefix="", description="Quantitative analysis tools.") +router.include_router(rolling_router) +router.include_router(stats_router) +router.include_router(performance_router) + + +@router.command( + methods=["POST"], + examples=[ + PythonEx( + description="Get Normality Statistics.", + code=[ + "stock_data = obb.equity.price.historical(symbol='TSLA', start_date='2023-01-01', provider='fmp').to_df()", # noqa: E501 + "obb.quantitative.normality(data=stock_data, target='close')", + ], + ), + APIEx(parameters={"target": "close", "data": APIEx.mock_data("timeseries", 8)}), + ], +) +def normality(data: list[Data], target: str) -> OBBject[NormalityModel]: + """Get Normality Statistics. + + - **Kurtosis**: whether the kurtosis of a sample differs from the normal distribution. + - **Skewness**: whether the skewness of a sample differs from the normal distribution. + - **Jarque-Bera**: whether the sample data has the skewness and kurtosis matching a normal distribution. + - **Shapiro-Wilk**: whether a random sample comes from a normal distribution. + - **Kolmogorov-Smirnov**: whether two underlying one-dimensional probability distributions differ. + + Parameters + ---------- + data : list[Data] + Time series data. + target : str + Target column name. + + Returns + ------- + OBBject[NormalityModel] + Normality tests summary. See qa_models.NormalityModel for details. + """ + # pylint: disable=import-outside-toplevel + from scipy import stats # noqa + from openbb_core.app.utils import ( # noqa + basemodel_to_df, + get_target_column, + ) + + df = basemodel_to_df(data) + series_target = get_target_column(df, target) + + kt_statistic, kt_pvalue = stats.kurtosistest(series_target) + sk_statistic, sk_pvalue = stats.skewtest(series_target) + jb_statistic, jb_pvalue = stats.jarque_bera(series_target) + sh_statistic, sh_pvalue = stats.shapiro(series_target) + ks_statistic, ks_pvalue = stats.kstest(series_target, "norm") + + norm_summary = NormalityModel( + kurtosis=TestModel(statistic=kt_statistic, p_value=kt_pvalue), + skewness=TestModel(statistic=sk_statistic, p_value=sk_pvalue), + jarque_bera=TestModel(statistic=jb_statistic, p_value=jb_pvalue), + shapiro_wilk=TestModel(statistic=sh_statistic, p_value=sh_pvalue), + kolmogorov_smirnov=TestModel(statistic=ks_statistic, p_value=ks_pvalue), + ) + + return OBBject(results=norm_summary) + + +@router.command( + methods=["POST"], + examples=[ + PythonEx( + description="Get Capital Asset Pricing Model (CAPM).", + code=[ + "stock_data = obb.equity.price.historical(symbol='TSLA', start_date='2023-01-01', provider='fmp').to_df()", # noqa: E501 + "obb.quantitative.capm(data=stock_data, target='close')", + ], + ), + APIEx( + parameters={"target": "close", "data": APIEx.mock_data("timeseries", 31)} + ), + ], +) +def capm(data: list[Data], target: str) -> OBBject[CAPMModel]: + """Get Capital Asset Pricing Model (CAPM). + + CAPM offers a streamlined way to assess the expected return on an investment while accounting for its risk relative + to the market. It's a cornerstone of modern financial theory that helps investors understand the trade-off between + risk and return, guiding more informed investment choices. + + Parameters + ---------- + data : list[Data] + Time series data. + target : str + Target column name. + + Returns + ------- + OBBject[CAPMModel] + CAPM model summary. + """ + # pylint: disable=import-outside-toplevel + import statsmodels.api as sm # noqa + from openbb_core.app.utils import ( # noqa`` + basemodel_to_df, + get_target_columns, + ) + from pandas import to_datetime # noqa + from openbb_quantitative.helpers import get_fama_raw # noqa + + df = basemodel_to_df(data) + + df_target = get_target_columns(df, ["date", target]) + df_target = df_target.set_index("date") + df_target.loc[:, "return"] = df_target.pct_change() + df_target = df_target.dropna() + df_target.index = to_datetime(df_target.index) + start_date = df_target.index.min().strftime("%Y-%m-%d") + end_date = df_target.index.max().strftime("%Y-%m-%d") + df_fama = get_fama_raw(start_date, end_date) + df_target = df_target.merge(df_fama, left_index=True, right_index=True) + df_target["excess_return"] = df_target["return"] - df_target["RF"] + df_target["excess_mkt"] = df_target["MKT-RF"] - df_target["RF"] + df_target = df_target.dropna() + + y = df_target[["excess_return"]] + x = df_target["excess_mkt"] + x = sm.add_constant(x) + model = sm.OLS(y, x).fit() + + results = CAPMModel( + market_risk=model.params["excess_mkt"], + systematic_risk=model.rsquared, + idiosyncratic_risk=1 - model.rsquared, + ) + + return OBBject(results=results) + + +@router.command( + methods=["POST"], + examples=[ + PythonEx( + description="Get Unit Root Test.", + code=[ + "stock_data = obb.equity.price.historical(symbol='TSLA', start_date='2023-01-01', provider='fmp').to_df()", # noqa: E501 + "obb.quantitative.unitroot_test(data=stock_data, target='close')", + ], + ), + APIEx(parameters={"target": "close", "data": APIEx.mock_data("timeseries", 5)}), + ], +) +def unitroot_test( + data: list[Data], + target: str, + fuller_reg: Literal["c", "ct", "ctt", "nc", "c"] = "c", + kpss_reg: Literal["c", "ct"] = "c", +) -> OBBject[UnitRootModel]: + """Get Unit Root Test. + + This function applies two renowned tests to assess whether your data series is stationary or if it contains a unit + root, indicating it may be influenced by time-based trends or seasonality. The Augmented Dickey-Fuller (ADF) test + helps identify the presence of a unit root, suggesting that the series could be non-stationary and potentially + unpredictable over time. On the other hand, the Kwiatkowski-Phillips-Schmidt-Shin (KPSS) test checks for the + stationarity of the series, where failing to reject the null hypothesis indicates a stable, stationary series. + Together, these tests provide a comprehensive view of your data's time series properties, essential for + accurate modeling and forecasting. + + Parameters + ---------- + data : list[Data] + Time series data. + target : str + Target column name. + fuller_reg : Literal["c", "ct", "ctt", "nc", "c"] + Regression type for ADF test. + kpss_reg : Literal["c", "ct"] + Regression type for KPSS test. + + Returns + ------- + OBBject[UnitRootModel] + Unit root tests summary. + """ + # pylint: disable=import-outside-toplevel + from openbb_core.app.utils import ( # noqa + basemodel_to_df, + get_target_column, + ) + from statsmodels.tsa import stattools # noqa + + df = basemodel_to_df(data) + series_target = get_target_column(df, target) + + adf = stattools.adfuller(series_target, regression=fuller_reg) + kpss = stattools.kpss(series_target, regression=kpss_reg, nlags="auto") + + unitroot_summary = UnitRootModel( + adf=ADFTestModel( + statistic=adf[0], + p_value=adf[1], + nlags=adf[2] if isinstance(adf[2], int) else 0, + nobs=adf[3] if isinstance(adf[3], int) else 0, + icbest=adf[5] if isinstance(adf[5], float) else 0.0, # type: ignore + ), + kpss=KPSSTestModel( + statistic=kpss[0], + p_value=kpss[1], + nlags=kpss[2], + ), + ) + return OBBject(results=unitroot_summary) + + +@router.command( + methods=["POST"], + examples=[ + PythonEx( + description="Get Summary Statistics.", + code=[ + "stock_data = obb.equity.price.historical(symbol='TSLA', start_date='2023-01-01', provider='fmp').to_df()", # noqa: E501 + "obb.quantitative.summary(data=stock_data, target='close')", + ], + ), + APIEx(parameters={"target": "close", "data": APIEx.mock_data("timeseries", 5)}), + ], +) +def summary(data: list[Data], target: str) -> OBBject[SummaryModel]: + """Get Summary Statistics. + + The summary that offers a snapshot of its central tendencies, variability, and distribution. + This command calculates essential statistics, including mean, standard deviation, variance, + and specific percentiles, to provide a detailed profile of your target column. B + y examining these metrics, you gain insights into the data's overall behavior, helping to identify patterns, + outliers, or anomalies. The summary table is an invaluable tool for initial data exploration, + ensuring you have a solid foundation for further analysis or reporting. + + Parameters + ---------- + data : list[Data] + Time series data. + target : str + Target column name. + + Returns + ------- + OBBject[SummaryModel] + Summary table. + """ + # pylint: disable=import-outside-toplevel + from openbb_core.app.utils import ( + basemodel_to_df, + get_target_column, + ) + + df = basemodel_to_df(data) + series_target = get_target_column(df, target) + + df_stats = series_target.describe(percentiles=[0.1, 0.25, 0.5, 0.75, 0.9]) + df_stats.loc["var"] = df_stats.loc["std"] ** 2 + results = SummaryModel( + count=df_stats.loc["count"], + mean=df_stats.loc["mean"], + std=df_stats.loc["std"], + var=df_stats.loc["var"], + min=df_stats.loc["min"], + p_25=df_stats.loc["25%"], + p_50=df_stats.loc["50%"], + p_75=df_stats.loc["75%"], + max=df_stats.loc["max"], + ) + + return OBBject(results=results) diff --git a/openbb_platform/extensions/quantitative/openbb_quantitative/rolling/rolling_router.py b/openbb_platform/extensions/quantitative/openbb_quantitative/rolling/rolling_router.py new file mode 100644 index 0000000000000000000000000000000000000000..b8d5361fdf078d2e4cb905f0ac960d9f5e3d45aa --- /dev/null +++ b/openbb_platform/extensions/quantitative/openbb_quantitative/rolling/rolling_router.py @@ -0,0 +1,448 @@ +"""Rolling submenu of quantitative models for rolling statistics.""" + +from openbb_core.app.model.example import APIEx, PythonEx +from openbb_core.app.model.obbject import OBBject +from openbb_core.app.router import Router +from openbb_core.provider.abstract.data import Data +from pydantic import NonNegativeFloat, PositiveInt + +router = Router(prefix="/rolling") + + +@router.command( + methods=["POST"], + examples=[ + PythonEx( + description="Get Rolling Mean.", + code=[ + 'stock_data = obb.equity.price.historical(symbol="TSLA", start_date="2023-01-01", provider="fmp").to_df()', # noqa: E501 + 'returns = stock_data["close"].pct_change().dropna()', + 'obb.quantitative.rolling.skew(data=returns, target="close")', + ], + ), + APIEx( + parameters={ + "target": "close", + "window": 2, + "data": APIEx.mock_data( + "timeseries", + sample={"date": "2023-01-01", "close": 0.05}, + ), + } + ), + ], +) +def skew( + data: list[Data], target: str, window: PositiveInt = 21, index: str = "date" +) -> OBBject[list[Data]]: + """Get Rolling Skew. + + Skew is a statistical measure that reveals the degree of asymmetry of a distribution around its mean. + Positive skewness indicates a distribution with an extended tail to the right, while negative skewness shows a tail + that stretches left. Understanding skewness can provide insights into potential biases in data and help anticipate + the nature of future data points. It's particularly useful for identifying the likelihood of extreme outcomes in + financial returns, enabling more informed decision-making based on the distribution's shape over a specified period. + + Parameters + ---------- + data : list[Data] + Time series data. + target : str + Target column name. + window : PositiveInt + Window size. + index : str, optional + Index column name, by default "date" + + Returns + ------- + OBBject[list[Data]] + Rolling skew. + + """ + # pylint: disable=import-outside-toplevel + from openbb_core.app.utils import ( + basemodel_to_df, + df_to_basemodel, + get_target_column, + ) + from openbb_quantitative.helpers import validate_window + from openbb_quantitative.statistics import skew_ + + df = basemodel_to_df(data, index=index) + series_target = get_target_column(df, target) + series_target.name = f"rolling_skew_{window}" + validate_window(series_target, window) + results = ( + series_target.rolling(window).apply(skew_).dropna().reset_index(drop=False) + ) + results = df_to_basemodel(results) + + return OBBject(results=results) + + +@router.command( + methods=["POST"], + examples=[ + PythonEx( + description="Get Rolling Variance.", + code=[ + 'stock_data = obb.equity.price.historical(symbol="TSLA", start_date="2023-01-01", provider="fmp").to_df()', # noqa: E501 + 'returns = stock_data["close"].pct_change().dropna()', + 'obb.quantitative.rolling.variance(data=returns, target="close", window=252)', + ], + ), + APIEx( + parameters={ + "target": "close", + "window": 2, + "data": APIEx.mock_data( + "timeseries", + sample={"date": "2023-01-01", "close": 0.05}, + ), + } + ), + ], +) +def variance( + data: list[Data], target: str, window: PositiveInt = 21, index: str = "date" +) -> OBBject[list[Data]]: + """ + Calculate the rolling variance of a target column within a given window size. + + Variance measures the dispersion of a set of data points around their mean. It is a key metric for + assessing the volatility and stability of financial returns or other time series data over a specified rolling window. + + Parameters + ---------- + data: list[Data] + The time series data as a list of data points. + target: str + The name of the column for which to calculate variance. + window: PositiveInt + The number of observations used for calculating the rolling measure. + index: str, optional + The name of the index column, default is "date". + + Returns + ------- + OBBject[list[Data]] + An object containing the rolling variance values. + """ + # pylint: disable=import-outside-toplevel + from openbb_core.app.utils import ( + basemodel_to_df, + df_to_basemodel, + get_target_column, + ) + from openbb_quantitative.helpers import validate_window + from openbb_quantitative.statistics import var_ + + df = basemodel_to_df(data, index=index) + series_target = get_target_column(df, target) + series_target.name = f"rolling_var_{window}" + validate_window(series_target, window) + results = series_target.rolling(window).apply(var_).dropna().reset_index(drop=False) + results = df_to_basemodel(results) + + return OBBject(results=results) + + +@router.command( + methods=["POST"], + examples=[ + PythonEx( + description="Get Rolling Standard Deviation.", + code=[ + 'stock_data = obb.equity.price.historical(symbol="TSLA", start_date="2023-01-01", provider="fmp").to_df()', # noqa: E501 + 'returns = stock_data["close"].pct_change().dropna()', + 'obb.quantitative.rolling.stdev(data=returns, target="close", window=252)', + ], + ), + APIEx( + parameters={ + "target": "close", + "window": 2, + "data": APIEx.mock_data( + "timeseries", + sample={"date": "2023-01-01", "close": 0.05}, + ), + } + ), + ], +) +def stdev( + data: list[Data], target: str, window: PositiveInt = 21, index: str = "date" +) -> OBBject[list[Data]]: + """ + Calculate the rolling standard deviation of a target column within a given window size. + + Standard deviation is a measure of the amount of variation or dispersion of a set of values. + It is widely used to assess the risk and volatility of financial returns or other time series data + over a specified rolling window. It is the square root of the variance. + + Parameters + ---------- + data: list[Data] + The time series data as a list of data points. + target: str + The name of the column for which to calculate standard deviation. + window: PositiveInt + The number of observations used for calculating the rolling measure. + index: str, optional + The name of the index column, default is "date". + + Returns + ------- + OBBject[list[Data]] + An object containing the rolling standard deviation values. + """ + # pylint: disable=import-outside-toplevel + from openbb_core.app.utils import ( + basemodel_to_df, + df_to_basemodel, + get_target_column, + ) + from openbb_quantitative.helpers import validate_window + from openbb_quantitative.statistics import std_dev_ + + df = basemodel_to_df(data, index=index) + series_target = get_target_column(df, target) + series_target.name = f"rolling_stdev_{window}" + validate_window(series_target, window) + results = ( + series_target.rolling(window).apply(std_dev_).dropna().reset_index(drop=False) + ) + results = df_to_basemodel(results) + + return OBBject(results=results) + + +@router.command( + methods=["POST"], + examples=[ + PythonEx( + description="Get Rolling Kurtosis.", + code=[ + 'stock_data = obb.equity.price.historical(symbol="TSLA", start_date="2023-01-01", provider="fmp").to_df()', # noqa: E501 + 'returns = stock_data["close"].pct_change().dropna()', + 'obb.quantitative.rolling.kurtosis(data=returns, target="close", window=252)', + ], + ), + APIEx( + parameters={ + "target": "close", + "window": 2, + "data": APIEx.mock_data( + "timeseries", + sample={"date": "2023-01-01", "close": 0.05}, + ), + } + ), + ], +) +def kurtosis( + data: list[Data], target: str, window: PositiveInt = 21, index: str = "date" +) -> OBBject[list[Data]]: + """ + Calculate the rolling kurtosis of a target column within a given window size. + + Kurtosis measures the "tailedness" of the probability distribution of a real-valued random variable. + High kurtosis indicates a distribution with heavy tails (outliers), suggesting a higher risk of extreme outcomes. + Low kurtosis indicates a distribution with lighter tails (less outliers), suggesting less risk of extreme outcomes. + This function helps in assessing the risk of outliers in financial returns or other time series data over a specified + rolling window. + + Parameters + ---------- + data: list[Data] + The time series data as a list of data points. + target: str + The name of the column for which to calculate kurtosis. + window: PositiveInt + The number of observations used for calculating the rolling measure. + index: str, optional + The name of the index column, default is "date". + + Returns + ------- + OBBject[list[Data]] + An object containing the rolling kurtosis values. + """ + # pylint: disable=import-outside-toplevel + from openbb_core.app.utils import ( + basemodel_to_df, + df_to_basemodel, + get_target_column, + ) + from openbb_quantitative.helpers import validate_window + from openbb_quantitative.statistics import kurtosis_ + + df = basemodel_to_df(data, index=index) + series_target = get_target_column(df, target) + series_target.name = f"rolling_kurtosis_{window}" + validate_window(series_target, window) + results = ( + series_target.rolling(window).apply(kurtosis_).dropna().reset_index(drop=False) + ) + results = df_to_basemodel(results) + + return OBBject(results=results) + + +@router.command( + methods=["POST"], + examples=[ + PythonEx( + description="Get Rolling Quantile.", + code=[ + 'stock_data = obb.equity.price.historical(symbol="TSLA", start_date="2023-01-01", provider="fmp").to_df()', # noqa: E501 + 'returns = stock_data["close"].pct_change().dropna()', + 'obb.quantitative.rolling.quantile(data=returns, target="close", window=252, quantile_pct=0.25)', + 'obb.quantitative.rolling.quantile(data=returns, target="close", window=252, quantile_pct=0.75)', + ], + ), + APIEx( + parameters={ + "target": "close", + "window": 2, + "data": APIEx.mock_data( + "timeseries", + sample={"date": "2023-01-01", "close": 0.05}, + ), + } + ), + ], +) +def quantile( + data: list[Data], + target: str, + window: PositiveInt = 21, + quantile_pct: NonNegativeFloat = 0.5, + index: str = "date", +) -> OBBject[list[Data]]: + """ + Calculate the rolling quantile of a target column within a given window size at a specified quantile percentage. + + Quantiles are points dividing the range of a probability distribution into intervals with equal probabilities, + or dividing the sample in the same way. This function is useful for understanding the distribution of data + within a specified window, allowing for analysis of trends, identification of outliers, and assessment of risk. + + Parameters + ---------- + data: list[Data] + The time series data as a list of data points. + target: str + The name of the column for which to calculate the quantile. + window: PositiveInt + The number of observations used for calculating the rolling measure. + quantile_pct: NonNegativeFloat, optional + The quantile percentage to calculate (e.g., 0.5 for median), default is 0.5. + index: str, optional + The name of the index column, default is "date". + + Returns + ------- + OBBject[list[Data]] + An object containing the rolling quantile values with the median. + """ + # pylint: disable=import-outside-toplevel + from openbb_core.app.utils import ( + basemodel_to_df, + df_to_basemodel, + get_target_column, + ) + from openbb_quantitative.helpers import validate_window + from pandas import concat + + df = basemodel_to_df(data, index=index) + series_target = get_target_column(df, target) + validate_window(series_target, window) + roll = series_target.rolling(window) + df_median = roll.median() + df_quantile = roll.quantile(quantile_pct) + results = ( + concat( + [df_median, df_quantile], + axis=1, + keys=[ + f"rolling_median_{window}", + f"rolling_quantile_{quantile_pct}_{window}", + ], + ) + .dropna() + .reset_index(drop=False) + ) + + results = df_to_basemodel(results) + + return OBBject(results=results) + + +@router.command( + methods=["POST"], + examples=[ + PythonEx( + description="Get Rolling Mean.", + code=[ + 'stock_data = obb.equity.price.historical(symbol="TSLA", start_date="2023-01-01", provider="fmp").to_df()', # noqa: E501 + 'returns = stock_data["close"].pct_change().dropna()', + 'obb.quantitative.rolling.mean(data=returns, target="close", window=252)', + ], + ), + APIEx( + parameters={ + "target": "close", + "window": 2, + "data": APIEx.mock_data( + "timeseries", + sample={"date": "2023-01-01", "close": 0.05}, + ), + } + ), + ], +) +def mean( + data: list[Data], target: str, window: PositiveInt = 21, index: str = "date" +) -> OBBject[list[Data]]: + """Calculate the rolling average of a target column within a given window size. + + The rolling mean is a simple moving average that calculates the average of a target variable over a specified window. + This function is widely used in financial analysis to smooth short-term fluctuations and highlight longer-term trends + or cycles in time series data. + + Parameters + ---------- + data: list[Data] + The time series data as a list of data points. + target: str + The name of the column for which to calculate the mean. + window: PositiveInt + The number of observations used for calculating the rolling measure. + index: str, optional + The name of the index column, default is "date". + + Returns + ------- + OBBject[list[Data]] + An object containing the rolling mean values. + """ + # pylint: disable=import-outside-toplevel + from openbb_core.app.utils import ( + basemodel_to_df, + df_to_basemodel, + get_target_column, + ) + from openbb_quantitative.helpers import validate_window + from openbb_quantitative.statistics import mean_ + + df = basemodel_to_df(data, index=index) + series_target = get_target_column(df, target) + series_target.name = f"rolling_mean_{window}" + validate_window(series_target, window) + results = ( + series_target.rolling(window).apply(mean_).dropna().reset_index(drop=False) + ) + results = df_to_basemodel(results) + + return OBBject(results=results) diff --git a/openbb_platform/extensions/quantitative/openbb_quantitative/statistics.py b/openbb_platform/extensions/quantitative/openbb_quantitative/statistics.py new file mode 100644 index 0000000000000000000000000000000000000000..e3339107364aee0f30211fcf90042191b32086c7 --- /dev/null +++ b/openbb_platform/extensions/quantitative/openbb_quantitative/statistics.py @@ -0,0 +1,47 @@ +"""Statistics Functions.""" + +from typing import Union + +from numpy import ( + mean as mean_np, + ndarray, + std, + var as var_np, +) +from pandas import DataFrame, Series +from scipy import stats + +# Because python is weird and these being the same name as the fastapi router functions +# which overwrites the function signature, we add the _ after the function name + + +def kurtosis_(data: Union[DataFrame, Series, ndarray]) -> float: + """Get Kurtosis. + + It is a measure of the "tailedness" of the probability distribution of a real-valued random variable. + """ + return stats.kurtosis(data) + + +def skew_(data: Union[DataFrame, Series, ndarray]) -> float: + """Get Skewness. + + It is a measure of the asymmetry of the probability distribution of a + real-valued random variable about its mean. + """ + return stats.skew(data) + + +def mean_(data: Union[DataFrame, Series, ndarray]) -> float: + """Get Mean which is the average of the numbers.""" + return mean_np(data) + + +def std_dev_(data: Union[DataFrame, Series, ndarray]) -> float: + """Get Standard deviation that is a measure of the amount of variation or dispersion of a set of values.""" + return std(data) + + +def var_(data: Union[DataFrame, Series, ndarray]) -> float: + """Get Variance that is a measure of the amount of variation or dispersion of a set of values.""" + return var_np(data) diff --git a/openbb_platform/extensions/quantitative/openbb_quantitative/stats/stats_router.py b/openbb_platform/extensions/quantitative/openbb_quantitative/stats/stats_router.py new file mode 100644 index 0000000000000000000000000000000000000000..343e0db77a824d8ec3df875ac31c91eba7c82615 --- /dev/null +++ b/openbb_platform/extensions/quantitative/openbb_quantitative/stats/stats_router.py @@ -0,0 +1,372 @@ +"""Rolling submenu of quantitative models for rolling statistics.""" + +from openbb_core.app.model.example import APIEx, PythonEx +from openbb_core.app.model.obbject import OBBject +from openbb_core.app.router import Router +from openbb_core.provider.abstract.data import Data +from pydantic import NonNegativeFloat + +router = Router(prefix="/stats") + + +@router.command( + methods=["POST"], + examples=[ + PythonEx( + description="Get Skewness.", + code=[ + 'stock_data = obb.equity.price.historical(symbol="TSLA", start_date="2023-01-01", provider="fmp").to_df()', # noqa: E501 + 'returns = stock_data["close"].pct_change().dropna()', + 'obb.quantitative.stats.skew(data=returns, target="close")', + ], + ), + APIEx( + parameters={ + "target": "close", + "data": APIEx.mock_data( + "timeseries", + sample={"date": "2023-01-01", "close": 0.05}, + ), + } + ), + ], +) +def skew( + data: list[Data], + target: str, +) -> OBBject[list[Data]]: + """Get the skew of the data set. + + Skew is a statistical measure that reveals the degree of asymmetry of a distribution around its mean. + Positive skewness indicates a distribution with an extended tail to the right, while negative skewness shows a tail + that stretches left. Understanding skewness can provide insights into potential biases in data and help anticipate + the nature of future data points. It's particularly useful for identifying the likelihood of extreme outcomes in + financial returns, enabling more informed decision-making based on the distribution's shape over a specified period. + + Parameters + ---------- + data : list[Data] + Time series data. + target : str + Target column name. + + Returns + ------- + OBBject[list[Data]] + Rolling skew. + """ + # pylint: disable=import-outside-toplevel + from openbb_core.app.utils import ( + basemodel_to_df, + df_to_basemodel, + get_target_column, + ) + from openbb_quantitative.statistics import skew_ + from pandas import DataFrame + + df = basemodel_to_df(data) + series_target = get_target_column(df, target) + results = DataFrame([skew_(series_target)], columns=["skew"]) + results = df_to_basemodel(results) + + return OBBject(results=results) + + +@router.command( + methods=["POST"], + examples=[ + PythonEx( + description="Get Variance.", + code=[ + 'stock_data = obb.equity.price.historical(symbol="TSLA", start_date="2023-01-01", provider="fmp").to_df()', # noqa: E501 + 'returns = stock_data["close"].pct_change().dropna()', + 'obb.quantitative.stats.variance(data=returns, target="close")', + ], + ), + APIEx( + parameters={ + "target": "close", + "data": APIEx.mock_data( + "timeseries", + sample={"date": "2023-01-01", "close": 0.05}, + ), + } + ), + ], +) +def variance(data: list[Data], target: str) -> OBBject[list[Data]]: + """Calculate the variance of a target column. + + Variance measures the dispersion of a set of data points around their mean. It is a key metric for + assessing the volatility and stability of financial returns or other time series data. + + Parameters + ---------- + data: list[Data] + The time series data as a list of data points. + target: str + The name of the column for which to calculate variance. + + Returns + ------- + OBBject[list[Data]] + An object containing the rolling variance values. + """ + # pylint: disable=import-outside-toplevel + from openbb_core.app.utils import ( + basemodel_to_df, + df_to_basemodel, + get_target_column, + ) + from openbb_quantitative.statistics import var_ + from pandas import DataFrame + + df = basemodel_to_df(data) + series_target = get_target_column(df, target) + results = DataFrame([var_(series_target)], columns=["variance"]) + results = df_to_basemodel(results) + + return OBBject(results=results) + + +@router.command( + methods=["POST"], + examples=[ + PythonEx( + description="Get Standard Deviation.", + code=[ + 'stock_data = obb.equity.price.historical(symbol="TSLA", start_date="2023-01-01", provider="fmp").to_df()', # noqa: E501 + 'returns = stock_data["close"].pct_change().dropna()', + 'obb.quantitative.stats.stdev(data=returns, target="close")', + ], + ), + APIEx( + parameters={ + "target": "close", + "data": APIEx.mock_data( + "timeseries", + sample={"date": "2023-01-01", "close": 0.05}, + ), + } + ), + ], +) +def stdev(data: list[Data], target: str) -> OBBject[list[Data]]: + """Calculate the rolling standard deviation of a target column. + + Standard deviation is a measure of the amount of variation or dispersion of a set of values. + It is widely used to assess the risk and volatility of financial returns or other time series data + It is the square root of the variance. + + Parameters + ---------- + data: list[Data] + The time series data as a list of data points. + target: str + The name of the column for which to calculate standard deviation. + + Returns + ------- + OBBject[list[Data]] + An object containing the rolling standard deviation values. + """ + # pylint: disable=import-outside-toplevel + from openbb_core.app.utils import ( + basemodel_to_df, + df_to_basemodel, + get_target_column, + ) + from openbb_quantitative.statistics import std_dev_ + from pandas import DataFrame + + df = basemodel_to_df(data) + series_target = get_target_column(df, target) + results = DataFrame([std_dev_(series_target)], columns=["stdev"]) + results = df_to_basemodel(results) + + return OBBject(results=results) + + +@router.command( + methods=["POST"], + examples=[ + PythonEx( + description="Get Kurtosis.", + code=[ + 'stock_data = obb.equity.price.historical(symbol="TSLA", start_date="2023-01-01", provider="fmp").to_df()', # noqa: E501 + 'returns = stock_data["close"].pct_change().dropna()', + 'obb.quantitative.stats.kurtosis(data=returns, target="close")', + ], + ), + APIEx( + parameters={ + "target": "close", + "data": APIEx.mock_data( + "timeseries", + sample={"date": "2023-01-01", "close": 0.05}, + ), + } + ), + ], +) +def kurtosis(data: list[Data], target: str) -> OBBject[list[Data]]: + """Calculate the rolling kurtosis of a target column. + + Kurtosis measures the "tailedness" of the probability distribution of a real-valued random variable. + High kurtosis indicates a distribution with heavy tails (outliers), suggesting a higher risk of extreme outcomes. + Low kurtosis indicates a distribution with lighter tails (less outliers), suggesting less risk of extreme outcomes. + This function helps in assessing the risk of outliers in financial returns or other time series data. + + Parameters + ---------- + data: list[Data] + The time series data as a list of data points. + target: str + The name of the column for which to calculate kurtosis. + + Returns + ------- + OBBject[list[Data]] + An object containing the kurtosis value + """ + # pylint: disable=import-outside-toplevel + from openbb_core.app.utils import ( + basemodel_to_df, + df_to_basemodel, + get_target_column, + ) + from openbb_quantitative.statistics import kurtosis_ + from pandas import DataFrame + + df = basemodel_to_df(data) + series_target = get_target_column(df, target) + results = DataFrame([kurtosis_(series_target)], columns=["kurtosis"]) + results = df_to_basemodel(results) + + return OBBject(results=results) + + +@router.command( + methods=["POST"], + examples=[ + PythonEx( + description="Get Quantile.", + code=[ + 'stock_data = obb.equity.price.historical(symbol="TSLA", start_date="2023-01-01", provider="fmp").to_df()', # noqa: E501 + 'returns = stock_data["close"].pct_change().dropna()', + 'obb.quantitative.stats.quantile(data=returns, target="close", quantile_pct=0.75)', + ], + ), + APIEx( + parameters={ + "target": "close", + "data": APIEx.mock_data( + "timeseries", + sample={"date": "2023-01-01", "close": 0.05}, + ), + } + ), + ], +) +def quantile( + data: list[Data], + target: str, + quantile_pct: NonNegativeFloat = 0.5, +) -> OBBject[list[Data]]: + """Calculate the quantile of a target column at a specified quantile percentage. + + Quantiles are points dividing the range of a probability distribution into intervals with equal probabilities, + or dividing the sample in the same way. + + Parameters + ---------- + data: list[Data] + The time series data as a list of data points. + target: str + The name of the column for which to calculate the quantile. + quantile_pct: NonNegativeFloat, optional + The quantile percentage to calculate (e.g., 0.5 for median), default is 0.5. + + Returns + ------- + OBBject[list[Data]] + An object containing the rolling quantile values with the median. + """ + # pylint: disable=import-outside-toplevel + from openbb_core.app.utils import ( + basemodel_to_df, + df_to_basemodel, + get_target_column, + ) + from pandas import DataFrame + + df = basemodel_to_df( + data, + ) + series_target = get_target_column(df, target) + results = DataFrame( + [series_target.quantile(quantile_pct)], columns=[f"{quantile_pct}_quantile"] + ) + results = df_to_basemodel(results) + return OBBject(results=results) + + +@router.command( + methods=["POST"], + examples=[ + PythonEx( + description="Get Mean.", + code=[ + 'stock_data = obb.equity.price.historical(symbol="TSLA", start_date="2023-01-01", provider="fmp").to_df()', # noqa: E501 + 'returns = stock_data["close"].pct_change().dropna()', + 'obb.quantitative.stats.mean(data=returns, target="close")', + ], + ), + APIEx( + parameters={ + "target": "close", + "data": APIEx.mock_data( + "timeseries", + sample={"date": "2023-01-01", "close": 0.05}, + ), + } + ), + ], +) +def mean( + data: list[Data], + target: str, +) -> OBBject[list[Data]]: + """Calculate the average of a target column. + + The rolling mean is a simple moving average that calculates the average of a target variable. + This function is widely used in financial analysis to smooth short-term fluctuations and highlight longer-term trends + or cycles in time series data. + + Parameters + ---------- + data: list[Data] + The time series data as a list of data points. + target: str + The name of the column for which to calculate the mean. + + Returns + ------- + OBBject[list[Data]] + An object containing the mean value. + """ + # pylint: disable=import-outside-toplevel + from openbb_core.app.utils import ( + basemodel_to_df, + df_to_basemodel, + get_target_column, + ) + from openbb_quantitative.statistics import mean_ + from pandas import DataFrame + + df = basemodel_to_df(data) + series_target = get_target_column(df, target) + results = DataFrame([mean_(series_target)], columns=["mean"]) + results = df_to_basemodel(results) + + return OBBject(results=results) diff --git a/openbb_platform/extensions/quantitative/poetry.lock b/openbb_platform/extensions/quantitative/poetry.lock new file mode 100644 index 0000000000000000000000000000000000000000..3dd716a5097a84008332b1cad371b13d469e0d8a --- /dev/null +++ b/openbb_platform/extensions/quantitative/poetry.lock @@ -0,0 +1,2080 @@ +# This file is automatically 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0000000000000000000000000000000000000000..eaa0b38bfd5ae2dd016b0aecf2aab1442e9a6500 --- /dev/null +++ b/openbb_platform/extensions/quantitative/pyproject.toml @@ -0,0 +1,20 @@ +[tool.poetry] +name = "openbb-quantitative" +version = "1.4.3" +description = "Quantitative Analysis extension for OpenBB" +authors = ["OpenBB Team "] +license = "AGPL-3.0-only" +readme = "README.md" +packages = [{ include = "openbb_quantitative" }] + +[tool.poetry.dependencies] +python = ">=3.9.21,<3.13" +openbb-core = "^1.4.6" +pandas-ta-openbb = "^0.4.20" + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" + +[tool.poetry.plugins."openbb_core_extension"] +quantitative = "openbb_quantitative.quantitative_router:router" diff --git a/openbb_platform/extensions/quantitative/tests/test_quantitative_helpers.py b/openbb_platform/extensions/quantitative/tests/test_quantitative_helpers.py new file mode 100644 index 0000000000000000000000000000000000000000..8a175957a0adf1e760700a313b969b1613fe68da --- /dev/null +++ b/openbb_platform/extensions/quantitative/tests/test_quantitative_helpers.py @@ -0,0 +1,15 @@ +"""Test the quantitative helpers.""" + +import pandas as pd +from extensions.quantitative.openbb_quantitative.helpers import ( + validate_window, +) + + +def test_validate_window(): + """Test the validate_window function.""" + input_data = pd.Series(range(1, 100)) + validate_window( + input_data=input_data, + window=20, + ) diff --git a/openbb_platform/extensions/quantitative/tests/test_statistics.py b/openbb_platform/extensions/quantitative/tests/test_statistics.py new file mode 100644 index 0000000000000000000000000000000000000000..d1a34e30c8c9248fb027219dc4e5db51cb436b03 --- /dev/null +++ b/openbb_platform/extensions/quantitative/tests/test_statistics.py @@ -0,0 +1,32 @@ +"""Tests for the statistics module.""" + +import pandas as pd +import pytest +from openbb_quantitative.statistics import kurtosis_, mean_, skew_, std_dev_, var_ + +test_data = pd.Series([1, 2, 3, 4, 5, 6, 7, 8, 9, 10]) + + +def test_kurtosis(): + """Test the kurtosis function.""" + assert kurtosis_(test_data) == pytest.approx(-1.224, abs=1e-3) + + +def test_skew(): + """Test the skewness function.""" + assert skew_(test_data) == pytest.approx(0.0, abs=1e-3) + + +def test_std_dev(): + """Test the standard deviation function.""" + assert std_dev_(test_data) == pytest.approx(2.872, abs=1e-3) + + +def test_mean(): + """Test the mean function.""" + assert mean_(test_data) == pytest.approx(5.5, abs=1e-3) + + +def test_var(): + """Test the variance function.""" + assert var_(test_data) == pytest.approx(8.25, abs=1e-3) diff --git a/openbb_platform/extensions/regulators/README.md b/openbb_platform/extensions/regulators/README.md new file mode 100644 index 0000000000000000000000000000000000000000..08c723016fec5e3f37f84f6dac2b0e7d7a95c102 --- /dev/null +++ b/openbb_platform/extensions/regulators/README.md @@ -0,0 +1,13 @@ +# OpenBB Regulators Extension + +This extension provides a structure for data sourced from various global market regulators. + +## Installation + +To install the extension, run the following command in this folder: + +```bash +pip install openbb-regulators +``` + +Documentation available [here](https://docs.openbb.co/platform/developer_guide/contributing). diff --git a/openbb_platform/extensions/regulators/integration/test_regulators_api.py b/openbb_platform/extensions/regulators/integration/test_regulators_api.py new file mode 100644 index 0000000000000000000000000000000000000000..3e97d88a554455dc15134e8ea8f043d462e37847 --- /dev/null +++ b/openbb_platform/extensions/regulators/integration/test_regulators_api.py @@ -0,0 +1,229 @@ +"""Integration tests for the regulators API.""" + +import base64 + +import pytest +import requests +from extensions.tests.conftest import parametrize +from openbb_core.env import Env +from openbb_core.provider.utils.helpers import get_querystring + + +@pytest.fixture(scope="session") +def headers(): + """Get the headers for the API request.""" + userpass = f"{Env().API_USERNAME}:{Env().API_PASSWORD}" + userpass_bytes = userpass.encode("ascii") + base64_bytes = base64.b64encode(userpass_bytes) + + return {"Authorization": f"Basic {base64_bytes.decode('ascii')}"} + + +# pylint: disable=redefined-outer-name + + +@parametrize( + "params", + [ + ({"symbol": "TSLA", "provider": "sec", "use_cache": None}), + ({"symbol": "SQQQ", "provider": "sec", "use_cache": None}), + ], +) +@pytest.mark.integration +def test_regulators_sec_cik_map(params, headers): + """Test the SEC CIK map endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/regulators/sec/cik_map?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ({"query": "berkshire hathaway", "provider": "sec", "use_cache": None}), + ], +) +@pytest.mark.integration +def test_regulators_sec_institutions_search(params, headers): + """Test the SEC institutions search endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/regulators/sec/institutions_search?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ({"query": "2022", "provider": "sec", "url": "", "use_cache": None}), + ( + { + "query": "", + "provider": "sec", + "url": "https://xbrl.fasb.org/us-gaap/2014/entire/", + "use_cache": None, + } + ), + ], +) +@pytest.mark.integration +def test_regulators_sec_schema_files(params, headers): + """Test the SEC schema files endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/regulators/sec/schema_files?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ({"query": "0000909832", "provider": "sec", "use_cache": None}), + ({"query": "0001067983", "provider": "sec", "use_cache": None}), + ], +) +@pytest.mark.integration +def test_regulators_sec_symbol_map(params, headers): + """Test the SEC symbol map endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/regulators/sec/symbol_map?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [({"provider": "sec"})], +) +@pytest.mark.integration +def test_regulators_sec_rss_litigation(params, headers): + """Test the SEC RSS litigation endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/regulators/sec/rss_litigation?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [({"query": "oil", "use_cache": False, "provider": "sec"})], +) +@pytest.mark.integration +def test_regulators_sec_sic_search(params, headers): + """Test the SEC SIC search endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/regulators/sec/sic_search?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ({"query": "grain", "provider": "cftc"}), + ], +) +@pytest.mark.integration +def test_regulators_cftc_cot_search(params, headers): + """Test the CFTC COT search endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/regulators/cftc/cot_search?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ( + { + "id": "045601", + "report_type": "legacy", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + "futures_only": False, + "provider": "cftc", + } + ), + ], +) +@pytest.mark.integration +def test_regulators_cftc_cot(params, headers): + """Test the CFTC COT endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/regulators/cftc/cot?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ( + { + "url": "https://www.sec.gov/Archives/edgar/data/21344/000155278124000634/", + "provider": "sec", + "use_cache": True, + } + ), + ], +) +@pytest.mark.integration +def test_regulators_sec_filing_headers(params, headers): + """Test the SEC Filing headers endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/regulators/sec/filing_headers?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ( + { + "url": "https://www.sec.gov/Archives/edgar/data/1990353/000110465925015513/tm256977d7_ex99-1.htm", + "provider": "sec", + "use_cache": True, + } + ), + ], +) +@pytest.mark.integration +def test_regulators_sec_htm_file(params, headers): + """Test the SEC HTM File endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/regulators/sec/htm_file?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 diff --git a/openbb_platform/extensions/regulators/integration/test_regulators_python.py b/openbb_platform/extensions/regulators/integration/test_regulators_python.py new file mode 100644 index 0000000000000000000000000000000000000000..734cbb429aab3b5aee8e0c6ae6f0531f93e02087 --- /dev/null +++ b/openbb_platform/extensions/regulators/integration/test_regulators_python.py @@ -0,0 +1,211 @@ +"""Test Regulators extension.""" + +import pytest +from extensions.tests.conftest import parametrize +from openbb_core.app.model.obbject import OBBject + + +# pylint: disable=inconsistent-return-statements +@pytest.fixture(scope="session") +def obb(pytestconfig): + """Fixture to setup obb.""" + + if pytestconfig.getoption("markexpr") != "not integration": + import openbb # pylint: disable=import-outside-toplevel + + return openbb.obb + + +# pylint: disable=redefined-outer-name + + +@parametrize( + "params", + [ + ({"symbol": "TSLA", "provider": "sec", "use_cache": None}), + ({"symbol": "SQQQ", "provider": "sec", "use_cache": None}), + ], +) +@pytest.mark.integration +def test_regulators_sec_cik_map(params, obb): + """Test the SEC CIK map endpoint.""" + result = obb.regulators.sec.cik_map(**params) + assert result + assert isinstance(result, OBBject) + assert hasattr(result.results, "cik") + assert isinstance(result.results.cik, str) + + +@parametrize( + "params", + [ + ({"query": "berkshire hathaway", "provider": "sec", "use_cache": None}), + ], +) +@pytest.mark.integration +def test_regulators_sec_institutions_search(params, obb): + """Test the SEC institutions search endpoint.""" + result = obb.regulators.sec.institutions_search(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ( + { + "query": "2022", + "provider": "sec", + "url": None, + "use_cache": None, + } + ), + ( + { + "query": "", + "provider": "sec", + "url": "https://xbrl.fasb.org/us-gaap/2014/entire/", + "use_cache": None, + } + ), + ], +) +@pytest.mark.integration +def test_regulators_sec_schema_files(params, obb): + """Test the SEC schema files endpoint.""" + result = obb.regulators.sec.schema_files(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results.files) > 0 + + +@parametrize( + "params", + [ + ({"query": "0000909832", "provider": "sec", "use_cache": None}), + ({"query": "0001067983", "provider": "sec", "use_cache": None}), + ], +) +@pytest.mark.integration +def test_regulators_sec_symbol_map(params, obb): + """Test the SEC symbol map endpoint.""" + result = obb.regulators.sec.symbol_map(**params) + assert result + assert isinstance(result, OBBject) + assert hasattr(result.results, "symbol") + assert isinstance(result.results.symbol, str) + + +@parametrize( + "params", + [({"provider": "sec"})], +) +@pytest.mark.integration +def test_regulators_sec_rss_litigation(params, obb): + """Test the SEC RSS litigation endpoint.""" + result = obb.regulators.sec.rss_litigation(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [({"query": "oil", "use_cache": False, "provider": "sec"})], +) +@pytest.mark.integration +def test_regulators_sec_sic_search(params, obb): + """Test the SEC SIC search endpoint.""" + result = obb.regulators.sec.sic_search(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ({"query": "grain", "provider": "cftc"}), + ], +) +@pytest.mark.integration +def test_regulators_cftc_cot_search(params, obb): + """Test the CFTC COT search endpoint.""" + result = obb.regulators.cftc.cot_search(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ( + { + "id": "045601", + "report_type": "legacy", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + "futures_only": False, + "provider": "cftc", + } + ), + ], +) +@pytest.mark.integration +def test_regulators_cftc_cot(params, obb): + """Test the CFTC COT endpoint.""" + result = obb.regulators.cftc.cot(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ( + { + "url": "https://www.sec.gov/Archives/edgar/data/21344/000155278124000634/", + "provider": "sec", + "use_cache": True, + } + ), + ], +) +@pytest.mark.integration +def test_regulators_sec_filing_headers(params, obb): + """Test the SEC Filing Headers endpoint.""" + from openbb_sec.models.sec_filing import SecFilingData + + result = obb.regulators.sec.filing_headers(**params) + assert result + assert isinstance(result, OBBject) + assert isinstance(result.results, SecFilingData) + assert hasattr(result.results, "cover_page") + + +@parametrize( + "params", + [ + ( + { + "url": "https://www.sec.gov/Archives/edgar/data/1990353/000110465925015513/tm256977d7_ex99-1.htm", + "provider": "sec", + "use_cache": True, + } + ), + ], +) +@pytest.mark.integration +def test_regulators_sec_htm_file(params, obb): + """Test the SEC HTM File endpoint.""" + from openbb_sec.models.htm_file import SecHtmFileData + + result = obb.regulators.sec.htm_file(**params) + assert result + assert isinstance(result, OBBject) + assert isinstance(result.results, SecHtmFileData) + assert hasattr(result.results, "content") diff --git a/openbb_platform/extensions/regulators/openbb_regulators/__init__.py b/openbb_platform/extensions/regulators/openbb_regulators/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..4167ad3a36f054a4359037422daab76cbc32c49d --- /dev/null +++ b/openbb_platform/extensions/regulators/openbb_regulators/__init__.py @@ -0,0 +1 @@ +"""OpenBB Regulators Extension.""" diff --git a/openbb_platform/extensions/regulators/openbb_regulators/cftc/__init__.py b/openbb_platform/extensions/regulators/openbb_regulators/cftc/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..42b690c9e1ba93496940f98d0faf25ed1909e464 --- /dev/null +++ b/openbb_platform/extensions/regulators/openbb_regulators/cftc/__init__.py @@ -0,0 +1 @@ +"""Commodity Futures Trading Commission (CFTC).""" diff --git a/openbb_platform/extensions/regulators/openbb_regulators/cftc/cftc_router.py b/openbb_platform/extensions/regulators/openbb_regulators/cftc/cftc_router.py new file mode 100644 index 0000000000000000000000000000000000000000..62512877acdfcc12484fd1a65e25c0415e2d915b --- /dev/null +++ b/openbb_platform/extensions/regulators/openbb_regulators/cftc/cftc_router.py @@ -0,0 +1,67 @@ +# pylint: disable=W0613:unused-argument +"""Commodity Futures Trading Commission (CFTC) Router.""" + +from openbb_core.app.model.command_context import CommandContext +from openbb_core.app.model.example import APIEx +from openbb_core.app.model.obbject import OBBject +from openbb_core.app.provider_interface import ( + ExtraParams, + ProviderChoices, + StandardParams, +) +from openbb_core.app.query import Query +from openbb_core.app.router import Router + +router = Router(prefix="/cftc") + + +@router.command( + model="COTSearch", + examples=[ + APIEx(parameters={"provider": "cftc"}), + APIEx(parameters={"query": "gold", "provider": "cftc"}), + ], +) +async def cot_search( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Get the current Commitment of Traders Reports. + + Search a list of the current Commitment of Traders Reports series information. + """ + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="COT", + examples=[ + APIEx(parameters={"provider": "ctfc"}), + APIEx( + description="Get the latest report for all items classified as, GOLD.", + parameters={"id": "gold", "provider": "cftc"}, + ), + APIEx( + description="Enter the entire history for a single CFTC Market Contract Code.", + parameters={"id": "088691", "provider": "cftc"}, + ), + APIEx( + description="Get the report for futures only.", + parameters={"id": "088691", "futures_only": True, "provider": "cftc"}, + ), + APIEx( + description="Get the most recent Commodity Index Traders Supplemental Report.", + parameters={"id": "all", "report_type": "supplemental", "provider": "cftc"}, + ), + ], +) +async def cot( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Get Commitment of Traders Reports.""" + return await OBBject.from_query(Query(**locals())) diff --git a/openbb_platform/extensions/regulators/openbb_regulators/regulators_router.py b/openbb_platform/extensions/regulators/openbb_regulators/regulators_router.py new file mode 100644 index 0000000000000000000000000000000000000000..c9ad4217950cdd8f3213ac283fc7371a63ea49cf --- /dev/null +++ b/openbb_platform/extensions/regulators/openbb_regulators/regulators_router.py @@ -0,0 +1,16 @@ +# pylint: disable=import-outside-toplevel +# pylint: disable=unused-import +# ruff: noqa: F401 +"""Regulators Router.""" + + +from openbb_core.app.router import Router + +from .cftc.cftc_router import ( + router as cftc_router, +) +from .sec.sec_router import router as sec_router + +router = Router(prefix="", description="Financial market regulators data.") +router.include_router(sec_router) +router.include_router(cftc_router) diff --git a/openbb_platform/extensions/regulators/openbb_regulators/sec/__init__.py b/openbb_platform/extensions/regulators/openbb_regulators/sec/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..4f3fc3a22e7365aceebe5c4b4d4b2f68064380ac --- /dev/null +++ b/openbb_platform/extensions/regulators/openbb_regulators/sec/__init__.py @@ -0,0 +1 @@ +"""Regulators for the SEC init.""" diff --git a/openbb_platform/extensions/regulators/openbb_regulators/sec/sec_router.py b/openbb_platform/extensions/regulators/openbb_regulators/sec/sec_router.py new file mode 100644 index 0000000000000000000000000000000000000000..31e5111646ae7e1c509db989468e97020a569298 --- /dev/null +++ b/openbb_platform/extensions/regulators/openbb_regulators/sec/sec_router.py @@ -0,0 +1,197 @@ +# pylint: disable=W0613:unused-argument +"""SEC Router.""" + +from openbb_core.app.model.command_context import CommandContext +from openbb_core.app.model.example import APIEx, PythonEx +from openbb_core.app.model.obbject import OBBject +from openbb_core.app.provider_interface import ( + ExtraParams, + ProviderChoices, + StandardParams, +) +from openbb_core.app.query import Query +from openbb_core.app.router import Router + +router = Router(prefix="/sec") + + +@router.command( + model="SecFiling", + examples=[ + APIEx( + parameters={ + "url": "https://www.sec.gov/Archives/edgar/data/317540/000119312524076556/d645509ddef14a.htm", + "provider": "sec", + } + ) + ], + openapi_extra={ + "widget_config": { + "description": "Get a list of all the documents associated with a filing, and their direct URLs.", + "gridData": { + "w": 30, + "h": 10, + }, + "refetchInterval": False, + "data": {"dataKey": "results.document_urls"}, + } + }, +) +async def filing_headers( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Download the index headers, and cover page if available, for any SEC filing.""" + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="SecHtmFile", + examples=[ + APIEx( + parameters={ + "url": "https://www.sec.gov/Archives/edgar/data/1723690/000119312525030074/d866336dex991.htm", + "provider": "sec", + } + ) + ], + openapi_extra={ + "widget_config": { + "name": "Open HTML", + "description": "Open a HTM/HTML document from the SEC website.", + "gridData": { + "w": 40, + "h": 25, + }, + "refetchInterval": False, + "type": "markdown", + "data": { + "dataKey": "results.content", + }, + } + }, +) +async def htm_file( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Download a raw HTML object from the SEC website.""" + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="CikMap", + examples=[APIEx(parameters={"symbol": "MSFT", "provider": "sec"})], +) +async def cik_map( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Map a ticker symbol to a CIK number.""" + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="InstitutionsSearch", + examples=[ + APIEx(parameters={"provider": "sec"}), + APIEx(parameters={"query": "blackstone real estate", "provider": "sec"}), + ], +) +async def institutions_search( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Search SEC-regulated institutions by name and return a list of results with CIK numbers.""" + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="SchemaFiles", + examples=[ + APIEx(parameters={"provider": "sec"}), + PythonEx( + description="Get a list of schema files.", + code=[ + "data = obb.regulators.sec.schema_files().results", + "data.files[0]", + "'https://xbrl.fasb.org/us-gaap/'", + "# The directory structure can be navigated by constructing a URL from the 'results' list.", + "url = data.files[0]+data.files[-1]", + "# The URL base will always be the 0 position in the list, feed the URL back in as a parameter.", + "obb.regulators.sec.schema_files(url=url).results.files", + "['https://xbrl.fasb.org/us-gaap/2024/'", + "'USGAAP2024FileList.xml'", + "'dis/'", + "'dqcrules/'", + "'ebp/'", + "'elts/'", + "'entire/'", + "'meta/'", + "'stm/'", + "'us-gaap-2024.zip']", + ], + ), + ], +) +async def schema_files( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Use tool for navigating the directory of SEC XML schema files by year.""" + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="SymbolMap", + examples=[APIEx(parameters={"query": "0000789019", "provider": "sec"})], +) +async def symbol_map( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Map a CIK number to a ticker symbol, leading 0s can be omitted or included.""" + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="RssLitigation", + examples=[APIEx(parameters={"provider": "sec"})], +) +async def rss_litigation( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Get the RSS feed that provides links to litigation releases concerning civil lawsuits brought by the Commission in federal court.""" # noqa: E501 pylint: disable=C0301 + return await OBBject.from_query(Query(**locals())) + + +@router.command( + model="SicSearch", + examples=[ + APIEx(parameters={"provider": "sec"}), + APIEx(parameters={"query": "real estate investment trusts", "provider": "sec"}), + ], +) +async def sic_search( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Search for Industry Titles, Reporting Office, and SIC Codes. 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b/openbb_platform/extensions/regulators/pyproject.toml new file mode 100644 index 0000000000000000000000000000000000000000..882f38a6e299339981b0022b6262c01a2e9d40c7 --- /dev/null +++ b/openbb_platform/extensions/regulators/pyproject.toml @@ -0,0 +1,19 @@ +[tool.poetry] +name = "openbb-regulators" +version = "1.4.2" +description = "Markets and Agency Regulators extension for OpenBB" +authors = ["OpenBB Team "] +license = "AGPL-3.0-only" +readme = "README.md" +packages = [{ include = "openbb_regulators" }] + +[tool.poetry.dependencies] +python = ">=3.9.21,<3.13" +openbb-core = "^1.4.6" + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" + +[tool.poetry.plugins."openbb_core_extension"] +regulators = "openbb_regulators.regulators_router:router" diff --git a/openbb_platform/extensions/technical/README.md b/openbb_platform/extensions/technical/README.md new file mode 100644 index 0000000000000000000000000000000000000000..acc5acc3a11163250755c919a97659b47210c7f0 --- /dev/null +++ b/openbb_platform/extensions/technical/README.md @@ -0,0 +1,17 @@ +# OpenBB Technical Analysis Extension + +This extension provides Technical Analysis tools for the OpenBB Platform. + +Features of the extension include various indicators and oscillators. + +This extension works nicely with a companion `openbb-charting` extension + +## Installation + +To install the extension, run the following command in this folder: + +```bash +pip install openbb-technical +``` + +Documentation available [here](https://docs.openbb.co/platform/developer_guide/contributing). diff --git a/openbb_platform/extensions/technical/integration/test_technical_api.py b/openbb_platform/extensions/technical/integration/test_technical_api.py new file mode 100644 index 0000000000000000000000000000000000000000..d7f787ae83893b8d5e8101782ed1e58f52105ab4 --- /dev/null +++ b/openbb_platform/extensions/technical/integration/test_technical_api.py @@ -0,0 +1,1003 @@ +"""Test technical api.""" + +# pylint: disable=use-dict-literal,too-many-lines + +import base64 +import json +import random +from typing import Literal + +import pytest +import requests +from extensions.tests.conftest import parametrize +from openbb_core.env import Env +from openbb_core.provider.utils.helpers import get_querystring + +data: dict = {} + + +def get_headers(): + """Get headers.""" + if "headers" in data: + return data["headers"] + + userpass = f"{Env().API_USERNAME}:{Env().API_PASSWORD}" + userpass_bytes = userpass.encode("ascii") + base64_bytes = base64.b64encode(userpass_bytes) + + data["headers"] = {"Authorization": f"Basic {base64_bytes.decode('ascii')}"} + return data["headers"] + + +def get_data(menu: Literal["equity", "crypto"]): + """Get data either from stocks or crypto.""" + funcs = {"equity": get_stocks_data, "crypto": get_crypto_data} + return funcs[menu]() + + +def request_data(menu: str, symbol: str, provider: str): + """Randomly pick a symbol and a provider and get data from the selected menu.""" + url = f"http://0.0.0.0:8000/api/v1/{menu}/price/historical?symbol={symbol}&provider={provider}" + result = requests.get(url, headers=get_headers(), timeout=10) + return result.json()["results"] + + +def get_stocks_data(): + """Get stocks data.""" + if "stocks_data" in data: + return data["stocks_data"] + + symbol = random.choice(["AAPL", "NVDA", "MSFT", "TSLA", "AMZN", "V"]) # noqa: S311 + provider = random.choice(["fmp", "polygon", "yfinance"]) # noqa: S311 + + data["stocks_data"] = request_data("equity", symbol=symbol, provider=provider) + return data["stocks_data"] + + +def get_crypto_data(): + """Get crypto data.""" + if "crypto_data" in data: + return data["crypto_data"] + + # TODO : add more crypto providers and symbols + symbol = random.choice(["BTCUSD"]) # noqa: S311 + provider = random.choice(["fmp"]) # noqa: S311 + + data["crypto_data"] = request_data( + menu="crypto", + symbol=symbol, + provider=provider, + ) + return data["crypto_data"] + + +@parametrize( + "params, data_type", + [ + ( + { + "data": "", + "index": "", + "length": "", + "mamode": "", + "drift": "", + "offset": "", + }, + "equity", + ), + ( + { + "data": "", + "index": "date", + "length": "15", + "mamode": "rma", + "drift": "2", + "offset": "1", + }, + "crypto", + ), + ], +) +@pytest.mark.integration +def test_technical_atr(params, data_type): + """Test ta atr.""" + params = {p: v for p, v in params.items() if v} + body = json.dumps(get_data(data_type)) + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/technical/atr?{query_str}" + result = requests.post(url, headers=get_headers(), timeout=15, data=body) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params, data_type", + [ + ( + { + "data": "", + "index": "", + "close_column": "", + "period": "", + "start_date": "", + "end_date": "", + }, + "equity", + ), + ( + { + "data": "", + "index": "date", + "close_column": "close", + "period": "125", + "start_date": "", + "end_date": "", + }, + "crypto", + ), + ], +) +@pytest.mark.integration +def test_technical_fib(params, data_type): + """Test ta fib.""" + params = {p: v for p, v in params.items() if v} + body = json.dumps(get_data(data_type)) + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/technical/fib?{query_str}" + result = requests.post(url, headers=get_headers(), timeout=10, data=body) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params, data_type", + [ + ({"data": "", "index": "", "offset": ""}, "equity"), + ({"data": "", "index": "date", "offset": "1"}, "crypto"), + ], +) +@pytest.mark.integration +def test_technical_obv(params, data_type): + """Test ta obv.""" + params = {p: v for p, v in params.items() if v} + body = json.dumps(get_data(data_type)) + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/technical/obv?{query_str}" + result = requests.post(url, headers=get_headers(), timeout=10, data=body) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params, data_type", + [ + ({"data": "", "index": "", "length": "", "signal": ""}, "equity"), + ({"data": "", "index": "date", "length": "15", "signal": "2"}, "crypto"), + ], +) +@pytest.mark.integration +def test_technical_fisher(params, data_type): + """Test ta fisher.""" + params = {p: v for p, v in params.items() if v} + body = json.dumps(get_data(data_type)) + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/technical/fisher?{query_str}" + result = requests.post(url, headers=get_headers(), timeout=10, data=body) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params, data_type", + [ + ( + { + "data": "", + "index": "", + "fast": "", + "slow": "", + "offset": "", + }, + "equity", + ), + ( + { + "data": "", + "index": "date", + "fast": "5", + "slow": "15", + "offset": "2", + }, + "crypto", + ), + ], +) +@pytest.mark.integration +def test_technical_adosc(params, data_type): + """Test ta adosc.""" + params = {p: v for p, v in params.items() if v} + body = json.dumps(get_data(data_type)) + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/technical/adosc?{query_str}" + result = requests.post(url, headers=get_headers(), timeout=10, data=body) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params, data_type", + [ + ( + { + "data": "", + "target": "", + "index": "", + "length": "", + "std": "", + "mamode": "", + "offset": "", + }, + "equity", + ), + ( + { + "data": "", + "target": "high", + "index": "date", + "length": "55", + "std": "3", + "mamode": "wma", + "offset": "1", + }, + "crypto", + ), + ], +) +@pytest.mark.integration +def test_technical_bbands(params, data_type): + """Test ta bbands.""" + params = {p: v for p, v in params.items() if v} + body = json.dumps(get_data(data_type)) + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/technical/bbands?{query_str}" + result = requests.post(url, headers=get_headers(), timeout=10, data=body) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params, data_type", + [ + ( + { + "data": "", + "target": "", + "index": "", + "length": "", + "offset": "", + }, + "equity", + ), + ( + { + "data": "", + "target": "high", + "index": "date", + "length": "55", + "offset": "5", + }, + "crypto", + ), + ], +) +@pytest.mark.integration +def test_technical_zlma(params, data_type): + """Test ta zlma.""" + params = {p: v for p, v in params.items() if v} + body = json.dumps(get_data(data_type)) + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/technical/zlma?{query_str}" + result = requests.post(url, headers=get_headers(), timeout=10, data=body) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params, data_type", + [ + ({"data": "", "index": "", "length": "", "scalar": ""}, "equity"), + ( + { + "data": "", + "index": "date", + "length": "30", + "scalar": "110", + }, + "crypto", + ), + ], +) +@pytest.mark.integration +def test_technical_aroon(params, data_type): + """Test ta aroon.""" + params = {p: v for p, v in params.items() if v} + body = json.dumps(get_data(data_type)) + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/technical/aroon?{query_str}" + result = requests.post(url, headers=get_headers(), timeout=10, data=body) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params, data_type", + [ + ( + { + "data": "", + "target": "", + "index": "", + "length": "", + "offset": "", + }, + "equity", + ), + ( + { + "data": "", + "target": "high", + "index": "date", + "length": "55", + "offset": "2", + }, + "crypto", + ), + ], +) +@pytest.mark.integration +def test_technical_sma(params, data_type): + """Test ta sma.""" + params = {p: v for p, v in params.items() if v} + body = json.dumps(get_data(data_type)) + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/technical/sma?{query_str}" + result = requests.post(url, headers=get_headers(), timeout=10, data=body) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params, data_type", + [ + ( + { + "data": "", + "index": "", + "target": "", + "show_all": "", + "asint": "", + "offset": "", + }, + "equity", + ), + ( + { + "data": "", + "index": "date", + "target": "high", + "show_all": "true", + "asint": "true", + "offset": "5", + }, + "crypto", + ), + ], +) +@pytest.mark.integration +def test_technical_demark(params, data_type): + """Test ta demark.""" + params = {p: v for p, v in params.items() if v} + body = json.dumps(get_data(data_type)) + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/technical/demark?{query_str}" + result = requests.post(url, headers=get_headers(), timeout=10, data=body) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params, data_type", + [ + ({"data": "", "index": "", "anchor": "", "offset": ""}, "equity"), + ({"data": "", "index": "date", "anchor": "W", "offset": "5"}, "crypto"), + ], +) +@pytest.mark.integration +def test_technical_vwap(params, data_type): + """Test ta vwap.""" + params = {p: v for p, v in params.items() if v} + body = json.dumps(get_data(data_type)) + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/technical/vwap?{query_str}" + result = requests.post(url, headers=get_headers(), timeout=10, data=body) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params, data_type", + [ + ( + { + "data": "", + "target": "", + "index": "", + "fast": "", + "slow": "", + "signal": "", + }, + "equity", + ), + ( + { + "data": "", + "target": "high", + "index": "date", + "fast": "10", + "slow": "30", + "signal": "10", + }, + "crypto", + ), + ], +) +@pytest.mark.integration +def test_technical_macd(params, data_type): + """Test ta macd.""" + params = {p: v for p, v in params.items() if v} + body = json.dumps(get_data(data_type)) + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/technical/macd?{query_str}" + result = requests.post(url, headers=get_headers(), timeout=10, data=body) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params, data_type", + [ + ( + { + "data": "", + "target": "", + "index": "", + "length": "", + "offset": "", + }, + "equity", + ), + ( + { + "data": "", + "target": "high", + "index": "date", + "length": "55", + "offset": "2", + }, + "crypto", + ), + ], +) +@pytest.mark.integration +def test_technical_hma(params, data_type): + """Test ta hma.""" + params = {p: v for p, v in params.items() if v} + body = json.dumps(get_data(data_type)) + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/technical/hma?{query_str}" + result = requests.post(url, headers=get_headers(), timeout=10, data=body) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params, data_type", + [ + ( + { + "data": "", + "index": "", + "lower_length": "", + "upper_length": "", + "offset": "", + }, + "equity", + ), + ( + { + "data": "", + "index": "date", + "lower_length": "30", + "upper_length": "40", + "offset": "5", + }, + "crypto", + ), + ], +) +@pytest.mark.integration +def test_technical_donchian(params, data_type): + """Test ta donchian.""" + params = {p: v for p, v in params.items() if v} + body = json.dumps(get_data(data_type)) + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/technical/donchian?{query_str}" + result = requests.post(url, headers=get_headers(), timeout=10, data=body) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params, data_type", + [ + ( + { + "data": "", + "index": "", + "conversion": "", + "base": "", + "lagging": "", + "offset": "", + "lookahead": "", + }, + "equity", + ), + ( + { + "data": "", + "index": "date", + "conversion": "10", + "base": "30", + "lagging": "50", + "offset": "30", + "lookahead": "true", + }, + "crypto", + ), + ], +) +@pytest.mark.integration +def test_technical_ichimoku(params, data_type): + """Test ta ichimoku.""" + params = {p: v for p, v in params.items() if v} + body = json.dumps(get_data(data_type)) + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/technical/ichimoku?{query_str}" + result = requests.post(url, headers=get_headers(), timeout=10, data=body) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params, data_type", + [ + ({"data": "", "index": "date", "target": "close", "period": "10"}, "equity"), + ( + { + "data": "", + "index": "date", + "target": "close", + "period": "95", + }, + "crypto", + ), + ], +) +@pytest.mark.integration +def test_technical_clenow(params, data_type): + """Test ta clenow.""" + params = {p: v for p, v in params.items() if v} + body = json.dumps(get_data(data_type)) + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/technical/clenow?{query_str}" + result = requests.post(url, headers=get_headers(), timeout=15, data=body) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params, data_type", + [ + ({"data": "", "index": "", "offset": ""}, "equity"), + ({"data": "", "index": "date", "offset": "5"}, "crypto"), + ], +) +@pytest.mark.integration +def test_technical_ad(params, data_type): + """Test ta ad.""" + params = {p: v for p, v in params.items() if v} + body = json.dumps(get_data(data_type)) + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/technical/ad?{query_str}" + result = requests.post(url, headers=get_headers(), timeout=10, data=body) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params, data_type", + [ + ( + { + "data": "", + "index": "", + "length": "", + "scalar": "", + "drift": "", + }, + "equity", + ), + ( + { + "data": "", + "index": "date", + "length": "60", + "scalar": "90.0", + "drift": "2", + }, + "crypto", + ), + ], +) +@pytest.mark.integration +def test_technical_adx(params, data_type): + """Test ta adx.""" + params = {p: v for p, v in params.items() if v} + body = json.dumps(get_data(data_type)) + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/technical/adx?{query_str}" + result = requests.post(url, headers=get_headers(), timeout=10, data=body) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params, data_type", + [ + ( + { + "data": "", + "target": "", + "index": "", + "length": "", + "offset": "", + }, + "equity", + ), + ( + { + "data": "", + "target": "high", + "index": "date", + "length": "60", + "offset": "10", + }, + "crypto", + ), + ], +) +@pytest.mark.integration +def test_technical_wma(params, data_type): + """Test ta wma.""" + params = {p: v for p, v in params.items() if v} + body = json.dumps(get_data(data_type)) + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/technical/wma?{query_str}" + result = requests.post(url, headers=get_headers(), timeout=10, data=body) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params, data_type", + [ + ({"data": "", "index": "", "length": "", "scalar": ""}, "equity"), + ( + { + "data": "", + "index": "date", + "length": "16", + "scalar": "0.02", + }, + "crypto", + ), + ], +) +@pytest.mark.integration +def test_technical_cci(params, data_type): + """Test ta cci.""" + params = {p: v for p, v in params.items() if v} + body = json.dumps(get_data(data_type)) + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/technical/cci?{query_str}" + result = requests.post(url, headers=get_headers(), timeout=10, data=body) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params, data_type", + [ + ( + { + "data": "", + "target": "", + "index": "", + "length": "", + "scalar": "", + "drift": "", + }, + "equity", + ), + ( + { + "data": "", + "target": "high", + "index": "date", + "length": "16", + "scalar": "90.0", + "drift": "2", + }, + "crypto", + ), + ], +) +@pytest.mark.integration +def test_technical_rsi(params, data_type): + """Test ta rsi.""" + params = {p: v for p, v in params.items() if v} + body = json.dumps(get_data(data_type)) + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/technical/rsi?{query_str}" + result = requests.post(url, headers=get_headers(), timeout=10, data=body) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params, data_type", + [ + ( + { + "data": "", + "index": "", + "fast_k_period": "", + "slow_d_period": "", + "slow_k_period": "", + }, + "equity", + ), + ( + { + "data": "", + "index": "date", + "fast_k_period": "12", + "slow_d_period": "2", + "slow_k_period": "2", + }, + "crypto", + ), + ], +) +@pytest.mark.integration +def test_technical_stoch(params, data_type): + """Test ta stoch.""" + params = {p: v for p, v in params.items() if v} + body = json.dumps(get_data(data_type)) + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/technical/stoch?{query_str}" + result = requests.post(url, headers=get_headers(), timeout=10, data=body) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params, data_type", + [ + ( + { + "data": "", + "index": "", + "length": "", + "scalar": "", + "mamode": "", + "offset": "", + }, + "equity", + ), + ( + { + "data": "", + "index": "date", + "length": "22", + "scalar": "24", + "mamode": "sma", + "offset": "5", + }, + "crypto", + ), + ], +) +@pytest.mark.integration +def test_technical_kc(params, data_type): + """Test ta kc.""" + params = {p: v for p, v in params.items() if v} + body = json.dumps(get_data(data_type)) + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/technical/kc?{query_str}" + result = requests.post(url, headers=get_headers(), timeout=10, data=body) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params, data_type", + [ + ({"data": "", "index": "", "length": ""}, "equity"), + ({"data": "", "index": "date", "length": "20"}, "crypto"), + ], +) +@pytest.mark.integration +def test_technical_cg(params, data_type): + """Test ta cg.""" + params = {p: v for p, v in params.items() if v} + body = json.dumps(get_data(data_type)) + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/technical/cg?{query_str}" + result = requests.post(url, headers=get_headers(), timeout=10, data=body) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params, data_type", + [ + ( + { + "data": "", + "index": "", + "lower_q": "", + "upper_q": "", + "model": "", + "is_crypto": "", + "trading_periods": "", + }, + "equity", + ), + ( + { + "data": "", + "index": "date", + "lower_q": "0.3", + "upper_q": "0.7", + "model": "parkinson", + "is_crypto": "True", + "trading_periods": "", + }, + "crypto", + ), + ], +) +@pytest.mark.integration +def test_technical_cones(params, data_type): + """Test ta cones.""" + params = {p: v for p, v in params.items() if v} + body = json.dumps(get_data(data_type)) + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/technical/cones?{query_str}" + result = requests.post(url, headers=get_headers(), timeout=10, data=body) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params, data_type", + [ + ( + { + "data": "", + "target": "close", + "index": "date", + "length": "", + "offset": "", + }, + "equity", + ), + ( + { + "data": "", + "target": "high", + "index": "", + "length": "60", + "offset": "10", + }, + "crypto", + ), + ], +) +@pytest.mark.integration +def test_technical_ema(params, data_type): + """Test ta ema.""" + params = {p: v for p, v in params.items() if v} + body = json.dumps(get_data(data_type)) + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1/technical/ema?{query_str}" + result = requests.post(url, headers=get_headers(), timeout=10, data=body) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@parametrize( + "params", + [ + ( + { + "data": "", + "study": "price", + "benchmark": "SPY", + "long_period": 252, + "short_period": 21, + "window": 21, + "trading_periods": 252, + "chart_params": {"show_tails": False}, + } + ), + ], +) +@pytest.mark.integration +def test_technical_relative_rotation(params): + """Test ta relative rotation.""" + params = {p: v for p, v in params.items() if v} + data_params = dict( + symbol="AAPL,MSFT,GOOGL,AMZN,SPY", + provider="yfinance", + start_date="2022-01-01", + end_date="2024-01-01", + ) + data_query_str = get_querystring(data_params, []) + data_url = f"http://0.0.0.0:8000/api/v1/equity/price/historical?{data_query_str}" + data_result = requests.get(data_url, headers=get_headers(), timeout=10).json()[ + "results" + ] + body = json.dumps({"data": data_result}) + query_str = get_querystring(params, ["data"]) + url = f"http://0.0.0.0:8000/api/v1/technical/relative_rotation?{query_str}" + result = requests.post(url, headers=get_headers(), timeout=10, data=body) + assert isinstance(result, requests.Response) + assert result.status_code == 200 diff --git a/openbb_platform/extensions/technical/integration/test_technical_python.py b/openbb_platform/extensions/technical/integration/test_technical_python.py new file mode 100644 index 0000000000000000000000000000000000000000..bfa82e4716f38d8ec718471dc041f8a1cc79054f --- /dev/null +++ b/openbb_platform/extensions/technical/integration/test_technical_python.py @@ -0,0 +1,971 @@ +"""Test ta extension.""" + +import random +from typing import Literal + +import pytest +from extensions.tests.conftest import parametrize +from openbb_core.app.model.obbject import OBBject + + +# pylint:disable=inconsistent-return-statements +@pytest.fixture(scope="session") +def obb(pytestconfig): + """Fixture to setup obb.""" + if pytestconfig.getoption("markexpr") != "not integration": + import openbb # pylint:disable=import-outside-toplevel + + return openbb.obb + + +# pylint:disable=redefined-outer-name + +data: dict = {} + + +def get_stocks_data(): + """Get stocks data.""" + import openbb # pylint:disable=import-outside-toplevel + + if "stocks_data" in data: + return data["stocks_data"] + + symbol = random.choice(["AAPL", "NVDA", "MSFT", "TSLA", "AMZN", "V"]) # noqa: S311 + provider = random.choice(["fmp", "polygon", "yfinance"]) # noqa: S311 + + data["stocks_data"] = openbb.obb.equity.price.historical( + symbol=symbol, provider=provider + ).results + return data["stocks_data"] + + +def get_crypto_data(): + """Get crypto data.""" + import openbb # pylint:disable=import-outside-toplevel + + if "crypto_data" in data: + return data["crypto_data"] + + # TODO : add more crypto providers and symbols + symbol = random.choice(["BTCUSD"]) # noqa: S311 + provider = random.choice(["fmp"]) # noqa: S311 + + data["crypto_data"] = openbb.obb.crypto.price.historical( + symbol=symbol, provider=provider + ).results + return data["crypto_data"] + + +def get_data(menu: Literal["stocks", "crypto"]): + """Get data.""" + funcs = {"stocks": get_stocks_data, "crypto": get_crypto_data} + return funcs[menu]() + + +@parametrize( + "params, data_type", + [ + ( + { + "data": "", + "index": "", + "length": "", + "mamode": "", + "drift": "", + "offset": "", + }, + "stocks", + ), + ( + { + "data": "", + "index": "date", + "length": "15", + "mamode": "rma", + "drift": "2", + "offset": "1", + }, + "crypto", + ), + ], +) +@pytest.mark.integration +def test_technical_atr(params, data_type, obb): + """Test atr.""" + params = {p: v for p, v in params.items() if v} + params["data"] = get_data(data_type) + + result = obb.technical.atr(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params, data_type", + [ + ( + { + "data": "", + "index": "", + "close_column": "", + "period": "", + "start_date": "", + "end_date": "", + }, + "stocks", + ), + ( + { + "data": "", + "index": "date", + "close_column": "adj_close", + "period": "125", + "start_date": "", + "end_date": "", + }, + "crypto", + ), + ], +) +@pytest.mark.integration +def test_technical_fib(params, data_type, obb): + """Test fib.""" + params = {p: v for p, v in params.items() if v} + params["data"] = get_data(data_type) + + result = obb.technical.fib(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params, data_type", + [ + ({"data": "", "index": "", "offset": ""}, "stocks"), + ({"data": "", "index": "date", "offset": "1"}, "crypto"), + ], +) +@pytest.mark.integration +def test_technical_obv(params, data_type, obb): + """Test obv.""" + params = {p: v for p, v in params.items() if v} + params["data"] = get_data(data_type) + + result = obb.technical.obv(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params, data_type", + [ + ({"data": "", "index": "", "length": "", "signal": ""}, "stocks"), + ({"data": "", "index": "date", "length": "15", "signal": "2"}, "crypto"), + ], +) +@pytest.mark.integration +def test_technical_fisher(params, data_type, obb): + """Test fisher.""" + params = {p: v for p, v in params.items() if v} + params["data"] = get_data(data_type) + + result = obb.technical.fisher(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params, data_type", + [ + ( + { + "data": "", + "index": "", + "fast": "", + "slow": "", + "offset": "", + }, + "stocks", + ), + ( + { + "data": "", + "index": "date", + "fast": "5", + "slow": "15", + "offset": "2", + }, + "crypto", + ), + ], +) +@pytest.mark.integration +def test_technical_adosc(params, data_type, obb): + """Test adosc.""" + params = {p: v for p, v in params.items() if v} + params["data"] = get_data(data_type) + + result = obb.technical.adosc(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params, data_type", + [ + ( + { + "data": "", + "target": "", + "index": "", + "length": "", + "std": "", + "mamode": "", + "offset": "", + }, + "stocks", + ), + ( + { + "data": "", + "target": "high", + "index": "date", + "length": "55", + "std": "3", + "mamode": "wma", + "offset": "1", + }, + "crypto", + ), + ], +) +@pytest.mark.integration +def test_technical_bbands(params, data_type, obb): + """Test bbands.""" + params = {p: v for p, v in params.items() if v} + params["data"] = get_data(data_type) + + result = obb.technical.bbands(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params, data_type", + [ + ( + { + "data": "", + "target": "", + "index": "", + "length": "", + "offset": "", + }, + "stocks", + ), + ( + { + "data": "", + "target": "high", + "index": "date", + "length": "55", + "offset": "5", + }, + "crypto", + ), + ], +) +@pytest.mark.integration +def test_technical_zlma(params, data_type, obb): + """Test zlma.""" + params = {p: v for p, v in params.items() if v} + params["data"] = get_data(data_type) + + result = obb.technical.zlma(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params, data_type", + [ + ({"data": "", "index": "", "length": "", "scalar": ""}, "stocks"), + ( + { + "data": "", + "index": "date", + "length": "30", + "scalar": "110", + }, + "crypto", + ), + ], +) +@pytest.mark.integration +def test_technical_aroon(params, data_type, obb): + """Test aroon.""" + params = {p: v for p, v in params.items() if v} + params["data"] = get_data(data_type) + + result = obb.technical.aroon(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params, data_type", + [ + ( + { + "data": "", + "target": "", + "index": "", + "length": "", + "offset": "", + }, + "stocks", + ), + ( + { + "data": "", + "target": "high", + "index": "date", + "length": "55", + "offset": "2", + }, + "crypto", + ), + ], +) +@pytest.mark.integration +def test_technical_sma(params, data_type, obb): + """Test sma.""" + params = {p: v for p, v in params.items() if v} + params["data"] = get_data(data_type) + + result = obb.technical.sma(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params, data_type", + [ + ( + { + "data": "", + "index": "", + "target": "", + "show_all": "", + "asint": "", + "offset": "", + }, + "stocks", + ), + ( + { + "data": "", + "index": "date", + "target": "high", + "show_all": "true", + "asint": "true", + "offset": "5", + }, + "crypto", + ), + ], +) +@pytest.mark.integration +def test_technical_demark(params, data_type, obb): + """Test demark.""" + params = {p: v for p, v in params.items() if v} + params["data"] = get_data(data_type) + + result = obb.technical.demark(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params, data_type", + [ + ({"data": "", "index": "", "anchor": "", "offset": ""}, "stocks"), + ({"data": "", "index": "date", "anchor": "W", "offset": "5"}, "crypto"), + ], +) +@pytest.mark.integration +def test_technical_vwap(params, data_type, obb): + """Test vwap.""" + params = {p: v for p, v in params.items() if v} + params["data"] = get_data(data_type) + + result = obb.technical.vwap(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params, data_type", + [ + ( + { + "data": "", + "target": "", + "index": "", + "fast": "", + "slow": "", + "signal": "", + }, + "stocks", + ), + ( + { + "data": "", + "target": "high", + "index": "date", + "fast": "10", + "slow": "30", + "signal": "10", + }, + "crypto", + ), + ], +) +@pytest.mark.integration +def test_technical_macd(params, data_type, obb): + """Test macd.""" + params = {p: v for p, v in params.items() if v} + params["data"] = get_data(data_type) + + result = obb.technical.macd(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params, data_type", + [ + ( + { + "data": "", + "target": "", + "index": "", + "length": "", + "offset": "", + }, + "stocks", + ), + ( + { + "data": "", + "target": "high", + "index": "date", + "length": "55", + "offset": "2", + }, + "crypto", + ), + ], +) +@pytest.mark.integration +def test_technical_hma(params, data_type, obb): + """Test hma.""" + params = {p: v for p, v in params.items() if v} + params["data"] = get_data(data_type) + + result = obb.technical.hma(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params, data_type", + [ + ( + { + "data": "", + "index": "", + "lower_length": "", + "upper_length": "", + "offset": "", + }, + "stocks", + ), + ( + { + "data": "", + "index": "date", + "lower_length": "30", + "upper_length": "40", + "offset": "5", + }, + "crypto", + ), + ], +) +@pytest.mark.integration +def test_technical_donchian(params, data_type, obb): + """Test donchian.""" + params = {p: v for p, v in params.items() if v} + params["data"] = get_data(data_type) + + result = obb.technical.donchian(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params, data_type", + [ + ( + { + "data": "", + "index": "", + "conversion": "", + "base": "", + "lagging": "", + "offset": "", + "lookahead": "", + }, + "stocks", + ), + ( + { + "data": "", + "index": "date", + "conversion": "10", + "base": "30", + "lagging": "50", + "offset": "30", + "lookahead": "true", + }, + "crypto", + ), + ], +) +@pytest.mark.integration +def test_technical_ichimoku(params, data_type, obb): + """Test ichimoku.""" + params = {p: v for p, v in params.items() if v} + params["data"] = get_data(data_type) + + result = obb.technical.ichimoku(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params, data_type", + [ + ({"data": "", "index": "", "target": "", "period": ""}, "stocks"), + ( + { + "data": "", + "index": "date", + "target": "close", + "period": "95", + }, + "crypto", + ), + ], +) +@pytest.mark.integration +def test_technical_clenow(params, data_type, obb): + """Test clenow.""" + params = {p: v for p, v in params.items() if v} + params["data"] = get_data(data_type) + + result = obb.technical.clenow(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params, data_type", + [ + ( + { + "data": "", + "index": "", + "length": "", + "scalar": "", + "drift": "", + }, + "stocks", + ), + ( + { + "data": "", + "index": "date", + "length": "60", + "scalar": "90.0", + "drift": "2", + }, + "crypto", + ), + ], +) +@pytest.mark.integration +def test_technical_adx(params, data_type, obb): + """Test adx.""" + params = {p: v for p, v in params.items() if v} + params["data"] = get_data(data_type) + + result = obb.technical.adx(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params, data_type", + [ + ({"data": "", "index": "", "offset": ""}, "stocks"), + ({"data": "", "index": "date", "offset": "5"}, "crypto"), + ], +) +@pytest.mark.integration +def test_technical_ad(params, data_type, obb): + """Test ad.""" + params = {p: v for p, v in params.items() if v} + params["data"] = get_data(data_type) + + result = obb.technical.ad(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params, data_type", + [ + ( + { + "data": "", + "target": "", + "index": "", + "length": "", + "offset": "", + }, + "stocks", + ), + ( + { + "data": "", + "target": "high", + "index": "date", + "length": "60", + "offset": "10", + }, + "crypto", + ), + ], +) +@pytest.mark.integration +def test_technical_wma(params, data_type, obb): + """Test wma.""" + params = {p: v for p, v in params.items() if v} + params["data"] = get_data(data_type) + + result = obb.technical.wma(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params, data_type", + [ + ({"data": "", "index": "", "length": "", "scalar": ""}, "stocks"), + ( + { + "data": "", + "index": "date", + "length": "16", + "scalar": "0.02", + }, + "crypto", + ), + ], +) +@pytest.mark.integration +def test_technical_cci(params, data_type, obb): + """Test cci.""" + params = {p: v for p, v in params.items() if v} + params["data"] = get_data(data_type) + + result = obb.technical.cci(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params, data_type", + [ + ( + { + "data": "", + "target": "", + "index": "", + "length": "", + "scalar": "", + "drift": "", + }, + "stocks", + ), + ( + { + "data": "", + "target": "high", + "index": "date", + "length": "16", + "scalar": "90.0", + "drift": "2", + }, + "crypto", + ), + ], +) +@pytest.mark.integration +def test_technical_rsi(params, data_type, obb): + """Test rsi.""" + params = {p: v for p, v in params.items() if v} + params["data"] = get_data(data_type) + + result = obb.technical.rsi(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params, data_type", + [ + ( + { + "data": "", + "index": "", + "fast_k_period": "", + "slow_d_period": "", + "slow_k_period": "", + }, + "stocks", + ), + ( + { + "data": "", + "index": "date", + "fast_k_period": "12", + "slow_d_period": "2", + "slow_k_period": "2", + }, + "crypto", + ), + ], +) +@pytest.mark.integration +def test_technical_stoch(params, data_type, obb): + """Test stoch.""" + params = {p: v for p, v in params.items() if v} + params["data"] = get_data(data_type) + + result = obb.technical.stoch(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params, data_type", + [ + ( + { + "data": "", + "index": "", + "length": "", + "scalar": "", + "mamode": "", + "offset": "", + }, + "stocks", + ), + ( + { + "data": "", + "index": "date", + "length": "22", + "scalar": "24", + "mamode": "sma", + "offset": "5", + }, + "crypto", + ), + ], +) +@pytest.mark.integration +def test_technical_kc(params, data_type, obb): + """Test kc.""" + params = {p: v for p, v in params.items() if v} + params["data"] = get_data(data_type) + + result = obb.technical.kc(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params, data_type", + [ + ({"data": "", "index": "", "length": ""}, "stocks"), + ({"data": "", "index": "date", "length": "20"}, "crypto"), + ], +) +@pytest.mark.integration +def test_technical_cg(params, data_type, obb): + """Test cg.""" + params = {p: v for p, v in params.items() if v} + params["data"] = get_data(data_type) + + result = obb.technical.cg(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params, data_type", + [ + ( + { + "data": "", + "index": "", + "lower_q": "", + "upper_q": "", + "model": "", + "is_crypto": "", + "trading_periods": "", + }, + "stocks", + ), + ( + { + "data": "", + "index": "date", + "lower_q": "0.3", + "upper_q": "0.7", + "model": "parkinson", + "is_crypto": "True", + "trading_periods": "", + }, + "crypto", + ), + ], +) +@pytest.mark.integration +def test_technical_cones(params, data_type, obb): + """Test cones.""" + params = {p: v for p, v in params.items() if v} + params["data"] = get_data(data_type) + + result = obb.technical.cones(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params, data_type", + [ + ( + { + "data": "", + "target": "close", + "index": "date", + "length": "", + "offset": "", + }, + "stocks", + ), + ( + { + "data": "", + "target": "high", + "index": "", + "length": "60", + "offset": "10", + }, + "crypto", + ), + ], +) +@pytest.mark.integration +def test_technical_ema(params, data_type, obb): + """Test ema.""" + params = {p: v for p, v in params.items() if v} + params["data"] = get_data(data_type) + + result = obb.technical.ema(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@parametrize( + "params", + [ + ( + { + "data": "", + "study": "price", + "benchmark": "SPY", + "long_period": 252, + "short_period": 21, + "window": 21, + "trading_periods": 252, + "chart_params": {"show_tails": False}, + } + ), + ], +) +@pytest.mark.integration +def test_technical_relative_rotation(params, obb): + """Test relative rotation.""" + params["data"] = obb.equity.price.historical( + "AAPL,MSFT,GOOGL,AMZN,SPY", + provider="yfinance", + start_date="2022-01-01", + end_date="2024-01-01", + ).results + result = obb.technical.relative_rotation( + data=params["data"], + benchmark=params["benchmark"], + study=params["study"], + long_period=params["long_period"], + short_period=params["short_period"], + window=params["window"], + trading_periods=params["trading_periods"], + ) + assert result + assert isinstance(result, OBBject) + assert hasattr(result.results, "rs_ratios") + assert len(result.results.rs_ratios) > 0 # type: ignore + assert hasattr(result.results, "rs_momentum") + assert len(result.results.rs_momentum) > 0 # type: ignore diff --git a/openbb_platform/extensions/technical/openbb_technical/__init__.py b/openbb_platform/extensions/technical/openbb_technical/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..9b5715fddf89fd76e537d7ceebb6bcbcdfff2465 --- /dev/null +++ b/openbb_platform/extensions/technical/openbb_technical/__init__.py @@ -0,0 +1 @@ +"""OpenBB Technical Analysis Extension.""" diff --git a/openbb_platform/extensions/technical/openbb_technical/helpers.py b/openbb_platform/extensions/technical/openbb_technical/helpers.py new file mode 100644 index 0000000000000000000000000000000000000000..2d971f8b048495b842ca736279e1c1f8d6ad289a --- /dev/null +++ b/openbb_platform/extensions/technical/openbb_technical/helpers.py @@ -0,0 +1,619 @@ +"""Technical Analysis Helpers.""" + +# pylint: disable=too-many-arguments,too-many-locals,too-many-positional-arguments + +from typing import TYPE_CHECKING, Any, List, Literal, Optional, Tuple, Union +from warnings import warn + +if TYPE_CHECKING: + from pandas import DataFrame, Series, Timestamp + + +def validate_data(data: list, length: Union[int, List[int]]) -> None: + """Validate data.""" + if isinstance(length, int): + length = [length] + for item in length: + if item > len(data): + raise ValueError( + f"Data length is less than required by parameters: {max(length)}" + ) + + +def parkinson( + data: "DataFrame", + window: int = 30, + trading_periods: Optional[int] = None, + is_crypto: bool = False, + clean=True, +) -> "DataFrame": + """Parkinson volatility. + + Uses the high and low price of the day rather than just close to close prices. + It is useful for capturing large price movements during the day. + + Parameters + ---------- + data : DataFrame + Dataframe of OHLC prices. + window : int [default: 30] + Length of window to calculate over. + trading_periods : Optional[int] [default: 252] + Number of trading periods in a year. + is_crypto : bool [default: False] + If true, trading_periods is defined as 365. + clean : bool [default: True] + Whether to clean the data or not by dropping NaN values. + + Returns + ------- + DataFrame : results + Dataframe with results. + """ + # pylint: disable=import-outside-toplevel + from numpy import log + + if window < 1: + warn("Error: Window must be at least 1, defaulting to 30.") + window = 30 + + if trading_periods and is_crypto: + warn("is_crypto is overridden by trading_periods.") + + if not trading_periods: + trading_periods = 365 if is_crypto else 252 + + rs = (1.0 / (4.0 * log(2.0))) * ((data["high"] / data["low"]).apply(log)) ** 2.0 + + def f(v): + return (trading_periods * v.mean()) ** 0.5 + + result = rs.rolling(window=window, center=False).apply(func=f) + + if clean: + return result.dropna() + + return result + + +def standard_deviation( + data: "DataFrame", + window: int = 30, + trading_periods: Optional[int] = None, + is_crypto: bool = False, + clean: bool = True, +) -> "DataFrame": + """Calculate the Standard deviation. + + Measures how widely returns are dispersed from the average return. + It is the most common (and biased) estimator of volatility. + + Parameters + ---------- + data : DataFrame + Dataframe of OHLC prices. + window : int [default: 30] + Length of window to calculate over. + trading_periods : Optional[int] [default: 252] + Number of trading periods in a year. + is_crypto : bool [default: False] + If true, trading_periods is defined as 365. + clean : bool [default: True] + Whether to clean the data or not by dropping NaN values. + + Returns + ------- + DataFrame : results + Dataframe with results. + """ + # pylint: disable=import-outside-toplevel + from numpy import log, sqrt + + if window < 2: + warn("Error: Window must be at least 2, defaulting to 30.") + window = 30 + + if trading_periods and is_crypto: + warn("is_crypto is overridden by trading_periods.") + + if not trading_periods: + trading_periods = 365 if is_crypto else 252 + + log_return = (data["close"] / data["close"].shift(1)).apply(log) + + result = log_return.rolling(window=window, center=False).std() * sqrt( + trading_periods + ) + + if clean: + return result.dropna() + + return result + + +def garman_klass( + data: "DataFrame", + window: int = 30, + trading_periods: Optional[int] = None, + is_crypto: bool = False, + clean=True, +) -> "DataFrame": + """Garman-Klass volatility. + + Extends Parkinson volatility by taking into account the opening and closing price. + As markets are most active during the opening and closing of a trading session. + It makes volatility estimation more accurate. + + Parameters + ---------- + data : DataFrame + Dataframe of OHLC prices. + window : int [default: 30] + Length of window to calculate over. + trading_periods : Optional[int] [default: 252] + Number of trading periods in a year. + is_crypto : bool [default: False] + If true, trading_periods is defined as 365. + clean : bool [default: True] + Whether to clean the data or not by dropping NaN values. + + Returns + ------- + DataFrame : results + Dataframe with results. + """ + # pylint: disable=import-outside-toplevel + from numpy import log + + if window < 1: + warn("Error: Window must be at least 1, defaulting to 30.") + window = 30 + + if trading_periods and is_crypto: + warn("is_crypto is overridden by trading_periods.") + + if not trading_periods: + trading_periods = 365 if is_crypto else 252 + + log_hl = (data["high"] / data["low"]).apply(log) + log_co = (data["close"] / data["open"]).apply(log) + + rs = 0.5 * log_hl**2 - (2 * log(2) - 1) * log_co**2 + + def f(v): + return (trading_periods * v.mean()) ** 0.5 + + result = rs.rolling(window=window, center=False).apply(func=f) + + if clean: + return result.dropna() + + return result + + +def hodges_tompkins( + data: "DataFrame", + window: int = 30, + trading_periods: Optional[int] = None, + is_crypto: bool = False, + clean=True, +) -> "DataFrame": + """Hodges-Tompkins volatility. + + Is a bias correction for estimation using an overlapping data sample. + It produces unbiased estimates and a substantial gain in efficiency. + + Parameters + ---------- + data : DataFrame + Dataframe of OHLC prices. + window : int [default: 30] + Length of window to calculate over. + trading_periods : Optional[int] [default: 252] + Number of trading periods in a year. + is_crypto : bool [default: False] + If true, trading_periods is defined as 365. + clean : bool [default: True] + Whether to clean the data or not by dropping NaN values. + + Returns + ------- + DataFrame : results + Dataframe with results. + + Example + ------- + >>> data = obb.equity.price.historical('BTC-USD') + >>> df = obb.technical.hodges_tompkins(data, is_crypto = True) + """ + # pylint: disable=import-outside-toplevel + from numpy import log, sqrt + + if window < 2: + warn("Error: Window must be at least 2, defaulting to 30.") + window = 30 + + if trading_periods and is_crypto: + warn("is_crypto is overridden by trading_periods.") + + if not trading_periods: + trading_periods = 365 if is_crypto else 252 + + log_return = (data["close"] / data["close"].shift(1)).apply(log) + + vol = log_return.rolling(window=window, center=False).std() * sqrt(trading_periods) + + h = window + n = (log_return.count() - h) + 1 + + adj_factor = 1.0 / (1.0 - (h / n) + ((h**2 - 1) / (3 * n**2))) + + result = vol * adj_factor + + if clean: + return result.dropna() + + return result + + +def rogers_satchell( + data: "DataFrame", + window: int = 30, + trading_periods: Optional[int] = None, + is_crypto: bool = False, + clean=True, +) -> "Series": + """Rogers-Satchell Estimator. + + Is an estimator for measuring the volatility with an average return not equal to zero. + Unlike Parkinson and Garman-Klass estimators, Rogers-Satchell incorporates a drift term, + mean return not equal to zero. + + Parameters + ---------- + data : DataFrame + Dataframe of OHLC prices. + window : int [default: 30] + Length of window to calculate over. + trading_periods : Optional[int] [default: 252] + Number of trading periods in a year. + is_crypto : bool [default: False] + If true, trading_periods is defined as 365. + clean : bool [default: True] + Whether to clean the data or not by dropping NaN values. + + Returns + ------- + Series : results + Pandas Series with results. + """ + # pylint: disable=import-outside-toplevel + from numpy import log + + if window < 1: + warn("Error: Window must be at least 1, defaulting to 30.") + window = 30 + + if trading_periods and is_crypto: + warn("is_crypto is overridden by trading_periods.") + + if not trading_periods: + trading_periods = 365 if is_crypto else 252 + + log_ho = (data["high"] / data["open"]).apply(log) + log_lo = (data["low"] / data["open"]).apply(log) + log_co = (data["close"] / data["open"]).apply(log) + + rs = log_ho * (log_ho - log_co) + log_lo * (log_lo - log_co) + + def f(v): + return (trading_periods * v.mean()) ** 0.5 + + result = rs.rolling(window=window, center=False).apply(func=f) + + if clean: + return result.dropna() + + return result + + +def yang_zhang( + data: "DataFrame", + window: int = 30, + trading_periods: Optional[int] = None, + is_crypto: bool = False, + clean=True, +) -> "DataFrame": + """Yang-Zhang Volatility. + + Is the combination of the overnight (close-to-open volatility). + It is a weighted average of the Rogers-Satchell volatility and the open-to-close volatility. + + Parameters + ---------- + data : DataFrame + Dataframe of OHLC prices. + window : int [default: 30] + Length of window to calculate standard deviation. + trading_periods : Optional[int] [default: 252] + Number of trading periods in a year. + is_crypto : bool [default: False] + If true, trading_periods is defined as 365. + clean : bool [default: True] + Whether to clean the data or not by dropping NaN values. + + Returns + ------- + DataFrame : results + Dataframe with results. + """ + # pylint: disable=import-outside-toplevel + from numpy import log, sqrt + + if window < 2: + warn("Error: Window must be at least 2, defaulting to 30.") + window = 30 + + if trading_periods and is_crypto: + warn("is_crypto is overridden by trading_periods.") + + if not trading_periods: + trading_periods = 365 if is_crypto else 252 + + log_ho = (data["high"] / data["open"]).apply(log) + log_lo = (data["low"] / data["open"]).apply(log) + log_co = (data["close"] / data["open"]).apply(log) + + log_oc = (data["open"] / data["close"].shift(1)).apply(log) + log_oc_sq = log_oc**2 + + log_cc = (data["close"] / data["close"].shift(1)).apply(log) + log_cc_sq = log_cc**2 + + rs = log_ho * (log_ho - log_co) + log_lo * (log_lo - log_co) + + close_vol = log_cc_sq.rolling(window=window, center=False).sum() * ( + 1.0 / (window - 1.0) + ) + open_vol = log_oc_sq.rolling(window=window, center=False).sum() * ( + 1.0 / (window - 1.0) + ) + window_rs = rs.rolling(window=window, center=False).sum() * (1.0 / (window - 1.0)) + + k = 0.34 / (1.34 + (window + 1) / (window - 1)) + result = (open_vol + k * close_vol + (1 - k) * window_rs).apply(sqrt) * sqrt( + trading_periods + ) + + if clean: + return result.dropna() + + return result + + +def calculate_cones( + data: "DataFrame", + lower_q: float, + upper_q: float, + is_crypto: bool, + model: Literal[ + "std", + "parkinson", + "garman_klass", + "hodges_tompkins", + "rogers_satchell", + "yang_zhang", + ], + trading_periods: Optional[int] = None, +) -> "DataFrame": + """Calculate Cones.""" + # pylint: disable=import-outside-toplevel + from pandas import DataFrame + + estimator = DataFrame() + + if lower_q > upper_q: + lower_q, upper_q = upper_q, lower_q + + if (lower_q >= 1) or (upper_q >= 1): + raise ValueError("Error: lower_q and upper_q must be between 0 and 1") + + lower_q_label = str(int(lower_q * 100)) + upper_q_label = str(int(upper_q * 100)) + quantiles = [lower_q, upper_q] + windows = [3, 10, 30, 60, 90, 120, 150, 180, 210, 240, 300, 360] + min_ = [] + max_ = [] + median = [] + top_q = [] + bottom_q = [] + realized = [] + allowed_windows = [] + data = data.sort_index(ascending=True) + + model_functions = { + "std": standard_deviation, + "parkinson": parkinson, + "garman_klass": garman_klass, + "hodges_tompkins": hodges_tompkins, + "rogers_satchell": rogers_satchell, + "yang_zhang": yang_zhang, + } + + for window in windows: + estimator = model_functions[model]( # type: ignore + window=window, + data=data, + is_crypto=is_crypto, + trading_periods=trading_periods, + ) + + if estimator.empty: + continue + + min_.append(estimator.min()) # type: ignore + max_.append(estimator.max()) # type: ignore + median.append(estimator.median()) # type: ignore + top_q.append(estimator.quantile(quantiles[1])) # type: ignore + bottom_q.append(estimator.quantile(quantiles[0])) # type: ignore + realized.append(estimator.iloc[-1]) # type: ignore + + allowed_windows.append(window) + + df_ = [realized, min_, bottom_q, median, top_q, max_] + df_windows = allowed_windows + df = DataFrame(df_, columns=df_windows) + df = df.rename( + index={ + 0: "realized", + 1: "min", + 2: f"lower_{lower_q_label}%", + 3: "median", + 4: f"upper_{upper_q_label}%", + 5: "max", + } + ) + cones_df = df.copy() + return cones_df.transpose().reset_index().rename(columns={"index": "window"}) + + +def clenow_momentum( + values: "Series", window: int = 90 +) -> Tuple[float, float, "Series"]: + """Clenow Volatility Adjusted Momentum. + + This is defined as the regression coefficient on log prices multiplied by the R^2 + value of the regression. + + Parameters + ---------- + values: Series + Values to perform regression for + window: int + Length of look back period + + Returns + ------- + float: + R2 of fit to log data + float: + Coefficient of linear regression + Series: + Values for best fit line + """ + # pylint: disable=import-outside-toplevel + from numpy import arange, exp, log + from pandas import Series + from sklearn.linear_model import LinearRegression + + if len(values) < window: + raise ValueError(f"Calculation asks for at least last {window} days of data") + + values = values[-window:] + + y = log(values) + X = arange(len(y)).reshape(-1, 1) # pylint: disable=invalid-name + + lr = LinearRegression() + lr.fit(X, y) + + r2 = lr.score(X, y) + coef = lr.coef_[0] + annualized_coef = (exp(coef) ** 252) - 1 + + return r2, annualized_coef, Series(lr.predict(X)) + + +def calculate_fib_levels( + data: "DataFrame", + close_col: str, + limit: int = 120, + start_date: Optional[Any] = None, + end_date: Optional[Any] = None, +) -> Tuple["DataFrame", "Timestamp", "Timestamp", float, float, str]: + """Calculate Fibonacci levels. + + Parameters + ---------- + data : DataFrame + Dataframe of prices + close_col : str + Column name of close prices + limit : int + Days to look back for retracement + start_date : Any + Custom start date for retracement + end_date : Any + Custom end date for retracement + + Returns + ------- + df : DataFrame + Dataframe of fib levels + min_date: Timestamp + Date of min point + max_date: Timestamp: + Date of max point + min_pr: float + Price at min point + max_pr: float + Price at max point + """ + # pylint: disable=import-outside-toplevel + from pandas import DataFrame + + if close_col not in data.columns: + raise ValueError(f"Column {close_col} not in data") + + if start_date and end_date: + if start_date not in data.index: + date0 = data.index[data.index.get_indexer([end_date], method="nearest")[0]] + warn(f"Start date not in data. Using nearest: {date0}") + else: + date0 = start_date + if end_date not in data.index: + date1 = data.index[data.index.get_indexer([end_date], method="nearest")[0]] + warn(f"End date not in data. Using nearest: {date1}") + else: + date1 = end_date + + data0 = data.loc[date0, close_col] + data1 = data.loc[date1, close_col] + + min_pr = min(data0, data1) + max_pr = max(data0, data1) + + if min_pr == data0: + min_date = date0 + max_date = date1 + else: + min_date = date1 + max_date = date0 + else: + data_to_use = data.iloc[-limit:, :][close_col] + + min_pr = data_to_use.min() + min_date = data_to_use.idxmin() + max_pr = data_to_use.max() + max_date = data_to_use.idxmax() + + fib_levels = [0, 0.235, 0.382, 0.5, 0.618, 0.65, 1] + + lvl_text: str = "left" if min_date < max_date else "right" + if min_date > max_date: + min_date, max_date = max_date, min_date + min_pr, max_pr = max_pr, min_pr + + price_dif = max_pr - min_pr + + levels = [ + round(max_pr - price_dif * f_lev, (2 if f_lev > 1 else 4)) + for f_lev in fib_levels + ] + + df = DataFrame() + df["Level"] = fib_levels + df["Level"] = df["Level"].apply(lambda x: str(x * 100) + "%") + df["Price"] = levels + + return df, min_date, max_date, min_pr, max_pr, lvl_text diff --git a/openbb_platform/extensions/technical/openbb_technical/py.typed b/openbb_platform/extensions/technical/openbb_technical/py.typed new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/openbb_platform/extensions/technical/openbb_technical/relative_rotation.py b/openbb_platform/extensions/technical/openbb_technical/relative_rotation.py new file mode 100644 index 0000000000000000000000000000000000000000..4911db4fc01936f02dcba5efe255508663ff4216 --- /dev/null +++ b/openbb_platform/extensions/technical/openbb_technical/relative_rotation.py @@ -0,0 +1,581 @@ +"""Relative Rotation Model.""" + +# pylint: disable=too-many-arguments, too-many-instance-attributes, protected-access +# pylint: disable=too-many-locals, too-few-public-methods, unused-argument + +from typing import TYPE_CHECKING, Any, Dict, List, Literal, Optional, Tuple, Union + +from openbb_core.provider.abstract.data import Data +from openbb_core.provider.abstract.fetcher import Fetcher +from openbb_core.provider.abstract.query_params import QueryParams +from pydantic import Field, field_validator + +if TYPE_CHECKING: + from pandas import DataFrame, Series + + +def absolute_maximum_scale(data: "Series") -> "Series": + """Absolute Maximum Scale Normaliztion Method.""" + return data / data.abs().max() + + +def min_max_scaling(data: "Series") -> "Series": + """Min/Max ScalingNormalization Method.""" + return (data - data.min()) / (data.max() - data.min()) + + +def z_score_standardization(data: "Series") -> "Series": + """Z-Score Standardization Method.""" + return (data - data.mean()) / data.std() + + +def normalize(data: "DataFrame", method: Literal["z", "m", "a"] = "z") -> "DataFrame": + """ + Normalize a Pandas DataFrame based on method. + + Parameters + ---------- + data: "DataFrame" + Pandas DataFrame with any number of columns to be normalized. + method: Literal["z", "m", "a"] + Normalization method. + z: Z-Score Standardization + m: Min/Max Scaling + a: Absolute Maximum Scale + + Returns + ------- + DataFrame + Normalized DataFrame. + """ + methods = { + "z": z_score_standardization, + "m": min_max_scaling, + "a": absolute_maximum_scale, + } + + df = data.copy() + + for col in df.columns: + df.loc[:, col] = methods[f"{method}"](df.loc[:, col]) + + return df + + +def standard_deviation( + data: "DataFrame", + window: int = 21, + trading_periods: int = 252, +) -> "DataFrame": + """ + Measures how widely returns are dispersed from the average return. + + It is the most common (and biased) estimator of volatility. + + Parameters + ---------- + data : pd.DataFrame + Dataframe of OHLC prices. + window : int [default: 21] + Length of window to calculate over. + trading_periods : Optional[int] [default: 252] + Number of trading periods in a year. + + Returns + ------- + pd.DataFrame : results + Dataframe with results. + """ + # pylint: disable=import-outside-toplevel + from numpy import log, sqrt + from pandas import DataFrame + + data = data.copy() + results = DataFrame() + if window < 2: + window = 21 + + for col in data.columns.tolist(): + log_return = (data[col] / data[col].shift(1)).apply(log) + + result = log_return.rolling(window=window, center=False).std() * sqrt( + trading_periods + ) + results[col] = result + + return results.dropna() + + +def calculate_momentum( + data: "Series", long_period: int = 252, short_period: int = 21 +) -> "Series": + """ + Momentum is calculated as the log trailing 12-month return minus trailing one-month return. + + Higher values indicate larger, positive momentum exposure. + + Momentum = ln(1 + r12) - ln(1 + r1) + + Parameters + ---------- + data: "Series" + Time series data to calculate the momentum for. + long_period: Optional[int] + Long period to base the calculation on. Default is one standard trading year. + short_period: Optional[int] + Short period to subtract from the long period. Default is one trading month. + + Returns + ------- + Series + Pandas Series with the calculated momentum. + """ + # pylint: disable=import-outside-toplevel + from numpy import log + + df = data.copy() + epsilon = 1e-10 + momentum_long = log(1 + df.pct_change(long_period) + epsilon) + momentum_short = log(1 + df.pct_change(short_period) + epsilon) + data = momentum_long - momentum_short # type: ignore + + return data + + +def get_momentum( + data: "DataFrame", long_period: int = 252, short_period: int = 21 +) -> "DataFrame": + """ + Calculate the Relative-Strength Momentum Indicator. + + Takes the Relative Strength Ratio as the input. + + Parameters + ---------- + data: "DataFrame" + Indexed time series data formatted with each column representing a ticker. + long_period: Optional[int] + Long period to base the calculation on. Default is one standard trading year. + short_period: Optional[int] + Short period to subtract from the long period. Default is one trading month. + + Returns + ------- + DataFrame + Pandas DataFrame with the calculated historical momentum factor exposure score. + """ + # pylint: disable=import-outside-toplevel + from pandas import DataFrame + + df = data.copy() + rs_momentum = DataFrame() + for ticker in df.columns.to_list(): + rs_momentum.loc[:, ticker] = calculate_momentum( + df.loc[:, ticker], long_period, short_period + ) # type: ignore + + return rs_momentum + + +def calculate_relative_strength_ratio( + symbols_data: "DataFrame", + benchmark_data: "DataFrame", +) -> "DataFrame": + """Calculate the Relative Strength Ratio for each ticker (column) in a DataFrame against the benchmark. + + Symbols data and benchmark data should have the same index, + and each column should represent a ticker. + + Parameters + ---------- + symbols_data: "DataFrame" + Pandas DataFrame with the symbols data to compare against the benchmark. + benchmark_data: "DataFrame" + Pandas DataFrame with the benchmark data. + + Returns + ------- + DataFrame + Pandas DataFrame with the calculated relative strength + ratio for each ticker joined with the benchmark values. + """ + return ( + symbols_data.div(benchmark_data.iloc[:, 0], axis=0) + .multiply(100) + .join(benchmark_data.iloc[:, 0]) + .dropna() + ) + + +def process_data( + symbols_data: "DataFrame", + benchmark_data: "DataFrame", + long_period: int = 252, + short_period: int = 21, + normalize_method: Literal["z", "m", "a"] = "z", +) -> Tuple["DataFrame", "DataFrame"]: + """Process the raw data into normalized indicator values. + + Parameters + ---------- + symbols_data: "DataFrame" + Indexed time series data formatted with each column representing a ticker. + benchmark_data: "DataFrame" + Indexed time series data of the benchmark symbol. + long_period: Optional[int] + Long period to base the calculation on. Default is one standard trading year. + short_period: Optional[int] + Short period to subtract from the long period. Default is one trading month. + normalize_method: Literal["z", "m", "a"] + + Returns + ------- + Tuple[DataFrame, DataFrame] + Tuple of Pandas DataFrames with the normalized ratio and momentum indicator values. + """ + ratio_data = calculate_relative_strength_ratio(symbols_data, benchmark_data) + momentum_data = get_momentum(ratio_data, long_period, short_period) + normalized_ratio = normalize(ratio_data, normalize_method) + normalized_momentum = normalize(momentum_data, normalize_method) + + return normalized_ratio, normalized_momentum + + +class RelativeRotation: + """Relative Rotation Class.""" + + def __init__( + self, + data: Union[List[Data], "DataFrame"], + benchmark: str, + study: Optional[Literal["price", "volume", "volatility"]] = "price", + long_period: Optional[int] = 252, + short_period: Optional[int] = 21, + window: Optional[int] = 21, + trading_periods: Optional[int] = 252, + ): + """Initialize the class.""" + # pylint: disable=import-outside-toplevel + import contextlib # noqa + from openbb_core.app.model.obbject import OBBject # noqa + from openbb_core.app.utils import ( # noqa + basemodel_to_df, + convert_to_basemodel, + df_to_basemodel, + ) + from pandas import DataFrame # noqa + + benchmark = benchmark.upper() + df = DataFrame() + + target_col = "volume" if study == "volume" else "close" + + if isinstance(data, OBBject): + data = data.results # type: ignore + + if isinstance(data, List) and ( + all(isinstance(d, Data) for d in data) + or all(isinstance(d, dict) for d in data) + ): + with contextlib.suppress(Exception): + df = basemodel_to_df(convert_to_basemodel(data), index="date") + + if isinstance(data, DataFrame) and not df.empty: + df = data.copy() + if "date" in df.columns: + df.set_index("date", inplace=True) + + if df.empty: + raise ValueError( + "Data must be a list of Data objects or a DataFrame with a 'date' column." + ) + + if "symbol" in df.columns: + df = df.pivot(columns="symbol", values=target_col) + + if benchmark not in df.columns: + raise RuntimeError("The benchmark symbol was not found in the data.") + + benchmark_data = df.pop(benchmark).to_frame() + symbols_data = df + + if len(symbols_data) <= 252 and study in ["price", "volume"]: # type: ignore + raise ValueError( + "Supplied data must be daily intervals and have more than one year of back data to calculate" + " the most recent day in the time series." + ) + + if study == "volatility" and len(symbols_data) <= 504: # type: ignore + raise ValueError( + "Supplied data must be daily intervals and have more than two years of back data to calculate" + " the most recent day in the time series as a volatility study." + ) + self.symbols = df.columns.to_list() + self.benchmark = benchmark + self.study = study + self.long_period = long_period + self.short_period = short_period + self.window = window + self.trading_periods = trading_periods + self.symbols_data = symbols_data # type: ignore + self.benchmark_data = benchmark_data # type: ignore + self._process_data() # type: ignore + self.symbols_data = df_to_basemodel(self.symbols_data.reset_index()) # type: ignore + self.benchmark_data = df_to_basemodel(self.benchmark_data.reset_index()) # type: ignore + + def _process_data(self): + """Process the data.""" + # pylint: disable=import-outside-toplevel + from openbb_core.app.utils import df_to_basemodel + from pandas import to_datetime + + if self.study == "volatility": + self.symbols_data = standard_deviation( + self.symbols_data, # type: ignore + window=self.window, # type: ignore + trading_periods=self.trading_periods, # type: ignore + ) + self.benchmark_data = standard_deviation( + self.benchmark_data, # type: ignore + window=self.window, # type: ignore + trading_periods=self.trading_periods, # type: ignore + ) + ratios, momentum = process_data( + self.symbols_data, # type: ignore + self.benchmark_data, # type: ignore + long_period=self.long_period, # type: ignore + short_period=self.short_period, # type: ignore + ) + # Re-index rs_ratios using the new index + index_after_dropping_nans = momentum.dropna().index + ratios = ratios.reindex(index_after_dropping_nans) + self.rs_ratios = df_to_basemodel(ratios.reset_index()) + self.rs_momentum = df_to_basemodel(momentum.dropna().reset_index()) + self.end_date = to_datetime(ratios.index[-1]).strftime("%Y-%m-%d") + self.start_date = to_datetime(ratios.index[0]).strftime("%Y-%m-%d") + return self + + +def _get_type_name(t): + """Get the type name of a type hint.""" + if hasattr(t, "__origin__"): + if hasattr(t.__origin__, "__name__"): + return f"{t.__origin__.__name__}[{', '.join([_get_type_name(arg) for arg in t.__args__])}]" + if hasattr(t.__origin__, "_name"): + return f"{t.__origin__._name}[{', '.join([_get_type_name(arg) for arg in t.__args__])}]" + if isinstance(t, str): + return t + if hasattr(t, "__name__"): + return t.__name__ + if hasattr(t, "_name"): + return t._name + return str(t) + + +class RelativeRotationQueryParams(QueryParams): + """Relative Rotation Query Parameters.""" + + data: List[Data] = Field( + description="The data to be used for the relative rotation calculations." + + " This should be the multi-symbol output from the" + + " 'equity.price.historical' endpoint, or similar, at a daily interval." + + " Or a pivot table with the 'date' column as the index, the symbols as the columns," + + " and the 'study' as the values." + + " It is recommended to use the 'equity.price.historical' endpoint to get the data," + + " and feed the results as-is." + ) + benchmark: str = Field(description="The symbol to be used as the benchmark.") + study: Literal["price", "volume", "volatility"] = Field( + default="price", + description="The data point for the calculations." + + " If 'price', the closing price will be used." + + " If 'volatility', the standard deviation of the closing price will be used." + + " If 'data' is supplied as a pivot table," + + " the 'study' will assume the values are the closing price and 'volume' will be ignored.", + ) + long_period: Optional[int] = Field( + default=252, + description="The length of the long period for momentum calculation, by default is 252." + + " Adjust this value, to 365, when supplying assets such as crypto.", + ) + short_period: Optional[int] = Field( + default=21, + description="The length of the short period for momentum calculation, by default is 21." + + " Adjust this value, to 30, when supplying assets such as crypto.", + ) + window: Optional[int] = Field( + default=21, + description="The length of window for the standard deviation calculation, by default is 21." + + " Adjust this value, to 30, when supplying assets such as crypto.", + ) + trading_periods: Optional[int] = Field( + default=252, + description="The number of trading periods per year," + + " for the standard deviation calculation, by default is 252." + + " Adjust this value, to 365, when supplying assets such as crypto.", + ) + chart_params: Optional[Dict[str, Any]] = Field( + default=None, + description="Additional parameters to pass when `chart=True` and the `openbb-charting` extension is installed." + + " Parameters can be passed again to redraw the chart using the charting.to_chart() method of the response." + + "\n" + + "\n ChartParams" + + "\n -----------" + + "\n date: Optional[str]" + + "\n A target end date within the data, by default is the last date in the data." + + "\n show_tails: bool" + + "\n Show the tails on the chart, by default is True." + + "\n tail_periods: Optional[int]" + + "\n Number of periods to show in the tails, by default is 16." + + "\n tail_interval: Literal['day', 'week', 'month']" + + "\n Interval to show the tails, by default is 'week'." + + "\n title: Optional[str]" + + "\n Title of the chart.", + ) + + @field_validator("benchmark", mode="before", check_fields=False) + @classmethod + def to_upper(cls, v): + """Convert the benchmark symbol to uppercase.""" + return v.upper() + + @field_validator("data", mode="before", check_fields=False) + @classmethod + def convert_data(cls, v): + """Validate the data format.""" + # pylint: disable=import-outside-toplevel + from openbb_core.app.model.obbject import OBBject + from openbb_core.app.utils import convert_to_basemodel, df_to_basemodel + from pandas import DataFrame + + if isinstance(v, OBBject): + return v.results + if isinstance(v, Data): + return v + if isinstance(v, (list, dict)): + return convert_to_basemodel(v) + if isinstance(v, DataFrame): + return df_to_basemodel(v.reset_index()) + return v + + def __init__(self, **data): + """Initialize the class.""" + super().__init__(**data) + fields = self.__class__.model_fields + doc_str = ( + "\n" + + self.__class__.__name__ + + "\n\n" + + " Parameters\n" + + " ----------\n" + + "\n".join( + [ + f" {k} : {_get_type_name(v.annotation)}\n {v.description}" + for k, v in fields.items() + ] + ) + + "\n" + ) + self.__doc__ = doc_str + + +class RelativeRotationData(Data): + """Relative Rotation Data Model.""" + + symbols: List[str] = Field( + description="The symbols that are being compared against the benchmark." + ) + benchmark: str = Field(description="The benchmark symbol, as entered by the user.") + study: Literal["price", "volume", "volatility"] = Field( + description="The data point for the study, as entered by the user." + ) + long_period: int = Field( + description="The length of the long period for momentum calculation," + + " as entered by the user." + ) + short_period: int = Field( + description="The length of the short period for momentum calculation," + + " as entered by the user." + ) + window: int = Field( + description="The length of window for the standard deviation calculation," + + " as entered by the user.", + ) + trading_periods: int = Field( + description="The number of trading periods per year," + + " for the standard deviation calculation, as entered by the user." + ) + start_date: str = Field( + description="The start date of the data after adjusting" + + " the length of the data for the calculations." + ) + end_date: str = Field(description="The end date of the data.") + symbols_data: List[Data] = Field( + description="The data representing the selected 'study' for each symbol." + ) + benchmark_data: List[Data] = Field( + description="The data representing the selected 'study' for the benchmark." + ) + rs_ratios: List[Data] = Field( + description="The normalized relative strength ratios data." + ) + rs_momentum: List[Data] = Field( + description="The normalized relative strength momentum data." + ) + + def __init__(self, **data): + """Initialize the class.""" + super().__init__(**data) + fields = self.__class__.model_fields + doc_str = ( + "\n" + + self.__class__.__name__ + + "\n\n" + + " Attributes\n" + + " ----------\n" + + "\n".join( + [ + f" {k} : {_get_type_name(v.annotation)}\n {v.description}" + for k, v in fields.items() + ] + ) + + "\n" + ) + self.__doc__ = doc_str + + +class RelativeRotationFetcher( + Fetcher[RelativeRotationQueryParams, RelativeRotationData] +): + """Relative Rotation Fetcher.""" + + @staticmethod + def transform_query(params: Dict[str, Any]) -> RelativeRotationQueryParams: + """Transform the query parameters.""" + return RelativeRotationQueryParams.model_validate(**params) + + @staticmethod + def extract_data( + query: RelativeRotationQueryParams, + credentials: Optional[Dict[str, str]], + **kwargs: Any, + ) -> Dict: + """Extract the data.""" + return RelativeRotation( + query.data, + query.benchmark, + study=query.study, + long_period=query.long_period, + short_period=query.short_period, + window=query.window, + trading_periods=query.trading_periods, + ).__dict__ + + @staticmethod + def transform_data( + query: RelativeRotationQueryParams, + data: Dict, + **kwargs: Any, + ) -> RelativeRotationData: + """Transform the data.""" + return RelativeRotationData.model_validate(data) diff --git a/openbb_platform/extensions/technical/openbb_technical/technical_router.py b/openbb_platform/extensions/technical/openbb_technical/technical_router.py new file mode 100644 index 0000000000000000000000000000000000000000..7ae19d5f3bc1e65eb27608bc4d7de1b1754e81d3 --- /dev/null +++ b/openbb_platform/extensions/technical/openbb_technical/technical_router.py @@ -0,0 +1,1916 @@ +"""Technical Analysis Router.""" + +# pylint: disable=too-many-lines,unused-import,too-many-arguments,too-many-positional-arguments + +from typing import Any, Literal, Optional + +from openbb_core.app.model.example import APIEx, PythonEx +from openbb_core.app.model.obbject import OBBject +from openbb_core.app.router import Router +from openbb_core.app.utils import ( + basemodel_to_df, + df_to_basemodel, + get_target_column, + get_target_columns, +) +from openbb_core.provider.abstract.data import Data +from pydantic import NonNegativeFloat, NonNegativeInt, PositiveFloat, PositiveInt + +from openbb_technical.helpers import ( + calculate_cones, + calculate_fib_levels, + clenow_momentum, + validate_data, +) +from openbb_technical.relative_rotation import ( + RelativeRotationData, + RelativeRotationFetcher, + RelativeRotationQueryParams, +) + +# TODO: Split this into multiple files +router = Router(prefix="", description="Technical Analysis tools.") + + +@router.command( + methods=["POST"], + examples=[ + PythonEx( + description="Calculate the Relative Strength Ratio and Relative Strength Momentum" + + " for a group of symbols against a benchmark.", + code=[ + "stock_data = obb.equity.price.historical(" + + "symbol='AAPL,MSFT,GOOGL,META,AMZN,TSLA,SPY', start_date='2022-01-01', provider='yfinance')", + "rr_data = obb.technical.relative_rotation(data=stock_data.results, benchmark='SPY')", + "rs_ratios = rr_data.results.rs_ratios", + "rs_momentum = rr_data.results.rs_momentum", + ], + ), + PythonEx( + description="When the assets are not traded 252 days per year," + + "adjust the momentum and volatility periods accordingly.", + code=[ + "crypto_data = obb.crypto.price.historical(" + + " symbol='BTCUSD,ETHUSD,SOLUSD', start_date='2021-01-01', provider='yfinance')", + "rr_data = obb.technical.relative_rotation(data=crypto_data.results, benchmark='BTC-USD'," + + " long_period=365, short_period=30, window=30, trading_periods=365)", + ], + ), + ], +) +async def relative_rotation( + data: list[Data], + benchmark: str, + study: Literal["price", "volume", "volatility"] = "price", + long_period: Optional[int] = 252, + short_period: Optional[int] = 21, + window: Optional[int] = 21, + trading_periods: Optional[int] = 252, + chart_params: Optional[dict[str, Any]] = None, +) -> OBBject[RelativeRotationData]: + """Calculate the Relative Strength Ratio and Relative Strength Momentum for a group of symbols against a benchmark. + + Parameters + ---------- + data : list[Data] + The data to be used for the relative rotation calculations. + This should be the multi-symbol output from the 'equity.price.historical' endpoint, or similar. + Or a pivot table with the 'date' column as the index, the symbols as the columns, and the 'study' as the values. + It is recommended to use the 'equity.price.historical' endpoint to get the data, and feed the results as-is. + benchmark : str + The symbol to be used as the benchmark. + study : Literal[price, volume, volatility] + The data point for the calculations. If 'price', the closing price will be used. + If 'volatility', the standard deviation of the closing price will be used. + If 'data' is supplied as a pivot table, + the 'study' will assume the values are the closing price and 'volume' will be ignored. + long_period : int, optional + The length of the long period for momentum calculation, by default 252. + Adjust this value when supplying a time series with an interval that is not daily. + For example, if the data is monthly, the long period should be 12. + short_period : int, optional + The length of the short period for momentum calculation, by default 21. + Adjust this value when supplying a time series with an interval that is not daily. + window : int, optional + The length of window for the standard deviation calculation, by default 21. + Adjust this value when supplying a time series with an interval that is not daily. + trading_periods : int, optional + The number of trading periods per year, for the standard deviation calculation, by default 252. + Adjust this value when supplying a time series with an interval that is not daily. + chart_params : dict[str, Any], optional + Additional parameters to pass when `chart=True` and the `openbb-charting` extension is installed. + Parameters can be passed again to redraw the chart using the charting.to_chart() method of the response. + + ChartParams + ----------- + date : str, optional + A target end date within the data to use for the chart, by default is the last date in the data. + show_tails : bool + Show the tails on the chart, by default True. + tail_periods : int + Number of periods to show in the tails, by default 16. + tail_interval : Literal[day, week, month] + Interval to show the tails, by default 'week'. + title : str, optional + Title of the chart. + + Returns + ------- + OBBject[RelativeRotationData] + results : RelativeRotationData + symbols : list[str]: + The symbols that are being compared against the benchmark. + benchmark : str + The benchmark symbol. + study : Literal[price, volume, volatility] + The data point for the selected. + long_period : int + The length of the long period for momentum calculation, as entered by the user. + short_period : int + The length of the short period for momentum calculation, as entered by the user. + window : int + The length of window for the standard deviation calculation. + trading_periods : int + The number of trading periods per year, for the standard deviation calculation. + start_date : str + The start date of the data after adjusting the length of the data for the calculations. + end_date : str + The end date of the data. + symbols_data : list[Data] + The data representing the selected 'study' for each symbol. + benchmark_data : list[Data] + The data representing the selected 'study' for the benchmark. + rs_ratios : list[Data] + The normalized relative strength ratios data. + rs_momentum : list[Data] + The normalized relative strength momentum data. + """ + params = RelativeRotationQueryParams( + data=data, + benchmark=benchmark, + study=study, + long_period=long_period, + short_period=short_period, + window=window, + trading_periods=trading_periods, + chart_params=chart_params, + ) + + return OBBject( + results=RelativeRotationFetcher.transform_data( + params, RelativeRotationFetcher.extract_data(params, {}) + ) + ) + + +@router.command( + methods=["POST"], + examples=[ + PythonEx( + description="Get the Average True Range.", + code=[ + "stock_data = obb.equity.price.historical(symbol='TSLA', start_date='2023-01-01', provider='fmp')", + "atr_data = obb.technical.atr(data=stock_data.results)", + ], + ), + APIEx(parameters={"length": 2, "data": APIEx.mock_data("timeseries")}), + ], +) +def atr( + data: list[Data], + index: str = "date", + length: PositiveInt = 14, + mamode: Literal["rma", "ema", "sma", "wma"] = "rma", + drift: NonNegativeInt = 1, + offset: int = 0, +) -> OBBject[list[Data]]: + """Calculate the Average True Range. + + Used to measure volatility, especially volatility caused by gaps or limit moves. + The ATR metric helps understand how much the values in your data change on average, + giving insights into the stability or unpredictability during a certain period. + It's particularly useful for spotting trends of increase or decrease in variations, + without getting into technical trading details. + The method considers not just the day-to-day changes but also accounts for any + sudden jumps or drops, ensuring you get a comprehensive view of movement. + + Parameters + ---------- + data : list[Data] + list of data to apply the indicator to. + index : str, optional + Index column name, by default "date" + length : PositiveInt, optional + It's period, by default 14 + mamode : Literal["rma", "ema", "sma", "wma"], optional + Moving average mode, by default "rma" + drift : NonNegativeInt, optional + The difference period, by default 1 + offset : int, optional + How many periods to offset the result, by default 0 + + Returns + ------- + OBBject[list[Data]] + list of data with the indicator applied. + """ + # pylint: disable=import-outside-toplevel + import pandas as pd + import pandas_ta as ta # noqa + + validate_data(data, length) + df = basemodel_to_df(data, index=index) + df_target = get_target_columns(df, ["high", "low", "close"]) + df_atr = pd.DataFrame( + df_target.ta.atr(length=length, mamode=mamode, drift=drift, offset=offset) + ) + + output = pd.concat([df, df_atr], axis=1) + results = df_to_basemodel(output.reset_index()) + + return OBBject(results=results) + + +@router.command( + methods=["POST"], + examples=[ + PythonEx( + description="Get the Bollinger Band Width.", + code=[ + "stock_data = obb.equity.price.historical(symbol='TSLA', start_date='2023-01-01', provider='fmp')", + "fib_data = obb.technical.fib(data=stock_data.results, period=120)", + ], + ), + APIEx(parameters={"data": APIEx.mock_data("timeseries")}), + ], +) +def fib( + data: list[Data], + index: str = "date", + close_column: Literal["close", "adj_close"] = "close", + period: PositiveInt = 120, + start_date: Optional[str] = None, + end_date: Optional[str] = None, +) -> OBBject[list[Data]]: + """Create Fibonacci Retracement Levels. + + This method draws from a classic technique to pinpoint significant price levels + that often indicate where the market might find support or resistance. + It's a tool used to gauge potential turning points in the data by applying a + mathematical approach rooted in nature's patterns. Is used to get insights into + where prices could head next, based on historical movements. + + Parameters + ---------- + data : list[Data] + list of data to apply the indicator to. + index : str, optional + Index column name, by default "date" + period : PositiveInt, optional + Period to calculate the indicator, by default 120 + + Returns + ------- + OBBject[list[Data]] + list of data with the indicator applied. + """ + df = basemodel_to_df(data, index=index) + + ( + df_fib, + min_date, + max_date, + min_pr, + max_pr, + lvl_text, + ) = calculate_fib_levels( + data=df, + close_col=close_column, + limit=period, + start_date=start_date, + end_date=end_date, + ) + + df_fib["min_date"] = min_date + df_fib["max_date"] = max_date + df_fib["min_pr"] = min_pr + df_fib["max_pr"] = max_pr + df_fib["lvl_text"] = lvl_text + + results = df_to_basemodel(df_fib) + + return OBBject(results=results) + + +@router.command( + methods=["POST"], + examples=[ + PythonEx( + description="Get the On Balance Volume (OBV).", + code=[ + "stock_data = obb.equity.price.historical(symbol='TSLA', start_date='2023-01-01', provider='fmp')", + "obv_data = obb.technical.obv(data=stock_data.results, offset=0)", + ], + ), + APIEx(parameters={"data": APIEx.mock_data("timeseries")}), + ], +) +def obv( + data: list[Data], + index: str = "date", + offset: int = 0, +) -> OBBject[list[Data]]: + """Calculate the On Balance Volume (OBV). + + Is a cumulative total of the up and down volume. When the close is higher than the + previous close, the volume is added to the running total, and when the close is + lower than the previous close, the volume is subtracted from the running total. + + To interpret the OBV, look for the OBV to move with the price or precede price moves. + If the price moves before the OBV, then it is a non-confirmed move. A series of rising peaks, + or falling troughs, in the OBV indicates a strong trend. If the OBV is flat, then the market + is not trending. + + Parameters + ---------- + data : list[Data] + list of data to apply the indicator to. + index : str, optional + Index column name, by default "date" + offset : int, optional + How many periods to offset the result, by default 0. + + Returns + ------- + OBBject[list[Data]] + list of data with the indicator applied. + """ + # pylint: disable=import-outside-toplevel + import pandas as pd + import pandas_ta as ta # noqa + + df = basemodel_to_df(data, index=index) + df_target = get_target_columns(df, ["close", "volume"]) + df_obv = pd.DataFrame(df_target.ta.obv(offset=offset)) + + output = pd.concat([df, df_obv], axis=1) + results = df_to_basemodel(output.reset_index()) + + return OBBject(results=results) + + +@router.command( + methods=["POST"], + examples=[ + PythonEx( + description="Perform the Fisher Transform.", + code=[ + "stock_data = obb.equity.price.historical(symbol='TSLA', start_date='2023-01-01', provider='fmp')", + "fisher_data = obb.technical.fisher(data=stock_data.results, length=14, signal=1)", + ], + ), + APIEx(parameters={"length": 2, "data": APIEx.mock_data("timeseries")}), + ], +) +def fisher( + data: list[Data], + index: str = "date", + length: PositiveInt = 14, + signal: PositiveInt = 1, +) -> OBBject[list[Data]]: + """Perform the Fisher Transform. + + A technical indicator created by John F. Ehlers that converts prices into a Gaussian + normal distribution. The indicator highlights when prices have moved to an extreme, + based on recent prices. + This may help in spotting turning points in the price of an asset. It also helps + show the trend and isolate the price waves within a trend. + + Parameters + ---------- + data : list[Data] + list of data to apply the indicator to. + index : str, optional + Index column name, by default "date" + length : PositiveInt, optional + Fisher period, by default 14 + signal : PositiveInt, optional + Fisher Signal period, by default 1 + + Returns + ------- + OBBject[list[Data]] + list of data with the indicator applied. + """ + # pylint: disable=import-outside-toplevel + import pandas as pd + import pandas_ta as ta # noqa + + validate_data(data, [length, signal]) + df = basemodel_to_df(data, index=index) + df_target = get_target_columns(df, ["high", "low"]) + df_fisher = pd.DataFrame(df_target.ta.fisher(length=length, signal=signal)) + + output = pd.concat([df, df_fisher], axis=1) + results = df_to_basemodel(output.reset_index()) + + return OBBject(results=results) + + +@router.command( + methods=["POST"], + examples=[ + PythonEx( + description="Get the Accumulation/Distribution Oscillator.", + code=[ + "stock_data = obb.equity.price.historical(symbol='TSLA', start_date='2023-01-01', provider='fmp')", + "adosc_data = obb.technical.adosc(data=stock_data.results, fast=3, slow=10, offset=0)", + ], + ), + APIEx(parameters={"fast": 2, "slow": 4, "data": APIEx.mock_data("timeseries")}), + ], +) +def adosc( + data: list[Data], + index: str = "date", + fast: PositiveInt = 3, + slow: PositiveInt = 10, + offset: int = 0, +) -> OBBject[list[Data]]: + """Calculate the Accumulation/Distribution Oscillator. + + Also known as the Chaikin Oscillator. + + Essentially a momentum indicator, but of the Accumulation-Distribution line + rather than merely price. It looks at both the strength of price moves and the + underlying buying and selling pressure during a given time period. The oscillator + reading above zero indicates net buying pressure, while one below zero registers + net selling pressure. Divergence between the indicator and pure price moves are + the most common signals from the indicator, and often flag market turning points. + + Parameters + ---------- + data : list[Data] + list of data to be used for the calculation. + fast : PositiveInt, optional + Number of periods to be used for the fast calculation, by default 3. + slow : PositiveInt, optional + Number of periods to be used for the slow calculation, by default 10. + offset : int, optional + Offset to be used for the calculation, by default 0. + + Returns + ------- + OBBject[list[Data]] + The calculated data. + """ + # pylint: disable=import-outside-toplevel + import pandas as pd + import pandas_ta as ta # noqa + + validate_data(data, [fast, slow]) + df = basemodel_to_df(data, index=index) + df_target = get_target_columns(df, ["open", "high", "low", "close", "volume"]) + df_adosc = pd.DataFrame(df_target.ta.adosc(fast=fast, slow=slow, offset=offset)) + + output = pd.concat([df, df_adosc], axis=1) + results = df_to_basemodel(output.reset_index()) + + return OBBject(results=results) + + +@router.command( + methods=["POST"], + examples=[ + PythonEx( + description="Get the Chande Momentum Oscillator.", + code=[ + "stock_data = obb.equity.price.historical(symbol='TSLA', start_date='2023-01-01', provider='fmp')", + "bbands_data = obb.technical.bbands(data=stock_data.results, target='close', length=50, std=2, mamode='sma')", # noqa: E501 + ], + ), + APIEx(parameters={"length": 2, "data": APIEx.mock_data("timeseries")}), + ], +) +def bbands( + data: list[Data], + target: str = "close", + index: str = "date", + length: int = 50, + std: NonNegativeFloat = 2, + mamode: Literal["sma", "ema", "wma", "rma"] = "sma", + offset: int = 0, +) -> OBBject[list[Data]]: + """Calculate the Bollinger Bands. + + Consist of three lines. The middle band is a simple moving average (generally 20 + periods) of the typical price (TP). The upper and lower bands are F standard + deviations (generally 2) above and below the middle band. + The bands widen and narrow when the volatility of the price is higher or lower, + respectively. + + Bollinger Bands do not, in themselves, generate buy or sell signals; + they are an indicator of overbought or oversold conditions. When the price is near the + upper or lower band it indicates that a reversal may be imminent. The middle band + becomes a support or resistance level. The upper and lower bands can also be + interpreted as price targets. When the price bounces off of the lower band and crosses + the middle band, then the upper band becomes the price target. + + Parameters + ---------- + data : list[Data] + list of data to be used for the calculation. + target : str + Target column name. + index : str, optional + Index column name to use with `data`, by default "date". + length : int, optional + Number of periods to be used for the calculation, by default 50. + std : NonNegativeFloat, optional + Standard deviation to be used for the calculation, by default 2. + mamode : Literal["sma", "ema", "wma", "rma"], optional + Moving average mode to be used for the calculation, by default "sma". + offset : int, optional + Offset to be used for the calculation, by default 0. + + Returns + ------- + OBBject[list[Data]] + The calculated data. + """ + # pylint: disable=import-outside-toplevel + import pandas as pd + import pandas_ta as ta # noqa + + validate_data(data, length) + df = basemodel_to_df(data, index=index) + df_target = get_target_column(df, target).to_frame() + bbands_df = pd.DataFrame( + df_target.ta.bbands( + length=length, + std=std, + mamode=mamode, + offset=offset, + close=target, + prefix=target, + ) + ) + + output = pd.concat([df, bbands_df], axis=1) + results = df_to_basemodel(output.reset_index()) + + return OBBject(results=results) + + +@router.command( + methods=["POST"], + examples=[ + PythonEx( + description="Get the Chande Momentum Oscillator.", + code=[ + "stock_data = obb.equity.price.historical(symbol='TSLA', start_date='2023-01-01', provider='fmp')", + "zlma_data = obb.technical.zlma(data=stock_data.results, target='close', length=50, offset=0)", + ], + ), + APIEx(parameters={"length": 2, "data": APIEx.mock_data("timeseries")}), + ], +) +def zlma( + data: list[Data], + target: str = "close", + index: str = "date", + length: int = 50, + offset: int = 0, +) -> OBBject[list[Data]]: + """Calculate the zero lag exponential moving average (ZLEMA). + + Created by John Ehlers and Ric Way. The idea is do a + regular exponential moving average (EMA) calculation but + on a de-lagged data instead of doing it on the regular data. + Data is de-lagged by removing the data from "lag" days ago + thus removing (or attempting to) the cumulative effect of + the moving average. + + Parameters + ---------- + data : list[Data] + list of data to be used for the calculation. + target : str + Target column name. + index : str, optional + Index column name to use with `data`, by default "date". + length : int, optional + Number of periods to be used for the calculation, by default 50. + offset : int, optional + Offset to be used for the calculation, by default 0. + + Returns + ------- + OBBject[list[Data]] + The calculated data. + """ + # pylint: disable=import-outside-toplevel + import pandas as pd + import pandas_ta as ta # noqa + + validate_data(data, length) + df = basemodel_to_df(data, index=index) + df_target = get_target_column(df, target).to_frame() + zlma_df = pd.DataFrame( + df_target.ta.zlma( + length=length, + offset=offset, + close=target, + prefix=target, + ) + ).dropna() + + output = pd.concat([df, zlma_df], axis=1) + results = df_to_basemodel(output.reset_index()) + + return OBBject(results=results) + + +@router.command( + methods=["POST"], + examples=[ + PythonEx( + description="Get the Chande Momentum Oscillator.", + code=[ + "stock_data = obb.equity.price.historical(symbol='TSLA', start_date='2023-01-01', provider='fmp')", + "aaron_data = obb.technical.aroon(data=stock_data.results, length=25, scalar=100)", + ], + ), + APIEx(parameters={"length": 2, "data": APIEx.mock_data("timeseries")}), + ], +) +def aroon( + data: list[Data], + index: str = "date", + length: int = 25, + scalar: float = 100, +) -> OBBject[list[Data]]: + """Calculate the Aroon Indicator. + + The word aroon is Sanskrit for "dawn's early light." The Aroon + indicator attempts to show when a new trend is dawning. The indicator consists + of two lines (Up and Down) that measure how long it has been since the highest + high/lowest low has occurred within an n period range. + + When the Aroon Up is staying between 70 and 100 then it indicates an upward trend. + When the Aroon Down is staying between 70 and 100 then it indicates an downward trend. + A strong upward trend is indicated when the Aroon Up is above 70 while the Aroon Down is below 30. + Likewise, a strong downward trend is indicated when the Aroon Down is above 70 while + the Aroon Up is below 30. Also look for crossovers. When the Aroon Down crosses above + the Aroon Up, it indicates a weakening of the upward trend (and vice versa). + + Parameters + ---------- + data : list[Data] + list of data to be used for the calculation. + index: str, optional + Index column name to use with `data`, by default "date". + length : int, optional + Number of periods to be used for the calculation, by default 25. + scalar : float, optional + Scalar to be used for the calculation, by default 100. + + Returns + ------- + OBBject[list[Data]] + The calculated data. + """ + # pylint: disable=import-outside-toplevel + import pandas as pd + import pandas_ta as ta # noqa + + validate_data(data, length) + df = basemodel_to_df(data, index=index) + df_target = get_target_columns(df, ["high", "low", "close"]) + df_aroon = pd.DataFrame(df_target.ta.aroon(length=length, scalar=scalar)).dropna() + + output = pd.concat([df, df_aroon], axis=1) + results = df_to_basemodel(output.reset_index()) + + return OBBject(results=results) + + +@router.command( + methods=["POST"], + examples=[ + PythonEx( + description="Get the Chande Momentum Oscillator.", + code=[ + "stock_data = obb.equity.price.historical(symbol='TSLA', start_date='2023-01-01', provider='fmp')", + "sma_data = obb.technical.sma(data=stock_data.results, target='close', length=50, offset=0)", + ], + ), + APIEx(parameters={"length": 2, "data": APIEx.mock_data("timeseries")}), + ], +) +def sma( + data: list[Data], + target: str = "close", + index: str = "date", + length: int = 50, + offset: int = 0, +) -> OBBject[list[Data]]: + """Calculate the Simple Moving Average (SMA). + + Moving Averages are used to smooth the data in an array to + help eliminate noise and identify trends. The Simple Moving Average is literally + the simplest form of a moving average. Each output value is the average of the + previous n values. In a Simple Moving Average, each value in the time period carries + equal weight, and values outside of the time period are not included in the average. + This makes it less responsive to recent changes in the data, which can be useful for + filtering out those changes. + + Parameters + ---------- + data : list[Data] + list of data to be used for the calculation. + target : str + Target column name. + index : str, optional + Index column name to use with `data`, by default "date". + length : int, optional + Number of periods to be used for the calculation, by default 50. + offset : int, optional + Offset from the current period, by default 0. + + Returns + ------- + OBBject[list[Data]] + The calculated data. + """ + # pylint: disable=import-outside-toplevel + import pandas as pd + import pandas_ta as ta # noqa + + validate_data(data, length) + df = basemodel_to_df(data, index=index) + df_target = get_target_column(df, target).to_frame() + sma_df = pd.DataFrame( + df_target.ta.sma( + length=length, + offset=offset, + close=target, + prefix=target, + ).dropna() + ) + + output = pd.concat([df, sma_df], axis=1) + results = df_to_basemodel(output.reset_index()) + + return OBBject(results=results) + + +@router.command( + methods=["POST"], + examples=[ + PythonEx( + description="Get the Demark Sequential Indicator.", + code=[ + "stock_data = obb.equity.price.historical(symbol='TSLA', start_date='2023-01-01', provider='fmp')", + "demark_data = obb.technical.demark(data=stock_data.results, offset=0)", + ], + ), + APIEx(parameters={"data": APIEx.mock_data("timeseries")}), + ], +) +def demark( + data: list[Data], + index: str = "date", + target: str = "close", + show_all: bool = True, + asint: bool = True, + offset: int = 0, +) -> OBBject[list[Data]]: + """Calculate the Demark sequential indicator. + + This indicator offers a strategic way to spot potential reversals in market trends. + It's designed to highlight moments when the current trend may be running out of steam, + suggesting a possible shift in direction. By focusing on specific patterns in price movements, it provides + valuable insights for making informed decisions on future changes and identifies trend exhaustion points + with precision. + + Parameters + ---------- + data : list[Data] + list of data to be used for the calculation. + index : str, optional + Index column name to use with `data`, by default "date". + target : str, optional + Target column name, by default "close". + show_all : bool, optional + Show 1 - 13. If set to False, show 6 - 9 + asint : bool, optional + If True, fill NAs with 0 and change type to int, by default True. + offset : int, optional + How many periods to offset the result + + Returns + ------- + OBBject[list[Data]] + The calculated data, with fields: [{index}, {target}, "up", "down"] + """ + # pylint: disable=import-outside-toplevel + import pandas_ta as ta # noqa + from pandas import concat + + df = basemodel_to_df(data, index=index) + df_target = get_target_column(df, target).to_frame() + _demark = ta.exhc(df_target[target], asint=asint, show_all=show_all, offset=offset) + demark_df = concat([df[[target]], _demark], axis=1).reset_index() + demark_df = demark_df.rename(columns={"EXHC_DNa": "down", "EXHC_UPa": "up"}) + results = df_to_basemodel(demark_df) + + return OBBject(results=results) + + +@router.command( + methods=["POST"], + examples=[ + PythonEx( + description="Get the Volume Weighted Average Price (VWAP).", + code=[ + "stock_data = obb.equity.price.historical(symbol='TSLA', start_date='2023-01-01', provider='fmp')", + "vwap_data = obb.technical.vwap(data=stock_data.results, anchor='D', offset=0)", + ], + ), + APIEx(parameters={"data": APIEx.mock_data("timeseries")}), + ], +) +def vwap( + data: list[Data], + index: str = "date", + anchor: str = "D", + offset: int = 0, +) -> OBBject[list[Data]]: + """Calculate the Volume Weighted Average Price (VWAP). + + Measures the average typical price by volume. + It is typically used with intraday charts to identify general direction. + It helps to understand the true average price factoring in the volume of transactions, + and serves as a benchmark for assessing the market's direction over short periods, such as a single trading day. + + Parameters + ---------- + data : list[Data] + list of data to be used for the calculation. + index : str, optional + Index column name to use with `data`, by default "date". + anchor : str, optional + Anchor period to use for the calculation, by default "D". + See Timeseries Offset Aliases below for additional options: + https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#timeseries-offset-aliases + offset : int, optional + Offset from the current period, by default 0. + + Returns + ------- + OBBject[list[Data]] + The calculated data. + """ + # pylint: disable=import-outside-toplevel + import pandas as pd + import pandas_ta as ta # noqa + + df = basemodel_to_df(data, index=index) + if index == "date": + df.index = pd.to_datetime(df.index) + df_target = get_target_columns(df, ["high", "low", "close", "volume"]) + df_vwap = pd.DataFrame(df_target.ta.vwap(anchor=anchor, offset=offset).dropna()) + + output = pd.concat([df, df_vwap], axis=1) + results = df_to_basemodel(output.reset_index()) + + return OBBject(results=results) + + +@router.command( + methods=["POST"], + examples=[ + PythonEx( + description="Get the Moving Average Convergence Divergence (MACD).", + code=[ + "stock_data = obb.equity.price.historical(symbol='TSLA', start_date='2023-01-01', provider='fmp')", + "macd_data = obb.technical.macd(data=stock_data.results, target='close', fast=12, slow=26, signal=9)", + ], + ), + APIEx( + description="Example with mock data.", + parameters={ + "fast": 2, + "slow": 3, + "signal": 1, + "data": APIEx.mock_data("timeseries"), + }, + ), + ], +) +def macd( + data: list[Data], + target: str = "close", + index: str = "date", + fast: int = 12, + slow: int = 26, + signal: int = 9, +) -> OBBject[list[Data]]: + """Calculate the Moving Average Convergence Divergence (MACD). + + Difference between two Exponential Moving Averages. The Signal line is an + Exponential Moving Average of the MACD. + + The MACD signals trend changes and indicates the start of new trend direction. + High values indicate overbought conditions, low values indicate oversold conditions. + Divergence with the price indicates an end to the current trend, especially if the + MACD is at extreme high or low values. When the MACD line crosses above the + signal line a buy signal is generated. When the MACD crosses below the signal line a + sell signal is generated. To confirm the signal, the MACD should be above zero for a buy, + and below zero for a sell. + + Parameters + ---------- + data : list[Data] + list of data to be used for the calculation. + target : str + Target column name. + fast : int, optional + Number of periods for the fast EMA, by default 12. + slow : int, optional + Number of periods for the slow EMA, by default 26. + signal : int, optional + Number of periods for the signal EMA, by default 9. + + Returns + ------- + OBBject[list[Data]] + The calculated data. + """ + # pylint: disable=import-outside-toplevel + import pandas as pd + import pandas_ta as ta # noqa + + validate_data(data, [fast, slow, signal]) + df = basemodel_to_df(data, index=index) + df_target = get_target_column(df, target).to_frame() + macd_df = pd.DataFrame( + df_target.ta.macd( + fast=fast, + slow=slow, + signal=signal, + close=target, + prefix=target, + ).dropna() + ) + output = pd.concat([df, macd_df], axis=1) + results = df_to_basemodel(output.reset_index()) + + return OBBject(results=results) + + +@router.command( + methods=["POST"], + examples=[ + PythonEx( + description="Calculate HMA with historical stock data.", + code=[ + "stock_data = obb.equity.price.historical(symbol='TSLA', start_date='2023-01-01', provider='fmp')", + "hma_data = obb.technical.hma(data=stock_data.results, target='close', length=50, offset=0)", + ], + ), + ], +) +def hma( + data: list[Data], + target: str = "close", + index: str = "date", + length: int = 50, + offset: int = 0, +) -> OBBject[list[Data]]: + """Calculate the Hull Moving Average (HMA). + + Solves the age old dilemma of making a moving average more responsive to current + price activity whilst maintaining curve smoothness. + In fact the HMA almost eliminates lag altogether and manages to improve smoothing + at the same time. + + Parameters + ---------- + data : list[Data] + list of data to be used for the calculation. + target : str + Target column name. + index : str, optional + Index column name to use with `data`, by default "date". + length : int, optional + Number of periods for the HMA, by default 50. + offset : int, optional + Offset of the HMA, by default 0. + + Returns + ------- + OBBject[list[Data]] + The calculated data. + """ + # pylint: disable=import-outside-toplevel + import pandas as pd + import pandas_ta as ta # noqa + + validate_data(data, length) + df = basemodel_to_df(data, index=index) + df_target = get_target_column(df, target).to_frame() + hma_df = pd.DataFrame( + df_target.ta.hma( + length=length, + offset=offset, + close=target, + prefix=target, + ).dropna() + ) + + output = pd.concat([df, hma_df], axis=1) + results = df_to_basemodel(output.reset_index()) + + return OBBject(results=results) + + +@router.command( + methods=["POST"], + examples=[ + PythonEx( + description="Get the Donchian Channels.", + code=[ + "stock_data = obb.equity.price.historical(symbol='TSLA', start_date='2023-01-01', provider='fmp')", + "donchian_data = obb.technical.donchian(data=stock_data.results, lower_length=20, upper_length=20, offset=0)", # noqa: E501 + ], + ), + APIEx( + parameters={ + "lower_length": 1, + "upper_length": 3, + "data": APIEx.mock_data("timeseries"), + } + ), + ], +) +def donchian( + data: list[Data], + index: str = "date", + lower_length: PositiveInt = 20, + upper_length: PositiveInt = 20, + offset: int = 0, +) -> OBBject[list[Data]]: + """Calculate the Donchian Channels. + + Three lines generated by moving average calculations that comprise an indicator + formed by upper and lower bands around a midrange or median band. The upper band + marks the highest price of a security over N periods while the lower band + marks the lowest price of a security over N periods. The area + between the upper and lower bands represents the Donchian Channel. + + Parameters + ---------- + data : list[Data] + list of data to be used for the calculation. + index : str, optional + Index column name to use with `data`, by default "date". + lower_length : PositiveInt, optional + Number of periods for the lower band, by default 20. + upper_length : PositiveInt, optional + Number of periods for the upper band, by default 20. + offset : int, optional + Offset of the Donchian Channel, by default 0. + + Returns + ------- + OBBject[list[Data]] + The calculated data. + """ + # pylint: disable=import-outside-toplevel + import pandas as pd + import pandas_ta as ta # noqa + + validate_data(data, [lower_length, upper_length]) + df = basemodel_to_df(data, index=index) + df_target = get_target_columns(df, ["high", "low"]) + donchian_df = pd.DataFrame( + df_target.ta.donchian( + lower_length=lower_length, upper_length=upper_length, offset=offset + ).dropna() + ) + + output = pd.concat([df, donchian_df], axis=1) + results = df_to_basemodel(output.reset_index()) + + return OBBject(results=results) + + +@router.command( + methods=["POST"], + examples=[ + PythonEx( + description="Get the Ichimoku Cloud.", + code=[ + "stock_data = obb.equity.price.historical(symbol='TSLA', start_date='2023-01-01', provider='fmp')", + "ichimoku_data = obb.technical.ichimoku(data=stock_data.results, conversion=9, base=26, lookahead=False)", + ], + ), + ], +) +def ichimoku( + data: list[Data], + index: str = "date", + conversion: PositiveInt = 9, + base: PositiveInt = 26, + lagging: PositiveInt = 52, + offset: PositiveInt = 26, + lookahead: bool = False, +) -> OBBject[list[Data]]: + """Calculate the Ichimoku Cloud. + + Also known as Ichimoku Kinko Hyo, is a versatile indicator that defines support and + resistance, identifies trend direction, gauges momentum and provides trading + signals. Ichimoku Kinko Hyo translates into "one look equilibrium chart". With + one look, chartists can identify the trend and look for potential signals within + that trend. + + Parameters + ---------- + data : list[Data] + list of data to be used for the calculation. + index : str, optional + Index column name to use with `data`, by default "date". + conversion : PositiveInt, optional + Number of periods for the conversion line, by default 9. + base : PositiveInt, optional + Number of periods for the base line, by default 26. + lagging : PositiveInt, optional + Number of periods for the lagging span, by default 52. + offset : PositiveInt, optional + Number of periods for the offset, by default 26. + lookahead : bool, optional + drops the Chikou Span Column to prevent potential data leak + + Returns + ------- + OBBject[list[Data]] + The calculated data. + """ + validate_data(data, [conversion, base, lagging]) + df = basemodel_to_df(data, index=index) + df_target = get_target_columns(df, ["high", "low", "close"]) + df_ichimoku, df_span = df_target.ta.ichimoku( + tenkan=conversion, + kijun=base, + senkou=lagging, + offset=offset, + lookahead=lookahead, + ) + + df_result = df.join(df_span.add_prefix("span_"), how="left") + df_result = df_result.join(df_ichimoku, how="left") + + results = df_to_basemodel(df_result.reset_index()) + + return OBBject(results=results) + + +@router.command( + methods=["POST"], + examples=[ + PythonEx( + description="Get the Clenow Volatility Adjusted Momentum.", + code=[ + "stock_data = obb.equity.price.historical(symbol='TSLA', start_date='2023-01-01', provider='fmp')", + "clenow_data = obb.technical.clenow(data=stock_data.results, period=90)", + ], + ), + APIEx(parameters={"period": 2, "data": APIEx.mock_data("timeseries")}), + ], +) +def clenow( + data: list[Data], + index: str = "date", + target: str = "close", + period: PositiveInt = 90, +) -> OBBject[list[Data]]: + """Calculate the Clenow Volatility Adjusted Momentum. + + The Clenow Volatility Adjusted Momentum is a sophisticated approach to understanding market momentum with a twist. + It adjusts for volatility, offering a clearer picture of true momentum by considering how price movements are + influenced by their volatility over a set period. It helps in identifying stronger, more reliable trends. + + Parameters + ---------- + data : list[Data] + list of data to be used for the calculation. + index : str, optional + Index column name to use with `data`, by default "date". + target : str, optional + Target column name, by default "close". + period : PositiveInt, optional + Number of periods for the momentum, by default 90. + + Returns + ------- + OBBject[list[Data]] + The calculated data. + """ + # pylint: disable=import-outside-toplevel + import pandas as pd + import pandas_ta as ta # noqa + + validate_data(data, period) + df = basemodel_to_df(data, index=index) + df_target = get_target_column(df, target) + + r2, coef, _ = clenow_momentum(df_target, period) + + df_clenow = pd.DataFrame.from_dict( + { + "r^2": f"{r2:.5f}", + "fit_coef": f"{coef:.5f}", + "factor": f"{coef * r2:.5f}", + }, + orient="index", + ).transpose() + + output = pd.concat([df, df_clenow], axis=1) + results = df_to_basemodel(output.reset_index()) + + return OBBject(results=results) + + +@router.command( + methods=["POST"], + examples=[ + PythonEx( + description="Get the Accumulation/Distribution Line.", + code=[ + "stock_data = obb.equity.price.historical(symbol='TSLA', start_date='2023-01-01', provider='fmp')", + "ad_data = obb.technical.ad(data=stock_data.results, offset=0)", + ], + ), + APIEx(parameters={"data": APIEx.mock_data("timeseries")}), + ], +) +def ad(data: list[Data], index: str = "date", offset: int = 0) -> OBBject[list[Data]]: + """Calculate the Accumulation/Distribution Line. + + Similar to the On Balance Volume (OBV). + Sums the volume times +1/-1 based on whether the close is higher than the previous + close. The Accumulation/Distribution indicator, however multiplies the volume by the + close location value (CLV). The CLV is based on the movement of the issue within a + single bar and can be +1, -1 or zero. + + + The Accumulation/Distribution Line is interpreted by looking for a divergence in + the direction of the indicator relative to price. If the Accumulation/Distribution + Line is trending upward it indicates that the price may follow. Also, if the + Accumulation/Distribution Line becomes flat while the price is still rising (or falling) + then it signals an impending flattening of the price. + + Parameters + ---------- + data : list[Data] + list of data to be used for the calculation. + index : str, optional + Index column name to use with `data`, by default "date". + offset : int, optional + Offset of the AD, by default 0. + + Returns + ------- + OBBject[list[Data]] + The calculated data. + """ + # pylint: disable=import-outside-toplevel + import pandas as pd + import pandas_ta as ta # noqa + + df = basemodel_to_df(data, index=index) + df_target = get_target_columns(df, ["high", "low", "close", "volume"]) + ad_df = pd.DataFrame(df_target.ta.ad(offset=offset).dropna()) + + output = pd.concat([df, ad_df], axis=1) + results = df_to_basemodel(output.reset_index()) + + return OBBject(results=results) + + +@router.command( + methods=["POST"], + examples=[ + PythonEx( + description="Get the Average Directional Index (ADX).", + code=[ + "stock_data = obb.equity.price.historical(symbol='TSLA', start_date='2023-01-01', provider='fmp')", + "adx_data = obb.technical.adx(data=stock_data.results, length=50, scalar=100.0, drift=1)", + ], + ), + APIEx(parameters={"length": 2, "data": APIEx.mock_data("timeseries")}), + ], +) +def adx( + data: list[Data], + index: str = "date", + length: int = 50, + scalar: float = 100.0, + drift: int = 1, +) -> OBBject[list[Data]]: + """Calculate the Average Directional Index (ADX). + + The ADX is a Welles Wilder style moving average of the Directional Movement Index (DX). + The values range from 0 to 100, but rarely get above 60. To interpret the ADX, consider + a high number to be a strong trend, and a low number, a weak trend. + + Parameters + ---------- + data : list[Data] + list of data to be used for the calculation. + index : str, optional + Index column name to use with `data`, by default "date". + length : int, optional + Number of periods for the ADX, by default 50. + scalar : float, optional + Scalar value for the ADX, by default 100.0. + drift : int, optional + Drift value for the ADX, by default 1. + + Returns + ------- + OBBject[list[Data]] + The calculated data. + """ + # pylint: disable=import-outside-toplevel + import pandas as pd + import pandas_ta as ta # noqa + + validate_data(data, length) + df = basemodel_to_df(data, index=index) + df_target = get_target_columns(df, ["close", "high", "low"]) + df_adx = pd.DataFrame( + df_target.ta.adx(length=length, scalar=scalar, drift=drift).dropna() + ) + + output = pd.concat([df, df_adx], axis=1) + results = df_to_basemodel(output.reset_index()) + + return OBBject(results=results) + + +@router.command( + methods=["POST"], + examples=[ + PythonEx( + description="Get the Average True Range (ATR).", + code=[ + "stock_data = obb.equity.price.historical(symbol='TSLA', start_date='2023-01-01', provider='fmp')", + "wma_data = obb.technical.wma(data=stock_data.results, target='close', length=50, offset=0)", + ], + ), + APIEx(parameters={"length": 2, "data": APIEx.mock_data("timeseries")}), + ], +) +def wma( + data: list[Data], + target: str = "close", + index: str = "date", + length: int = 50, + offset: int = 0, +) -> OBBject[list[Data]]: + """Calculate the Weighted Moving Average (WMA). + + A Weighted Moving Average puts more weight on recent data and less on past data. + This is done by multiplying each bar's price by a weighting factor. Because of its + unique calculation, WMA will follow prices more closely than a corresponding Simple + Moving Average. + + Parameters + ---------- + data : list[Data] + The data to use for the calculation. + target : str + Target column name. + index : str, optional + Index column name to use with `data`, by default "date". + length : int, optional + The length of the WMA, by default 50. + offset : int, optional + The offset of the WMA, by default 0. + + Returns + ------- + OBBject[list[Data]] + The WMA data. + """ + # pylint: disable=import-outside-toplevel + import pandas as pd + import pandas_ta as ta # noqa + + validate_data(data, length) + df = basemodel_to_df(data, index=index) + df_target = get_target_column(df, target).to_frame() + df_wma = pd.DataFrame( + df_target.ta.wma( + length=length, + offset=offset, + close=target, + prefix=target, + ).dropna() + ) + + output = pd.concat([df, df_wma], axis=1) + results = df_to_basemodel(output.reset_index()) + + return OBBject(results=results) + + +@router.command( + methods=["POST"], + examples=[ + PythonEx( + description="Get the Commodity Channel Index (CCI).", + code=[ + "stock_data = obb.equity.price.historical(symbol='TSLA', start_date='2023-01-01', provider='fmp')", + "cci_data = obb.technical.cci(data=stock_data.results, length=14, scalar=0.015)", + ], + ), + APIEx(parameters={"length": 2, "data": APIEx.mock_data("timeseries")}), + ], +) +def cci( + data: list[Data], + index: str = "date", + length: PositiveInt = 14, + scalar: PositiveFloat = 0.015, +) -> OBBject[list[Data]]: + """Calculate the Commodity Channel Index (CCI). + + The CCI is designed to detect beginning and ending market trends. + The range of 100 to -100 is the normal trading range. CCI values outside of this + range indicate overbought or oversold conditions. You can also look for price + divergence in the CCI. If the price is making new highs, and the CCI is not, + then a price correction is likely. + + Parameters + ---------- + data : list[Data] + The data to use for the CCI calculation. + index : str, optional + Index column name to use with `data`, by default "date". + length : PositiveInt, optional + The length of the CCI, by default 14. + scalar : PositiveFloat, optional + The scalar of the CCI, by default 0.015. + + Returns + ------- + OBBject[list[Data]] + The CCI data. + """ + # pylint: disable=import-outside-toplevel + import pandas as pd + import pandas_ta as ta # noqa + + validate_data(data, length) + df = basemodel_to_df(data, index=index) + df_target = get_target_columns(df, ["close", "high", "low"]) + cci_df = pd.DataFrame(df_target.ta.cci(length=length, scalar=scalar).dropna()) + + output = pd.concat([df, cci_df], axis=1) + results = df_to_basemodel(output.reset_index()) + + return OBBject(results=results) + + +@router.command( + methods=["POST"], + examples=[ + PythonEx( + description="Get the Relative Strength Index (RSI).", + code=[ + "stock_data = obb.equity.price.historical(symbol='TSLA', start_date='2023-01-01', provider='fmp')", + "rsi_data = obb.technical.rsi(data=stock_data.results, target='close', length=14, scalar=100.0, drift=1)", + ], + ), + APIEx(parameters={"length": 2, "data": APIEx.mock_data("timeseries")}), + ], +) +def rsi( + data: list[Data], + target: str = "close", + index: str = "date", + length: int = 14, + scalar: float = 100.0, + drift: int = 1, +) -> OBBject[list[Data]]: + """Calculate the Relative Strength Index (RSI). + + RSI calculates a ratio of the recent upward price movements to the absolute price + movement. The RSI ranges from 0 to 100. + The RSI is interpreted as an overbought/oversold indicator when + the value is over 70/below 30. You can also look for divergence with price. If + the price is making new highs/lows, and the RSI is not, it indicates a reversal. + + Parameters + ---------- + data : list[Data] + The data to use for the RSI calculation. + target : str + Target column name. + index : str, optional + Index column name to use with `data`, by default "date" + length : int, optional + The length of the RSI, by default 14 + scalar : float, optional + The scalar to use for the RSI, by default 100.0 + drift : int, optional + The drift to use for the RSI, by default 1 + + Returns + ------- + OBBject[list[Data]] + The RSI data. + """ + # pylint: disable=import-outside-toplevel + import pandas as pd + import pandas_ta as ta # noqa + + validate_data(data, length) + df = basemodel_to_df(data, index=index) + df_target = get_target_column(df, target).to_frame() + rsi_df = pd.DataFrame( + df_target.ta.rsi( + length=length, + scalar=scalar, + drift=drift, + close=target, + prefix=target, + ).dropna() + ) + + output = pd.concat([df, rsi_df], axis=1) + results = df_to_basemodel(output.reset_index()) + + return OBBject(results=results) + + +@router.command( + methods=["POST"], + examples=[ + PythonEx( + description="Get the Stochastic Oscillator.", + code=[ + "stock_data = obb.equity.price.historical(symbol='TSLA', start_date='2023-01-01', provider='fmp')", + "stoch_data = obb.technical.stoch(data=stock_data.results, fast_k_period=14, slow_d_period=3, slow_k_period=3)", # noqa: E501 # pylint: disable=line-too-long + ], + ), + ], +) +def stoch( + data: list[Data], + index: str = "date", + fast_k_period: NonNegativeInt = 14, + slow_d_period: NonNegativeInt = 3, + slow_k_period: NonNegativeInt = 3, +) -> OBBject[list[Data]]: + """Calculate the Stochastic Oscillator. + + The Stochastic Oscillator measures where the close is in relation + to the recent trading range. The values range from zero to 100. %D values over 75 + indicate an overbought condition; values under 25 indicate an oversold condition. + When the Fast %D crosses above the Slow %D, it is a buy signal; when it crosses + below, it is a sell signal. The Raw %K is generally considered too erratic to use + for crossover signals. + + Parameters + ---------- + data : list[Data] + The data to use for the Stochastic Oscillator calculation. + index : str, optional + Index column name to use with `data`, by default "date". + fast_k_period : NonNegativeInt, optional + The fast %K period, by default 14. + slow_d_period : NonNegativeInt, optional + The slow %D period, by default 3. + slow_k_period : NonNegativeInt, optional + The slow %K period, by default 3. + + Returns + ------- + OBBject[list[Data]] + The Stochastic Oscillator data. + """ + # pylint: disable=import-outside-toplevel + import pandas as pd + import pandas_ta as ta # noqa + + validate_data(data, [fast_k_period, slow_d_period, slow_k_period]) + df = basemodel_to_df(data, index=index) + df_target = get_target_columns(df, ["close", "high", "low"]) + stoch_df = pd.DataFrame( + df_target.ta.stoch( + fast_k_period=fast_k_period, + slow_d_period=slow_d_period, + slow_k_period=slow_k_period, + ).dropna() + ) + + output = pd.concat([df, stoch_df], axis=1) + results = df_to_basemodel(output.reset_index()) + + return OBBject(results=results) + + +@router.command( + methods=["POST"], + examples=[ + PythonEx( + description="Get the Keltner Channels.", + code=[ + "stock_data = obb.equity.price.historical(symbol='TSLA', start_date='2023-01-01', provider='fmp')", + "kc_data = obb.technical.kc(data=stock_data.results, length=20, scalar=20, mamode='ema', offset=0)", + ], + ), + APIEx(parameters={"length": 2, "data": APIEx.mock_data("timeseries")}), + ], +) +def kc( + data: list[Data], + index: str = "date", + length: PositiveInt = 20, + scalar: PositiveFloat = 20, + mamode: Literal["ema", "sma", "wma", "hma", "zlma"] = "ema", + offset: NonNegativeInt = 0, +) -> OBBject[list[Data]]: + """Calculate the Keltner Channels. + + Keltner Channels are volatility-based bands that are placed + on either side of an asset's price and can aid in determining + the direction of a trend.The Keltner channel uses the average + true range (ATR) or volatility, with breaks above or below the top + and bottom barriers signaling a continuation. + + Parameters + ---------- + data : list[Data] + The data to use for the Keltner Channels calculation. + index : str, optional + Index column name to use with `data`, by default "date" + length : PositiveInt, optional + The length of the Keltner Channels, by default 20 + scalar : PositiveFloat, optional + The scalar to use for the Keltner Channels, by default 20 + mamode : Literal["ema", "sma", "wma", "hma", "zlma"], optional + The moving average mode to use for the Keltner Channels, by default "ema" + offset : NonNegativeInt, optional + The offset to use for the Keltner Channels, by default 0 + + Returns + ------- + OBBject[list[Data]] + The Keltner Channels data. + """ + # pylint: disable=import-outside-toplevel + import pandas as pd + import pandas_ta as ta # noqa + + validate_data(data, length) + df = basemodel_to_df(data, index=index) + df_target = get_target_columns(df, ["high", "low", "close"]) + kc_df = pd.DataFrame( + df_target.ta.kc( + length=length, + scalar=scalar, + mamode=mamode, + offset=offset, + ).dropna() + ) + output = pd.concat([df, kc_df], axis=1) + results = df_to_basemodel(output.reset_index()) + + return OBBject(results=results) + + +@router.command( + methods=["POST"], + examples=[ + PythonEx( + description="Get the Center of Gravity (CG).", + code=[ + "stock_data = obb.equity.price.historical(symbol='TSLA', start_date='2023-01-01', provider='fmp')", + "cg_data = obb.technical.cg(data=stock_data.results, length=14)", + ], + ), + APIEx(parameters={"length": 2, "data": APIEx.mock_data("timeseries")}), + ], +) +def cg( + data: list[Data], index: str = "date", length: PositiveInt = 14 +) -> OBBject[list[Data]]: + """Calculate the Center of Gravity. + + The Center of Gravity indicator, in short, is used to anticipate future price movements + and to trade on price reversals as soon as they happen. However, just like other oscillators, + the COG indicator returns the best results in range-bound markets and should be avoided when + the price is trending. Traders who use it will be able to closely speculate the upcoming + price change of the asset. + + Parameters + ---------- + data : list[Data] + The data to use for the COG calculation. + index : str, optional + Index column name to use with `data`, by default "date" + length : PositiveInt, optional + The length of the COG, by default 14 + + Returns + ------- + OBBject[list[Data]] + The COG data. + """ + # pylint: disable=import-outside-toplevel + import pandas as pd + import pandas_ta as ta # noqa + + validate_data(data, length) + df = basemodel_to_df(data, index=index) + df_target = get_target_columns(df, ["high", "low", "close"]) + cg_df = pd.DataFrame(df_target.ta.cg(length=length).dropna()) + + output = pd.concat([df, cg_df], axis=1) + results = df_to_basemodel(output.reset_index()) + + return OBBject(results=results) + + +@router.command( + methods=["POST"], + examples=[ + PythonEx( + description="Realized Volatility Cones.", + code=[ + "stock_data = obb.equity.price.historical(symbol='TSLA', start_date='2023-01-01', provider='yfinance')", + "cones_data = obb.technical.cones(data=stock_data.results, lower_q=0.25, upper_q=0.75, model='std')", + ], + ), + APIEx(parameters={"data": APIEx.mock_data("timeseries")}), + ], +) +def cones( + data: list[Data], + index: str = "date", + lower_q: float = 0.25, + upper_q: float = 0.75, + model: Literal[ + "std", + "parkinson", + "garman_klass", + "hodges_tompkins", + "rogers_satchell", + "yang_zhang", + ] = "std", + is_crypto: bool = False, + trading_periods: Optional[int] = None, +) -> OBBject[list[Data]]: + """Calculate the realized volatility quantiles over rolling windows of time. + + The cones indicator is designed to map out the ebb and flow of price movements through a detailed analysis of + volatility quantiles. By examining the range of volatility within specific time frames, it offers a nuanced view of + market behavior, highlighting periods of stability and turbulence. + + The model for calculating volatility is selectable and can be one of the following: + - Standard deviation + - Parkinson + - Garman-Klass + - Hodges-Tompkins + - Rogers-Satchell + - Yang-Zhang + + Read more about it in the model parameter description. + + Parameters + ---------- + data : list[Data] + The data to use for the calculation. + index : str, optional + Index column name to use with `data`, by default "date" + lower_q : float, optional + The lower quantile value for calculations + upper_q : float, optional + The upper quantile value for calculations + model : Literal["std", "parkinson", "garman_klass", "hodges_tompkins", "rogers_satchell", "yang_zhang"], optional + The model used to calculate realized volatility + + Standard deviation measures how widely returns are dispersed from the average return. + It is the most common (and biased) estimator of volatility. + + Parkinson volatility uses the high and low price of the day rather than just close to close prices. + It is useful for capturing large price movements during the day. + + Garman-Klass volatility extends Parkinson volatility by taking into account the opening and closing price. + As markets are most active during the opening and closing of a trading session; + it makes volatility estimation more accurate. + + Hodges-Tompkins volatility is a bias correction for estimation using an overlapping data sample. + It produces unbiased estimates and a substantial gain in efficiency. + + Rogers-Satchell is an estimator for measuring the volatility with an average return not equal to zero. + Unlike Parkinson and Garman-Klass estimators, Rogers-Satchell incorporates a drift term, + mean return not equal to zero. + + Yang-Zhang volatility is the combination of the overnight (close-to-open volatility). + It is a weighted average of the Rogers-Satchell volatility and the open-to-close volatility. + is_crypto : bool, optional + Whether the data is crypto or not. If True, volatility is calculated for 365 days instead of 252 + trading_periods : Optional[int] [default: 252] + Number of trading periods in a year. + + Returns + ------- + OBBject[list[Data]] + The cones data. + """ + if lower_q > upper_q: + lower_q, upper_q = upper_q, lower_q + + df = basemodel_to_df(data, index=index) + df_cones = calculate_cones( + data=df, + lower_q=lower_q, + upper_q=upper_q, + model=model, + is_crypto=is_crypto, + trading_periods=trading_periods, + ) + results = df_to_basemodel(df_cones) + + return OBBject(results=results) + + +@router.command( + methods=["POST"], + examples=[ + PythonEx( + description="Get the Exponential Moving Average (EMA).", + code=[ + "stock_data = obb.equity.price.historical(symbol='TSLA', start_date='2023-01-01', provider='fmp')", + "ema_data = obb.technical.ema(data=stock_data.results, target='close', length=50, offset=0)", + ], + ), + APIEx(parameters={"length": 2, "data": APIEx.mock_data("timeseries")}), + ], +) +def ema( + data: list[Data], + target: str = "close", + index: str = "date", + length: int = 50, + offset: int = 0, +) -> OBBject[list[Data]]: + """Calculate the Exponential Moving Average (EMA). + + EMA is a cumulative calculation, including all data. Past values have + a diminishing contribution to the average, while more recent values have a greater + contribution. This method allows the moving average to be more responsive to changes + in the data. + + Parameters + ---------- + data : list[Data] + The data to use for the calculation. + target : str + Target column name. + index : str, optional + Index column name to use with `data`, by default "date" + length : int, optional + The length of the calculation, by default 50. + offset : int, optional + The offset of the calculation, by default 0. + + Returns + ------- + OBBject[list[Data]] + The calculated data. + """ + # pylint: disable=import-outside-toplevel + import pandas as pd + import pandas_ta as ta # noqa + + validate_data(data, length) + df = basemodel_to_df(data, index=index) + df_target = get_target_column(df, target).to_frame() + ema_df = pd.DataFrame( + df_target.ta.ema( + length=length, offset=offset, close=target, prefix=target + ).dropna() + ) + + output = pd.concat([df, ema_df], axis=1) + results = df_to_basemodel(output.reset_index()) + + return OBBject(results=results) diff --git a/openbb_platform/extensions/technical/openbb_technical/technical_views.py b/openbb_platform/extensions/technical/openbb_technical/technical_views.py new file mode 100644 index 0000000000000000000000000000000000000000..1957a6f955120bc98c21e39b40ad3070057eba2c --- /dev/null +++ b/openbb_platform/extensions/technical/openbb_technical/technical_views.py @@ -0,0 +1,556 @@ +"""Views for the technical Extension.""" + +# pylint: disable=too-many-locals,use-dict-literal + +from typing import TYPE_CHECKING, Any + +from openbb_charting.core.to_chart import to_chart +from openbb_charting.styles.colors import LARGE_CYCLER + +if TYPE_CHECKING: + from openbb_charting.core.openbb_figure import OpenBBFigure + + +class TechnicalViews: + """Technical Views.""" + + @staticmethod + def technical_sma(**kwargs) -> tuple["OpenBBFigure", dict[str, Any]]: + """Plot simple moving average chart.""" + if "ma_type" not in kwargs: + kwargs["ma_type"] = "sma" + return _ta_ma(**kwargs) + + @staticmethod + def technical_ema(**kwargs) -> tuple["OpenBBFigure", dict[str, Any]]: + """Exponential moving average chart.""" + if "ma_type" not in kwargs: + kwargs["ma_type"] = "ema" + return _ta_ma(**kwargs) + + @staticmethod + def technical_hma(**kwargs) -> tuple["OpenBBFigure", dict[str, Any]]: + """Hull moving average chart.""" + if "ma_type" not in kwargs: + kwargs["ma_type"] = "hma" + return _ta_ma(**kwargs) + + @staticmethod + def technical_wma(**kwargs) -> tuple["OpenBBFigure", dict[str, Any]]: + """Weighted moving average chart.""" + if "ma_type" not in kwargs: + kwargs["ma_type"] = "wma" + return _ta_ma(**kwargs) + + @staticmethod + def technical_zlma(**kwargs) -> tuple["OpenBBFigure", dict[str, Any]]: + """Zero lag moving average chart.""" + if "ma_type" not in kwargs: + kwargs["ma_type"] = "zlma" + return _ta_ma(**kwargs) + + @staticmethod + def technical_aroon(**kwargs) -> tuple["OpenBBFigure", dict[str, Any]]: + """Technical Aroon Chart.""" + # pylint: disable=import-outside-toplevel + from openbb_charting.core.plotly_ta.ta_class import PlotlyTA + from openbb_core.app.utils import basemodel_to_df + from pandas import DataFrame + + if "data" in kwargs and isinstance(kwargs["data"], DataFrame): + data = kwargs["data"] + else: + data = basemodel_to_df( + kwargs["obbject_item"], index=kwargs.get("index", "date") + ) + + if "date" in data.columns: + data = data.set_index("date") + + if "symbol" in data.columns and len(data.symbol.unique()) > 1: + raise ValueError( + "Please provide data with only one symbol and columns for OHLC." + ) + + symbol = kwargs.get("symbol", "") + + volume = kwargs.get("volume") is True + title = f"Aroon Indicator & Oscillator {symbol}" + + length = kwargs.get("length", 25) + scalar = kwargs.get("scalar", 100) + symbol = kwargs.get("symbol", "") + + ta = PlotlyTA() + fig = ta.plot( # type: ignore + data, + dict(aroon=dict(length=length, scalar=scalar)), + title, + False, + volume=volume, + ) + + content = fig.show(external=True).to_plotly_json() + + return fig, content + + @staticmethod + def technical_macd(**kwargs) -> tuple["OpenBBFigure", dict[str, Any]]: + """Plot moving average convergence divergence chart.""" + # pylint: disable=import-outside-toplevel + from openbb_charting.core.plotly_ta.ta_class import PlotlyTA + from openbb_core.app.utils import basemodel_to_df + from pandas import DataFrame + + if "data" in kwargs and isinstance(kwargs["data"], DataFrame): + data = kwargs["data"] + else: + data = basemodel_to_df( + kwargs["obbject_item"], index=kwargs.get("index", "date") + ) + + if "date" in data.columns: + data = data.set_index("date") + + if "symbol" in data.columns and len(data.symbol.unique()) > 1: + raise ValueError( + "Please provide data with only one symbol and columns for OHLC." + ) + + fast = kwargs.get("fast", 12) + slow = kwargs.get("slow", 26) + signal = kwargs.get("signal", 9) + symbol = kwargs.get("symbol", "") + + title = f"{symbol.upper()} MACD" + volume = kwargs.get("volume") is True + + ta = PlotlyTA() + fig = ta.plot( # type: ignore + data, + dict(macd=dict(fast=fast, slow=slow, signal=signal)), + title, + False, + volume=volume, + ) + content = fig.show(external=True).to_plotly_json() + + return fig, content + + @staticmethod + def technical_adx(**kwargs) -> tuple["OpenBBFigure", dict[str, Any]]: + """Average directional movement index chart.""" + # pylint: disable=import-outside-toplevel + from openbb_charting.core.plotly_ta.ta_class import PlotlyTA + from openbb_core.app.utils import basemodel_to_df + from pandas import DataFrame + + if "data" in kwargs and isinstance(kwargs["data"], DataFrame): + data = kwargs["data"] + else: + data = basemodel_to_df( + kwargs["obbject_item"], index=kwargs.get("index", "date") + ) + + if "date" in data.columns: + data = data.set_index("date") + + if "symbol" in data.columns and len(data.symbol.unique()) > 1: + raise ValueError( + "Please provide data with only one symbol and columns for OHLC." + ) + + length = kwargs.get("length", 14) + scalar = kwargs.get("scalar", 100.0) + drift = kwargs.get("drift", 1) + symbol = kwargs.get("symbol", "") + + ta = PlotlyTA() + fig = ta.plot( # type: ignore + data, + dict(adx=dict(length=length, scalar=scalar, drift=drift)), + f"Average Directional Movement Index (ADX) {symbol}", + False, + volume=False, + ) + content = fig.show(external=True).to_plotly_json() + + return fig, content + + @staticmethod + def technical_rsi(**kwargs) -> tuple["OpenBBFigure", dict[str, Any]]: + """Relative strength index chart.""" + # pylint: disable=import-outside-toplevel + from openbb_charting.core.plotly_ta.ta_class import PlotlyTA + from openbb_core.app.utils import basemodel_to_df + from pandas import DataFrame + + if "data" in kwargs and isinstance(kwargs["data"], DataFrame): + data = kwargs["data"] + else: + data = basemodel_to_df( + kwargs["obbject_item"], index=kwargs.get("index", "date") + ) + + if "date" in data.columns: + data = data.set_index("date") + + if "symbol" in data.columns and len(data.symbol.unique()) > 1: + raise ValueError( + "Please provide data with only one symbol and columns for OHLC." + ) + + window = kwargs.get("window", 14) + scalar = kwargs.get("scalar", 100.0) + drift = kwargs.get("drift", 1) + symbol = kwargs.get("symbol", "") + + ta = PlotlyTA() + fig = ta.plot( # type: ignore + data, + dict(rsi=dict(length=window, scalar=scalar, drift=drift)), + f"{symbol.upper()} RSI {window}", + False, + volume=False, + ) + content = fig.show(external=True).to_plotly_json() + + return fig, content + + @staticmethod + def technical_cones(**kwargs) -> tuple["OpenBBFigure", dict[str, Any]]: + """Volatility Cones Chart.""" + # pylint: disable=import-outside-toplevel + from openbb_charting.core.chart_style import ChartStyle + from openbb_charting.core.openbb_figure import OpenBBFigure + from openbb_core.app.utils import basemodel_to_df + from pandas import DataFrame + + data = kwargs.get("data") + + if isinstance(data, DataFrame) and not data.empty and "window" in data.columns: + df_ta = data.set_index("window") + else: + df_ta = basemodel_to_df(kwargs["obbject_item"], index="window") # type: ignore + + df_ta.columns = [col.title().replace("_", " ") for col in df_ta.columns] + + # Check if the data is formatted as expected. + if not all( + col in df_ta.columns for col in ["Realized", "Min", "Median", "Max"] + ): + raise ValueError("Data supplied does not match the expected format.") + + model = ( + str(kwargs.get("model")) + .replace("std", "Standard Deviation") + .replace("_", "-") + .title() + if kwargs.get("model") + else "Standard Deviation" + ) + + symbol = str(kwargs.get("symbol")) + " - " if kwargs.get("symbol") else "" + + title = ( + str(kwargs.get("title")) + if kwargs.get("title") + else f"{symbol}Realized Volatility Cones - {model} Model" + ) + + colors = [ + "green", + "red", + "burlywood", + "grey", + "orange", + "blue", + ] + + fig = OpenBBFigure() + + fig.update_layout(ChartStyle().plotly_template.get("layout", {})) + + text_color = "black" if ChartStyle().plt_style == "light" else "white" + + for i, col in enumerate(df_ta.columns): + fig.add_scatter( + x=df_ta.index, + y=df_ta[col], + name=col, + mode="lines+markers", + hovertemplate=f"{col}: %{{y}}", + marker=dict( + color=colors[i], + size=11, + ), + ) + + fig.set_title(title) + + fig.update_layout( + paper_bgcolor=( + "rgba(0,0,0,0)" if text_color == "white" else "rgba(255,255,255,0)" + ), + plot_bgcolor=( + "rgba(0,0,0,0)" if text_color == "white" else "rgba(255,255,255,0)" + ), + font=dict(color=text_color), + legend=dict( + orientation="h", + yanchor="bottom", + xanchor="right", + y=1.02, + x=1, + bgcolor=( + "rgba(0,0,0,0)" if text_color == "white" else "rgba(255,255,255,0)" + ), + ), + yaxis=dict( + ticklen=0, + showgrid=True, + showline=True, + mirror=True, + zeroline=False, + gridcolor="rgba(128,128,128,0.3)", + ), + xaxis=dict( + type="category", + tickmode="array", + ticklen=0, + tickvals=df_ta.index, + ticktext=df_ta.index, + title_text="Period", + showgrid=False, + showline=True, + mirror=True, + zeroline=False, + ), + margin=dict(l=20, r=20, b=20), + dragmode="pan", + ) + + content = fig.to_plotly_json() + + return fig, content + + @staticmethod + def technical_relative_rotation( + **kwargs: Any, + ) -> tuple["OpenBBFigure", dict[str, Any]]: + """Relative Rotation Chart.""" + # pylint: disable=import-outside-toplevel + from openbb_charting.charts import relative_rotation # noqa + from openbb_charting.core.chart_style import ChartStyle # noqa + from openbb_charting.core.openbb_figure import OpenBBFigure # noqa + from openbb_core.app.utils import basemodel_to_df # noqa + + ratios_df = basemodel_to_df(kwargs["obbject_item"].rs_ratios, index="date") # type: ignore + momentum_df = basemodel_to_df(kwargs["obbject_item"].rs_momentum, index="date") # type: ignore + benchmark_symbol = kwargs["obbject_item"].benchmark # type: ignore + study = kwargs.get("study") + study = str(kwargs["obbject_item"].study) if study is None else str(study) + show_tails = kwargs.get("show_tails") + show_tails = True if show_tails is None else show_tails + tail_periods = int(kwargs.get("tail_periods")) if "tail_periods" in kwargs else 16 # type: ignore + tail_interval = str(kwargs.get("tail_interval")) if "tail_interval" in kwargs else "week" # type: ignore + date = kwargs.get("date") if "date" in kwargs else None # type: ignore + show_tails = False if date is not None else show_tails + if ratios_df.empty or momentum_df.empty: + raise RuntimeError("Error: No data to plot.") + + if show_tails is True: + fig = relative_rotation.create_rrg_with_tails( + ratios_df, momentum_df, study, benchmark_symbol, tail_periods, tail_interval # type: ignore + ) + + if show_tails is False: + fig = relative_rotation.create_rrg_without_tails( + ratios_df, momentum_df, benchmark_symbol, study, date # type: ignore + ) + + figure = OpenBBFigure(fig) # pylint: disable=E0606 + font_color = "black" if ChartStyle().plt_style == "light" else "white" + figure.update_layout( + paper_bgcolor=( + "rgba(0,0,0,0)" if font_color == "white" else "rgba(255,255,255,255)" + ), + plot_bgcolor="rgba(255,255,255,1)", + font=dict(color=font_color), + yaxis=dict( + showgrid=True, + gridcolor="rgba(128,128,128,0.3)", + side="left", + showline=True, + zeroline=True, + mirror=True, + ticklen=0, + tickfont=dict(size=14), + title=dict(font=dict(size=16)), + ), + xaxis=dict( + showgrid=True, + gridcolor="rgba(128,128,128,0.3)", + showline=True, + zeroline=True, + mirror=True, + ticklen=0, + tickfont=dict(size=14), + title=dict(font=dict(size=16)), + hoverformat="", + ), + hoverlabel=dict( + font_size=12, + ), + ) + + if kwargs.get("title") is not None: + figure.set_title(str(kwargs.get("title"))) + content = figure.to_plotly_json() + + return figure, content + + +def _ta_ma(**kwargs): + """Plot moving average helper.""" + # pylint: disable=import-outside-toplevel + from openbb_charting.core.chart_style import ChartStyle + from openbb_charting.core.openbb_figure import OpenBBFigure + from openbb_core.app.utils import basemodel_to_df + from pandas import DataFrame + + index = ( + kwargs.get("index") + if "index" in kwargs and kwargs.get("index") is not None + else "date" + ) + data = kwargs.get("data") + ma_type = ( + kwargs["ma_type"] + if "ma_type" in kwargs and kwargs.get("ma_type") is not None + else "sma" + ) + ma_types = ma_type.split(",") if isinstance(ma_type, str) else ma_type + + if isinstance(data, DataFrame) and not data.empty: + data = data.set_index(index) if index in data.columns else data + + if data is None: + data = basemodel_to_df(kwargs["obbject_item"], index=index) + + if isinstance(data, list): + data = basemodel_to_df(data, index=index) + + window = ( + kwargs.get("length", []) + if "length" in kwargs and kwargs.get("length") is not None + else [50] + ) + offset = kwargs.get("offset", 0) + target = ( + kwargs.get("target") + if "target" in kwargs and kwargs.get("target") is not None + else "close" + ) + + if target not in data.columns and "close" in data.columns: + target = "close" + + if target not in data.columns and "close" not in data.columns: + raise ValueError(f"Column '{target}', or 'close', not found in the data.") + + df = data.copy() + if target in data.columns: + df = df[[target]] + df.columns = ["close"] + title = ( + kwargs.get("title") + if "title" in kwargs and kwargs.get("title") is not None + else f"{ma_type.upper()}" + ) + + fig = OpenBBFigure() + fig = fig.create_subplots( + 1, + 1, + shared_xaxes=True, + vertical_spacing=0.06, + horizontal_spacing=0.01, + row_width=[1], + specs=[[{"secondary_y": True}]], + ) + fig.update_layout(ChartStyle().plotly_template.get("layout", {})) + font_color = "black" if ChartStyle().plt_style == "light" else "white" + ma_df = DataFrame() + window = [window] if isinstance(window, int) else window + for w in window: + for ma_type in ma_types: + ma_df[f"{ma_type.upper()} {w}"] = getattr(df.ta, ma_type)( + length=w, offset=offset + ) + + if kwargs.get("dropnan") is True: + ma_df = ma_df.dropna() + data = data.iloc[-len(ma_df) :] + + if ( + "candles" in kwargs + and kwargs.get("candles") is True + and kwargs.get("target") is None + ): + volume = kwargs.get("volume") is True + fig, _ = to_chart(data, candles=True, volume=volume) + + else: + ma_df[f"{target}".title()] = data[target] + + for i, col in enumerate(ma_df.columns): + name = col.replace("_", " ") + fig.add_scatter( + x=ma_df.index, + y=ma_df[col], + name=name, + mode="lines", + hovertemplate=f"{name}: %{{y}}", + line=dict(width=1, color=LARGE_CYCLER[i]), + showlegend=True, + ) + + fig.update_layout( + title=dict(text=title, x=0.5, font=dict(size=16)), + paper_bgcolor=( + "rgba(0,0,0,0)" if font_color == "white" else "rgba(255,255,255,255)" + ), + plot_bgcolor=( + "rgba(0,0,0,0)" if font_color == "white" else "rgba(255,255,255,0)" + ), + showlegend=True, + legend=dict( + orientation="h", + yanchor="bottom", + xanchor="right", + y=1.02, + 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OpenBB" +authors = ["OpenBB Team "] +license = "AGPL-3.0-only" +readme = "README.md" +packages = [{ include = "openbb_technical" }] + +[tool.poetry.dependencies] +python = ">=3.9.21,<3.13" +openbb-core = "^1.4.6" +pandas-ta-openbb = "^0.4.20" +scikit-learn = "^1.6.0" + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" + +[tool.poetry.plugins."openbb_core_extension"] +technical = "openbb_technical.technical_router:router" + +[tool.poetry.plugins."openbb_charting_extension"] +technical = "openbb_technical.technical_views:TechnicalViews" diff --git a/openbb_platform/extensions/technical/tests/test_technical_helpers.py b/openbb_platform/extensions/technical/tests/test_technical_helpers.py new file mode 100644 index 0000000000000000000000000000000000000000..9b4b7a9d10259c55533e3ef627c3ab207be55b80 --- /dev/null +++ b/openbb_platform/extensions/technical/tests/test_technical_helpers.py @@ -0,0 +1,101 @@ +"""Test the technical helpers module.""" + +import numpy as np +import pandas as pd +import pytest +from extensions.technical.openbb_technical.helpers import ( + calculate_cones, + calculate_fib_levels, + clenow_momentum, + garman_klass, + hodges_tompkins, + parkinson, + rogers_satchell, + validate_data, + yang_zhang, +) + +# pylint: disable=redefined-outer-name + + +@pytest.fixture(scope="module") +def mock_data(top_range: int = 100): + """Mock data for testing.""" + _open = pd.Series(np.arange(1, top_range)) + _high = pd.Series(np.arange(1, top_range)) + _low = pd.Series(np.arange(1, top_range)) + _close = pd.Series(np.arange(1, top_range)) + _volume = pd.Series(np.arange(1, top_range)) + _date = pd.Series(pd.date_range("2021-01-01", periods=top_range - 1, freq="D")) + return pd.DataFrame( + { + "open": _open, + "high": _high, + "low": _low, + "close": _close, + "volume": _volume, + "date": _date, + } + ).set_index("date") + + +def test_parkinson_with_mock_data(mock_data): + """Test parkinson with valid input.""" + result = parkinson(mock_data) + assert not result.empty + + +def test_garman_klass_with_mock_data(mock_data): + """Test garman_klass with valid input.""" + result = garman_klass(mock_data) + assert not result.empty + + +def test_hodges_tompkins_with_mock_data(mock_data): + """Test hodges_tompkins with valid input.""" + result = hodges_tompkins(mock_data) + assert not result.empty + + +def test_rogers_satchell_with_mock_data(mock_data): + """Test rogers_satchell with valid input.""" + result = rogers_satchell(mock_data) + assert not result.empty + + +def test_yang_zhang_with_mock_data(mock_data): + """Test yang_zhang with valid input.""" + result = yang_zhang(mock_data) + assert not result.empty + + +def test_clenow_momentum_with_mock_data(mock_data): + """Test clenow_momentum with valid input.""" + result = clenow_momentum(mock_data["close"]) + assert result + + +def test_calculate_cones_with_mock_data(mock_data): + """Test calculate_cones with valid input.""" + result = calculate_cones( + mock_data, + lower_q=0.1, + upper_q=0.9, + is_crypto=False, + model="std", + ) + assert not result.empty + + +def test_calculate_fib_levels_with_mock_data(mock_data): + """Test calculate_fib_levels with valid input.""" + result = calculate_fib_levels(mock_data, "close") + assert result + + +def test_validate_data_with_mock_data(mock_data): + """Test validate_data with valid input.""" + try: + validate_data(mock_data["close"].tolist(), 20) + except ValueError: + pytest.fail("validate_data raised ValueError unexpectedly!") diff --git a/openbb_platform/extensions/tests/__init__.py b/openbb_platform/extensions/tests/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..d420712d8bbf0195c02e3317e4395b2c2ffeacf6 --- /dev/null +++ b/openbb_platform/extensions/tests/__init__.py @@ -0,0 +1 @@ +"""Tests.""" diff --git a/openbb_platform/extensions/tests/conftest.py b/openbb_platform/extensions/tests/conftest.py new file mode 100644 index 0000000000000000000000000000000000000000..c71c47faab525cd9891232b598af0ca156393c57 --- /dev/null +++ b/openbb_platform/extensions/tests/conftest.py @@ -0,0 +1,62 @@ +"""Custom pytest configuration for the extensions.""" + +from typing import Dict, List + +import pytest +from openbb_core.app.router import CommandMap + +from extensions.tests.utils.helpers import list_openbb_extensions + +cm = CommandMap() +commands = list(cm.map.keys()) + +# ruff: noqa: SIM114 + + +def parametrize(argnames: str, argvalues: List, **kwargs): + """Apply a custom parametrize decorator that filters test cases based on the environment.""" + + routers, providers, obbject_ext = list_openbb_extensions() + + def decorator(function): + """Patch the pytest.mark.parametrize decorator.""" + filtered_argvalues: List[Dict] = [] + name = function.__name__.split("_")[1] + # This is a patch to handle the charting extension name + extension_name = "openbb_" + name if name == "charting" else name + function_name = "/" + "/".join(function.__name__.split("_")[1:]) + # this handles edge cases where the function name has an underscore + function_name_v2 = ( + function_name.rsplit("/", 1)[0] + "_" + function_name.rsplit("/", 1)[1] + ) + function_name_v3 = ( + function_name.rsplit("/", 2)[0] + + "_" + + function_name.rsplit("/", 2)[1] + + "_" + + function_name.rsplit("/", 1)[1].replace("/", "_") + ) + if extension_name in routers | obbject_ext: + for args in argvalues: + if "provider" in args and args["provider"] in providers: + filtered_argvalues.append(args) + elif "provider" not in args and function_name in commands: + # Run the standard test case + filtered_argvalues.append(args) + elif "provider" not in args and function_name_v2 in commands: + # Handle edge case + filtered_argvalues.append(args) + elif "provider" not in args and function_name_v3 in commands: + # Handle edge case + filtered_argvalues.append(args) + elif extension_name in obbject_ext: + filtered_argvalues.append(args) + + # If filtered_argvalues is empty, pytest will skip the test! + return pytest.mark.parametrize(argnames, filtered_argvalues, **kwargs)( + function + ) + + return pytest.mark.skip(function) + + return decorator diff --git a/openbb_platform/extensions/tests/test_docstring_examples.py b/openbb_platform/extensions/tests/test_docstring_examples.py new file mode 100644 index 0000000000000000000000000000000000000000..3229300db56ed4cc5a4fdd97b3c93b0e70d60e12 --- /dev/null +++ b/openbb_platform/extensions/tests/test_docstring_examples.py @@ -0,0 +1,12 @@ +"""Test utils.""" + +import pytest + +from extensions.tests.utils.helpers import check_docstring_examples + + +@pytest.mark.integration +def test_docstring_examples(): + """Test that the docstring examples execute without errors.""" + errors = check_docstring_examples() + assert not errors, "\n".join(errors) diff --git a/openbb_platform/extensions/tests/test_integration_tests_api.py b/openbb_platform/extensions/tests/test_integration_tests_api.py new file mode 100644 index 0000000000000000000000000000000000000000..222791133872464dda7e25e7d91fa72c109967f2 --- /dev/null +++ b/openbb_platform/extensions/tests/test_integration_tests_api.py @@ -0,0 +1,71 @@ +"""Test the integration tests.""" + +from typing import Literal + +from openbb_charting import Charting + +from extensions.tests.utils.integration_tests_testers import ( + check_missing_integration_test_params, + check_missing_integration_test_providers, + check_missing_integration_tests, + check_outdated_integration_tests, + check_wrong_integration_test_params, + get_integration_tests, + get_module_functions, +) + + +def run_test(test_type: Literal["api", "python"], check_function) -> None: + """Run tests helper function.""" + integration_tests = get_integration_tests(test_type=test_type) + functions = get_module_functions(integration_tests) + missing_items = check_function(functions) + + assert not missing_items, "\n".join(missing_items) + + +def test_api_interface_integration_test_providers() -> None: + """Test if there are any missing providers for integration tests.""" + run_test("api", check_missing_integration_test_providers) + + +def test_api_interface_integration_test_params() -> None: + """Test if there are any missing params for integration tests.""" + run_test("api", check_missing_integration_test_params) + + +def test_api_interface_wrong_integration_test_params() -> None: + """Test if there are any wrong params for integration tests.""" + run_test("api", check_wrong_integration_test_params) + + +def test_charting_extension_function_coverage() -> None: + """Test if all charting extension functions are covered by integration tests.""" + functions = Charting.functions() + + test_names = [f"test_charting_{func}" for func in functions] + integration_tests_modules = get_integration_tests( + test_type="api", filter_charting_ext=False + ) + charting_module = [ + module for module in integration_tests_modules if "charting" in module.__name__ + ] + integration_tests_functions = get_module_functions(charting_module) + + missing_items = [ + test for test in test_names if test not in integration_tests_functions + ] + + assert missing_items == [], "\n".join(missing_items) + + +def test_missing_api_integration_tests() -> None: + """Check if there are missing tests.""" + missing = check_missing_integration_tests(test_type="api") + assert not missing, "\n".join(missing) + + +def test_outdated_api_integration_tests() -> None: + """Check if there are outdated tests.""" + outdated = check_outdated_integration_tests(test_type="api") + assert not outdated, "\n".join(outdated) diff --git a/openbb_platform/extensions/tests/test_integration_tests_python.py b/openbb_platform/extensions/tests/test_integration_tests_python.py new file mode 100644 index 0000000000000000000000000000000000000000..abed14fa30136cb5ee7cada5e2c1a5d356ebb245 --- /dev/null +++ b/openbb_platform/extensions/tests/test_integration_tests_python.py @@ -0,0 +1,71 @@ +"""Test the integration tests.""" + +from typing import Literal + +from openbb_charting import Charting + +from extensions.tests.utils.integration_tests_testers import ( + check_missing_integration_test_params, + check_missing_integration_test_providers, + check_missing_integration_tests, + check_outdated_integration_tests, + check_wrong_integration_test_params, + get_integration_tests, + get_module_functions, +) + + +def run_test(test_type: Literal["api", "python"], check_function) -> None: + """Run tests helper function.""" + integration_tests = get_integration_tests(test_type=test_type) + functions = get_module_functions(integration_tests) + missing_items = check_function(functions) + + assert not missing_items, "\n".join(missing_items) + + +def test_python_interface_integration_test_providers() -> None: + """Test if there are any missing providers for integration tests.""" + run_test("python", check_missing_integration_test_providers) + + +def test_python_interface_integration_test_params() -> None: + """Test if there are any missing params for integration tests.""" + run_test("python", check_missing_integration_test_params) + + +def test_python_interface_wrong_integration_test_params() -> None: + """Test if there are any wrong params for integration tests.""" + run_test("python", check_wrong_integration_test_params) + + +def test_charting_extension_function_coverage() -> None: + """Test if all charting extension functions are covered by integration tests.""" + functions = Charting.functions() + + test_names = [f"test_charting_{func}" for func in functions] + integration_tests_modules = get_integration_tests( + test_type="python", filter_charting_ext=False + ) + charting_module = [ + module for module in integration_tests_modules if "charting" in module.__name__ + ] + integration_tests_functions = get_module_functions(charting_module) + + missing_items = [ + test for test in test_names if test not in integration_tests_functions + ] + + assert missing_items == [], "\n".join(missing_items) + + +def test_missing_python_integration_tests() -> None: + """Check if there are missing tests.""" + missing = check_missing_integration_tests(test_type="python") + assert not missing, "\n".join(missing) + + +def test_outdated_python_integration_tests() -> None: + """Check if there are outdated tests.""" + outdated = check_outdated_integration_tests(test_type="python") + assert not outdated, "\n".join(outdated) diff --git a/openbb_platform/extensions/tests/test_routers.py b/openbb_platform/extensions/tests/test_routers.py new file mode 100644 index 0000000000000000000000000000000000000000..cc16eed757f5b1ef406372e33ece0505bc111f7b --- /dev/null +++ b/openbb_platform/extensions/tests/test_routers.py @@ -0,0 +1,31 @@ +"""Test the routers.""" + +from extensions.tests.utils.router_testers import ( + check_router_command_examples, + check_router_function_models, + check_router_model_functions_signature, +) + + +def test_router_function_models() -> None: + """Test if the models in the router functions exist in the provider interface map.""" + missing_models = check_router_function_models() + assert not missing_models, "\n".join(missing_models) + + +def test_router_model_functions_signature() -> None: + """Test if the router functions have the correct signature.""" + missing_args = check_router_model_functions_signature() + assert not missing_args, "\n".join(missing_args) + + +def test_router_examples_rules() -> None: + """Test if the router examples follow certain rules. + + Rules: + - All endpoints should have examples. + - At least one example using all required parameters. + - All params are valid for the command. + """ + invalid_examples = check_router_command_examples() + assert not invalid_examples, "\n".join(sorted(invalid_examples)) diff --git a/openbb_platform/extensions/tests/utils/__init__.py b/openbb_platform/extensions/tests/utils/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..479fdbbe1fbac2867cf4c837ccd797c8ff6e37e9 --- /dev/null +++ b/openbb_platform/extensions/tests/utils/__init__.py @@ -0,0 +1 @@ +"""Testing utilities.""" diff --git a/openbb_platform/extensions/tests/utils/helpers.py b/openbb_platform/extensions/tests/utils/helpers.py new file mode 100644 index 0000000000000000000000000000000000000000..a00ba7c55838e93ce33d177042373de80ac4dcd7 --- /dev/null +++ b/openbb_platform/extensions/tests/utils/helpers.py @@ -0,0 +1,314 @@ +"""Test helpers.""" + +import doctest +import glob +import importlib +import inspect +import logging +import os +import re +from ast import AsyncFunctionDef, Call, FunctionDef, Name, parse, unparse +from dataclasses import dataclass +from importlib.metadata import EntryPoint, entry_points +from inspect import getmembers, isfunction +from sys import version_info +from typing import Any, Dict, List, Optional, Set, Tuple, Union + +from importlib_metadata import EntryPoints +from openbb_core.app.provider_interface import ProviderInterface + +pi = ProviderInterface() + +logging.basicConfig(level=logging.INFO) + + +def get_packages_info() -> Dict[str, str]: + """Get the paths and names of all the static packages.""" + paths_and_names: Dict[str, str] = {} + package_paths = glob.glob("openbb_platform/openbb/package/*.py") + for path in package_paths: + name = os.path.basename(path).split(".")[0] + paths_and_names[path] = name + + paths_and_names = { + path: name for path, name in paths_and_names.items() if not name.startswith("_") + } + return paths_and_names + + +def execute_docstring_examples(module_name: str, path: str) -> List[str]: + """Execute the docstring examples of a module.""" + errors = [] + module = importlib.import_module(f"openbb.package.{module_name}") + doc_tests = doctest.DocTestFinder().find(module) + + for dt in doc_tests: + code = "".join([ex.source for ex in dt.examples]) + try: + print(f"* Executing example from {path}") # noqa: T201 + exec(code) # pylint: disable=exec-used # noqa: S102 + except Exception as e: + error = ( + f"{'_'*136}\nPath: {path}\nCode:\n{code}\nError: {str(e)}\n{'_'*136}" + ) + print(error) # noqa: T201 + errors.append(error) + + return errors + + +def check_docstring_examples() -> List[str]: + """Test that the docstring examples execute without errors.""" + errors = [] + paths_and_names = get_packages_info() + + for path, name in paths_and_names.items(): + result = execute_docstring_examples(name, path) + if result: + errors.extend(result) + + return errors + + +def filter_eps(eps: Union[EntryPoints, dict], group: str) -> Tuple[EntryPoint, ...]: + if version_info[:2] == (3, 12): + return eps.select(group=group) or () # type: ignore[union-attr] + return eps.get(group, ()) # type: ignore[union-attr] + + +def list_openbb_extensions() -> Tuple[Set[str], Set[str], Set[str]]: + """List installed openbb extensions and providers. + + Returns + ------- + Tuple[Set[str], Set[str], Set[str]] + First element: set of installed core extensions. + Second element: set of installed provider extensions. + Third element: set of installed obbject extensions. + """ + + core_extensions = set() + provider_extensions = set() + obbject_extensions = set() + + entry_points_dict = entry_points() + + # Compatibility for different Python versions + if hasattr(entry_points_dict, "select"): # Python 3.12+ + core_entry_points = entry_points_dict.select(group="openbb_core_extension") + provider_entry_points = entry_points_dict.select( + group="openbb_provider_extension" + ) + obbject_entry_points = entry_points_dict.select( + group="openbb_obbject_extension" + ) + else: + core_entry_points = entry_points_dict.get("openbb_core_extension", []) + provider_entry_points = entry_points_dict.get("openbb_provider_extension", []) + obbject_entry_points = entry_points_dict.get("openbb_obbject_extension", []) + + for entry_point in core_entry_points: + core_extensions.add(f"{entry_point.name}") + + for entry_point in provider_entry_points: + provider_extensions.add(f"{entry_point.name}") + + for entry_point in obbject_entry_points: + obbject_extensions.add(f"{entry_point.name}") + + return core_extensions, provider_extensions, obbject_extensions + + +def collect_routers(target_dir: str) -> List[str]: + """Collect all routers in the target directory.""" + current_dir = os.path.dirname(__file__) + base_path = os.path.abspath(os.path.join(current_dir, "../../../")) + + full_target_path = os.path.abspath(os.path.join(base_path, target_dir)) + routers = [] + + for root, _, files in os.walk(full_target_path): + for name in files: + if name.endswith("_router.py"): + full_path = os.path.join(root, name) + # Convert the full path to a module path + relative_path = os.path.relpath(full_path, base_path) + module_path = relative_path.replace("/", ".").replace(".py", "") + routers.append(module_path) + + return routers + + +def import_routers(routers: List) -> List: + """Import all routers.""" + loaded_routers: List = [] + for router in routers: + module = importlib.import_module(router) + loaded_routers.append(module) + + return loaded_routers + + +def collect_router_functions(loaded_routers: List) -> Dict: + """Collect all router functions.""" + router_functions = {} + for router in loaded_routers: + router_functions[router.__name__] = [ + function[1] + for function in getmembers(router, isfunction) + if function[0] != "router" + ] + + return router_functions + + +def find_decorator(file_path: str, function_name: str) -> str: + """Find the @router.command decorator of the function in the file, supporting multiline decorators.""" + this_dir = os.path.dirname(os.path.abspath(__file__)) + file_path = os.path.join( + this_dir.split("openbb_platform/")[0], "openbb_platform", file_path + ) + + with open(file_path) as file: + lines = file.readlines() + + decorator_lines = [] + capturing_decorator = False + for line in lines: + stripped_line = line.strip() + # Start capturing lines if we encounter a decorator + if stripped_line.startswith("@router.command"): + capturing_decorator = True + decorator_lines.append(stripped_line) + elif capturing_decorator: + # If we're currently capturing a decorator and the line is part of it (indentation or open parenthesis) + if ( + stripped_line.startswith("@") + or "def" in stripped_line + and function_name in stripped_line + ): + # If we've reached another decorator or the function definition, stop capturing + capturing_decorator = False + # If it's the target function, break, else clear decorator_lines for the next decorator + if "def" in stripped_line and function_name in stripped_line: + break + decorator_lines = [] + else: + # It's part of the multiline decorator + decorator_lines.append(stripped_line) + + decorator = " ".join(decorator_lines) + return decorator + + +@dataclass +class Decorator: + """Decorator.""" + + name: str + args: Optional[dict] = None + kwargs: Optional[dict] = None + + +def get_decorator_details(function) -> Optional[Decorator]: + """Extract decorators and their arguments from a function.""" + source = inspect.getsource(function) + parsed_source = parse(source) + + if isinstance(parsed_source.body[0], (FunctionDef, AsyncFunctionDef)): + func_def = parsed_source.body[0] + for decorator in func_def.decorator_list: + if isinstance(decorator, Call): + name = ( + decorator.func.id + if isinstance(decorator.func, Name) + else unparse(decorator.func) + ) + args = {i: unparse(arg) for i, arg in enumerate(decorator.args)} + kwargs = {kw.arg: unparse(kw.value) for kw in decorator.keywords} + else: + name = ( + decorator.id if isinstance(decorator, Name) else unparse(decorator) + ) + return Decorator(name, args, kwargs) + return None + + +def find_missing_router_function_models( + router_functions: Dict, pi_map: Dict +) -> List[str]: + """Find the missing models in the router functions.""" + missing_models: List[str] = [] + for router_name, functions in router_functions.items(): + for function in functions: + decorator = find_decorator( + os.path.join(*router_name.split(".")) + ".py", + function.__name__, + ) + if ( + decorator + and "model" in decorator + and "POST" not in decorator + and "GET" not in decorator + ): + model = decorator.split("model=")[1].split(",")[0].strip('"') + if ( + model not in pi_map + and "POST" not in decorator + and "GET" not in decorator + ): + missing_models.append( + f"{function.__name__} in {router_name} model doesn't exist in the provider interface map." + ) + + return missing_models + + +def parse_example_string(example_string: str) -> Dict[str, Any]: + """Parse a string of examples into nested dictionaries. + + This is capturing all instances of PythonEx and APIEx, including their "parameters", "code", and "description". + """ + # Initialize the result dictionary + result = {} + + # Regular expression patterns to find PythonEx and APIEx examples + pythonex_pattern = r"PythonEx\(.*?code=(\[.*?\]).*?\)" + apiex_pattern = r"APIEx\(.*?parameters=(\{.*?\}).*?\)" + + # Function to parse individual examples + def parse_examples(matches, example_type): + examples = [] + for match in matches: + examples.append( + {"code": [match]} if example_type == "PythonEx" else {"params": match} + ) + return examples + + # Find and parse all PythonEx examples + pythonex_matches = re.findall(pythonex_pattern, example_string, re.DOTALL) + result["PythonEx"] = parse_examples(pythonex_matches, "PythonEx") + + # Find and parse all APIEx examples + apiex_matches = re.findall(apiex_pattern, example_string, re.DOTALL) + result["APIEx"] = parse_examples(apiex_matches, "APIEx") + + return result + + +def get_required_fields(model: str) -> List[str]: + """Get the required fields of a model.""" + fields = pi.map[model]["openbb"]["QueryParams"]["fields"] + return [field for field, info in fields.items() if info.is_required()] + + +def get_all_fields(model: str) -> List[str]: + """Get all the fields of a model.""" + all_fields: List[str] = [] + info = pi.map[model] + # for every key, grab the fields + for _, provider_info in info.items(): + for field, _ in provider_info["QueryParams"]["fields"].items(): + all_fields.append(field) + + return all_fields diff --git a/openbb_platform/extensions/tests/utils/integration_tests_api_generator.py b/openbb_platform/extensions/tests/utils/integration_tests_api_generator.py new file mode 100644 index 0000000000000000000000000000000000000000..559b3a166ed48154e30c9964432225750e05fe6d --- /dev/null +++ b/openbb_platform/extensions/tests/utils/integration_tests_api_generator.py @@ -0,0 +1,228 @@ +"""Generate API integration tests.""" + +import argparse +import os +from pathlib import Path +from typing import Dict, List, Literal, Type, Union, get_type_hints + +import requests +from openbb_charting import Charting +from openbb_core.app.provider_interface import ProviderInterface +from openbb_core.app.router import CommandMap + +from extensions.tests.utils.integration_tests_generator import get_test_params + + +def get_http_method(api_paths: Dict[str, dict], route: str): + """Given a set of paths and a route, return the http method for that route.""" + route_info = api_paths.get(route) + if not route_info: + return route_info + return list(route_info.keys())[0] + + +def get_post_flat_params(hints: Dict[str, Type]): + """Flattens the params for a post request.""" + return list(hints.keys()) + + +def write_init_test_template(http_method: str, path: str): + """Write some common initialization for the tests with the defined template.""" + http_template_imports = {"get": "", "post": "import json"} + template = http_template_imports[http_method] + template += """import base64 + +import pytest +import requests +from openbb_core.env import Env +from openbb_core.provider.utils.helpers import get_querystring +from extensions.tests.conftest import parametrize + + +@pytest.fixture(scope="session") +def headers(): + userpass = f"{Env().API_USERNAME}:{Env().API_PASSWORD}" + userpass_bytes = userpass.encode("ascii") + base64_bytes = base64.b64encode(userpass_bytes) + + return {"Authorization": f"Basic {base64_bytes.decode('ascii')}"} + + +# pylint: disable=redefined-outer-name + +""" + + with open(path, "w") as f: + f.write(template) + + +def write_test_w_template( + http_method: Literal["post", "get"], + params_list: List[Dict[str, Union[str, bool]]], + route: str, + path: str, + chart: bool = False, +): + """Write the test with the defined template.""" + params_str = ",\n".join([f"({params})" for params in params_list]) + + http_template_request = { + "get": "requests.get(url, headers=headers, timeout=10)", + "post": "requests.post(url, headers=headers, timeout=10, data=body)", + } + + http_template_params = {"get": "", "post": "body = json.dumps(params.pop('data'))"} + + test_name_extra = "chart_" if chart else "" + + chart_extra_template = """ + assert result.json()["chart"] + assert list(result.json()["chart"].keys()) == ["content", "format"] + """ + + template = f""" +@parametrize( + "params", + [{params_str}], +) +@pytest.mark.integration +def test_{test_name_extra}{route.replace("/", "_")[1:]}(params, headers): + params = {{p: v for p, v in params.items() if v}} + {http_template_params[http_method]} + + query_str = get_querystring(params, []) + url = f"http://0.0.0.0:8000/api/v1{route}?{{query_str}}" + result = {http_template_request[http_method]} + assert isinstance(result, requests.Response) + assert result.status_code == 200 +""" + if chart: + template += chart_extra_template + + with open(path, "a") as f: + f.write(template) + + +def test_exists(route: str, path: str): + """Check if a test exists.""" + with open(path) as f: + return route.replace("/", "_")[1:] in f.read() + + +# pylint: disable=W0621 +def write_commands_integration_tests( + command_map: CommandMap, + provider_interface: ProviderInterface, + api_paths: Dict[str, dict], +) -> List[str]: + """Write the commands integration tests.""" + commands_not_found = [] + + cm_map = command_map.map + cm_models = command_map.commands_model + provider_interface_map = provider_interface.map + + for route in cm_map: + http_method = get_http_method(api_paths, f"/api/v1{route}") + + menu = route.split("/")[1] + path = os.path.join( + "openbb_platform", "extensions", menu, "integration", f"test_{menu}_api.py" + ) + if not os.path.exists(path): + write_init_test_template(http_method=http_method, path=path) + + if not http_method: + commands_not_found.append(route) + else: + hints = get_type_hints(cm_map[route]) + hints.pop("cc", None) + hints.pop("return", None) + + params_list = ( + [{k: "" for k in get_post_flat_params(hints)}] + if http_method == "post" + else get_test_params( + model_name=cm_models[route], # type: ignore + provider_interface_map=provider_interface_map, + ) + ) + + if not test_exists(route=route, path=path): + write_test_w_template( + http_method=http_method, + params_list=params_list, + route=route, + path=path, + ) + + return commands_not_found + + +def write_charting_extension_integration_tests(): + """Write the charting extension integration tests.""" + import openbb_charting # pylint: disable=import-outside-toplevel + + functions = Charting.functions() + + charting_ext_path = Path(openbb_charting.__file__).parent.parent + test_file = charting_ext_path / "integration" / "test_charting_api.py" + + for function in functions: + route = "/" + function.replace("_", "/") + if not test_exists(route=function, path=str(test_file)): + write_test_w_template( + http_method="post", + params_list=[{"chart": True}], + route=route, + path=str(test_file), + chart=True, + ) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + prog="API Integration Tests Generator", + description="Generate API integration tests", + ) + parser.add_argument( + "--charting", + dest="charting", + default=True, + action="store_false", + help="Generate charting extension integration tests", + ) + parser.add_argument( + "--commands", + dest="commands", + default=True, + action="store_false", + help="Generate commands integration tests", + ) + + args = parser.parse_args() + charting = args.charting + commands = args.commands + + r = requests.get("http://0.0.0.0:8000/openapi.json", timeout=10).json() + + if not r: + raise Exception("Could not get openapi.json") + + command_map = CommandMap() + provider_interface = ProviderInterface() + + if commands: + commands_not_found_in_openapi = write_commands_integration_tests( + command_map=command_map, + provider_interface=provider_interface, + api_paths=r["paths"], + ) + if commands_not_found_in_openapi: + print( # noqa + f"Commands not found in openapi.json: {commands_not_found_in_openapi}" + ) + + if charting: + write_charting_extension_integration_tests() diff --git a/openbb_platform/extensions/tests/utils/integration_tests_generator.py b/openbb_platform/extensions/tests/utils/integration_tests_generator.py new file mode 100644 index 0000000000000000000000000000000000000000..0e7663b03e40c9fe3bee8fc79eacc1cd5d5bb6a5 --- /dev/null +++ b/openbb_platform/extensions/tests/utils/integration_tests_generator.py @@ -0,0 +1,334 @@ +"""Integration test generator.""" + +import argparse +from pathlib import Path, PosixPath +from typing import ( + Any, + Dict, + List, + Literal, + Optional, + Tuple, + Type, + get_origin, + get_type_hints, +) + +from openbb_charting import Charting +from openbb_core.app.provider_interface import ProviderInterface +from openbb_core.app.router import CommandMap +from pydantic.fields import FieldInfo +from pydantic_core import PydanticUndefined + +ROOT_DIR = Path(__file__).parent.parent.parent.parent + +TEST_TEMPLATE = """\n\n@parametrize( + "params", + [ + {params} + ], +) +@pytest.mark.integration +def test_{test_name}(params, obb): + params = {{p: v for p, v in params.items() if v}} + + result = obb.{command_name}(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + {extra} +""" + + +def find_extensions(filter_chart: Optional[bool] = True): + """Find extensions.""" + filter_ext = ["tests", "__pycache__"] + if filter_chart: + filter_ext.append("charting") + extensions = [x for x in (ROOT_DIR / "extensions").iterdir() if x.is_dir()] + extensions.extend( + [x for x in (ROOT_DIR / "obbject_extensions").iterdir() if x.is_dir()] + ) + extensions = [x for x in extensions if x.name not in filter_ext] + return extensions + + +def create_integration_test_files(extensions: List[PosixPath]) -> None: + """Create integration test files for the python interface.""" + for extension in extensions: + extension_name = extension.name + test_file_name = f"test_{extension_name}_python.py" + if not (extension / "integration").exists(): + (extension / "integration").mkdir() + test_file = extension / "integration" / test_file_name + if not test_file.exists(): + with open(test_file, "w", encoding="utf-8", newline="\n") as f: + f.write( + f'''"""Test {extension_name} extension.""" +import pytest +from openbb_core.app.model.obbject import OBBject +from extensions.tests.conftest import parametrize + + +@pytest.fixture(scope="session") +def obb(pytestconfig): # pylint: disable=inconsistent-return-statements + """Fixture to setup obb.""" + + if pytestconfig.getoption("markexpr") != "not integration": + import openbb # pylint: disable=import-outside-toplevel + + return openbb.obb + + +# pylint: disable=redefined-outer-name +''' + ) + + +def get_model_params(param_fields: Dict[str, FieldInfo]) -> Dict[str, Any]: + """Get the test params for the fetcher based on the required standard params.""" + test_params: Dict[str, Any] = {} + for field_name, field in param_fields.items(): + if field.default and field.default is not PydanticUndefined: + test_params[field_name] = field.default + elif not field.default or field.default is PydanticUndefined: + example_dict = { + "symbol": "AAPL", + "symbols": "AAPL,MSFT", + "start_date": "2023-01-01", + "end_date": "2023-06-06", + "country": "Portugal", + "date": "2023-01-01", + "countries": ["portugal", "spain"], + } + if field_name in example_dict: + test_params[field_name] = example_dict[field_name] + elif field.annotation == str: + test_params[field_name] = "TEST_STRING" + elif field.annotation == int: + test_params[field_name] = 1 + elif field.annotation == float: + test_params[field_name] = 1.0 + elif field.annotation == bool: + test_params[field_name] = True + elif get_origin(field.annotation) is Literal: + test_params[field_name] = field.annotation.__args__[0] # type: ignore + + return test_params + + +def get_test_params( + model_name: str, provider_interface_map: Dict[str, Any] +) -> List[Dict[str, Any]]: + """Get the test params for the integration test.""" + test_params_list: List[Dict[str, Any]] = [] + standard_params = get_model_params( + param_fields=provider_interface_map[model_name]["openbb"]["QueryParams"][ + "fields" + ] + ) + + test_params_list.append(standard_params) + + for provider in provider_interface_map[model_name]: + if provider != "openbb": + test_params: Dict[str, Any] = get_model_params( + param_fields=provider_interface_map[model_name][provider][ + "QueryParams" + ]["fields"] + ) + + # We add provider even if `test_params` is empty to test the provider itself + test_params["provider"] = provider + test_params.update(standard_params) + test_params_list.append(test_params) + + return test_params_list + + +def get_test_params_data_processing(hints: Dict[str, Type]): + """Get the test params for the data processing commands.""" + return list(hints.keys()) + + +def get_full_command_name_and_test_name(route: str) -> Tuple[str, str]: + """Get the full command name and test name.""" + cmd_parts = route.split("/") + del cmd_parts[0] + + menu = cmd_parts[0] + command = cmd_parts[-1] + sub_menus = cmd_parts[1:-1] + + sub_menus_str_test_name = f"_{'_'.join(sub_menus)}" if sub_menus else "" + sub_menu_str_cmd = f".{'.'.join(sub_menus)}" if sub_menus else "" + + full_command = f"{menu}{sub_menu_str_cmd}.{command}" + test_name = f"{menu}{sub_menus_str_test_name}_{command}" + + return test_name, full_command + + +def test_exists(command_name: str, path: str): + """Check if test exists.""" + with open(path) as f: + return command_name in f.read() + + +def write_to_file_w_template(test_file, params_list, full_command, test_name, extra=""): + """Write to file with template.""" + params = ",\n".join(f"({test_params})" for test_params in params_list) + + if not test_exists(command_name=full_command, path=test_file): + with open(test_file, "a", encoding="utf-8", newline="\n") as f: + f.write( + TEST_TEMPLATE.format( + test_name=test_name, + command_name=full_command, + params=params, + extra=extra, + ) + ) + + +def write_test( + test_file: PosixPath, + commands_model: Dict[str, str], + extension_name: str, + provider_interface_map: Dict[str, Any], +): + """Write test.""" + for route, model in commands_model.items(): + if extension_name in route and route.startswith(f"/{extension_name}/"): + test_name, full_command = get_full_command_name_and_test_name(route=route) + + test_params_list = get_test_params( + model_name=model, + provider_interface_map=provider_interface_map, + ) + + write_to_file_w_template( + test_file=test_file, + params_list=test_params_list, + full_command=full_command, + test_name=test_name, + ) + + +def write_test_data_processing( + test_file: PosixPath, commands_map: Dict[str, str], extension_name: str +): + """Write test for data processing commands.""" + for route, _ in commands_map.items(): + if extension_name in route and route.startswith(f"/{extension_name}/"): + test_name, full_command = get_full_command_name_and_test_name(route=route) + + hints = get_type_hints(commands_map[route]) + hints.pop("cc", None) + hints.pop("return", None) + test_params_list = [{k: "" for k in get_test_params_data_processing(hints)}] + + write_to_file_w_template( + test_file=test_file, + params_list=test_params_list, + full_command=full_command, + test_name=test_name, + ) + + +def add_test_commands_to_file( # pylint: disable=W0102 + extensions: List[PosixPath], +) -> None: + """Add test commands to file.""" + provider_interface = ProviderInterface() + provider_interface_map = provider_interface.map + + cm = CommandMap() + commands_model = cm.commands_model + commands_map = cm.map + + extensions_names = [path.name for path in extensions] + extensions_w_models = list({route.split("/")[1] for route in commands_model}) + extensions_data_processing = [ + ext for ext in extensions_names if ext not in extensions_w_models + ] + + for extension in extensions: + extension_name = extension.name + test_file_name = f"test_{extension_name}_python.py" + test_file = extension / "integration" / test_file_name + + if extension_name in extensions_data_processing: + write_test_data_processing( + test_file=test_file, + commands_map=commands_map, # type: ignore + extension_name=extension_name, + ) + else: + write_test( + test_file=test_file, + commands_model=commands_model, # type: ignore + extension_name=extension_name, + provider_interface_map=provider_interface_map, + ) + + +def write_commands_integration_tests() -> None: + """Write integration test.""" + extensions = find_extensions() + create_integration_test_files(extensions) + add_test_commands_to_file(extensions) + + +def write_charting_extension_integration_tests(): + """Write charting extension integration tests.""" + import openbb_charting # pylint: disable=import-outside-toplevel + + functions = Charting.functions() + + charting_ext_path = Path(openbb_charting.__file__).parent.parent + test_file = charting_ext_path / "integration" / "test_charting_python.py" + + extra = """assert result.chart.content + assert isinstance(result.chart.fig, OpenBBFigure)""" + + for func in functions: + write_to_file_w_template( + test_file=test_file, + params_list=[{"chart": "True"}], + full_command=func.replace("_", "."), + test_name=f"chart_{func}", # TODO: fix the name of the charting library + extra=extra, + ) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + prog="API Integration Tests Generator", + description="Generate API integration tests", + ) + parser.add_argument( + "--charting", + dest="charting", + default=False, + action="store_true", + help="Generate charting extension integration tests", + ) + parser.add_argument( + "--commands", + dest="commands", + default=False, + action="store_true", + help="Generate commands integration tests", + ) + + args = parser.parse_args() + charting = args.charting + commands = args.commands + + if commands: + write_commands_integration_tests() + + if charting: + write_charting_extension_integration_tests() diff --git a/openbb_platform/extensions/tests/utils/integration_tests_testers.py b/openbb_platform/extensions/tests/utils/integration_tests_testers.py new file mode 100644 index 0000000000000000000000000000000000000000..a259f60debd18511cdca6120d09baceb6041e3de --- /dev/null +++ b/openbb_platform/extensions/tests/utils/integration_tests_testers.py @@ -0,0 +1,357 @@ +"""Test if there are any missing providers for python interface integration tests.""" + +import importlib.util +import inspect +import os +from typing import ( + Any, + Callable, + Dict, + List, + Literal, + Optional, + Tuple, + Union, + get_type_hints, +) + +from openbb_core.app.provider_interface import ProviderInterface +from openbb_core.app.router import CommandMap + +from extensions.tests.utils.integration_tests_generator import ( + find_extensions, + get_test_params_data_processing, +) + + +def get_integration_tests( + test_type: Literal["api", "python"], filter_charting_ext: Optional[bool] = True +) -> List[Any]: + """Get integration tests for the OpenBB Platform.""" + integration_tests: List[Any] = [] + + if test_type == "python": + file_end = "_python.py" + elif test_type == "api": + file_end = "_api.py" + else: + raise ValueError(f"test_type '{test_type}' not valid") + + for extension in find_extensions(filter_charting_ext): + integration_folder = os.path.join(extension, "integration") + for file in os.listdir(integration_folder): + if file.endswith(file_end): + file_path = os.path.join(integration_folder, file) + module_name = file[:-3] # Remove .py from file name + + spec = importlib.util.spec_from_file_location(module_name, file_path) + if spec: + module = importlib.util.module_from_spec(spec) + if spec.loader: + spec.loader.exec_module(module) + integration_tests.append(module) + + return integration_tests + + +def get_module_functions(module_list: List[Any]) -> Dict[str, Any]: + """Get all functions from a list of modules.""" + functions = {} + for module in module_list: + for name, obj in inspect.getmembers(module): + if inspect.isfunction(obj): + functions[name] = obj + return functions + + +def check_missing_providers( + command_params: Union[Dict[str, Dict[str, dict]], List[Tuple[Dict[str, str], str]]], + function_params: List[dict], + function, + processing: bool = False, +) -> List[str]: + """Check if there are any missing providers for a command.""" + if processing or not isinstance(command_params, dict): + return [] + + missing_providers: List[str] = [] + providers = list(command_params.keys()) + providers.remove("openbb") + + for test_params in function_params: + provider = test_params.get("provider", None) + if provider: + try: # noqa + providers.remove(provider) + except ValueError: + pass + + if providers: + # if there is only one provider left and the length of the + # test_params is 1, we can ignore because it is picked up by default + if len(providers) == 1 and len(function_params) == 1: + pass + else: + missing_providers.append(f"Missing providers for {function}: {providers}") + + return missing_providers + + +def check_wrong_params( + command_params: Union[Dict[str, Dict[str, dict]], List[Tuple[Dict[str, str], str]]], + function_params: List[dict], + function, + processing: bool = False, +) -> List[str]: + """Check if there are any wrong params passed to a command.""" + wrong_params = [] + if not processing: + for i, test_params in enumerate(function_params): + if "provider" in test_params and i != 0: + provider = test_params["provider"] + if provider in command_params: + for param in test_params: + if ( + param + not in command_params[provider]["QueryParams"]["fields"] + and param + not in command_params["openbb"]["QueryParams"]["fields"] # type: ignore + and param != "provider" + ): + wrong_params.append( + f"Wrong param {param} for provider {provider} in function {function}" + ) + elif isinstance(command_params, dict): + providers = list(command_params.keys()) + providers.remove("openbb") + for param in test_params: + is_wrong_param = True + for provider in providers: + if ( + param in command_params[provider]["QueryParams"]["fields"] + or param + in command_params["openbb"]["QueryParams"]["fields"] + or param == "provider" + ): + is_wrong_param = False + break + + if is_wrong_param: + wrong_params.append( + f"Wrong param {param} in function {function}" + ) + + else: + for test_params in function_params: + if isinstance(command_params, list): + try: + iter_commands_params = command_params[0][0] + except KeyError: + iter_commands_params = command_params[0] # type: ignore + + if isinstance(test_params, dict): + param_keys = test_params.keys() + elif isinstance(test_params, tuple) and all( + isinstance(item, dict) for item in test_params + ): + param_keys = [key for item in test_params for key in item] + else: + continue # Skip this iteration if test_params is neither a dict nor a tuple of dicts + + for key in param_keys: + if key not in iter_commands_params and key != "return": + wrong_params.append(f"Wrong param {key} in function {function}") + return wrong_params + + +def check_missing_params( + command_params: Union[Dict[str, Dict[str, dict]], List[Tuple[Dict[str, str], str]]], + function_params: List[dict], + function, + processing: bool = False, +) -> List[str]: + """Check if there are any missing params for a command.""" + missing_params = [] + if not processing: + for i, test_params in enumerate(function_params): + if "provider" in test_params and i != 0: + provider = test_params["provider"] + if provider in command_params: + for expected_param in command_params[provider]["QueryParams"][ + "fields" + ]: + if expected_param not in test_params: + missing_params.append( + f"Missing param {expected_param} for provider {provider} in function {function}" + ) + elif isinstance(command_params, dict): + for expected_param in command_params["openbb"]["QueryParams"]["fields"]: + if expected_param not in test_params: + missing_params.append( + f"Missing standard param {expected_param} in function {function}" + ) + else: + for test_params in function_params: + if isinstance(command_params, list): + try: + iter_commands_params = command_params[0][0] + except KeyError: + iter_commands_params = command_params[0] # type: ignore + + for expected_param in iter_commands_params: + try: + used_params = test_params[0].keys() + except KeyError: + used_params = test_params.keys() + if expected_param not in used_params and expected_param not in ( + "return", + "chart", + ): + missing_params.append( + f"Missing param {expected_param} in function {function}" + ) + return missing_params + + +def check_integration_tests( + functions: Dict[str, Any], + check_function: Callable[ + [ + Union[Dict[str, Dict[str, dict]], List[Tuple[Dict[str, str], str]]], + List[dict], + str, + bool, + ], + List[str], + ], +) -> List[str]: + """Check if there are any missing items for integration tests.""" + pi = ProviderInterface() + provider_interface_map = pi.map + cm = CommandMap(coverage_sep=".") + + function_params: List[dict] = [] + all_missing_items: List[str] = [] + used_functions: List[str] = [] + + for command, model in cm.commands_model.items(): + for function in functions: + if command[1:].replace(".", "_") == function.replace("test_", ""): + command_params: Dict[str, Dict[str, dict]] = provider_interface_map[ + model + ] + try: + function_params = functions[function].pytestmark[1].args[1] + except IndexError: + # Another decorator is below the parametrize decorator + function_params = functions[function].pytestmark[2].args[1] + missing_items = check_function( + command_params, function_params, function, False + ) + all_missing_items.extend(missing_items) + used_functions.append(function) + + # the processing commands are the ones that are left + processing_functions = [ + function for function in functions if function not in used_functions + ] + + for route, _ in cm.map.items(): + for function in processing_functions: + if route.replace("/", "_")[1:] == function.replace("test_", ""): + hints = get_type_hints(cm.map[route]) + processing_command_params = [ + {k: "" for k in get_test_params_data_processing(hints)} + ] + try: + function_params = functions[function].pytestmark[1].args[1] + except IndexError: + # Another decorator is below the parametrize decorator + function_params = functions[function].pytestmark[2].args[1] + + missing_items = check_function( + processing_command_params, function_params, function, True # type: ignore + ) + + # if "chart" is in missing_items, remove it + if "chart" in missing_items: + missing_items.remove("chart") + + all_missing_items.extend(missing_items) + + return all_missing_items + + +def check_missing_integration_tests(test_type: Literal["api", "python"]) -> List[str]: + """Check if all endpoints have integration tests.""" + cm = CommandMap(coverage_sep=".") + routes = [route[1:].replace("/", "_") for route in cm.map] + missing_integration_tests: List[str] = [] + + if test_type == "api": + functions = get_module_functions(get_integration_tests(test_type="api")) + else: + functions = get_module_functions(get_integration_tests(test_type="python")) + + tested_functions = [ + function.replace("test_", "", 1) + for function in functions + if function.startswith("test_") + ] + + for route in routes: + if route not in tested_functions: + # TODO: See how to handle edge cases that are excluded from the schema + # on purpose. This is currently on the econometrics router. + if test_type == "api" and "econometrics" in route: + continue + missing_integration_tests.append( + f"Missing {test_type} integration test for route {route}" + ) + + return missing_integration_tests + + +def check_outdated_integration_tests(test_type: Literal["api", "python"]) -> List[str]: + """Check if there are any outdated integration tests.""" + cm = CommandMap(coverage_sep=".") + routes = [route[1:].replace("/", "_") for route in cm.map] + outdated_integration_tests: List[str] = [] + + if test_type == "api": + functions = get_module_functions(get_integration_tests(test_type="api")) + else: + functions = get_module_functions(get_integration_tests(test_type="python")) + + for function, f_callable in functions.items(): + if function.startswith("test_"): + route = function.replace("test_", "", 1) + try: + if f_callable.pytestmark[1].name != "skip": + args = f_callable.pytestmark[1].args[1] + else: + continue + except IndexError: + continue + if route not in routes and len(args) > 0: + # If it doesn't have any args it is because it is not installed. + outdated_integration_tests.append( + f"Outdated {test_type} integration test for route {route}" + ) + + return outdated_integration_tests + + +def check_missing_integration_test_providers(functions: Dict[str, Any]) -> List[str]: + """Check if there are any missing providers for integration tests.""" + return check_integration_tests(functions, check_missing_providers) + + +def check_missing_integration_test_params(functions: Dict[str, Any]) -> List[str]: + """Check if there are any missing params for integration tests.""" + return check_integration_tests(functions, check_missing_params) + + +def check_wrong_integration_test_params(functions: Dict[str, Any]) -> List[str]: + """Check if there are any wrong params for integration tests.""" + return check_integration_tests(functions, check_wrong_params) diff --git a/openbb_platform/extensions/tests/utils/router_testers.py b/openbb_platform/extensions/tests/utils/router_testers.py new file mode 100644 index 0000000000000000000000000000000000000000..dd8574b328e94a2fd0026e39dd36a882acc71606 --- /dev/null +++ b/openbb_platform/extensions/tests/utils/router_testers.py @@ -0,0 +1,159 @@ +"""Router testers.""" + +import ast +import os +from typing import Any, Dict, List, Optional + +from openbb_core.app.provider_interface import ProviderInterface + +from extensions.tests.utils.helpers import ( + collect_router_functions, + collect_routers, + find_decorator, + find_missing_router_function_models, + get_all_fields, + get_decorator_details, + get_required_fields, + import_routers, + parse_example_string, +) + + +def check_router_function_models() -> List[str]: + """Check if the models in the router functions exist in the provider interface map.""" + pi = ProviderInterface() + pi_map = pi.map + routers = collect_routers("extensions") + loaded_routers = import_routers(routers) + router_functions = collect_router_functions(loaded_routers) + missing_models = find_missing_router_function_models(router_functions, pi_map) + + return missing_models + + +def check_router_model_functions_signature() -> List[str]: + """Check if the router model functions have the correct signature.""" + expected_args = ["cc", "provider_choices", "standard_params", "extra_params"] + expected_return_type = "OBBject" + missing_args: List[str] = [] + missing_return_type: List[str] = [] + + routers = collect_routers("extensions") + loaded_routers = import_routers(routers) + router_functions = collect_router_functions(loaded_routers) + + for router_name, functions in router_functions.items(): + for function in functions: + decorator = find_decorator( + os.path.join(*router_name.split(".")) + ".py", + function.__name__, + ) + if decorator: + if "POST" in decorator or "GET" in decorator: + continue + args = list(function.__code__.co_varnames) + + if ( + args + and not all(arg in args for arg in expected_args) + and "model" in decorator + ): + missing_args.append( + f"{function.__name__} in {router_name} missing expected args: {expected_args}" + ) + if expected_return_type not in str(function.__annotations__["return"]): + missing_return_type.append( + f"{function.__name__} in {router_name} " + f"doesn't have the expected return type: {expected_return_type}" + ) + + return missing_args + missing_return_type + + +def check_general( + keywords: Dict, examples: List, router_name: str, function: Any +) -> List[str]: + """Check for general violations in the router command examples.""" + general_violation: List[str] = [] + + # Check if the endpoint has examples + if "examples" not in keywords or not examples: + general_violation.append( + f"'{router_name}' > '{function.__name__}': missing examples" + ) + return general_violation + + return general_violation + + +def check_api( + examples: str, router_name: str, model: Optional[str], function: Any +) -> List[str]: + """Check for API examples.""" + api_example_violation: List[str] = [] + parsed_examples = parse_example_string(examples) + if model and "APIEx" in parsed_examples: + required_fields = set(get_required_fields(model.strip("'"))) + all_fields = get_all_fields(model.strip("'")) + all_fields.append("provider") + required_fields_met = False + + for api_example in parsed_examples["APIEx"]: + params = ast.literal_eval(api_example.get("params", "{}")) + if not required_fields_met and required_fields.issubset(params.keys()): + required_fields_met = True + + # Check for unsupported parameters + for param in params: + if param not in all_fields: + api_example_violation.append( + f"'{router_name}' > '{function.__name__}': param '{param}' is not supported by the command." + ) + + # If after checking all examples, required fields are still not met + if not required_fields_met: + api_example_violation.append( + f"'{router_name}' > '{function.__name__}': missing example with required fields only > {required_fields}" + ) + + return api_example_violation + + +def check_router_command_examples() -> List[str]: + """Check if the router command examples satisfy criteria.""" + general_violation: List[str] = [] + api_example_violation: List[str] = [] + python_example_violation: List[str] = [] + + routers = collect_routers("extensions") + loaded_routers = import_routers(routers) + router_functions = collect_router_functions(loaded_routers) + + for router_name, functions in router_functions.items(): + for function in functions: + if ( + "basemodel_to_df" in function.__name__ + or "router" not in function.__module__ + ): + continue + decorator = find_decorator( + os.path.join(*router_name.split(".")) + ".py", + function.__name__, + ) + if decorator: + decorator_details = get_decorator_details(function) + if decorator_details and decorator_details.name == "router.command": + keywords = decorator_details.kwargs or {} + examples = keywords.get("examples", []) + # General checks + general_violation += check_general( + keywords, examples, router_name, function + ) + if examples: + # API example checks + model = keywords.get("model", None) + api_example_violation += check_api( + examples, router_name, model, function + ) + + return general_violation + api_example_violation + python_example_violation