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openbb_platform/providers/alpha_vantage/README.md

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+# OpenBB Alpha Vantage Provider
+
+This extension integrates the [Alpha Vantage](https://www.alphavantage.co/) data provider into the OpenBB Platform.
+
+## Installation
+
+To install the extension:
+
+```bash
+pip install openbb-alpha-vantage
+```
+
+Documentation available [here](https://docs.openbb.co/platform/developer_guide/contributing).

openbb_platform/providers/alpha_vantage/__init__.py

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+"""Alpha Vantage Provider."""

openbb_platform/providers/alpha_vantage/openbb_alpha_vantage/__init__.py

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+"""Alpha Vantage Provider module."""
+
+from openbb_alpha_vantage.models.equity_historical import AVEquityHistoricalFetcher
+from openbb_alpha_vantage.models.historical_eps import AVHistoricalEpsFetcher
+from openbb_core.provider.abstract.provider import Provider
+
+alpha_vantage_provider = Provider(
+    name="alpha_vantage",
+    website="https://www.alphavantage.co",
+    description="""Alpha Vantage provides realtime and historical
+financial market data through a set of powerful and developer-friendly data APIs
+and spreadsheets. From traditional asset classes (e.g., stocks, ETFs, mutual funds)
+to economic indicators, from foreign exchange rates to commodities,
+from fundamental data to technical indicators, Alpha Vantage
+is your one-stop-shop for enterprise-grade global market data delivered through
+cloud-based APIs, Excel, and Google Sheets. """,
+    credentials=["api_key"],
+    fetcher_dict={
+        "EquityHistorical": AVEquityHistoricalFetcher,
+        "HistoricalEps": AVHistoricalEpsFetcher,
+        "EtfHistorical": AVEquityHistoricalFetcher,
+    },
+    repr_name="Alpha Vantage",
+    deprecated_credentials={"API_KEY_ALPHAVANTAGE": "alpha_vantage_api_key"},
+    instructions='Go to: https://www.alphavantage.co/support/#api-key\n\n![AlphaVantage](https://user-images.githubusercontent.com/46355364/207820936-46c2ba00-81ff-4cd3-98a4-4fa44412996f.png)\n\nFill out the form, pass Captcha, and click on, "GET FREE API KEY".',  # noqa: E501  pylint: disable=line-too-long
+)

openbb_platform/providers/alpha_vantage/openbb_alpha_vantage/models/__init__.py

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+"""Alpha Vantage models."""

openbb_platform/providers/alpha_vantage/openbb_alpha_vantage/models/equity_historical.py

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+"""Alpha Vantage Equity Historical Price Model."""
+
+# pylint: disable=unused-argument
+
+from datetime import datetime
+from typing import Any, Dict, List, Literal, Optional
+from warnings import warn
+
+from openbb_core.provider.abstract.fetcher import Fetcher
+from openbb_core.provider.standard_models.equity_historical import (
+    EquityHistoricalData,
+    EquityHistoricalQueryParams,
+)
+from openbb_core.provider.utils.descriptions import (
+    DATA_DESCRIPTIONS,
+    QUERY_DESCRIPTIONS,
+)
+from pydantic import (
+    Field,
+    NonNegativeFloat,
+    PositiveFloat,
+)
+
+
+class AVEquityHistoricalQueryParams(EquityHistoricalQueryParams):
+    """Alpha Vantage Equity Historical Price Query.
+
+    Source: https://www.alphavantage.co/documentation/#time-series-data
+    """
+
+    __json_schema_extra__ = {
+        "symbol": {"multiple_items_allowed": True},
+        "interval": {"choices": ["1m", "5m", "15m", "30m", "60m", "1d", "1W", "1M"]},
+    }
+
+    interval: Literal["1m", "5m", "15m", "30m", "60m", "1d", "1W", "1M"] = Field(
+        default="1d",
+        description=QUERY_DESCRIPTIONS.get("interval", ""),
+    )
+    adjustment: Literal["splits_only", "splits_and_dividends", "unadjusted"] = Field(
+        description="The adjustment factor to apply. 'splits_only' is not supported for intraday data.",
+        default="splits_only",
+    )
+    extended_hours: bool = Field(
+        description="Include Pre and Post market data.",
+        default=False,
+    )
+
+
+class AVEquityHistoricalData(EquityHistoricalData):
+    """Alpha Vantage Equity Historical Price Data."""
+
+    __alias_dict__ = {
+        "adj_close": "adjusted_close",
+        "dividend": "dividend_amount",
+        "split_ratio": "split_factor",
+    }
+
+    adj_close: Optional[PositiveFloat] = Field(
+        default=None, description=DATA_DESCRIPTIONS.get("adj_close", "")
+    )
+    dividend: Optional[NonNegativeFloat] = Field(
+        default=None,
+        description="Dividend amount, if a dividend was paid.",
+    )
+    split_ratio: Optional[NonNegativeFloat] = Field(
+        default=None,
+        description="Split coefficient, if a split occurred.",
+    )
+
+
+class AVEquityHistoricalFetcher(
+    Fetcher[
+        AVEquityHistoricalQueryParams,
+        List[AVEquityHistoricalData],
+    ]
+):
+    """Transform the query, extract and transform the data from the AlphaVantage endpoints."""
+
+    @staticmethod
+    def transform_query(params: Dict[str, Any]) -> AVEquityHistoricalQueryParams:
+        """Transform the query."""
+        # pylint: disable=import-outside-toplevel
+        from dateutil.relativedelta import relativedelta
+
+        transformed_params = params
+
+        now = datetime.now().date()
+        if params.get("start_date") is None:
+            transformed_params["start_date"] = now - relativedelta(years=1)
+
+        if params.get("end_date") is None:
+            transformed_params["end_date"] = now
+
+        return AVEquityHistoricalQueryParams(**transformed_params)
+
+    @staticmethod
+    async def aextract_data(
+        query: AVEquityHistoricalQueryParams,
+        credentials: Optional[Dict[str, str]],
+        **kwargs: Any,
+    ) -> List[Dict]:
+        """Return the raw data from the Alpha Vantage endpoint."""
+        # pylint: disable=import-outside-toplevel
+        import asyncio  # noqa
+        from io import BytesIO  # noqa
+        from openbb_alpha_vantage.utils.helpers import (  # noqa
+            INTERVALS_DICT,
+            calculate_adjusted_prices,
+            get_interval,
+        )
+        from openbb_core.provider.utils.helpers import (  # noqa
+            amake_request,
+            amake_requests,
+            get_querystring,
+        )
+        from pandas import date_range, read_csv, to_datetime  # noqa
+
+        api_key = credentials.get("alpha_vantage_api_key") if credentials else ""
+        intraday = False
+        interval = get_interval(query.interval)
+        query_str = get_querystring(
+            query.model_dump(by_alias=True),
+            [
+                "start_date",
+                "end_date",
+                "interval",
+                "symbol",
+                "adjustment",
+                "extended_hours",
+            ],
+        )
+
+        function = INTERVALS_DICT[query.interval[-1]]
+
+        if query.adjustment != "unadjusted":
+            function += "_ADJUSTED"
+        query_str += f"&function={function}&outputsize=full&datatype=csv"
+
+        if "INTRADAY" in function:
+            query_str += f"&interval={interval}&extended_hours={str(query.extended_hours).lower()}"
+            intraday = True
+
+        results = []
+
+        async def callback(response, _):
+            """Use callback function to process the response."""
+            try:
+                result = await response.json()
+                if "Information" in result:
+                    warn(str(result.get("Information")))
+            except Exception as _:
+                # TODO: This is hacky, find a better solution.
+                return await response.read()
+            return await response.read()
+
+        async def intraday_callback(response, _):
+            """Use callback function to process the intraday response."""
+            symbol = response.url.query.get("symbol", None)
+            data = await response.read()
+            if data:
+                df = read_csv(BytesIO(data))
+                if len(df) > 0:
+                    df.rename(
+                        columns={
+                            "timestamp": "date",
+                        },
+                        inplace=True,
+                    )
+                if len(query.symbol.split(",")) > 1:
+                    df.loc[:, "symbol"] = symbol
+                results.extend(df.to_dict("records"))
+            if not data:
+                warn(f"Symbol Error: No data found for {symbol}")
+            return results
+
+        async def get_one(symbol, intraday: bool = False):
+            """Get data for one symbol."""
+            if intraday is True:
+                adjusted = query.adjustment != "unadjusted"
+                if query.adjustment == "splits_only":
+                    warn(
+                        "Intraday does not support 'splits_only'. Using 'splits_and_dividends' instead."
+                    )
+                url = (
+                    f"https://www.alphavantage.co/query?{query_str.replace('_ADJUSTED', '')}"
+                    f"&symbol={symbol}&adjusted={str(adjusted).lower()}"
+                )
+                dates = (
+                    date_range(start=query.start_date, end=query.end_date)
+                    .strftime("%Y-%m")
+                    .unique()
+                    .tolist()
+                )
+                urls = [f"{url}&month={date}&apikey={api_key}" for date in dates]
+                return await amake_requests(urls, response_callback=intraday_callback)
+
+            # We will resample the intervals ourselves to get the correct data.
+            url = (
+                f"https://www.alphavantage.co/query?{query_str.replace('MONTHLY', 'DAILY').replace('WEEKLY', 'DAILY')}"
+                f"&symbol={symbol}&apikey={api_key}"
+            )
+            result = await amake_request(url, response_callback=callback)
+            if not result:
+                warn(f"Symbol Error: No data found for {symbol}")
+            if result:
+                data = read_csv(BytesIO(result))  # type: ignore
+                if len(data) > 0:
+                    data.rename(
+                        columns={
+                            "timestamp": "date",
+                            "dividend_amount": "dividend",
+                            "adjusted close": "adj_close",
+                            "dividend amount": "dividend",
+                            "adjusted_close": "adj_close",
+                            "split_coefficient": "split_factor",
+                        },
+                        inplace=True,
+                    )
+                    if "date" in data.columns:
+                        data["date"] = data["date"].apply(to_datetime)
+                        data.set_index("date", inplace=True)
+                        # The returned data when 'adjusted=true' from the API does not return a usable OHLCV data set.
+                        # We need to calculate the adjusted prices manually.
+                        if query.adjustment != "unadjusted":
+                            temp = data.copy()
+                            temp["dividend_factor"] = (
+                                temp["close"] - temp["dividend"]
+                            ) / temp["close"]
+                            temp["volume_factor"] = temp["split_factor"]
+                            temp["split_factor"] = 1 / temp["split_factor"]
+                            adj_cols = ["open", "high", "low", "close", "volume"]
+                            divs = query.adjustment == "splits_and_dividends"
+                            for col in adj_cols:
+                                divs = False if col == "volume" else divs
+                                if col in temp.columns:
+                                    temp = calculate_adjusted_prices(temp, col, divs)
+                            temp["adj_dividend"] = (
+                                temp["adj_close"] * (1 - temp["dividend_factor"])
+                                if query.adjustment == "splits_only"
+                                else temp["close"] * (1 - temp["dividend_factor"])
+                            )
+                            data["open"] = round(temp["adj_open"], 4)
+                            data["high"] = round(temp["adj_high"], 4)
+                            data["low"] = round(temp["adj_low"], 4)
+                            data["close"] = round(temp["adj_close"], 4)
+                            data["volume"] = round(temp["adj_volume"]).astype(int)
+                            data["dividend"] = round(temp["adj_dividend"], 4)
+                            data.drop(columns=["adj_close"], inplace=True)
+                        # Resample the daily data for the interval requested.
+                        freq = ""
+                        agg_dict = {
+                            "open": "first",
+                            "high": "max",
+                            "low": "min",
+                            "close": "last",
+                            "volume": "sum",
+                            "dividend": "sum",
+                            "split_factor": "prod",
+                        }
+                        if query.adjustment == "unadjusted":
+                            agg_dict.pop("dividend")
+                            agg_dict.pop("split_factor")
+                        if query.interval == "1M":
+                            freq = "M"
+                        if query.interval == "1W":
+                            freq = "W-FRI"
+                        if freq in ["M", "W-FRI"]:
+                            data = data.resample(freq).agg({**agg_dict})
+                        if len(query.symbol.split(",")) > 1:
+                            data.loc[:, "symbol"] = symbol
+
+                        data = data.reset_index()
+                        if intraday is False:
+                            data["date"] = data["date"].dt.date
+
+                        results.extend(data.to_dict("records"))
+
+            return results
+
+        tasks = [get_one(symbol, intraday) for symbol in query.symbol.split(",")]
+        await asyncio.gather(*tasks)
+
+        return results
+
+    @staticmethod
+    def transform_data(
+        query: AVEquityHistoricalQueryParams, data: List[Dict], **kwargs: Any
+    ) -> List[AVEquityHistoricalData]:
+        """Transform the data to the standard format."""
+        # pylint: disable=import-outside-toplevel
+        from pandas import to_datetime
+
+        if data == []:
+            return []
+        if "{" in data[0]:
+            warn(str(data[0]["{"].strip()))
+            return []
+        return [
+            AVEquityHistoricalData.model_validate(d)
+            for d in sorted(
+                data,
+                key=(
+                    lambda x: (x["date"], x["symbol"]) if "symbol" in x else x["date"]
+                ),
+            )
+            if query.start_date <= to_datetime(d["date"]).date() <= query.end_date
+        ]

openbb_platform/providers/alpha_vantage/openbb_alpha_vantage/models/historical_eps.py

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+"""AlphaVantage Historical EPS Model."""
+
+# pylint: disable=unused-argument
+
+from datetime import date as dateType
+from typing import Any, Dict, List, Literal, Optional, Union
+from warnings import warn
+
+from openbb_core.provider.abstract.fetcher import Fetcher
+from openbb_core.provider.standard_models.historical_eps import (
+    HistoricalEpsData,
+    HistoricalEpsQueryParams,
+)
+from openbb_core.provider.utils.descriptions import QUERY_DESCRIPTIONS
+from openbb_core.provider.utils.errors import EmptyDataError
+from pydantic import Field, field_validator
+
+
+class AlphaVantageHistoricalEpsQueryParams(HistoricalEpsQueryParams):
+    """
+    AlphaVantage Historical EPS Query Params.
+
+    Source: https://www.alphavantage.co/documentation/#earnings
+    """
+
+    __json_schema_extra__ = {"symbol": {"multiple_items_allowed": True}}
+
+    period: Literal["annual", "quarter"] = Field(
+        default="quarter", description=QUERY_DESCRIPTIONS.get("period", "")
+    )
+    limit: Optional[int] = Field(
+        default=None, description=QUERY_DESCRIPTIONS.get("limit", "")
+    )
+
+
+class AlphaVantageHistoricalEpsData(HistoricalEpsData):
+    """AlphaVantage Historical EPS Data."""
+
+    __alias_dict__ = {
+        "date": "fiscalDateEnding",
+        "eps_actual": "reportedEPS",
+        "eps_estimated": "estimatedEPS",
+        "surprise_percent": "surprisePercentage",
+        "reported_date": "reportedDate",
+    }
+
+    surprise: Optional[float] = Field(
+        default=None,
+        description="Surprise in EPS (Actual - Estimated).",
+    )
+    surprise_percent: Optional[Union[float, str]] = Field(
+        default=None,
+        description="EPS surprise as a normalized percent.",
+        json_schema_extra={"x-unit_measurement": "percent", "x-frontend_multiply": 100},
+    )
+    reported_date: Optional[dateType] = Field(
+        default=None,
+        description="Date of the earnings report.",
+    )
+
+    @field_validator(
+        "eps_estimated",
+        "eps_actual",
+        "surprise",
+        mode="before",
+        check_fields=False,
+    )
+    @classmethod
+    def validate_null(cls, v):
+        """Clean None returned as a string."""
+        return None if str(v).strip() == "None" or str(v) == "0" else v
+
+    @field_validator("surprise_percent", mode="before", check_fields=False)
+    @classmethod
+    def normalize_percent(cls, v):
+        """Normalize percent values."""
+        if isinstance(v, str) and v == "None" or str(v) == "0":
+            return None
+        return float(v) / 100
+
+
+class AVHistoricalEpsFetcher(
+    Fetcher[AlphaVantageHistoricalEpsQueryParams, List[AlphaVantageHistoricalEpsData]]
+):
+    """AlphaVantage Historical EPS Fetcher."""
+
+    @staticmethod
+    def transform_query(params: Dict[str, Any]) -> AlphaVantageHistoricalEpsQueryParams:
+        """Transform the query params."""
+        return AlphaVantageHistoricalEpsQueryParams(**params)
+
+    @staticmethod
+    async def aextract_data(
+        query: AlphaVantageHistoricalEpsQueryParams,
+        credentials: Optional[Dict[str, str]],
+        **kwargs: Any,
+    ) -> List[Dict]:
+        """Return the raw data from the AlphaVantage endpoint."""
+        # pylint: disable=import-outside-toplevel
+        from openbb_core.provider.utils.helpers import (
+            ClientResponse,
+            ClientSession,
+            amake_requests,
+        )
+
+        api_key = credentials.get("alpha_vantage_api_key") if credentials else ""
+        BASE_URL = "https://www.alphavantage.co/query?function=EARNINGS&"
+        # We are allowing multiple symbols to be passed in the query, so we need to handle that.
+        symbols = query.symbol.split(",")
+        urls = [f"{BASE_URL}symbol={symbol}&apikey={api_key}" for symbol in symbols]
+        results: List = []
+        messages: List = []
+
+        # We need to make a custom callback function for this async request.
+        async def response_callback(response: ClientResponse, _: ClientSession):
+            """Response callback function."""
+            symbol = response.url.query.get("symbol", None)
+            data = await response.json()
+            target = (
+                "annualEarnings" if query.period == "annual" else "quarterlyEarnings"
+            )
+            message = data.get("Information", "")  # type: ignore
+            if message:
+                messages.append(message)
+                warn(f"Symbol Error for {symbol}: {message}")
+            result: List = []
+            # If data is returned, append it to the results list.
+            if data:
+                result = [
+                    {
+                        "symbol": symbol,
+                        **d,
+                    }
+                    for d in data.get(target, [])  # type: ignore
+                ]
+                if query.limit is not None:
+                    results.extend(result[: query.limit])
+                else:
+                    results.extend(result)
+            # If no data is returned, raise a warning and move on to the next symbol.
+            if not data:
+                warn(f"Symbol Error: No data found for {symbol}")
+
+        await amake_requests(urls, response_callback, **kwargs)  # type: ignore
+
+        if not results:
+            raise EmptyDataError(f"No data was returned -> \n{messages[-1]}")
+
+        return results
+
+    @staticmethod
+    def transform_data(
+        query: AlphaVantageHistoricalEpsQueryParams,
+        data: List[Dict],
+        **kwargs: Any,
+    ) -> List[AlphaVantageHistoricalEpsData]:
+        """Transform the raw data into the standard model."""
+        if not data:
+            raise EmptyDataError("No data found.")
+        return [AlphaVantageHistoricalEpsData.model_validate(d) for d in data]

openbb_platform/providers/alpha_vantage/openbb_alpha_vantage/utils/__init__.py

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+"""Alpha Vantage utilities."""

openbb_platform/providers/alpha_vantage/openbb_alpha_vantage/utils/helpers.py

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+"""Alpha Vantage Helpers Module."""
+
+from datetime import datetime
+from typing import TYPE_CHECKING, Any, Dict, List
+
+if TYPE_CHECKING:
+    from pandas import DataFrame
+
+INTERVALS_DICT = {
+    "m": "TIME_SERIES_INTRADAY",
+    "d": "TIME_SERIES_DAILY",
+    "W": "TIME_SERIES_WEEKLY",
+    "M": "TIME_SERIES_MONTHLY",
+}
+
+
+def get_interval(value: str) -> str:
+    """Get the intervals for the Alpha Vantage API."""
+    intervals = {
+        "m": "min",
+        "d": "day",
+        "W": "week",
+        "M": "month",
+    }
+
+    return f"{value[:-1]}{intervals[value[-1]]}"
+
+
+def extract_key_name(key):
+    """Extract the alphabetical part of the key using regex."""
+    # pylint: disable=import-outside-toplevel
+    import re
+
+    match = re.search(r"\d+\.\s+([a-z]+)", key, re.I)
+    return match.group(1) if match else key
+
+
+def filter_by_dates(
+    data: List[Dict[str, Any]], start_date: datetime, end_date: datetime
+) -> List[Dict[str, Any]]:
+    """Filter the data by start and end dates."""
+    return list(
+        filter(
+            lambda x: start_date
+            <= datetime.strptime(x["date"], "%Y-%m-%d").date()
+            <= end_date,
+            data,
+        )
+    )
+
+
+def calculate_adjusted_prices(
+    df: "DataFrame", column: str, dividends: bool = False
+) -> "DataFrame":
+    """Calculate the split-adjusted prices, or split and dividend adjusted prices.
+
+    Parameters
+    ----------
+    df: DataFrame
+        DataFrame with unadjusted OHLCV values + split_factor + dividend
+    column: str
+        The column name to adjust.
+    dividends: bool
+        Whether to adjust for both splits and dividends. Default is split-adjusted only.
+
+    Returns
+    -------
+    DataFrame
+        DataFrame with adjusted prices.
+    """
+    # pylint: disable=import-outside-toplevel
+    from numpy import zeros
+
+    df = df.copy()
+    adj_column = "adj_" + column
+
+    # Reverse the DataFrame order, sorting by date in descending order
+    df.sort_index(ascending=False, inplace=True)
+
+    price_col = df[column]
+    split_col = df["volume_factor"] if column == "volume" else df["split_factor"]
+    dividend_col = df["dividend"] if dividends else zeros(len(price_col))
+    adj_price_col = zeros(len(df.index))
+    adj_price_col[0] = price_col.iloc[0]
+
+    for i in range(1, len(price_col)):
+        adj_price_col[i] = adj_price_col[i - 1] + adj_price_col[i - 1] * (
+            (
+                (price_col.iloc[i] * split_col.iloc[i - 1])
+                - price_col.iloc[i - 1]
+                - dividend_col[i - 1]
+            )
+            / price_col.iloc[i - 1]
+        )
+    df[adj_column] = adj_price_col
+
+    # Change the DataFrame order back to dates ascending
+    df.sort_index(ascending=True, inplace=True)
+    return df

openbb_platform/providers/alpha_vantage/pyproject.toml

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+[tool.poetry]
+name = "openbb-alpha-vantage"
+version = "1.4.1"
+description = "Alpha Vantage extension for OpenBB"
+authors = ["OpenBB Team <hello@openbb.co>"]
+license = "AGPL-3.0-only"
+readme = "README.md"
+packages = [{ include = "openbb_alpha_vantage" }]
+
+[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_provider_extension"]
+alpha_vantage = "openbb_alpha_vantage:alpha_vantage_provider"

openbb_platform/providers/alpha_vantage/tests/__init__.py

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+"""Alpha Vantage tests."""

openbb_platform/providers/alpha_vantage/tests/record/http/test_alpha_vantage_fetchers/test_av_equity_historical_fetcher_urllib3_v1.yaml

@@ -0,0 +1,316 @@
+interactions:
+- request:
+    body: null
+    headers:
+      Accept:
+      - application/json
+      Accept-Encoding:
+      - gzip, deflate
+      Connection:
+      - keep-alive
+    method: GET
+    uri: https://www.alphavantage.co/query?adjusted=true&apikey=MOCK_API_KEY&datatype=csv&extended_hours=false&function=TIME_SERIES_INTRADAY&interval=15min&month=2023-01&outputsize=full&symbol=AAPL
+  response:
+    body:
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+        12:00:00,123.8990,124.2090,123.8350,124.1310,2027857\r\n2023-01-03 11:45:00,124.1070,124.1450,123.6360,123.8950,3044469\r\n2023-01-03
+        11:30:00,124.4950,124.5220,123.9740,124.1170,2546604\r\n2023-01-03 11:15:00,123.9780,124.6110,123.9050,124.4930,2773011\r\n2023-01-03
+        11:00:00,124.7380,125.0190,123.8740,123.9790,4858972\r\n2023-01-03 10:45:00,124.5140,124.8750,124.3710,124.7420,3326033\r\n2023-01-03
+        10:30:00,124.6040,124.9290,124.1920,124.4980,4825857\r\n2023-01-03 10:15:00,125.0610,125.2020,124.3710,124.6080,4607743\r\n2023-01-03
+        10:00:00,126.0740,126.4390,124.9170,125.0590,4751302\r\n2023-01-03 09:45:00,128.9940,129.2500,126.0100,126.0710,6031150\r\n2023-01-03
+        09:30:00,129.3610,130.0150,128.5520,129.0030,7092582\r\n"
+    headers:
+      Allow:
+      - GET, HEAD, OPTIONS
+      CF-Cache-Status:
+      - DYNAMIC
+      CF-RAY:
+      - 85ce38b1794b2db9-YVR
+      Connection:
+      - keep-alive
+      Content-Disposition:
+      - attachment; filename=intraday_15min_AAPL.csv
+      Content-Length:
+      - '33793'
+      Content-Type:
+      - application/x-download
+      Cross-Origin-Opener-Policy:
+      - same-origin
+      Date:
+      - Thu, 29 Feb 2024 04:39:11 GMT
+      Nel:
+      - '{"report_to":"heroku-nel","max_age":3600,"success_fraction":0.005,"failure_fraction":0.05,"response_headers":["Via"]}'
+      Referrer-Policy:
+      - same-origin
+      Report-To:
+      - '{"group":"heroku-nel","max_age":3600,"endpoints":[{"url":"https://nel.heroku.com/reports?ts=1709181545&sid=1b10b0ff-8a76-4548-befa-353fc6c6c045&s=981PZd8mTWGiCra0E4yBSk28yyW4n2S%2FVCrZT0YNQm8%3D"}]}'
+      Reporting-Endpoints:
+      - heroku-nel=https://nel.heroku.com/reports?ts=1709181545&sid=1b10b0ff-8a76-4548-befa-353fc6c6c045&s=981PZd8mTWGiCra0E4yBSk28yyW4n2S%2FVCrZT0YNQm8%3D
+      Server:
+      - cloudflare
+      Vary:
+      - Cookie, Origin
+      Via:
+      - 1.1 vegur
+      X-Content-Type-Options:
+      - nosniff
+      X-Frame-Options:
+      - DENY
+    status:
+      code: 200
+      message: OK
+version: 1

openbb_platform/providers/alpha_vantage/tests/record/http/test_alpha_vantage_fetchers/test_av_equity_historical_fetcher_urllib3_v2.yaml

@@ -0,0 +1,316 @@
+interactions:
+- request:
+    body: null
+    headers:
+      Accept:
+      - application/json
+      Accept-Encoding:
+      - gzip, deflate
+      Connection:
+      - keep-alive
+    method: GET
+    uri: https://www.alphavantage.co/query?adjusted=true&apikey=MOCK_API_KEY&datatype=csv&extended_hours=false&function=TIME_SERIES_INTRADAY&interval=15min&month=2023-01&outputsize=full&symbol=AAPL
+  response:
+    body:
+      string: "timestamp,open,high,low,close,volume\r\n2023-01-31 15:45:00,142.4710,143.3640,142.1380,143.3200,6111148\r\n2023-01-31
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+        15:00:00,142.8880,143.0660,142.6600,142.7730,1206452\r\n2023-01-31 14:45:00,142.9580,143.0260,142.8230,142.8830,852602\r\n2023-01-31
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openbb_platform/providers/alpha_vantage/tests/record/http/test_alpha_vantage_fetchers/test_av_historical_eps_fetcher_urllib3_v1.yaml

@@ -0,0 +1,193 @@
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+        AAA=
+    headers:
+      Allow:
+      - GET, HEAD, OPTIONS
+      CF-Cache-Status:
+      - DYNAMIC
+      CF-RAY:
+      - 85d74473e8ad8453-YVR
+      Connection:
+      - keep-alive
+      Content-Encoding:
+      - gzip
+      Content-Type:
+      - application/json
+      Cross-Origin-Opener-Policy:
+      - same-origin
+      Date:
+      - Fri, 01 Mar 2024 06:59:58 GMT
+      Nel:
+      - '{"report_to":"heroku-nel","max_age":3600,"success_fraction":0.005,"failure_fraction":0.05,"response_headers":["Via"]}'
+      Referrer-Policy:
+      - same-origin
+      Report-To:
+      - '{"group":"heroku-nel","max_age":3600,"endpoints":[{"url":"https://nel.heroku.com/reports?ts=1709276398&sid=1b10b0ff-8a76-4548-befa-353fc6c6c045&s=HYLkepa3xuJf8ZP93wYuVeE7fHXoMnoAaRfYOuePI1s%3D"}]}'
+      Reporting-Endpoints:
+      - heroku-nel=https://nel.heroku.com/reports?ts=1709276398&sid=1b10b0ff-8a76-4548-befa-353fc6c6c045&s=HYLkepa3xuJf8ZP93wYuVeE7fHXoMnoAaRfYOuePI1s%3D
+      Server:
+      - cloudflare
+      Transfer-Encoding:
+      - chunked
+      Vary:
+      - Cookie, Origin
+      Via:
+      - 1.1 vegur
+      X-Content-Type-Options:
+      - nosniff
+      X-Frame-Options:
+      - DENY
+    status:
+      code: 200
+      message: OK
+version: 1

openbb_platform/providers/alpha_vantage/tests/record/http/test_alpha_vantage_fetchers/test_av_historical_eps_fetcher_urllib3_v2.yaml

@@ -0,0 +1,195 @@
+interactions:
+- request:
+    body: null
+    headers:
+      Accept:
+      - application/json
+      Accept-Encoding:
+      - gzip, deflate
+      Connection:
+      - keep-alive
+    method: GET
+    uri: https://www.alphavantage.co/query?apikey=MOCK_API_KEY&function=EARNINGS&symbol=AAPL
+  response:
+    body:
+      string: !!binary |
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+      CF-RAY:
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+      Connection:
+      - keep-alive
+      Content-Encoding:
+      - gzip
+      Content-Type:
+      - application/json
+      Cross-Origin-Opener-Policy:
+      - same-origin
+      Date:
+      - Thu, 27 Jun 2024 10:11:56 GMT
+      Nel:
+      - '{"report_to":"heroku-nel","max_age":3600,"success_fraction":0.005,"failure_fraction":0.05,"response_headers":["Via"]}'
+      Referrer-Policy:
+      - same-origin
+      Report-To:
+      - '{"group":"heroku-nel","max_age":3600,"endpoints":[{"url":"https://nel.heroku.com/reports?ts=1719483116&sid=1b10b0ff-8a76-4548-befa-353fc6c6c045&s=VvE1FMSjgc8ZK9stjN0j9fAim74NZBpZJko2Ddrcx%2BM%3D"}]}'
+      Reporting-Endpoints:
+      - heroku-nel=https://nel.heroku.com/reports?ts=1719483116&sid=1b10b0ff-8a76-4548-befa-353fc6c6c045&s=VvE1FMSjgc8ZK9stjN0j9fAim74NZBpZJko2Ddrcx%2BM%3D
+      Server:
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+      Transfer-Encoding:
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+      Via:
+      - 1.1 vegur
+      X-Content-Type-Options:
+      - nosniff
+      X-Frame-Options:
+      - DENY
+    status:
+      code: 200
+      message: OK
+- request:
+    body: null
+    headers:
+      Accept:
+      - application/json
+      Accept-Encoding:
+      - gzip, deflate
+      Connection:
+      - keep-alive
+    method: GET
+    uri: https://www.alphavantage.co/query?apikey=MOCK_API_KEY&function=EARNINGS&symbol=MSFT
+  response:
+    body:
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openbb_platform/providers/alpha_vantage/tests/test_alpha_vantage_fetchers.py

@@ -0,0 +1,48 @@
+"""Test the Alpha Vantage fetchers."""
+
+from datetime import date
+
+import pytest
+from openbb_alpha_vantage.models.equity_historical import AVEquityHistoricalFetcher
+from openbb_alpha_vantage.models.historical_eps import AVHistoricalEpsFetcher
+from openbb_core.app.service.user_service import UserService
+
+test_credentials = UserService().default_user_settings.credentials.model_dump(
+    mode="json"
+)
+
+
+@pytest.fixture(scope="module")
+def vcr_config():
+    """VCR configuration."""
+    return {
+        "filter_headers": [("User-Agent", None)],
+        "filter_query_parameters": [
+            ("apikey", "MOCK_API_KEY"),
+        ],
+    }
+
+
+@pytest.mark.record_http
+def test_av_equity_historical_fetcher(credentials=test_credentials):
+    """Test the Alpha Vantage Equity Historical fetcher."""
+    params = {
+        "symbol": "AAPL",
+        "start_date": date(2023, 1, 1),
+        "end_date": date(2023, 1, 10),
+        "interval": "15m",
+    }
+
+    fetcher = AVEquityHistoricalFetcher()
+    result = fetcher.test(params, credentials)
+    assert result is None
+
+
+@pytest.mark.record_http
+def test_av_historical_eps_fetcher(credentials=test_credentials):
+    """Test the Alpha Vantage Historical Earnings fetcher."""
+    params = {"symbol": "AAPL,MSFT", "period": "quarter", "limit": 4}
+
+    fetcher = AVHistoricalEpsFetcher()
+    result = fetcher.test(params, credentials)
+    assert result is None