"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "\n",
+ "# Plot the analysis results\n",
+ "plot_ma_analysis(analysis_results, acquirer_symbol, announcement_date)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7fa552be",
+ "metadata": {},
+ "source": [
+ "### Generate Summary Report"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "87d9358d",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "M&A Impact Analysis Report\n",
+ "=========================\n",
+ "Acquirer: MSFT\n",
+ "Target: LNKD\n",
+ "Announcement Date: 2016-06-13\n",
+ "\n",
+ "Performance Metrics\n",
+ "-----------------\n",
+ "Pre-Merger Period:\n",
+ "- Total Return: -10.67%\n",
+ "- Volatility: 26.41%\n",
+ "- Beta: 1.25\n",
+ "\n",
+ "Post-Merger Period:\n",
+ "- Total Return: 23.59%\n",
+ "- Volatility: 19.10%\n",
+ "- Beta: 1.15\n",
+ "\n",
+ "Impact Analysis\n",
+ "--------------\n",
+ "- Return Impact: 34.27%\n",
+ "- Volatility Impact: -7.30%\n",
+ "- Beta Impact: -0.10\n",
+ "\n",
+ "Summary\n",
+ "-------\n",
+ "The merger announcement appears to have positively impacted the acquirer's stock performance, with a 34.27% change in returns.\n",
+ "Risk metrics show that the company's volatility has decreased by 7.30% and beta has decreased by 0.10.\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "\n",
+ "# Generate and print the summary report\n",
+ "print(generate_ma_report(analysis_results, acquirer_symbol, target_symbol, announcement_date))\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ff3278e6",
+ "metadata": {},
+ "source": [
+ "\n",
+ "---\n",
+ "\n",
+ "### Conclusion\n",
+ "\n",
+ "The notebook provides a streamlined approach to assessing M&A impact, leveraging OpenBB's data retrieval capabilities to analyze stock performance pre- and post-announcement.\n"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "venv",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.12.4"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/examples/openbb-apachebeam/README.md b/examples/openbb-apachebeam/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..3e2d1d4ad21e9f2eca25379cb07844685ea62629
--- /dev/null
+++ b/examples/openbb-apachebeam/README.md
@@ -0,0 +1,12 @@
+# OBB Dataflow Sample
+
+
+This is a sample how to invoke OBB fetchers in an Apache Beam pipeline. (GCP dataflow is build on Apache Beam)
+Pre-requisites
+- You need to create a Conda environment (or a virtual env) using requirements.txt in this directory
+- The script exercise 3 OBB endpoints, all of which require no credentials
+- Run the test from the main directory
+ python -m unittest .\tests\test_obb_pipeline.py
+
+The script will run a pipeline consisting of 3 task which will fetch AAPL quote, profile and news
+This is just a very basic sample which can be used as building block to create more complex scenarios
diff --git a/examples/openbb-apachebeam/requirements.txt b/examples/openbb-apachebeam/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..57071e8de92be3c3e7f60339fe2583f72f8fd685
--- /dev/null
+++ b/examples/openbb-apachebeam/requirements.txt
@@ -0,0 +1,2 @@
+apache-beam
+openbb-yfinance
\ No newline at end of file
diff --git a/examples/openbb-apachebeam/tests/__init__.py b/examples/openbb-apachebeam/tests/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/examples/openbb-apachebeam/tests/test_obb_pipeline.py b/examples/openbb-apachebeam/tests/test_obb_pipeline.py
new file mode 100644
index 0000000000000000000000000000000000000000..097df423703720859d4731a1c7263d10556ba275
--- /dev/null
+++ b/examples/openbb-apachebeam/tests/test_obb_pipeline.py
@@ -0,0 +1,48 @@
+import unittest
+from apache_beam.testing.test_pipeline import TestPipeline
+from apache_beam.options.pipeline_options import PipelineOptions
+import asyncio
+import apache_beam as beam
+from openbb_yfinance.models.equity_quote import YFinanceEquityQuoteFetcher as quote_fetcher
+from openbb_yfinance.models.equity_profile import YFinanceEquityProfileFetcher as profile_fetcher
+from openbb_yfinance.models.company_news import YFinanceCompanyNewsFetcher as news_fetcher
+
+
+class AsyncProcess(beam.DoFn):
+
+ def __init__(self, credentials, fetcher):
+ self.credentials = credentials
+ self.fetcher = fetcher
+
+ async def fetch_data(self, element: str):
+ params = dict(symbol=element)
+ data = await self.fetcher.fetch_data(params, self.credentials)
+ return [d.model_dump(exclude_none=True) for d in data]
+
+ def process(self, element: str):
+ return asyncio.run(self.fetch_data(element))
+
+class MyTestCase(unittest.TestCase):
+
+
+ def test_sample_pipeline(self):
+ credentials = {} # Running OBB endpoints which do not require credentials
+ debug_sink = beam.Map(print)
+ ticker = 'AAPL'
+
+ with TestPipeline(options=PipelineOptions()) as p:
+ quote = (p | 'Start Quote' >> beam.Create([ticker])
+ | 'Run Quote' >> beam.ParDo(AsyncProcess(credentials, quote_fetcher))
+ | 'Print quote' >> debug_sink)
+
+ profile = (p | 'Start Profile' >> beam.Create([ticker])
+ | 'Run Profile' >> beam.ParDo(AsyncProcess(credentials, profile_fetcher))
+ | 'Print profile' >> debug_sink)
+
+ news = (p | 'Start News' >> beam.Create([ticker])
+ | 'Run News' >> beam.ParDo(AsyncProcess(credentials, news_fetcher))
+ | 'Print nes' >> debug_sink)
+
+
+if __name__ == '__main__':
+ unittest.main()
diff --git a/examples/openbbPlatformAsLLMTools.ipynb b/examples/openbbPlatformAsLLMTools.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..aee9a09c9b700b3c602509d12c3f2c6fef7b6041
--- /dev/null
+++ b/examples/openbbPlatformAsLLMTools.ipynb
@@ -0,0 +1,6228 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# OpenBB Tools"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Getting Started"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Install dependencies"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "!pip install openbb\n",
+ "!pip install openbb-charting\n",
+ "\n",
+ "!pip install pydantic==2.5\n",
+ "\n",
+ "!pip install langchain\n",
+ "!pip install langchain_community\n",
+ "!pip install langchain_openai\n",
+ "# need to restart runtime after installing pydantic"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Set the OpenAI API Key"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "os.environ['OPENAI_API_KEY'] = \"your-api-key"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Import packages"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from openbb import obb"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Login"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "obb.account.login(pat=\"PAT\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## OpenBB tool pre-requisites"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Callable"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " open \n",
+ " high \n",
+ " low \n",
+ " close \n",
+ " volume \n",
+ " vwap \n",
+ " transactions \n",
+ " \n",
+ " \n",
+ " date \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 2023-02-07 \n",
+ " 150.640 \n",
+ " 155.2300 \n",
+ " 150.6400 \n",
+ " 154.65 \n",
+ " 83322551.0 \n",
+ " 153.4202 \n",
+ " 661767 \n",
+ " \n",
+ " \n",
+ " 2023-02-08 \n",
+ " 153.880 \n",
+ " 154.5800 \n",
+ " 151.1680 \n",
+ " 151.92 \n",
+ " 63620079.0 \n",
+ " 152.3636 \n",
+ " 524140 \n",
+ " \n",
+ " \n",
+ " 2023-02-09 \n",
+ " 153.775 \n",
+ " 154.3300 \n",
+ " 150.4200 \n",
+ " 150.87 \n",
+ " 55994243.0 \n",
+ " 152.2769 \n",
+ " 471973 \n",
+ " \n",
+ " \n",
+ " 2023-02-10 \n",
+ " 149.460 \n",
+ " 151.3401 \n",
+ " 149.2200 \n",
+ " 151.01 \n",
+ " 57388108.0 \n",
+ " 150.4054 \n",
+ " 443405 \n",
+ " \n",
+ " \n",
+ " 2023-02-13 \n",
+ " 150.952 \n",
+ " 154.2600 \n",
+ " 150.9200 \n",
+ " 153.85 \n",
+ " 62199013.0 \n",
+ " 153.2310 \n",
+ " 506668 \n",
+ " \n",
+ " \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " \n",
+ " \n",
+ " 2024-02-01 \n",
+ " 183.985 \n",
+ " 186.9500 \n",
+ " 183.8200 \n",
+ " 186.86 \n",
+ " 64885408.0 \n",
+ " 185.5688 \n",
+ " 820977 \n",
+ " \n",
+ " \n",
+ " 2024-02-02 \n",
+ " 179.860 \n",
+ " 187.3300 \n",
+ " 179.2500 \n",
+ " 185.85 \n",
+ " 102527680.0 \n",
+ " 184.7440 \n",
+ " 1108465 \n",
+ " \n",
+ " \n",
+ " 2024-02-05 \n",
+ " 188.150 \n",
+ " 189.2500 \n",
+ " 185.8400 \n",
+ " 187.68 \n",
+ " 69654320.0 \n",
+ " 187.6824 \n",
+ " 804748 \n",
+ " \n",
+ " \n",
+ " 2024-02-06 \n",
+ " 186.860 \n",
+ " 189.3100 \n",
+ " 186.7695 \n",
+ " 189.30 \n",
+ " 43490528.0 \n",
+ " 188.4788 \n",
+ " 530823 \n",
+ " \n",
+ " \n",
+ " 2024-02-07 \n",
+ " 190.630 \n",
+ " 191.0500 \n",
+ " 188.6100 \n",
+ " 189.00 \n",
+ " 31515523.0 \n",
+ " 189.4285 \n",
+ " 474967 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
252 rows × 7 columns
\n",
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " date \n",
+ " 2024-08-22 18:02:00+00:00 \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " title \n",
+ " Natural Grocers® Teams Up With Local Artist, S... \n",
+ " \n",
+ " \n",
+ " text \n",
+ " Natural Grocers®, the leading family-operated ... \n",
+ " \n",
+ " \n",
+ " url \n",
+ " https://finance.yahoo.com/news/natural-grocers... \n",
+ " \n",
+ " \n",
+ " source \n",
+ " yahoo \n",
+ " \n",
+ " \n",
+ " id \n",
+ " new_DDGR2v \n",
+ " \n",
+ " \n",
+ " company \n",
+ " {'id': 'com_g4Q8NX', 'ticker': 'NGVC', 'name':... \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " date \n",
+ " 2024-08-22 14:46:33-04:00 \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " title \n",
+ " Behind the Scenes of Vertiv Hldgs's Latest Opt... \n",
+ " \n",
+ " \n",
+ " images \n",
+ " [{'size': 'thumb', 'url': 'https://cdn.benzing... \n",
+ " \n",
+ " \n",
+ " text \n",
+ " <p>Whales with a lot of money to spend have ta... \n",
+ " \n",
+ " \n",
+ " url \n",
+ " https://www.benzinga.com/insights/options/24/0... \n",
+ " \n",
+ " \n",
+ " id \n",
+ " 40515079 \n",
+ " \n",
+ " \n",
+ " author \n",
+ " Benzinga Insights \n",
+ " \n",
+ " \n",
+ " teaser \n",
+ " \n",
+ " \n",
+ " channels \n",
+ " Options,Markets \n",
+ " \n",
+ " \n",
+ " stocks \n",
+ " VRT \n",
+ " \n",
+ " \n",
+ " tags \n",
+ " BZI-UOA \n",
+ " \n",
+ " \n",
+ " updated \n",
+ " 2024-08-22 14:46:33-04:00 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
Whales with a lot of money to spend have ta...\n",
+ "url https://www.benzinga.com/insights/options/24/0...\n",
+ "id 40515079\n",
+ "author Benzinga Insights\n",
+ "teaser \n",
+ "channels Options,Markets\n",
+ "stocks VRT\n",
+ "tags BZI-UOA\n",
+ "updated 2024-08-22 14:46:33-04:00"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "obb.news.world(limit=1, provider=\"benzinga\").to_df().T"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n",
+ "\n",
+ "\n",
+ "---\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Standardization of input and output schemas is done with Pydantic models"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### This is a standard model"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'date': FieldInfo(annotation=datetime, required=True, alias_priority=1, validation_alias='date', serialization_alias='date', description='The date of the data. The published date of the article.'),\n",
+ " 'title': FieldInfo(annotation=str, required=True, alias_priority=1, validation_alias='title', serialization_alias='title', description='Title of the article.'),\n",
+ " 'images': FieldInfo(annotation=Union[List[Dict[str, str]], NoneType], required=False, default=None, alias_priority=1, validation_alias='images', serialization_alias='images', description='Images associated with the article.'),\n",
+ " 'text': FieldInfo(annotation=Union[str, NoneType], required=False, default=None, alias_priority=1, validation_alias='text', serialization_alias='text', description='Text/body of the article.'),\n",
+ " 'url': FieldInfo(annotation=Union[str, NoneType], required=False, default=None, alias_priority=1, validation_alias='url', serialization_alias='url', description='URL to the article.')}"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from openbb_core.provider.standard_models.world_news import WorldNewsData\n",
+ "\n",
+ "WorldNewsData.__fields__"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### These are provider models"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from openbb_intrinio.models.world_news import IntrinioWorldNewsData\n",
+ "from openbb_benzinga.models.world_news import BenzingaWorldNewsData"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'date': FieldInfo(annotation=datetime, required=True, alias_priority=1, validation_alias='date', serialization_alias='date', description='The date of the data. The published date of the article.'),\n",
+ " 'title': FieldInfo(annotation=str, required=True, alias_priority=1, validation_alias='title', serialization_alias='title', description='Title of the article.'),\n",
+ " 'images': FieldInfo(annotation=Union[List[Dict[str, str]], NoneType], required=False, default=None, alias_priority=1, validation_alias='images', serialization_alias='images', description='Images associated with the article.'),\n",
+ " 'text': FieldInfo(annotation=Union[str, NoneType], required=False, default=None, alias_priority=1, validation_alias='text', serialization_alias='text', description='Text/body of the article.'),\n",
+ " 'url': FieldInfo(annotation=Union[str, NoneType], required=False, default=None, alias_priority=1, validation_alias='url', serialization_alias='url', description='URL to the article.'),\n",
+ " 'source': FieldInfo(annotation=Union[str, NoneType], required=False, default=None, alias_priority=1, validation_alias='source', serialization_alias='source', description='The source of the news article.'),\n",
+ " 'summary': FieldInfo(annotation=Union[str, NoneType], required=False, default=None, alias_priority=1, validation_alias='summary', serialization_alias='summary', description='The summary of the news article.'),\n",
+ " 'topics': FieldInfo(annotation=Union[str, NoneType], required=False, default=None, alias_priority=1, validation_alias='topics', serialization_alias='topics', description='The topics related to the news article.'),\n",
+ " 'word_count': FieldInfo(annotation=Union[int, NoneType], required=False, default=None, alias_priority=1, validation_alias='wordCount', serialization_alias='word_count', description='The word count of the news article.'),\n",
+ " 'business_relevance': FieldInfo(annotation=Union[float, NoneType], required=False, default=None, alias_priority=1, validation_alias='businessRelevance', serialization_alias='business_relevance', description=' \\tHow strongly correlated the news article is to the business'),\n",
+ " 'sentiment': FieldInfo(annotation=Union[str, NoneType], required=False, default=None, alias_priority=1, validation_alias='sentiment', serialization_alias='sentiment', description='The sentiment of the news article - i.e, negative, positive.'),\n",
+ " 'sentiment_confidence': FieldInfo(annotation=Union[float, NoneType], required=False, default=None, alias_priority=1, validation_alias='sentimentConfidence', serialization_alias='sentiment_confidence', description='The confidence score of the sentiment rating.'),\n",
+ " 'language': FieldInfo(annotation=Union[str, NoneType], required=False, default=None, alias_priority=1, validation_alias='language', serialization_alias='language', description='The language of the news article.'),\n",
+ " 'spam': FieldInfo(annotation=Union[bool, NoneType], required=False, default=None, alias_priority=1, validation_alias='spam', serialization_alias='spam', description='Whether the news article is spam.'),\n",
+ " 'copyright': FieldInfo(annotation=Union[str, NoneType], required=False, default=None, alias_priority=1, validation_alias='copyright', serialization_alias='copyright', description='The copyright notice of the news article.'),\n",
+ " 'id': FieldInfo(annotation=str, required=True, alias_priority=1, validation_alias='id', serialization_alias='id', description='Article ID.'),\n",
+ " 'company': FieldInfo(annotation=Union[IntrinioCompany, NoneType], required=False, default=None, alias_priority=1, validation_alias='company', serialization_alias='company', description='The Intrinio Company object. Contains details company reference data.'),\n",
+ " 'security': FieldInfo(annotation=Union[IntrinioSecurity, NoneType], required=False, default=None, alias_priority=1, validation_alias='security', serialization_alias='security', description='The Intrinio Security object. Contains the security details related to the news article.')}"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "IntrinioWorldNewsData.__fields__"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'date': FieldInfo(annotation=datetime, required=True, alias_priority=1, validation_alias='date', serialization_alias='date', description='The date of the data. The published date of the article.'),\n",
+ " 'title': FieldInfo(annotation=str, required=True, alias_priority=1, validation_alias='title', serialization_alias='title', description='Title of the article.'),\n",
+ " 'images': FieldInfo(annotation=Union[List[Dict[str, str]], NoneType], required=False, default=None, alias_priority=1, validation_alias='images', serialization_alias='images', description='Images associated with the article.'),\n",
+ " 'text': FieldInfo(annotation=Union[str, NoneType], required=False, default=None, alias_priority=1, validation_alias='text', serialization_alias='text', description='Text/body of the article.'),\n",
+ " 'url': FieldInfo(annotation=Union[str, NoneType], required=False, default=None, alias_priority=1, validation_alias='url', serialization_alias='url', description='URL to the article.'),\n",
+ " 'id': FieldInfo(annotation=str, required=True, alias_priority=1, validation_alias='id', serialization_alias='id', description='Article ID.'),\n",
+ " 'author': FieldInfo(annotation=Union[str, NoneType], required=False, default=None, alias_priority=1, validation_alias='author', serialization_alias='author', description='Author of the news.'),\n",
+ " 'teaser': FieldInfo(annotation=Union[str, NoneType], required=False, default=None, alias_priority=1, validation_alias='teaser', serialization_alias='teaser', description='Teaser of the news.'),\n",
+ " 'channels': FieldInfo(annotation=Union[str, NoneType], required=False, default=None, alias_priority=1, validation_alias='channels', serialization_alias='channels', description='Channels associated with the news.'),\n",
+ " 'stocks': FieldInfo(annotation=Union[str, NoneType], required=False, default=None, alias_priority=1, validation_alias='stocks', serialization_alias='stocks', description='Stocks associated with the news.'),\n",
+ " 'tags': FieldInfo(annotation=Union[str, NoneType], required=False, default=None, alias_priority=1, validation_alias='tags', serialization_alias='tags', description='Tags associated with the news.'),\n",
+ " 'updated': FieldInfo(annotation=Union[datetime, NoneType], required=False, default=None, alias_priority=1, validation_alias='updated', serialization_alias='updated', description='Updated date of the news.')}"
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "BenzingaWorldNewsData.__fields__"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Inheritance, field mapping and quality assurance"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Provider models inherit from Standard Models"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "True"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "issubclass(BenzingaWorldNewsData, WorldNewsData)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Provider models use aliases to map to standard fields"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'date': 'created', 'text': 'body', 'images': 'image'}"
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "BenzingaWorldNewsData.__alias_dict__"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Provider models implement field validation"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'date_validate': Decorator(cls_ref='openbb_intrinio.models.world_news.IntrinioWorldNewsData:140298041431344', cls_var_name='date_validate', func=>, shim=None, info=FieldValidatorDecoratorInfo(fields=('publication_date',), mode='before', check_fields=False)),\n",
+ " 'topics_validate': Decorator(cls_ref='openbb_intrinio.models.world_news.IntrinioWorldNewsData:140298041431344', cls_var_name='topics_validate', func=>, shim=None, info=FieldValidatorDecoratorInfo(fields=('topics',), mode='before', check_fields=False)),\n",
+ " 'copyright_validate': Decorator(cls_ref='openbb_intrinio.models.world_news.IntrinioWorldNewsData:140298041431344', cls_var_name='copyright_validate', func=>, shim=None, info=FieldValidatorDecoratorInfo(fields=('copyright',), mode='before', check_fields=False))}"
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "IntrinioWorldNewsData.__dict__[\"__pydantic_decorators__\"].field_validators"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'date_validate': Decorator(cls_ref='openbb_benzinga.models.world_news.BenzingaWorldNewsData:140297991464784', cls_var_name='date_validate', func=>, shim=None, info=FieldValidatorDecoratorInfo(fields=('date', 'updated'), mode='before', check_fields=False)),\n",
+ " 'list_validate': Decorator(cls_ref='openbb_benzinga.models.world_news.BenzingaWorldNewsData:140297991464784', cls_var_name='list_validate', func=>, shim=None, info=FieldValidatorDecoratorInfo(fields=('stocks', 'channels', 'tags'), mode='before', check_fields=False)),\n",
+ " 'id_validate': Decorator(cls_ref='openbb_benzinga.models.world_news.BenzingaWorldNewsData:140297991464784', cls_var_name='id_validate', func=>, shim=None, info=FieldValidatorDecoratorInfo(fields=('id', 'text', 'teaser', 'title', 'author'), mode='before', check_fields=False)),\n",
+ " 'empty_list': Decorator(cls_ref='openbb_benzinga.models.world_news.BenzingaWorldNewsData:140297991464784', cls_var_name='empty_list', func=>, shim=None, info=FieldValidatorDecoratorInfo(fields=('images',), mode='before', check_fields=False))}"
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "BenzingaWorldNewsData.__dict__[\"__pydantic_decorators__\"].field_validators"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Example:\n",
+ "\n",
+ "```python\n",
+ "@field_validator(\"date\")\n",
+ "def date_validate(cls, v):\n",
+ " \"\"\"Return the date as a datetime object.\"\"\"\n",
+ " return datetime.strptime(v, \"%a, %d %b %Y %H:%M:%S %z\")\n",
+ "```\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "---"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Modularity"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "obb"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Each extension and provider integration is a separate python package"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "!pip list | grep openbb"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Install/Uninstall a provider as python packages"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "!pip uninstall openbb-yfinance"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "To learn more about how it works, here are a few links to the [documentation](https://docs.openbb.co/platform):\n",
+ "\n",
+ "- [Architecture. Data, Query Parameters and Fetchers.](https://docs.openbb.co/platform/developer_guide/architecture_overview)\n",
+ "- [Integrating a new provider.](https://docs.openbb.co/platform/user_guides/add_data_provider_extension)\n",
+ "- [Building standalone extensions.](https://docs.openbb.co/platform/getting_started/create_new_provider_extension)\n",
+ "- and more in the [Development](https://docs.openbb.co/platform/developer_guide) section of the docs...\n"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "venv",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.12.4"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/examples/portfolioOptimizationUsingModernPortfolioTheory.ipynb b/examples/portfolioOptimizationUsingModernPortfolioTheory.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..ae2e21fd1502d6bae5c5c7351b8f83e489cab1fb
--- /dev/null
+++ b/examples/portfolioOptimizationUsingModernPortfolioTheory.ipynb
@@ -0,0 +1,2777 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "HRMC-0N0sSjJ"
+ },
+ "source": [
+ "## Portfolio Optimization Using Modern Portfolio Theory\n",
+ "\n",
+ "#### Description\n",
+ "This notebook utilizes OpenBB’s data for portfolio optimization based on MPT principles. We would be optimizing a portfolio of top 10 crypto assets, using the daily close data from 1st october 2023 to 1st october 2024.\n",
+ "\n",
+ "The portfolio optimization would be done using the mean-variance approach. The mean-variance approach helps determine the optimal allocation of assets in a portfolio to minimize overall risk while maximizing expected returns.\n",
+ "\n",
+ "#### Author\n",
+ "[Ambrose Ikpele](https://github.com/ambroseikpele)\n",
+ "\n",
+ "[](https://colab.research.google.com/github/OpenBB-Finance/OpenBB/blob/develop/examples/[Notebook_Name].ipynb)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "### Introduction"
+ ],
+ "metadata": {
+ "id": "-Ih2c6xRxklu"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "Modern Portfolio Theory (MPT) is a mathematical framework for constructing a portfolio of assets to maximize expected return based on a given level of risk. In this notebook, we will implement MPT to construct an optimal portfolio with minimum volatility using a selection of the top cryptocurrencies as our assets. The notebook will fetch historical price data for these assets, calculate the portfolio's expected return and risk, and visualize the optimal portfolio based on risk-return trade-offs."
+ ],
+ "metadata": {
+ "id": "mWd0TC0DxZm-"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "Install external packages"
+ ],
+ "metadata": {
+ "id": "_rMPfEH2KJjA"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "!pip install openbb\n",
+ "!pip install PyPortfolioOpt"
+ ],
+ "metadata": {
+ "id": "iexQsZ1XvYa8"
+ },
+ "execution_count": 17,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "Import necessary packages"
+ ],
+ "metadata": {
+ "id": "CH4i_WQGRmG-"
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "id": "B8m_9BassSjK"
+ },
+ "outputs": [],
+ "source": [
+ "from openbb import obb\n",
+ "\n",
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "from pypfopt import EfficientFrontier\n",
+ "from pypfopt import CovarianceShrinkage, CLA, expected_returns"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "### Define the assets and fetch the data"
+ ],
+ "metadata": {
+ "id": "w3XB7egmzlmI"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "Fetch the daily data of the top crypto currencies for a period of one year using openbb"
+ ],
+ "metadata": {
+ "id": "SyF2ROKDyKdJ"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "top_crypto= ['ADA-USD', 'BNB-USD', 'BTC-USD', 'DOT-USD', 'ETH-USD', 'LTC-USD','MATIC-USD', 'SOL-USD', 'TRX-USD', 'XRP-USD']\n",
+ "\n",
+ "ohlc_data= obb.crypto.price.historical(top_crypto, provider=\"yfinance\", interval='1d', start_date='2023-10-01', end_date='2024-10-01').to_df()\n",
+ "ohlc_data"
+ ],
+ "metadata": {
+ "id": "C3UwCtY8vDaQ",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 455
+ },
+ "outputId": "fd4a3544-32f9-49c1-d7ad-bca9efa88baa"
+ },
+ "execution_count": 2,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ " open high low close \\\n",
+ "date \n",
+ "2023-10-01 0.254043 0.267471 0.254019 0.265895 \n",
+ "2023-10-01 214.800323 219.133835 213.506516 218.047134 \n",
+ "2023-10-01 26967.396484 28047.238281 26965.093750 27983.750000 \n",
+ "2023-10-01 4.105477 4.279937 4.103880 4.261009 \n",
+ "2023-10-01 1671.161499 1750.595703 1670.082153 1733.810425 \n",
+ "... ... ... ... ... \n",
+ "2024-09-30 69.314308 69.321297 66.454277 66.820450 \n",
+ "2024-09-30 0.421419 0.421627 0.394318 0.395917 \n",
+ "2024-09-30 158.632416 159.508926 152.019836 152.618469 \n",
+ "2024-09-30 0.156474 0.156746 0.154867 0.155915 \n",
+ "2024-09-30 0.641945 0.652411 0.610951 0.611492 \n",
+ "\n",
+ " volume symbol \n",
+ "date \n",
+ "2023-10-01 1.650882e+08 ADA-USD \n",
+ "2023-10-01 3.874081e+08 BNB-USD \n",
+ "2023-10-01 9.503917e+09 BTC-USD \n",
+ "2023-10-01 8.294334e+07 DOT-USD \n",
+ "2023-10-01 5.054880e+09 ETH-USD \n",
+ "... ... ... \n",
+ "2024-09-30 3.003743e+08 LTC-USD \n",
+ "2024-09-30 3.730373e+07 MATIC-USD \n",
+ "2024-09-30 2.376781e+09 SOL-USD \n",
+ "2024-09-30 3.565544e+08 TRX-USD \n",
+ "2024-09-30 2.051369e+09 XRP-USD \n",
+ "\n",
+ "[3660 rows x 6 columns]"
+ ],
+ "text/html": [
+ "\n",
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\n",
+ "\n",
+ "
\n",
+ " \n",
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+ " open \n",
+ " high \n",
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+ " 159.508926 \n",
+ " 152.019836 \n",
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+ " 0.156746 \n",
+ " 0.154867 \n",
+ " 0.155915 \n",
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+ " TRX-USD \n",
+ " \n",
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+ " ADA-USD \n",
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+ " \n",
+ " \n",
+ " BTC-USD \n",
+ " 0.952337 \n",
+ " \n",
+ " \n",
+ " DOT-USD \n",
+ " 0.319752 \n",
+ " \n",
+ " \n",
+ " ETH-USD \n",
+ " 0.591286 \n",
+ " \n",
+ " \n",
+ " LTC-USD \n",
+ " 0.173647 \n",
+ " \n",
+ " \n",
+ " MATIC-USD \n",
+ " -0.058596 \n",
+ " \n",
+ " \n",
+ " SOL-USD \n",
+ " 2.288214 \n",
+ " \n",
+ " \n",
+ " TRX-USD \n",
+ " 0.616572 \n",
+ " \n",
+ " \n",
+ " XRP-USD \n",
+ " 0.344980 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
dtype: float64\n",
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " symbol \n",
+ " ADA-USD \n",
+ " BNB-USD \n",
+ " BTC-USD \n",
+ " DOT-USD \n",
+ " ETH-USD \n",
+ " LTC-USD \n",
+ " MATIC-USD \n",
+ " SOL-USD \n",
+ " TRX-USD \n",
+ " XRP-USD \n",
+ " \n",
+ " \n",
+ " symbol \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " ADA-USD \n",
+ " 0.366706 \n",
+ " 0.150286 \n",
+ " 0.180182 \n",
+ " 0.304589 \n",
+ " 0.203891 \n",
+ " 0.205121 \n",
+ " 0.278896 \n",
+ " 0.297128 \n",
+ " 0.083817 \n",
+ " 0.194349 \n",
+ " \n",
+ " \n",
+ " BNB-USD \n",
+ " 0.150286 \n",
+ " 0.231405 \n",
+ " 0.122723 \n",
+ " 0.150846 \n",
+ " 0.139246 \n",
+ " 0.109135 \n",
+ " 0.173927 \n",
+ " 0.172109 \n",
+ " 0.047007 \n",
+ " 0.093584 \n",
+ " \n",
+ " \n",
+ " BTC-USD \n",
+ " 0.180182 \n",
+ " 0.122723 \n",
+ " 0.190093 \n",
+ " 0.176476 \n",
+ " 0.172781 \n",
+ " 0.127462 \n",
+ " 0.170640 \n",
+ " 0.222148 \n",
+ " 0.055094 \n",
+ " 0.120736 \n",
+ " \n",
+ " \n",
+ " DOT-USD \n",
+ " 0.304589 \n",
+ " 0.150846 \n",
+ " 0.176476 \n",
+ " 0.386458 \n",
+ " 0.207296 \n",
+ " 0.193352 \n",
+ " 0.288592 \n",
+ " 0.333958 \n",
+ " 0.086146 \n",
+ " 0.192491 \n",
+ " \n",
+ " \n",
+ " ETH-USD \n",
+ " 0.203891 \n",
+ " 0.139246 \n",
+ " 0.172781 \n",
+ " 0.207296 \n",
+ " 0.259972 \n",
+ " 0.160428 \n",
+ " 0.225087 \n",
+ " 0.232898 \n",
+ " 0.063453 \n",
+ " 0.133452 \n",
+ " \n",
+ " \n",
+ " LTC-USD \n",
+ " 0.205121 \n",
+ " 0.109135 \n",
+ " 0.127462 \n",
+ " 0.193352 \n",
+ " 0.160428 \n",
+ " 0.270809 \n",
+ " 0.191384 \n",
+ " 0.191960 \n",
+ " 0.055784 \n",
+ " 0.158367 \n",
+ " \n",
+ " \n",
+ " MATIC-USD \n",
+ " 0.278896 \n",
+ " 0.173927 \n",
+ " 0.170640 \n",
+ " 0.288592 \n",
+ " 0.225087 \n",
+ " 0.191384 \n",
+ " 0.415755 \n",
+ " 0.290945 \n",
+ " 0.075620 \n",
+ " 0.181063 \n",
+ " \n",
+ " \n",
+ " SOL-USD \n",
+ " 0.297128 \n",
+ " 0.172109 \n",
+ " 0.222148 \n",
+ " 0.333958 \n",
+ " 0.232898 \n",
+ " 0.191960 \n",
+ " 0.290945 \n",
+ " 0.595302 \n",
+ " 0.095612 \n",
+ " 0.185187 \n",
+ " \n",
+ " \n",
+ " TRX-USD \n",
+ " 0.083817 \n",
+ " 0.047007 \n",
+ " 0.055094 \n",
+ " 0.086146 \n",
+ " 0.063453 \n",
+ " 0.055784 \n",
+ " 0.075620 \n",
+ " 0.095612 \n",
+ " 0.098694 \n",
+ " 0.057615 \n",
+ " \n",
+ " \n",
+ " XRP-USD \n",
+ " 0.194349 \n",
+ " 0.093584 \n",
+ " 0.120736 \n",
+ " 0.192491 \n",
+ " 0.133452 \n",
+ " 0.158367 \n",
+ " 0.181063 \n",
+ " 0.185187 \n",
+ " 0.057615 \n",
+ " 0.267912 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " returns \n",
+ " volatility \n",
+ " weights \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 \n",
+ " 2.288214 \n",
+ " 0.771558 \n",
+ " [[0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.... \n",
+ " \n",
+ " \n",
+ " 1 \n",
+ " 2.288214 \n",
+ " 0.771558 \n",
+ " [[0.0], [8.540177112501205e-18], [0.0], [0.0],... \n",
+ " \n",
+ " \n",
+ " 2 \n",
+ " 2.288214 \n",
+ " 0.771558 \n",
+ " [[0.0], [1.708035422500241e-17], [0.0], [0.0],... \n",
+ " \n",
+ " \n",
+ " 3 \n",
+ " 2.288214 \n",
+ " 0.771558 \n",
+ " [[0.0], [2.5620531337503614e-17], [0.0], [0.0]... \n",
+ " \n",
+ " \n",
+ " 4 \n",
+ " 2.288214 \n",
+ " 0.771558 \n",
+ " [[0.0], [3.416070845000482e-17], [0.0], [0.0],... \n",
+ " \n",
+ " \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " \n",
+ " \n",
+ " 74 \n",
+ " 0.699686 \n",
+ " 0.289411 \n",
+ " [[0.0], [0.14064237710852637], [0.110488877515... \n",
+ " \n",
+ " \n",
+ " 75 \n",
+ " 0.695988 \n",
+ " 0.289372 \n",
+ " [[0.0], [0.1384244599412712], [0.1077026598376... \n",
+ " \n",
+ " \n",
+ " 76 \n",
+ " 0.692291 \n",
+ " 0.289344 \n",
+ " [[0.0], [0.136206542774016], [0.10491644215937... \n",
+ " \n",
+ " \n",
+ " 77 \n",
+ " 0.688593 \n",
+ " 0.289328 \n",
+ " [[0.0], [0.1339886256067608], [0.1021302244811... \n",
+ " \n",
+ " \n",
+ " 78 \n",
+ " 0.684895 \n",
+ " 0.289322 \n",
+ " [[0.0], [0.1317707084395056], [0.0993440068028... \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
79 rows × 3 columns
\n",
+ "
\n",
+ "
\n",
+ "
"
+ ],
+ "image/png": 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\n"
+ },
+ "metadata": {}
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "Get the weights (in perentage) of the portfolio with the lowest volatility"
+ ],
+ "metadata": {
+ "id": "v4jE07br-DEJ"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "optimized_weight=np.array(list(cla.max_sharpe().values()))\n",
+ "optimized_weight= np.round(optimized_weight, 4)\n",
+ "\n",
+ "pie_df=pd.DataFrame(optimized_weight*100, index=prices.columns, columns=['weights'])\n",
+ "pie_df= pie_df.sort_values(by=['weights'], ascending=False)\n",
+ "pie_df"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 394
+ },
+ "id": "FzvioR9k11Tx",
+ "outputId": "9eae848f-52ba-47bc-c17f-4cf753c82504"
+ },
+ "execution_count": 9,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ " weights\n",
+ "symbol \n",
+ "SOL-USD 37.11\n",
+ "TRX-USD 32.71\n",
+ "BNB-USD 30.17\n",
+ "ADA-USD 0.00\n",
+ "BTC-USD -0.00\n",
+ "DOT-USD 0.00\n",
+ "ETH-USD 0.00\n",
+ "LTC-USD 0.00\n",
+ "MATIC-USD 0.00\n",
+ "XRP-USD 0.00"
+ ],
+ "text/html": [
+ "\n",
+ " \n",
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " weights \n",
+ " \n",
+ " \n",
+ " symbol \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " SOL-USD \n",
+ " 37.11 \n",
+ " \n",
+ " \n",
+ " TRX-USD \n",
+ " 32.71 \n",
+ " \n",
+ " \n",
+ " BNB-USD \n",
+ " 30.17 \n",
+ " \n",
+ " \n",
+ " ADA-USD \n",
+ " 0.00 \n",
+ " \n",
+ " \n",
+ " BTC-USD \n",
+ " -0.00 \n",
+ " \n",
+ " \n",
+ " DOT-USD \n",
+ " 0.00 \n",
+ " \n",
+ " \n",
+ " ETH-USD \n",
+ " 0.00 \n",
+ " \n",
+ " \n",
+ " LTC-USD \n",
+ " 0.00 \n",
+ " \n",
+ " \n",
+ " MATIC-USD \n",
+ " 0.00 \n",
+ " \n",
+ " \n",
+ " XRP-USD \n",
+ " 0.00 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
\n",
+ "
"
+ ],
+ "image/png": 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\n"
+ },
+ "metadata": {}
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "The above weights forms a portfolio with maximum sharpe ratio"
+ ],
+ "metadata": {
+ "id": "GAT1yTIEXGh4"
+ }
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "obb",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "name": "python",
+ "version": "3.9.19"
+ },
+ "colab": {
+ "provenance": []
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/examples/riskReturnAnalysis.ipynb b/examples/riskReturnAnalysis.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..8176bcd37a1ff121eb69874402b85abbf87e88ec
--- /dev/null
+++ b/examples/riskReturnAnalysis.ipynb
@@ -0,0 +1,1012 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Advanced Risk-Return Analysis and Portfolio Optimization with OpenBB\n",
+ "===========================================================\n",
+ "#### Description\n",
+ "This notebook demonstrates advanced techniques for risk-return analysis and portfolio optimization using OpenBB. We'll explore various asset classes, implement modern portfolio theory, and utilize OpenBB's extensive financial analysis capabilities. This comprehensive guide covers:\n",
+ "\n",
+ "1. Data collection and preprocessing\n",
+ "2. Exploratory data analysis with visualizations\n",
+ "3. Risk and return calculations\n",
+ "4. Efficient frontier computation\n",
+ "5. Portfolio optimization techniques\n",
+ "6. Advanced risk metrics (VaR, CVaR)\n",
+ "7. Performance attribution\n",
+ "8. Scenario analysis and stress testing\n",
+ "\n",
+ "By the end of this notebook, you'll have a deep understanding of how to use OpenBB for sophisticated financial analysis and portfolio management.\n",
+ "\n",
+ "#### Author\n",
+ "[Amit Kumar](https://github.com/HmbleCreator)\n",
+ "\n",
+ "[](https://colab.research.google.com/github/OpenBB-Finance/OpenBB/blob/develop/examples/[Notebook_Name].ipynb)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "If you are running this notebook in Colab, you can run the following command to install the OpenBB Platform:\n",
+ "\n",
+ "```python\n",
+ "!pip install openbb\n",
+ "```\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "!pip install openbb\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Dependencies\n",
+ "To run this notebook, you'll need to install the following dependencies:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "!pip install pandas numpy matplotlib seaborn scipy scikit-learn"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Imports"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Import libraries\n",
+ "from openbb import obb\n",
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "import seaborn as sns\n",
+ "from datetime import datetime, timedelta"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Set plotting style\n",
+ "plt.style.use('default') # Use the default Matplotlib style\n",
+ "sns.set_theme(style=\"whitegrid\") # Set Seaborn style\n",
+ "sns.set_palette(\"viridis\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Login "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from openbb import obb\n",
+ "obb.account.login(pat=\"Enter Your PAT Key\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 1. Data Collection and Preprocessing\n",
+ "Let's collect historical data for a diverse set of asset classes:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Combined Closing Prices:\n",
+ " US Large Cap US Small Cap International Developed \\\n",
+ "date \n",
+ "2023-10-19 426.429993 168.619995 67.169998 \n",
+ "2023-10-20 421.190002 166.440002 66.570000 \n",
+ "2023-10-23 420.459991 165.029999 66.620003 \n",
+ "2023-10-24 423.630005 166.470001 67.000000 \n",
+ "2023-10-25 417.549988 163.720001 66.519997 \n",
+ "\n",
+ " Emerging Markets US Aggregate Bonds US Treasury Bonds \\\n",
+ "date \n",
+ "2023-10-19 37.240002 91.669998 82.769997 \n",
+ "2023-10-20 36.790001 92.000000 83.239998 \n",
+ "2023-10-23 36.759998 92.360001 84.239998 \n",
+ "2023-10-24 37.230000 92.690002 85.349998 \n",
+ "2023-10-25 36.740002 92.000000 83.449997 \n",
+ "\n",
+ " Real Estate Gold Commodities \n",
+ "date \n",
+ "2023-10-19 72.889999 183.089996 25.320000 \n",
+ "2023-10-20 72.400002 183.589996 25.129999 \n",
+ "2023-10-23 71.690002 182.970001 24.969999 \n",
+ "2023-10-24 72.610001 182.949997 24.700001 \n",
+ "2023-10-25 71.050003 183.720001 24.879999 \n",
+ "\n",
+ "Daily Returns:\n",
+ " US Large Cap US Small Cap International Developed \\\n",
+ "date \n",
+ "2023-10-20 -0.012288 -0.012928 -0.008933 \n",
+ "2023-10-23 -0.001733 -0.008472 0.000751 \n",
+ "2023-10-24 0.007539 0.008726 0.005704 \n",
+ "2023-10-25 -0.014352 -0.016519 -0.007164 \n",
+ "2023-10-26 -0.011975 0.002565 -0.007216 \n",
+ "\n",
+ " Emerging Markets US Aggregate Bonds US Treasury Bonds \\\n",
+ "date \n",
+ "2023-10-20 -0.012084 0.003600 0.005678 \n",
+ "2023-10-23 -0.000816 0.003913 0.012013 \n",
+ "2023-10-24 0.012786 0.003573 0.013177 \n",
+ "2023-10-25 -0.013161 -0.007444 -0.022261 \n",
+ "2023-10-26 -0.004899 0.006739 0.015339 \n",
+ "\n",
+ " Real Estate Gold Commodities \n",
+ "date \n",
+ "2023-10-20 -0.006722 0.002731 -0.007504 \n",
+ "2023-10-23 -0.009807 -0.003377 -0.006367 \n",
+ "2023-10-24 0.012833 -0.000109 -0.010813 \n",
+ "2023-10-25 -0.021485 0.004209 0.007287 \n",
+ "2023-10-26 0.019141 0.001578 -0.008440 \n",
+ "Data exported successfully to financial_data.xlsx\n"
+ ]
+ }
+ ],
+ "source": [
+ "import pandas as pd\n",
+ "from openbb import obb\n",
+ "from datetime import datetime, timedelta\n",
+ "import time\n",
+ "\n",
+ "# Define asset classes and their tickers\n",
+ "assets = {\n",
+ " 'US Large Cap': 'SPY',\n",
+ " 'US Small Cap': 'IWM',\n",
+ " 'International Developed': 'EFA',\n",
+ " 'Emerging Markets': 'EEM',\n",
+ " 'US Aggregate Bonds': 'AGG',\n",
+ " 'US Treasury Bonds': 'TLT',\n",
+ " 'Real Estate': 'VNQ',\n",
+ " 'Gold': 'GLD',\n",
+ " 'Commodities': 'DBC'\n",
+ "}\n",
+ "\n",
+ "# Set date range\n",
+ "end_date = datetime.now().date()\n",
+ "start_date = end_date - timedelta(days=365) # Adjusted to 1 year for testing\n",
+ "\n",
+ "# Function to fetch historical data with retries\n",
+ "def fetch_historical_data(ticker, retries=3):\n",
+ " for attempt in range(retries):\n",
+ " try:\n",
+ " # Fetch historical data using the correct function and parameters\n",
+ " historical_data = obb.equity.price.historical(ticker)\n",
+ "\n",
+ " # Convert the OBBject to a DataFrame using the to_dataframe() method\n",
+ " df = historical_data.to_dataframe()\n",
+ "\n",
+ " # Check if DataFrame is empty and return it if not\n",
+ " if df is not None and not df.empty:\n",
+ " return df\n",
+ "\n",
+ " except Exception as e:\n",
+ " print(f\"Attempt {attempt + 1} failed for {ticker}: {str(e)}\")\n",
+ " time.sleep(2) # Wait before retrying\n",
+ "\n",
+ " return None\n",
+ "\n",
+ "# Fetch data and combine into a single DataFrame\n",
+ "combined_data = {}\n",
+ "for asset_name, ticker in assets.items():\n",
+ " df = fetch_historical_data(ticker)\n",
+ "\n",
+ " if df is not None and not df.empty:\n",
+ " # Store closing prices with asset name as key\n",
+ " combined_data[asset_name] = df['close']\n",
+ " else:\n",
+ " print(f\"No data returned for {asset_name} ({ticker}).\")\n",
+ "\n",
+ "# Combine all asset closing prices into a single DataFrame\n",
+ "df_combined = pd.DataFrame(combined_data)\n",
+ "\n",
+ "# Check if we have any data\n",
+ "if df_combined.empty:\n",
+ " raise ValueError(\"No data was successfully retrieved. Please check your tickers and date range.\")\n",
+ "\n",
+ "# Calculate daily returns with specified fill method to avoid FutureWarning\n",
+ "returns = df_combined.pct_change(fill_method=None).dropna()\n",
+ "\n",
+ "# Display the combined DataFrame for better readability\n",
+ "print(\"Combined Closing Prices:\")\n",
+ "print(df_combined.head()) # Display first few rows of closing prices\n",
+ "\n",
+ "print(\"\\nDaily Returns:\")\n",
+ "print(returns.head()) # Display first few rows of returns\n",
+ "\n",
+ "\n",
+ "# Export the combined DataFrame to an Excel file\n",
+ "output_file = \"financial_data.xlsx\"\n",
+ "df_combined.to_excel(output_file)\n",
+ "\n",
+ "print(f\"Data exported successfully to {output_file}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 2. Exploratory Data Analysis\n",
+ "Let's visualize our data to better understand the relationships between different asset classes."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Heatmap data (preview): \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAABLAAAAPoCAYAAADOWwfbAAAAOX...\"\n",
+ "Cumulative returns data (preview): \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAoAAAAHgCAYAAAA10dzkAAAAOX...\"\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import io\n",
+ "import base64\n",
+ "from IPython.display import HTML\n",
+ "\n",
+ "def fig_to_base64(fig):\n",
+ " buf = io.BytesIO()\n",
+ " fig.savefig(buf, format='png')\n",
+ " buf.seek(0)\n",
+ " return base64.b64encode(buf.getvalue()).decode('utf-8')\n",
+ "\n",
+ "# Correlation heatmap\n",
+ "plt.figure(figsize=(12, 10))\n",
+ "sns.heatmap(returns.corr(), annot=True, cmap='coolwarm', linewidths=0.5)\n",
+ "plt.title('Correlation Heatmap of Asset Returns')\n",
+ "heatmap_base64 = fig_to_base64(plt.gcf())\n",
+ "plt.close()\n",
+ "\n",
+ "# Cumulative returns plot\n",
+ "cumulative_returns = (1 + returns).cumprod()\n",
+ "plt.figure(figsize=(12, 6))\n",
+ "cumulative_returns.plot()\n",
+ "plt.title('Cumulative Returns of Asset Classes')\n",
+ "plt.ylabel('Cumulative Return')\n",
+ "plt.legend(loc='center left', bbox_to_anchor=(1, 0.5))\n",
+ "plt.tight_layout()\n",
+ "cumulative_returns_base64 = fig_to_base64(plt.gcf())\n",
+ "plt.close()\n",
+ "\n",
+ "# Display images\n",
+ "display(HTML(f'"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "import matplotlib.pyplot as plt\n",
+ "import seaborn as sns\n",
+ "\n",
+ "# Check if returns DataFrame is not empty before calculations\n",
+ "if not returns.empty:\n",
+ " # Calculate annualized returns and volatility\n",
+ " annual_returns = returns.mean() * 252\n",
+ " annual_volatility = returns.std() * np.sqrt(252)\n",
+ "\n",
+ " # Calculate Sharpe Ratio (assuming risk-free rate of 2%)\n",
+ " risk_free_rate = 0.02\n",
+ " sharpe_ratio = (annual_returns - risk_free_rate) / annual_volatility\n",
+ "\n",
+ " # Combine metrics into a DataFrame\n",
+ " risk_return_metrics = pd.DataFrame({\n",
+ " 'Return': annual_returns,\n",
+ " 'Volatility': annual_volatility,\n",
+ " 'Sharpe Ratio': sharpe_ratio\n",
+ " })\n",
+ "\n",
+ " # Sort and display the metrics\n",
+ " print(risk_return_metrics.sort_values('Sharpe Ratio', ascending=False))\n",
+ "\n",
+ " # Visualize risk-return tradeoff\n",
+ " plt.figure(figsize=(12, 8))\n",
+ " sns.scatterplot(data=risk_return_metrics, x='Volatility', y='Return', size='Sharpe Ratio',\n",
+ " sizes=(50, 500), legend='brief', hue='Sharpe Ratio', palette='viridis')\n",
+ "\n",
+ " # Annotate points with asset names\n",
+ " for i, asset in enumerate(risk_return_metrics.index):\n",
+ " plt.annotate(asset,\n",
+ " (risk_return_metrics['Volatility'][i], risk_return_metrics['Return'][i]),\n",
+ " xytext=(5, 5), textcoords='offset points')\n",
+ "\n",
+ " plt.title('Risk-Return Tradeoff of Various Asset Classes')\n",
+ " plt.xlabel('Risk (Annualized Volatility)')\n",
+ " plt.ylabel('Return (Annualized)')\n",
+ " plt.tight_layout()\n",
+ " plt.show()\n",
+ "else:\n",
+ " print(\"No return data available for calculations.\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 4. Efficient Frontier Computation\n",
+ "Let's compute the efficient frontier to understand the optimal risk-return tradeoffs."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Optimal Portfolio Weights:\n",
+ "US Large Cap: 0.5582\n",
+ "US Small Cap: 0.0000\n",
+ "International Developed: 0.0000\n",
+ "Emerging Markets: 0.0000\n",
+ "US Aggregate Bonds: 0.0000\n",
+ "US Treasury Bonds: 0.0436\n",
+ "Real Estate: 0.0000\n",
+ "Gold: 0.3982\n",
+ "Commodities: 0.0000\n"
+ ]
+ }
+ ],
+ "source": [
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "import matplotlib.pyplot as plt\n",
+ "import seaborn as sns\n",
+ "from scipy.optimize import minimize\n",
+ "\n",
+ "def portfolio_performance(weights, returns):\n",
+ " portfolio_return = np.sum(returns.mean() * weights) * 252 # Annualized return\n",
+ " portfolio_volatility = np.sqrt(np.dot(weights.T, np.dot(returns.cov() * 252, weights))) # Annualized volatility\n",
+ " return portfolio_return, portfolio_volatility\n",
+ "\n",
+ "def negative_sharpe_ratio(weights, returns, risk_free_rate):\n",
+ " p_return, p_volatility = portfolio_performance(weights, returns)\n",
+ " return -(p_return - risk_free_rate) / p_volatility # Negative Sharpe Ratio for minimization\n",
+ "\n",
+ "def calculate_efficient_frontier(returns, num_portfolios=1000):\n",
+ " num_assets = len(returns.columns)\n",
+ " results = np.zeros((3, num_portfolios))\n",
+ "\n",
+ " for i in range(num_portfolios):\n",
+ " weights = np.random.random(num_assets)\n",
+ " weights /= np.sum(weights) # Normalize weights\n",
+ " p_return, p_volatility = portfolio_performance(weights, returns)\n",
+ " results[0,i] = p_return\n",
+ " results[1,i] = p_volatility\n",
+ " results[2,i] = p_return / p_volatility # Sharpe Ratio\n",
+ "\n",
+ " return results.T\n",
+ "\n",
+ "# Calculate efficient frontier\n",
+ "efficient_frontier = calculate_efficient_frontier(returns)\n",
+ "\n",
+ "# Find the optimal portfolio (maximum Sharpe ratio)\n",
+ "num_assets = len(returns.columns)\n",
+ "risk_free_rate = 0.02 # Define risk-free rate if not already defined\n",
+ "args = (returns, risk_free_rate)\n",
+ "constraints = ({'type': 'eq', 'fun': lambda x: np.sum(x) - 1}) # Weights sum to 1\n",
+ "bound = (0.0, 1.0)\n",
+ "bounds = tuple(bound for asset in range(num_assets))\n",
+ "\n",
+ "result = minimize(negative_sharpe_ratio, num_assets*[1./num_assets], args=args,\n",
+ " method='SLSQP', bounds=bounds, constraints=constraints)\n",
+ "\n",
+ "optimal_weights = result.x\n",
+ "optimal_return, optimal_volatility = portfolio_performance(optimal_weights, returns)\n",
+ "\n",
+ "# Plot efficient frontier\n",
+ "plt.figure(figsize=(12, 8))\n",
+ "plt.scatter(efficient_frontier[:,1], efficient_frontier[:,0], c=efficient_frontier[:,2], cmap='viridis')\n",
+ "plt.colorbar(label='Sharpe Ratio')\n",
+ "plt.xlabel('Volatility')\n",
+ "plt.ylabel('Return')\n",
+ "plt.title('Efficient Frontier')\n",
+ "\n",
+ "# Plot individual assets\n",
+ "for i, asset in enumerate(returns.columns):\n",
+ " plt.scatter(risk_return_metrics.loc[asset, 'Volatility'],\n",
+ " risk_return_metrics.loc[asset, 'Return'],\n",
+ " marker='o', s=200, label=asset)\n",
+ "\n",
+ "# Plot optimal portfolio\n",
+ "plt.scatter(optimal_volatility, optimal_return, c='red', s=200, marker='*', label='Optimal Portfolio')\n",
+ "\n",
+ "plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left')\n",
+ "plt.tight_layout()\n",
+ "plt.show()\n",
+ "\n",
+ "print(\"Optimal Portfolio Weights:\")\n",
+ "for asset, weight in zip(returns.columns, optimal_weights):\n",
+ " print(f\"{asset}: {weight:.4f}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 5. Advanced Risk Metrics\n",
+ "Let's calculate Value at Risk (VaR) and Conditional Value at Risk (CVaR) for our optimal portfolio."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "95% VaR: -0.0097\n",
+ "95% CVaR: -0.0135\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "import matplotlib.pyplot as plt\n",
+ "import seaborn as sns\n",
+ "\n",
+ "def calculate_var_cvar(returns, weights, confidence_level=0.95):\n",
+ " # Calculate portfolio returns\n",
+ " portfolio_returns = np.sum(returns * weights, axis=1)\n",
+ "\n",
+ " # Calculate VaR\n",
+ " var = np.percentile(portfolio_returns, 100 * (1 - confidence_level))\n",
+ "\n",
+ " # Calculate CVaR\n",
+ " cvar = portfolio_returns[portfolio_returns <= var].mean()\n",
+ "\n",
+ " return var, cvar\n",
+ "\n",
+ "# Check if returns DataFrame is not empty before proceeding\n",
+ "if not returns.empty and optimal_weights is not None:\n",
+ " # Calculate optimal portfolio returns\n",
+ " optimal_portfolio_returns = np.sum(returns * optimal_weights, axis=1)\n",
+ "\n",
+ " # Calculate 95% VaR and CVaR\n",
+ " var_95, cvar_95 = calculate_var_cvar(returns, optimal_weights)\n",
+ "\n",
+ " # Print results\n",
+ " print(f\"95% VaR: {var_95:.4f}\")\n",
+ " print(f\"95% CVaR: {cvar_95:.4f}\")\n",
+ "\n",
+ " # Visualize VaR and CVaR\n",
+ " plt.figure(figsize=(12, 6))\n",
+ " sns.histplot(optimal_portfolio_returns, kde=True)\n",
+ " plt.axvline(var_95, color='r', linestyle='dashed', label='95% VaR')\n",
+ " plt.axvline(cvar_95, color='g', linestyle='dashed', label='95% CVaR')\n",
+ " plt.title('Distribution of Optimal Portfolio Returns with VaR and CVaR')\n",
+ " plt.xlabel('Daily Return')\n",
+ " plt.ylabel('Frequency')\n",
+ " plt.legend()\n",
+ " plt.show()\n",
+ "else:\n",
+ " print(\"Returns data or optimal weights are not available for calculations.\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 6. Scenario Analysis and Stress Testing\n",
+ "Let's perform a simple scenario analysis to see how our optimal portfolio would perform under different market conditions."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " Mean Return Volatility VaR 95% CVaR 95%\n",
+ "Base Case 0.001439 0.011118 -0.018049 -0.022964\n",
+ "Market Crash -0.538561 0.011118 -0.558049 -0.562964\n",
+ "Economic Boom 0.361439 0.011118 0.341951 0.337036\n",
+ "Rising Interest Rates -0.049824 0.002629 -0.054325 -0.055655\n",
+ "Commodity Boom 0.110169 0.002985 0.105341 0.103996\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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//36kpqaiQoUKsLW1hY6OTr7Oy1a5cmU4ODggKCgInTp1QlBQEBo1aqT444nEi8UbiVpsbCw6d+4MX19flfE6tWvXxrBhwxQ3HBQtWhTApwHln3v79i1u376N+vXrw8TEBI0bN0a/fv1UjpUxnicnwsLCMHbsWPTs2RP9+/dXtA7MmzcvRzcafK5YsWJZDvZ++fIlgE9dxJlFRETgzp07GDJkCBo2bKi07uTJk1i/fj1u3Lih6P7Lr/dTsWJFNGzYEIcPH8b79+9haGiItm3bKtabmJhg9OjRGD16NB49eoQTJ05gxYoVmDp1KtauXauyv9xunxOurq7YsGEDbt26hZCQELRt2zbLgf8mJiawsLDA/Pnzs9xPhQoV8vS851SJEiXg5+eHoUOHYubMmYruwYzv9/nz58PCwkLldV/7oyHz2LuMVtj8EBwcjDlz5mDkyJHw9PRUFF++vr64detWjvdTvHhxaGlpqVwvGYVsTnTu3BnTpk3D69evERQUhFKlSilN25OT6zFzYTxz5kwcOXIEixcvRuPGjRUt7RnDCfJT586dMX78eERFRan88UTixW5TErWSJUtCR0cHW7duhVQqVVn/6NEj6Ovro3LlyqhatSpMTU1x4sQJpW2Cg4MxYMAASKVSNGzYEJGRkahVqxasra1hbW2NunXrYv369Yquh5wIDw9Heno6fHx8FL/A09LSFF2vX2td+ZyDgwNOnTql1AKRlpaGQ4cOwdraGnp6eiqv2b17N/T09NC3b184Ojoq/evfvz+0tbWzHauW1+/H09MTFy5cwIEDB+Di4qJoOXn27BlatGihGAtVtWpVDBgwAI0bN8aLFy9U9pPb7XOqfv36KF++PIKDg3Hy5MksWyqBT3enPn/+HCVKlFB8b1hbW+PixYtYu3YttLW18/S850bbtm3RrFkzHDx4UHHzRr169aCrq4vY2FilvLq6uliwYIFibGVGy9XnjI2NVT7Ta9eu5Ut2ALh69SpMTEzw22+/KQq3xMREXL16NVefmb6+PmxtbXH06FGlFq2TJ0/meB9ubm7Q1dXFiRMnEBoaik6dOin94fYt1+PVq1fh6OiIH374QVG4/fvvv3j9+rXS+8vqXORUduNEf/zxRxgZGcHPzw8GBgZKfzyReLF4I1HT1tbGlClTcP/+fXTu3Bnbtm3D5cuX8ffff2PWrFlYsmQJhgwZgmLFikFbWxtDhw7FkSNHMGXKFJw/fx5btmzB4sWL0b17d5iZmWHQoEH477//4O3tjePHj+Ps2bMYOnQoDh06BCsrqxznsrGxAQBMmzYN//zzD44ePYp+/foppizITSvGkCFDIJPJ0Lt3bxw+fBgnTpzAr7/+iidPnmDEiBEq28tkMhw6dAgtWrRQGpeToXTp0oo5rd69e5fv7+fHH3+Evr4+bty4oRj0DQDly5eHubk5ZsyYgd27d+Py5ctYt24d/v77b/z4448q+8np9tevX8d///2Xo/eVoV27dti6dSuKFCmi0lKZwcPDA+XKlUO/fv2wb98+/PPPP1i4cCEWLVqE0qVLQ1dXN0/Pe25NmDABurq6mDFjBlJTU2Fqaopff/0VS5YsweLFi3Hx4kUEBQXh999/x3///af4fs5ooTt48KBiSpyWLVvi0KFD2Lp1Ky5evIgxY8YgOjo637Lb2Njg/fv3mDNnDi5duoTg4GD06NEDr169+uoYwMxGjBiBhw8fYsiQIThz5gw2btyIpUuX5vj1xsbGaNeuHdauXYvHjx+jc+fOSutzez1mvL9z584pfj5t3LgRAwYMgEQiUXp/RYsWRXh4OK5cuZLr7tSMa/3ixYtKU84YGhrCzc0Nly9fhqur61eHOpA4sHgj0WvZsiV27tyJmjVrYtWqVejfvz9GjBiBO3fuYNGiRUpjfnr06IE5c+YgLCwM3t7eWLduHby8vBRTDVhZWWHLli2QSCQYM2YMfHx88PLlSyxfvjxXf7E6OjrCz88P4eHhGDBgAGbPno1y5copbufPTRdajRo1sHXrVpQsWRITJkzA6NGjIZfLsXHjRjRu3Fhl++PHj+Pt27dZDrrP4O7ujo8fP2Lfvn35/n709fXh5OQECwsL2NnZKa3LmNJgyZIl8PLywrZt2zBkyBDFXa+Z5WT7rl27YsWKFTl6XxlcXV2RkpICFxeXbFs/jIyMsGXLFjRo0AD+/v4YMGAAjh49ipEjR2L8+PHf/Tl9r6pVq6JXr164f/8+Nm/eDAAYNmwYxo0bh2PHjmHAgAHw9/dHgwYNsHnzZsUv+7Zt28La2hrjxo1TPJopY64wf39/+Pj4wNDQECNHjsy37O7u7hg8eDAOHz6MAQMGYOnSpbC3t8e0adPw9u1bREZG5nhf9vb2WLNmDWJjYzFkyBBs3779i9OUZMXT0xOPHz+Gg4MDqlSporQut9cj8GlOvB9++AGLFy+Gt7c3du3ahd9//x0///wzwsPDFV3UAwcOxK1btzBgwAA8f/48V5mNjY3Rr18/HD9+HL/++qvS3JUZ0958/scTiZtEzqfYElE++vjxI1q0aAFvb294eXkJHYdI40yZMgVXr17lc08LEd6wQET54tmzZ9i3b59ivFduJ4Alou+TMaHxjh07eKNCIcPijYjyhZaWFjZt2gQjIyMsXLgwy/F3RJR/wsLCcPbsWfTq1YtzuxUy7DYlIiIiEhHesEBEREQkIqIo3tLT07F06VI0a9YM9erVg5eXV45vWw8ODoalpaXK8yKJiIiIxEgUxduKFSuwfft2zJgxAzt27IBEIsGAAQOUboXOyrNnzzB16tQCSklERESU/9R+zJtMJkOjRo0wevRodO/eHQDw7t07NGvWDLNmzcp2NvT09HT07NkTurq6+Oeff3DixAlUqFDhmzKEh4dDLpdn+cgcIiIioryQkpICiUQCW1vbL26n9i1vd+/eRWJiIho1aqRYVrRoUdSuXRtXrlzJ9nWrVq1CSkoKvL29vzuDXC7P14cHC0Uul0MmkxXK91YY8XyJC8+XuPB8iUthPV85rTfUfqqQjOfrlS1bVml56dKls52B+ubNm1i3bh12796N2NjY786gq6sLuVyOatWqffe+1ElycjIeP36M8uXLK543SeqL50tceL7EhedLXArr+YqMjIREIvnqdmpfvGU89y3zw3719fWRkJCgsn1SUhJGjRqFUaNGwcLCIk+KN+BTU+adO3fyZF/q5vHjx0JHoFzg+RIXni9x4fkSl8J4vjLXO1lR++It4yG6MplM6YG6Uqk0y2p7xowZsLCwQLdu3fI0h66uLqpXr56n+xRaxl8uFhYWheovl8KK50tceL7EhedLXArr+crpc3zVvnjL6C6Ni4tDpUqVFMvj4uJgZWWlsv2ePXugp6enGOyX8cDf9u3bo0OHDpg2bdo35ZBIJDAyMvqm16o7Q0PDQvveCiOeL3Hh+RIXni9xKWznKyddpoAIijcrKysYGxvj0qVLiuLt3bt3iIiIQM+ePVW2P3r0qNLXN27cwOjRo/Hnn38WujFrREREpHnUvnjT09NDz549MX/+fJiZmaF8+fLw9/eHubk52rRpg7S0NLx+/RomJiYwMDBA5cqVlV6fccNDuXLlUKJECSHeAhEREVGeUfviDQB8fHyQmpqKSZMm4ePHj3BwcEBgYCD09PTw9OlTtG7dGrNnz4aHh4fQUYmISIOkpaUhJSVF6BgaRyqVKv6rpaX2s54B+DR2XltbO0/2JYriTVtbG6NHj8bo0aNV1lWoUAH37t3L9rWOjo5fXE9ERJRbcrkcL168wNu3b4WOopHS09Oho6ODmJgY0RRvAFC8eHGYm5vneGxbdkRRvBEREamTjMKtdOnSMDIy+u5fxpQ7aWlpkEql0NfXz7PWrPwkl8uRlJSEuLg4AKpz1+YWizciIqJcSEtLUxRuHEstjIyZJAwMDERRvAFQTGkSFxeH0qVLf1du8bQ1EhGJiEQi4fOQC6mMMW6FaYoKKhgZ3zPfO06SxRsRaZS0tPQCOY6hoSFq167N7rRCjOeWciuvvmfYbUpEGkVbWwtzJu3Gk6hX+XqcilVKYtwMT96JSER5jsUbEWmcJ1GvEHn3udAxqBBKS0uHtrYwnVrfcmxnZ2c8e/YM48aNQ79+/VTW+/n5YceOHRgyZAiGDh2aV1Fzbe/evRg/frzSMi0tLRgbG8PGxgajR4/O8qlL2YmJiUF4eDjc3NzyOmqBYPFGRESURwqqZTezjJbeb6Grq4vQ0FCV4i01NRVHjx5Vq+7hc+fOAfh0w0JSUhJiYmIwd+5ceHl54fjx4zkehzh27FiUL1+exRsRERGJr2XXyckJZ8+exfPnz5WmsPjnn39gZGSkVg9+L1WqFIBPxdvHjx9RuXJl+Pn5oWfPnvjnn3/g7OwscMKCwRsWiIiINJiNjQ3KlSuH0NBQpeUhISFwcXFRaXm7du0aevToARsbG7Rs2RJTp07Fhw8fFOtfvHiBUaNGoXHjxqhTpw5atGiBRYsWIT39081Ce/fuhbOzM/bt24c2bdqgbt266Ny5M8LDw78pv76+PgAoTb1x6tQpeHh4wMbGBm3atMHixYshk8kAAL169cLly5exb98+RbHn7OyMZcuWKe23V69eGDduHADg0qVLsLS0xJo1a+Do6Ah3d3f8999/sLS0xOHDh9GlSxdYW1ujdevW2L179ze9j9xg8UZERKThXFxclIo3mUyG48ePq3Qr3r17F3379kWTJk1w4MABzJ8/H7dv34aXlxfkcjkAwNvbG69fv0ZgYCBCQ0Px66+/YtWqVTh58qRiP3Fxcdi+fTv8/f2xY8cOaGlpYezYsYp95NTTp0/h7++PcuXKwcHBAQBw5swZ+Pr6okuXLjh48CAmT56Mw4cPK57StGzZMtja2sLFxSXXhdbp06exY8cOzJo1S/Fkhzlz5mDgwIEICgqCk5MT/vjjDzx58iRX+80tFm9EREQazsXFBTdu3MDz55+6e8+fPw9TU1PUrl1babvAwEA4OTlh0KBBsLCwgL29PRYsWIAbN27g8uXL+PjxIzp27Ijp06ejVq1aqFixInr16oXSpUsrPaoyJSUFU6ZMQf369VGnTh14e3sjOjoaL1++/GJOW1tb2NraokGDBnB0dESnTp1QvHhxbNq0STHebdWqVfD09ET37t1RqVIlNG3aFFOnTkVoaCiePn2K4sWLQ1dXFwYGBjAzM8vV5+Tl5QULCwvUqlVLsaxfv35o3bo1qlWrhrFjxyI9PR03btzI1X5zi2PeiIiINFzdunVRsWJFxY0LISEhaN++vcp2ERERiI6Ohq2trcq6hw8fwtHRET179kRoaCg2bNiA6Oho3L17F3FxcYpu0wzVqlVT/L+JiQmAr09eGxQUBACIjY3FkiVL8ObNGwwbNgwVKlRQynjz5k3s27dPsSyjRe/hw4dK2+aWhYWFyrJveR/fi8UbERERKbpOf/nlF5w4cQK7du1S2SY9PR0//fQTBg4cqLLOzMwMycnJ6NGjB5KTk+Hi4oKOHTvijz/+QI8ePVS219PTU1n2tW7TypUrAwAqVKiAJUuWoE+fPujfvz/27dsHU1NTRcZff/0V7u7uKq/PuOEhK5mPnVUBljG+7nvfx/ditykREREpuk53796NihUrKrUoZahRowYePHiAypUrK/6lpaVh9uzZeP78Oc6ePYvbt29j06ZN8PHxgaurK4yNjREfH5/nBY2hoSHmzZuHV69eYdq0aUoZHz16pJQxNjYW8+bNQ2JiYpb70tXVxfv37xVfp6en4+nTp3maNy+xeCMiIiLUqlULlStXxsKFC7Od/8zLywt37tyBn58fIiMjcePGDYwaNQpRUVGwsLCAubk5AODAgQN49uwZwsLCMGjQIKSkpCju9sxLVlZW+PXXXxESEqK4IWLAgAE4evQoli1bhqioKFy8eBHjx4/Hu3fvFC1vRYoUwbNnz/DixQsAgJ2dHUJCQnDlyhVERUVhypQpSsWcumG3KRERUR6qWKWkaI/p4uKClStXwtXVNcv19evXx9q1a7FkyRJ4eHjA0NAQjRo1wtixY6GnpwcbGxuMHz8e69evx+LFi1GmTBm4urqibNmy+TaIf9CgQThy5AimTp2Khg0bol27dli0aBFWr16N1atXo1ixYmjVqpXiblMA6NatG8aOHYsOHTrg4sWLGD58OBISEjBgwAAYGhqiS5cucHV1zffuz28lkatrMjVy69YtAIC1tbXASfJWUlIS7ty5g1q1auV4VmoSDs9X3hncY1W+T6Ja3aoslm8ZiOTkZLWa5JSylpvr6+PHj4iKikKVKlVgYGCgtE5sj8cSq4xJeg0MDJTmd1N3X/reAXJeb2jGWSYiIioAQhZPmlK4EYs3IiIiIlFh8UZEREQkIizeiIiIiESExRsRERGRiLB4IyIiIhIRFm9EREREIsLijYiIiEhEWLwRERERiQiLNyIiIiIRYfFGRESUR9LS00Vz7F69eqFjx47Zrvfz84Ozs/MXn+/59OlTWFpa4tKlSzk+7oMHD3D69GnF15aWlti7dy8AYNy4cejVq1eW+05KSsKWLVtyfJzCjA+mJyIiyiPaWlrwWxmCx89eF+hxLcqbYdrvWT9MPjuenp4YM2YMHjx4gBo1aiitk8lkCA0NRe/evSGRSPIyKry9veHu7o6WLVsCAM6dOwcTExOV7cqWLYtz586hWLFiAIB169Zh79696NGjR57mESMWb0RERHno8bPXuBcdJ3SMr/rxxx8xffp0BAcHY8SIEUrrTpw4gffv36Nz5875nqNUqVJZLtfW1lZa96UWQE3DblMiIiINZGBggPbt2+PgwYMqhdH+/fvRpEkTlC1bFkFBQejQoQNsbGzg7OyMVatWIT2bLlqZTIYFCxbghx9+QN26deHo6IgRI0bgzZs3AABnZ2c8e/YMAQEBiu7Rz7tNP/d5t+myZcsQEBCAZ8+ewdLSEnfv3oWdnR3CwsKUXjN8+HAMGTIkLz4etcbijYiISEN5enri2bNnuHr1qmJZfHw8zp49iy5dumD9+vX4448/0LVrVxw4cADDhw9HYGAg5s2bl+X+5s2bh4MHD2LmzJk4cuQI5s6di/Pnz2PlypUAgN27d8Pc3BxeXl5YtmxZjnN6eXnBy8sL5ubmOHfuHGrUqAErKyvs379fsc379+9x4sQJeHh4fOOnIR4s3oiIiDRU3bp1YWVlheDgYMWy4OBgFC1aFM7OzlizZg169uyJHj16wMLCAj/99BN8fHywefNmvH//XmV/1tbWmDt3LhwdHVG+fHm0bNkSTZs2xb179wAAZmZm0NbWhpGREYoXL57jnEWKFIGRkZGiK1VbWxsdOnTA0aNHIZVKAQCHDx+GiYkJmjdv/n0figiweCMiItJgnp6eCA0NRUpKCgAgKCgInTp1wrt37/Dq1Ss0aNBAaXsHBwekpKTg0aNHKvvq2LEj0tLSsHDhQvj4+MDNzQ0hISHZdrN+D1dXV0ilUpw4cQIAsG/fPnTo0AE6OoV/OD+LNyIiIg32008/ISkpCWfOnMH9+/dx584deHp6ZnuDQFpaGgBkWSRNmTIFvr6++PjxI1q2bIn58+fDzc0tX3IXLVoUrVu3xoEDB/DkyROEh4drRJcpwLtNiYiINFrx4sXRpk0bhIaGwtzcHHZ2dqhWrRrkcjlKlCiBq1ev4ocfflBsHxYWBl1dXVSqVAkJCQmK5W/evMG2bduwaNEiuLr+/7Qljx49gpGR0XfnzGrKEg8PDwwePBj79++HtbW1ypQnhRVb3oiIiDScp6cnTp8+jdDQUHh6egL4VCx5eXlh8+bN2LJlC6KjoxEcHIyAgAB07dpVZW42ExMTmJiY4MSJE4iOjsa9e/fwxx9/4Pbt25DJZIrtihQpgsePH+PVq1e5ymhkZISEhARERUUpunidnJxQsmRJrFmzRmNa3QC2vBEREeUpi/Jmojumk5MTTExMEB8fDxcXF8XyX3/9FXp6etiwYQNmz54Nc3NzDBgwAP3791fZh46ODpYsWYI5c+bgp59+QrFixRRThaxatQpJSUkwMjJCr169MHfuXDx48AAHDhzIcca2bdti586d6NChAzZs2ABLS0toaWmhQ4cO+Ouvv/Kte1YdSeQimPUuPT0dAQEB2LVrF969e4cGDRpg8uTJqFy5cpbbP3jwAP7+/rhx4wa0tLTg4OCAcePGoVy5ct90/Fu3bgH4dBdNYZKUlIQ7d+6gVq1aedKkTfmL5yvvDO6xCpF3n+frMapblcXyLQORnJwMQ0PDfD0Wfb/cXF8fP35EVFQUqlSpAgMDA6V1aenp0NYSplNLyGMXtLS0NHz8+BEGBgaYNGkSUlJSMH/+fKFjfdWXvneAnNcbojjLK1aswPbt2zFjxgzs2LEDEokEAwYMUGqGzfDmzRv069cPRYoUwebNm7FmzRq8efMGv/76q+J2YiIiovwgZPGkKYVbhn/++QebNm3CwYMH0bt3b6HjFCi1P9MymQzr1q3D0KFD0aJFC1hZWWHRokWIjY3FsWPHVLY/fvw4kpOTMWfOHNSoUQN169aFv78/Hj58iGvXrgnwDoiIiCiv7d+/H0uXLsXQoUNhY2MjdJwCpfZj3u7evYvExEQ0atRIsaxo0aKoXbs2rly5otLH7eTkhOXLl0NfX19lX5/fFUNERETiNXv2bBgYGEBbW1voKAVO7Yu3Fy9eAADKli2rtLx06dJ4/lx1zEqFChVQoUIFpWWrV6+Gvr4+HBwcvjmHXC5HUlLSN79eHSUnJyv9l9Qbz9f3k0gkBT7+TCqV8oHaIpCb60sqlSI9PR1paWmKOc+oYGVcU3K5XFTnIC0tDenp6UhOTs5y4mK5XJ7llCiZqX3xlnEh6enpKS3X19fPUUvaxo0bsXXrVowfPx4lSpT45hwpKSm4c+fON79enT1+/FjoCJQLPF/fztDQELVr1y7QY8bExLDgFpGcXl86OjocR60GxHYOpFIpUlNTs3w6RYbM9U5W1L54y7gbQyaTKd2ZIZVKv/gXtFwux5IlS7By5Up4e3ujb9++35VDV1cX1atX/659qJvk5GQ8fvwYFhYWvBtOBHi+vl9O/qLNa+XKlcvRD2MSVm6uL6lUipiYGOjp6WV5xyDlP7lcDqlUCn19fUGu628ll8uho6ODSpUqZTm8KzIyMkf7UfviLaO7NC4uDpUqVVIsj4uLg5WVVZavSUlJwfjx43Hw4EGMGTMmy/locksikRTa6RkMDQ0L7XsrjHi+xEVfX5/Ftojk5PrS19fHixcvIJVKYWxsXEDJ6HMZXaUSiURUY96kUim0tLRQtGjRLHPntBBV++LNysoKxsbGuHTpkqJ4e/fuHSIiItCzZ88sXzNmzBgcO3YMCxYs0KhJ+4iIKP9pa2ujePHiiIuLA/Bp5n8xtf4UBmlpaYouUzEUbxnj5uPi4lC8ePHvzqz2xZuenh569uyJ+fPnw8zMDOXLl4e/vz/Mzc3Rpk0bpKWl4fXr1zAxMYGBgQH27t2LkJAQjBkzBg0bNsTLly8V+8rYhoiI6HuYm5sDgKKAo4KVnp6O1NRU6OjoQEtE89sVL15c8b3zPdS+eAMAHx8fpKamYtKkSfj48SMcHBwQGBgIPT09PH36FK1bt8bs2bPh4eGBgwcPAgDmzZuHefPmKe0nYxsiIqLvIZFIULZsWZQuXVrxnE0qOMnJyXj06BEqVaokmmEJurq6edZKKIriTVtbG6NHj8bo0aNV1lWoUAH37t1TfL1u3bqCjEZERBpMW1tbFN12hU3GNBv6+voa2aMmnrZGIiIiImLxRkRERCQmLN6IiIiIRITFGxEREZGIsHgjIiIiEhEWb0REREQiwuKNiIiISERYvBERERGJCIs3IiIiIhFh8UZEREQkIizeiIiIiESExRsRERGRiLB4IyIiIhIRFm9EREREIsLijYiIiEhEWLwRERGRqEgkEujq6godQzAs3oiIiChPpKWlF8hxDA0NUbt2bUgkkgI5nrrREToAERERFQ7a2lqYM2k3nkS9ytfjVKxSEuNmeCIlJSVfj6OuWLwRERFRnnkS9QqRd58LHaNQY7cpERERkYiweCMiIiISERZvRERERCLC4o2IiIhIRFi8EREREYkIizciIiIiEWHxRkRERCQiLN6IiIiIRITFGxEREZGIsHgjIiIiEhEWb0REREQiwuKNiIiISERYvBERERGJCIs3IiIiIhFh8UZEREQkIizeiIiIiESExRsRERGRiLB4IyIiIhIRFm9EREREIiKK4i09PR1Lly5Fs2bNUK9ePXh5eSE6Ojrb7d+8eYORI0fCwcEBDg4O+OOPP5CUlFSAiYmIiIjyhyiKtxUrVmD79u2YMWMGduzYAYlEggEDBkAmk2W5vY+PD548eYL169dj6dKlOH/+PKZOnVrAqcVBV1cXEolE6BiUQzxf4sPzRUR5Te2LN5lMhnXr1mHo0KFo0aIFrKyssGjRIsTGxuLYsWMq24eHh+Py5cuYPXs26tSpAycnJ0ybNg379+9HbGysAO9AfUkkEtSuXQeGhoYFcry09PQCOU5hxfMlLqYljJGWng4DA4MCOR7PF5Hm0BE6wNfcvXsXiYmJaNSokWJZ0aJFUbt2bVy5cgVubm5K24eFhaFUqVKoVq2aYlnDhg0hkUhw9epVuLq6Flh2MdDR0YbfyhA8fvY6X49jUd4M037nZ/+9eL7Ew9jEANpaWjxfRPlIU1u21b54e/HiBQCgbNmySstLly6N58+fq2wfGxursq2enh6KFy+e5fY5JZfLC3TcXEF8Q6ampub7MTJLTk6GXC4v8OPmp4L64cHz9f0kEgkMDQ1RsUrJfD9WmXLF8/0YmRW28wUU3PUlk8lgaGgImUxWIMcsbOcJKNjrq079igXbsp2WDplMmu/nTS6X5+j7T+2Lt+TkZACfCrDP6evrIyEhIcvtM2+bsb1UKv3mHCkpKbhz5843vz43ypYti3LlyhXIsdLS0grsL/bU1DQ8ePAAKSkpBXK8gqCrq4vatWtDR6dgLqW09HSer++Qcb7GzfAskOPxfH2fgry+DA0NUbRoUWhpFcxoopiYmO9qUFBHQlxfBUUulxfY9ZVVDZOZ2hdvGVW1TCZTqrClUmmWY38MDAyyvJFBKpXCyMjom3Po6uqievXq3/z63MooWvOTVCpFTEwMypUrB319/Xw/HoAC/QwLgkQigY6ODuZM2o0nUa/y9VgVq5TEuBmeeP/+fYEVi4XtfAGf/hAriB/AUqkUz58/R9myZXl9faPCfH0VL14cxYsXz/fjFDReX98nMjIyR9upffGW0QUaFxeHSpUqKZbHxcXByspKZXtzc3McP35caZlMJsPbt29RpkyZb84hkUi+q/hTR3K5XNFSWVCD4AurJ1GvEHm3YP6K1tHR4fkSgYyhFry+vh+vL8qssF5fOe2yV/u7Ta2srGBsbIxLly4plr179w4RERGwt7dX2d7BwQEvXrxQmgcu47V2dnb5H5iIiIgoH31Ty5tMJsPu3btx4cIFvHz5ErNmzcLly5dRp04d2NjY5GlAPT099OzZE/Pnz4eZmRnKly8Pf39/mJubo02bNkhLS8Pr169hYmICAwMD1KtXD3Z2dhg+fDimTJmCpKQkTJ48GZ06dfquljciIiIidZDrlrfXr1+jc+fOmDlzJqKjo3Hz5k18/PgRf//9N3r16oXw8PA8D+nj4wNPT09MmjQJ3bt3h7a2NgIDA6Gnp4fnz5+jadOmCAkJAfCpyTEgIAAVKlRAnz59MGzYMDRv3hxTpkzJ81xEREREBS3XLW/z5s1DYmIiQkJCUL58edStWxcAsGTJEvTv3x9Lly7FX3/9lachtbW1MXr0aIwePVplXYUKFXDv3j2lZSVKlMDSpUvzNAMRERGROsh1y9upU6fg6+uLypUrKw2s09fXh5eXF27fvp2nAYmIiIjo/+W6eJNKpdne3qytrV2o5hgiIiIiUje5Lt6sra2xdevWLNcFBwcrulGJiIiIKO/lesybr68v+vbti44dO6JFixaQSCQ4ePAgli1bhnPnzmHt2rX5kZOIiIiI8A0tb/b29vjrr79gaGiItWvXQi6XY/369Xj58iVWr16t9AB5IiIiIspb3zTPm4ODA7Zv346PHz8iISEBxsbGKFKkSF5nIyIiKpAHnRfEMYjyync9HsvAwEDpeaNERER5KS0tvcAedJ6amlogxyH6Xrku3qysrL767K07d+58cyAiIqIM2toF8xTH5ORkPHjwoEAePk70vXJdvA0ePFileEtMTMS1a9fw33//YdSoUXkWjoiIqCDI5XJOdUWikevibejQodmuGzt2LP7991907tz5u0IRERERUdbytD26U6dOimeMEhEREVHey9Pi7fHjxxzwSURERJSPct1tGhAQoLIsPT0dz58/R0hICJydnfMkGBERERGpypPiDQCMjY3Rpk0bjB8//rtDEREREVHWcl283b17Nz9yEBEREVEOFMwEOkRERESUJ3LU8ta7d+8c71AikWDDhg3fHIiIiIiIspej4k0ul+d4h7nZloiIiIhyJ0fF26ZNm/I7BxERERHlQJ6OeUtKSsKZM2fycpdERERE9Jlc32367Nkz+Pn54cqVK9k+B44PpiciIiLKH7ku3mbPno3w8HD8/PPPuHbtGgwNDVG/fn2cP38e9+/fx7Jly/IjJxERERHhG7pNr1y5gmHDhmHSpEno3Lkz9PT0MHr0aOzZswcODg44ceJEfuQkIiIiInxD8ZaYmIhatWoBAKpVq6boItXW1kaPHj3wzz//5G1CIiIiIlLIdfFWunRpvHz5EgBQuXJlJCQkIC4uDgBQrFgxxMfH521CIiIiIlLIdfHWokULLFmyBNeuXUPZsmVhbm6OdevW4cOHD9izZw/KlCmTHzmJiIiICDks3rp27Ypdu3YhKSkJPj4+KFq0KJYuXQoAGD58ODZu3AgHBwcEBwejX79++RqYiIiISJPl6G7Tjx8/4o8//sDs2bPh6uqKCRMmoEKFCgCADh06oFy5crh+/TpsbGzQsGHDfA1MREREpMlyVLzt378fd+/eRVBQEA4ePIg9e/agWrVq8PT0RIcOHWBvbw97e/v8zkpERESk8XI85s3Kygrjxo3DmTNnsGrVKlSvXh2LFi1CixYt4Ovri/Pnz+dnTiIiIiLCN0zSq6WlhRYtWqBFixb48OEDDh06hP379+PXX39F2bJl4eHhgSFDhuRHViIiIiKN913PNjU2NkbXrl2xdetWbNy4EXp6eli+fHleZSMiIiKiTHLd8va52NhYHDp0CMHBwbh79y7Kly+PoUOH5lU2IiIiIsok18Xbhw8fcOTIEQQHB+PKlSvQ0dHBDz/8gDFjxsDJySk/MhIRERHR/+SoeEtNTcXff/+NAwcO4PTp05BKpahduzYmTJiADh06wMTEJL9zEhERERFyWLw1adIE7969Q9GiRdGlSxd4enrCysoqv7MRERERUSY5Kt7q1KkDT09P/PDDD9DT08vvTERERESUjRwVb+vWrcvvHERERESUA981VUhBkEqlmDp1KpycnGBrawsfHx/Ex8d/8TXXrl1Dr1690KBBAzRr1gwTJ07E27dvCyYwERERUT5S++JtypQpOH/+PJYtW4YNGzbgyZMn8PX1zXb7qKgo9O/fH1ZWVti1axcWLVqEmzdvwsfHpwBTExEREeUPtS7eYmNjERQUhEmTJsHe3h42NjZYuHAhrly5guvXr2f5mqCgIJQuXRoTJkxA1apVYW9vj8mTJ+PSpUt48uRJwb4BIiIiojym1sXb1atXAQCOjo6KZVWqVEGZMmVw5cqVLF/ToUMHzJ07FxKJRGUdu06JiIhI7L7rCQv5LTY2FqamptDX11daXrp0aTx//jzL11SrVk1l2Zo1a1CqVKnvmt5ELpcjKSnpm1+vjpKTk5X+S7knkUhgaGhYoMeUSqWQy+UFekzKPV5f4sLzJS6F9XzJ5fIsG58yE7R4e/r0KVq3bp3tel9f3yynJtHX14dUKs3RMebMmYO///4bS5cuha6u7jdnTUlJwZ07d7759ers8ePHQkcQLUNDQ9SuXbtAjxkTE1PofmAVZry+xIXnS1wK4/nKyZRsghZvZcqUQUhISLbr//77b8hkMpXlUqn0q60dKSkp8PPzw759+zB58mS0bdv2u7Lq6uqievXq37UPdZOcnIzHjx/DwsKiwFuPCouc/IWU18qVK8f5FkWA15e48HyJS2E9X5GRkTnaTtDiTVdXN8tuzgz37t3D27dvIZPJlH5ZxcXFwdzcPNvXffjwAUOGDEFYWBgWLFgANze3784qkUhgZGT03ftRR4aGhoX2vRVG+vr6heqHVWHH60tceL7EpbCdr5w2CKj1DQsNGjRAenq64sYFAHj06BFiY2Nhb2+f5WtkMhm8vb1x69YtrF27Nk8KNyIiIiJ1odbFW5kyZeDm5oZJkybh0qVLuHnzJkaOHImGDRuifv36AD4Vay9fvlR0r65evRpXr17FtGnTUK1aNbx8+VLxL6suWCIiIiIxUeviDQCmT58OJycnDBkyBP3790fVqlWxdOlSxfrw8HA0bdoU4eHhAICDBw9CLpdjxIgRaNq0qdK/jG2IiIiIxEqtpwoBACMjI8yYMQMzZszIcr2joyPu3bun+PrIkSMFFY2IiIiowKl9yxsRERER/T8Wb0REREQiwuKNiIiISERYvBERERGJCIs3IiIiIhFh8UZEREQkIizeiIiIiESExRsRERGRiLB4IyIiIhIRFm9EREREIsLijYiIiEhEWLwRERERiQiLNyIiIiIRYfFGREREJCIs3oiIiIhEhMUbERERkYiweCMiIiISERZvRERERCLC4o2IiIhIRFi8EREREYkIizciIiIiEWHxRkRERCQiLN6IiIiIRITFGxEREZGIsHgjIiIiEhEWb0REREQiwuKNiIiISERYvBERERGJCIs3IiIiIhFh8UZEREQkIizeiIiIiESExRsRERGRiLB4IyIiIhIRFm9EREREIsLijYiIiEhEWLwRERERiQiLNyIiIiIRYfFGREREJCJqX7xJpVJMnToVTk5OsLW1hY+PD+Lj43P8+pUrV8LS0jIfExIREREVHLUv3qZMmYLz589j2bJl2LBhA548eQJfX98cvfbmzZsICAjI54REREREBUeti7fY2FgEBQVh0qRJsLe3h42NDRYuXIgrV67g+vXrX3xtUlISRo8eDXt7+4IJS0RERFQA1Lp4u3r1KgDA0dFRsaxKlSooU6YMrly58sXXzpw5EzVr1kTHjh3zNSMRERFRQdIROsCXxMbGwtTUFPr6+krLS5cujefPn2f7umPHjuHvv/9GcHAwTp06lSdZ5HI5kpKS8mRf6iI5OVnpv5R7EokEhoaGBXpMqVQKuVxeoMek3OP1JS48X+JSWM+XXC6HRCL56naCFm9Pnz5F69ats13v6+sLPT09leX6+vqQSqVZviY2NhZ//PEH5s2bB1NT0zzLmpKSgjt37uTZ/tTJ48ePhY4gWoaGhqhdu3aBHjMmJqbQ/cAqzHh9iQvPl7gUxvOVVd2TmaDFW5kyZRASEpLt+r///hsymUxluVQqzbK1Qy6XY9y4cXBxcUHz5s3zNKuuri6qV6+ep/sUWnJyMh4/fgwLC4sCbz0qLHLyF1JeK1euXI4ubhIWry9x4fkSl8J6viIjI3O0naDFm66uLqpVq5bt+nv37uHt27eQyWRKv6zi4uJgbm6usn1MTAwuXLiAa9euISgoCACQmpoKALC1tYW3tzcGDhz4TVklEgmMjIy+6bXqztDQsNC+t8JIX1+/UP2wKux4fYkLz5e4FLbzldMGAbUe89agQQOkp6fj6tWrcHJyAgA8evQIsbGxWd5FWqZMGRw9elRp2dGjRzF//nwEBQWhWLFiBZKbiIiIKL+odfFWpkwZuLm5YdKkSZg1axYMDQ0xefJkNGzYEPXr1wcAyGQyJCQkoFixYtDT00PlypWV9lGiRAkAUFlOREREJEZqPVUIAEyfPh1OTk4YMmQI+vfvj6pVq2Lp0qWK9eHh4WjatCnCw8MFTElERERUMNS65Q0AjIyMMGPGDMyYMSPL9Y6Ojrh37162r/fw8ICHh0d+xSMiIiIqUGrf8kZERERE/4/FGxEREZGIsHgjIiIiEhEWb0REREQiwuKNiIiISERYvBERERGJCIs3IiIiIhFh8UZEREQkIizeiIiIiESExRsRERGRiLB4IyIiIhIRFm9EREREIsLijYiIiEhEWLwRERERiQiLNyIiIiIRYfFGREREJCIs3oiIiIhEhMUbERERkYiweCMiIiISERZvRERERCLC4o2IiIhIRFi8EREREYkIizciIiIiEWHxRkRERCQiLN6IiIiIRITFGxEREZGIsHgjIiIiEhEdoQMQFQYVq5QsFMcgIiL1x+KN6DulpaVj3AzPAjlWampqgRyHiIjUF7tNib6TtnbBXEbJycmIiIiAXC4vkOMREZF6YvFGJBJyuRwpKSlCxyAiIoGxeCMiIiISERZvRERERCLC4o2IiIhIRFi8EREREYkIizciIiIiEWHxRkRERCQiLN6IiIiIREQi54yfX3Xt2jXI5XLo6ekJHSVPZcwbpqurC4lEInQc+gqeL3Hh+RIXni9xKaznSyaTQSKRwM7O7ovb8fFYOVCYvjE+J5FICl1BWpjxfIkLz5e48HyJS2E9XxKJJEc1B1veiIiIiESEY96IiIiIRITFGxEREZGIsHgjIiIiEhEWb0REREQiwuKNiIiISERYvBERERGJCIs3IiIiIhFh8UZEREQkIizeiIiIiESExRsRERGRiLB4IyIiIhIRFm9EREREIsLijYiINF5KSgpu3bqFxMREoaMQfZWO0AGo4MXExODhw4dwcHBAYmIiSpQoIXQkyoJcLse+ffvw77//4uPHj5DL5UrrZ8+eLVAyyk5CQgKio6MhlUpV1jk4OAiQiLLz/PlzTJw4EcOGDYOlpSU6d+6MyMhIFCtWDOvXr0etWrWEjkiZREZG4v79+5DJZCrrOnXqVPCBBMTiTYPIZDKMHTsWhw8fhpaWFo4cOYK5c+fi/fv3CAgIgImJidAR6TNz587F+vXrYWlpiaJFiwodh74iKCgIkydPhkwmUym0JRIJ7ty5I1Ayysrs2bPx/v17mJmZ4ciRI3j27Bm2bt2K3bt3w9/fH+vWrRM6In3mzz//xMKFC7NcJ5FINK54k8gz/5ShQmvJkiUIDQ3FlClTMHDgQBw4cADPnz/HhAkT0LhxY0ybNk3oiPSZRo0aYcyYMfDw8BA6CuVAy5Yt0bx5c/Tt2xf6+voq68uXLy9AKspOw4YNsWHDBtSqVQsjR45EamoqlixZgqioKHh4eCA8PFzoiPSZJk2aoFu3bvD29oaenp7QcQTHMW8a5NChQ/jjjz/g6OioWNawYUNMnz4dJ0+eFDAZZUUqlSqdK1JvCQkJ8PLyQtWqVVG+fHmVf6ReUlJSUKxYMQDAxYsX0bhxYwBAeno6dHTYKaVuUlJS0KFDBxZu/8PiTYPExsaiUqVKKsvLli2Ld+/eCZCIvqRZs2Y4deqU0DEoh9q2bYu///5b6BiUQ7Vr18auXbuwbds2vHnzBi1atIBMJsOaNWtgZWUldDzKpGPHjti5c6fQMdQG/7zQINWqVcOFCxfw888/Ky0/ePAgqlevLlAq+lxAQIDi/01NTTFnzhxcu3YNFhYW0NJS/ltryJAhBR2PvmD06NFwc3PD0aNHUbFiRUgkEqX1vMFEvYwdOxYDBw7EmzdvMGDAAJibm2PKlCk4fvw4AgMDhY5Hmfz666/o0KEDQkJCUKFCBZXra+PGjQIlEwaLNw0ydOhQDBs2DPfv30daWhr27duHR48e4ejRo1i0aJHQ8QjA3r17lb4uXbo0rl+/juvXrystl0gkLN7UzOzZs5GYmAiZTIZnz54JHYe+wsbGBufPn8f79+8VNwT16dMHvr6+MDU1FTgdZTZp0iQAQL169WBoaChwGuHxhgUNc+bMGaxevRoRERFIT09HjRo1MGDAAPz4449CRyMSNVtbWyxevBgtWrQQOgrlwpUrV/Dw4UO0b98eL168QOXKlaGrqyt0LMqkXr16WL9+PWxtbYWOohbY8qZhmjdvjubNmwsdg3Lo48eP0NLSgp6eHh4+fIjTp0/D1tYWdnZ2QkejTIoUKZLlmFJSTx8+fED//v1x48YNSCQSNGnSBPPnz8fjx4+xfv16mJubCx2RPlOyZEkUKVJE6BhqgzcsaJjg4GC8ePECALBixQq0b98efn5+WU4qSsK6cuUKmjdvjqtXr+Lly5fo0qULVq5ciZ49e+Lw4cNCx6NMvL29sXjxYnz48EHoKJQDCxcuhEQiwbFjx2BgYAAAGDNmDIyMjDBv3jyB01FmI0eOxIwZM/Dw4UOkpaUJHUdw7DbVICtWrMCqVauwfv16SCQSdO/eHV26dMHly5fRvHlzTJw4UeiI9Jnu3bvDwsICEydOxJ49exAYGIijR49iz5492LVrF4KCgoSOSJ/p168fwsLCIJfLUaJECZXpJk6cOCFQMspKq1atsGDBAtjZ2cHW1hYHDhxAxYoVcePGDQwcOBAXL14UOiJ9pm3btoiJicm2cNO0SbDZbapB9uzZg7lz58LOzg5z585F/fr1MX36dISFhWH48OEs3tRMREQE5s2bB2NjY5w7dw4tW7aEgYEBWrZsiblz5wodjzJp0KABGjRoIHQMyqHXr1+jVKlSKsuNjY2RnJwsQCL6kt9//13oCGqFxZsGiYuLUwz2vHDhAtq0aQOA87ypK0NDQ8hkMshkMoSFhWHWrFkAgFevXvFRZmqId/+Ki7W1NUJCQuDt7a20fOPGjahdu7ZAqSg77u7uQkdQKyzeNIi5uTmioqIgk8lw7949TJkyBQAQFhbGwblqyNHREf7+/opZ4Js1a4Y7d+5gxowZfPKCmrp9+zYCAwNx79496OjooHr16ujTpw9sbGyEjkaZjBgxAv369UN4eDhSU1OxcuVKREZGIiIigvO8qalTp05h1apVStdX//79FQ0RmoQ3LGiQbt26wdfXFz169IClpSVsbW2xZcsWTJ48WWXiXhLe5MmToauri3v37sHf3x/GxsbYv38/dHR0MH78eKHjUSZhYWHo1q0boqOj0bRpUzg4OCAqKgq//PILrl69KnQ8ysTOzg47duxA0aJFUblyZVy/fh1ly5bFli1b+MeRGjp+/DgGDRqEMmXKYMSIERgyZAhKlCgBX19fjRxPyhsWNMzJkyfx5MkTdOjQAaampjhw4ACkUim6dOkidDTKAZlMxmf7qalffvkFVlZW8PPzU1o+depUREZGYtOmTQIlo6wEBQXB1dVV5XpKSkrCzp070bdvX2GCUZbc3d3xww8/YPDgwUrLAwICcPr0aezevVugZMJg8Uakxl6/fo2oqCikp6cDAORyOWQyGW7cuKHyQ4yEVa9ePezbtw9Vq1ZVWv7w4UN4enoiPDxcoGSU4fXr1/j48SMAoHXr1ti9e7fK0xQiIiIwYsQI3Lx5U4iIlA0bGxsEBwejcuXKSssfP36Mjh074saNGwIlEwbHvGkQmUyGHTt24N69e0q3W8tkMty6dQtHjx4VMB1ldujQIUyYMAFSqRQSiQRyuVzxPL/y5cuzeFMzpqamiI+PVyne4uPj2VqqJs6cOYNx48YpridPT0+VbeRyOZ+SoYZKly6Nx48fZ1m8aeINXCzeNMisWbOwd+9e1KlTBzdu3ICtrS2io6MRHx/PLgI1tGrVKrRv3x4DBgzAzz//jHXr1iEuLg5Tp07F0KFDhY5HmbRq1QrTp0/HokWLUK1aNQBAZGQkZs6ciVatWgmcjgCgU6dOKF++PNLT09GnTx8sXbpUcUMQ8OmZwUZGRqhZs6aAKSkr7du3x9SpUzF58mTFlDxXr17FtGnT0K5dO4HTFTwWbxrk+PHjmDNnDlxdXdG2bVtMnz4dFStWxPDhw5GSkiJ0PMrk8ePHWLJkCSwsLFCrVi28fv0azs7OSE1NxapVq9CxY0ehI9Jnhg0bhn79+qF9+/YwMTGBRCLBu3fvULNmTYwZM0boePQ/Dg4OAD5NCWJnZ6cymTKpp99//x3379+Ht7e3ogcio5V05MiRAqcrePyu1SBv375F/fr1AQA1a9ZEREQEqlatCm9vbwwbNgyTJk0SNiAp0dfXVzwg28LCAg8ePEDz5s1Rt25dREdHC5yOMitWrBh2796Ns2fP4sGDB5DL5ahZsyaaNm0KbW1toeNRJg0bNsTdu3dx//79LMeUZsyrSOpBX18fK1aswMOHD3H//n3I5XJYWloqWrk1DYs3DVKyZEnEx8ejXLlyqFSpEu7fvw/g01idV69eCZyOMrOxscH27dsxevRoVK9eHadOnUL//v0RGRmpKOpIvWhpaaFixYpISkqCrq4uKlasyMJNTW3cuFFRoGWMgcv4f3t7eyGj0RfExMTg2bNn0NHRQbFixWBhYaGR1xiLNw3SokULTJ48GbNnz4adnR1mzpyJNm3aICQkhJP0qqHBgwejf//+MDMzg4eHBwICAuDm5obnz5/D1dVV6HiUiUwmw6hRo3Ds2DGlQqBVq1ZYvHgxb1pQM5s3b4a3tzcGDx6MVq1aYe/evXj79i1GjhyJ1q1bCx2PMnn37h28vLzw77//omjRokhPT8eHDx9Qp04d/PXXXyhatKjQEQsUJ+nVIKNGjYK5uTnCwsLQunVr1KhRA126dMGmTZvg4+MjdDzKxM7ODkeOHEHbtm1hamqKbdu2wcnJCYMHD1aZS4yEt2jRIty8eRMrVqxAWFgYLl26hGXLliEiIgLLli0TOh5lEhMTA09PT+jp6cHKygq3bt2CpaUlxo0bp3FzhonB3LlzIZVKceDAAVy+fBlhYWEICgqCTCbDggULhI5X4DjPm4aLiIhAyZIlUbp0aaGjUCYeHh6YNWsWrKyshI5COdCsWTPMmDFDZZqJU6dOYerUqTh9+rQwwShLjo6O2L59O6pUqYLp06ejVKlSGDhwoKJlm/PyqZdGjRph2bJlihtOMly+fBnDhw/H+fPnBUomDLa8aYjw8HDF5JQZQkNDkZiYyMJNTT179gxGRkZCx6Ac+vDhg8ocVABQpUoVvH79WoBE9CX29vZYtWoVEhMTYWVlhRMnTiA9PR1hYWEoUqSI0PEok9TUVJiZmaksL1GiBD58+CBAImGxeCvk5HI5Jk2ahF9++QXXr19XWrdv3z707t0b06dPFyYcfdGAAQMwceJEnD59Go8ePUJMTIzSP1IvNWvWRGhoqMrykJAQVKlSRYBE9CXDhg3D+fPnsW3bNri6uiI+Ph4NGzbEuHHj4OHhIXQ8yqROnTrYtm2byvKtW7eiVq1aAiQSFrtNC7kdO3Zgzpw5mDp1Ktq3bw8trf+v1+VyOYKDg+Hn54fJkyfD3d1dwKSU2efdpRnzGgFQPGnhzp07QsSibJw+fRqDBg1C27ZtYWdnB4lEgrCwMBw7dgzz58/nTSZq6OPHj0hKSoKZmRni4+MRHBwMc3NzjZz0Vd2Fh4ejd+/esLKyUrq+7t69izVr1sDJyUnoiAWKxVsh5+HhgS5duqB79+7ZbvPnn3/i2LFj2LVrVwEmo6+5fPnyF9c3bNiwgJJQTh0/fhx//vmnYh6qmjVron///iwGRGbDhg3o06eP0DEok5s3b+Kvv/5Sur769u2rmL9Uk7B4K+Ts7OwQFBSESpUqZbvNw4cP0bVrV4SFhRVgMsotmUzG6SaIvtNff/2FgwcPQltbGx07dkSPHj0U6x48eICJEyfi1q1bbNkmtcYxb4Wcvr6+yo0KWdHESQ7V1YMHD9C/f3/8999/SsvHjh2LPn36ICoqSqBklJ3ExEQkJiYqvo6KioK/vz/mzZvHIkCNBAQEYO7cuTA2Nkbx4sUxe/ZsbN++HQAQGBgIDw8PREdHY/bs2QInpc9FRkYiMjJS8XVYWBiGDRsGHx8fnDx5UsBkwuEkvYVc7dq1cfr06S8+aPnEiROoWrVqAaai7ERHR6Nnz54oXbo0ZDKZ0rrWrVtj9erV+OWXX7Br1y5UqFBBoJSU4cOHD5g0aRKOHj0KiUQCV1dXDBw4EF27dkVaWhrkcjk2bNiAVatWoVmzZkLH1XjBwcHw8fHBoEGDAABBQUFYs2YNXr58ieXLl6Ndu3bw8/PL8q5GKngvX77EoEGDcOvWLUgkEtjZ2WHYsGHw8vJCuXLlIJfLMXjwYMybNw8//fST0HELFFveCrlffvkFK1euxN9//53l+tOnT2PFihXo2rVrASejrCxfvhx169bF3r17Ub16daV17du3x65du1C1alWsWLFCoIT0OX9/f9y/fx/z5s3DkiVL8PTpU/Ts2RNOTk64dOkSLl++DBcXF/z5559CRyUAL168gIuLi+JrV1dXPHr0CBs2bMCcOXOwePFiFm5qZM6cOdDR0cG2bduwb98+mJqaYsCAAXB3d8eRI0dw9OhR9OnTB5s2bRI6aoFjy1sh17p1a3Tv3h3e3t6oXbs2bG1tUbRoUbx9+xbXr1/H3bt30bVrV3Tq1EnoqIRPNyksXLgw22eXGhgYYPDgwZg0aVIBJ6OsnDhxAgsXLlTcPFK3bl20bNkSffr0UYxP/O2335TGVZFwpFKp0mOU9PT0YGBggOHDh/NnoBo6f/48/vzzT9jY2AAApk+fDicnJ3Tu3FmxTbdu3bBz506hIgqGxZsGGDNmDBo1aoRt27bhyJEjSEhIgJmZGWxtbTF69Gg0btxY6Ij0P2/evEGZMmW+uE2lSpUQHx9fQInoS16/fq10M5C5uTn09fVRsmRJxTIzMzOl8XCkfvgzUD0lJCQo/Tw0NTWFgYEBihcvrlhmbGyM5ORkAdIJi8WbhmjevDmaN28udAz6CnNzczx+/Bjly5fPdpvHjx8rFQcknPT0dJVWUi0tLZUbgHhTv3rT0eGvQnUkl8tVzo1EIlGar1RT8TuWSI20bt0aK1euhKOjY5a/UFJTU7F69Wo0adJEgHSUmUQiUZpAmdTfunXrYGhoqPg6NTUVGzduRLFixZS2GzJkSEFHo0yyur54vX3Ced6I1MibN2/QqVMnlCtXDt7e3qhXrx6KFSuGt2/fIjw8HGvWrMF///2HXbt2fbF1jgqGlZUVbG1tlVrfwsLCYG1tDX19fQBASkoKrl+/zilD1ICzs3OOtpNIJDhx4kQ+p6GvsbKyQtmyZZVa2mJiYlCmTBlF63Z6ejpevHihcdcXW96I1IipqSk2bNiAMWPGYODAgSqPxbKzs8PGjRtZuKmJrB4pl9W5sbCwKIA09DWaOieYWLH1M3tseSNSUxEREbh16xYSEhJgamoKOzs7VKtWTehYREQkMBZvGigmJgYPHz6Eg4MDEhMTUaJECaEjERERUQ6x21SDyGQyjB07FocPH4aWlhaOHDmCuXPn4v379wgICICJiYnQEYmIiOgreL+tBlm5ciXu3r2LDRs2KAZT9+7dG8+ePYO/v7/A6YiIiCgnWLxpkEOHDuGPP/6Ao6OjYlnDhg0xffp0DuQlIo0SFBSk8vxgAEhKSsL69esLPhBRLrB40yCxsbFKs8FnKFu2LN69eydAIvqSK1euIDU1VWW5VCrFkSNHBEhEX7N7926EhIQovh4yZAj2798vYCL63OvXrxETE4OYmBiMHz8eDx48UHyd8e/ChQtYuHCh0FEpEw8PD2zZsgUJCQlCR1ELLN40SLVq1XDhwgWV5QcPHlR5CDoJr3fv3lkW1ZGRkRg9erQAiehL1q9fjxkzZig9qqdcuXKYPHkydu3aJWAyynDmzBk4OzujdevWkMvl8PT0ROvWrZX+DRkyBE5OTkJHpUwaN26MNWvWoFmzZhg2bBjOnj2r0U8u4d2mGuTUqVMYNmwYunTpgp07d+LXX3/Fo0ePcPToUSxatAg//vij0BE13vr16zF37lwAn+Z1y242cRsbG+zYsaMgo9FXtGnTBiNHjkS7du2Ulh86dAjLli1DaGioQMnoc1euXEF6ejr69OmDZcuWKT1ZQSKRwMjICDVr1lR57BkJTy6X48KFCwgKCsLx48dhYmICd3d3dOrUCVWqVBE6XoFi8aZhzpw5g9WrVyMiIgLp6emoUaMGBgwYwMJNTaSmpuLgwYNIT0/HhAkTMGHCBKW7gDN+uTRq1AhFixYVMCllVq9ePQQHB6sMTXjy5Anc3Nxw8+ZNgZJRVi5fvgw7Ozs+11SkkpOTsWnTJqxYsQJSqRR2dnbo06cP2rZtK3S0AsHvWg3DB9SrNx0dHXTq1AnAp0LNzc0Nenp6woaiHKlSpQqOHTuG/v37Ky0/ffo0KlasKFAqyk7Dhg0RHBwMBwcHmJubY8WKFQgJCYGdnR0mTpyouCOf1EtcXBwOHDiAAwcO4P79+7Czs4O7uztiY2MxadIkXLlyBRMnThQ6Zr5jy5uG4Q8rcYmLi8POnTsRFRWFCRMm4PLly6hZsyaftKCGDh48iDFjxsDV1RX16tWDRCLBrVu3cOjQIcyYMUNRlJN6WLFiBVatWoX169dDIpGge/fu6NKlCy5fvozmzZtrRAEgJvv378f+/ftx6dIlmJmZoVOnTujcubPSo+d2796NmTNnIjw8XLigBYTFmwbhDytxiY6Oxs8//wxjY2PExsbi8OHD8Pf3x9mzZxEYGAg7OzuhI1ImoaGhWL9+Pe7duwddXV1Uq1YNv/32G1q1aiV0NMqkdevWGDVqFFxcXDB37lyEh4dj+/btCAsLw/Dhw3H27FmhI9Jn6tati1atWqFz585o3ry50sPqM1y9ehWnTp3CqFGjBEhYsFi8aRD+sBKX33//HWZmZpgxYwbs7Oxw4MABlCtXDuPGjcPz58+xefNmoSMSiZa1tTWOHTsGc3NzdOzYEW3atMGQIUPw7NkzuLq64saNG0JHpM+EhoaidevWKjeSSKVSnD59WuPGbXPMmwaJi4uDra0tAODChQto06YNAM7zpq7Cw8OxefNmpTtOtbW1MXDgQPz8888CJqMMQUFBcHV1hZ6eHoKCgr64LbtN1Yu5uTmioqIgk8lw7949TJkyBQAQFhYGc3NzYcORiuHDh+P8+fMwMzNTWp4xdRKLNyq0+MNKXNLS0pCenq6y/MOHD9DW1hYgEWU2btw4NGvWDCVKlMC4ceOy3U4ikbB4UzPdunWDr68v9PX1YWlpCVtbW2zZsgX+/v4YOnSo0PEIqlMnNWnSJMvtbGxsCjKWWmDxpkH4w0pcmjZtipUrV2L+/PmKZW/evIG/vz8aNWokYDLKcPfu3Sz/n9Rf//79UaVKFTx58gQdOnQAAJiYmGDixIno0qWLwOkIAHr27InixYsrpk4aP358tlMnaRqOedMwJ0+eVPywMjU1xYEDByCVSvnDSg3Fxsaid+/eePv2Ld6/f4+qVavi2bNnKF68ODZv3ozy5csLHZGoUJDJZJySR83t27ePUyd9hsUbkRpLTk7GwYMHcefOHcWkyh07doSxsbHQ0SiTW7duYerUqXjw4EGWDzy/c+eOAKnoS7Zt24Y1a9bgxYsXOHLkCNauXYtSpUphyJAhQkcjcEzpl7B40yAymQw7duzAvXv3kJaWprT81q1bOHr0qIDpKLP//vtPZbZ+AEhJSUFAQACGDx8uQCrKTseOHaGvrw8PD48s50x0d3cXIBVlJzg4GFOnTkWfPn2wdu1aHDx4EKdOncL8+fMxdOhQDBgwQOiIGs/Kygrnz59HiRIlYGVlle12EolE4/44YvGmQaZMmYK9e/eiTp06uHHjBmxtbREdHY34+Hj07dsXY8eOFToifaZVq1Yq3aP//vsvxo0bh6dPn+L69evChSMV9erVw+7du1GjRg2ho1AOuLu7o3fv3nB3d4etrS0OHDiAihUrYt++fVi5ciX/mCW1pjrLHRVax48fx5w5c7Bt2zZUqFAB06dPx6lTp9C6dWukpKQIHY8yqVevHnr16oUXL14gJSUFCxcuRNeuXWFqavrVLgQqeNbW1nj27JnQMSiHoqKiYG9vr7Lc3t4eL168ECARUc7xblMN8vbtW9SvXx8AULNmTURERKBq1arw9vbGsGHDMGnSJGEDkpKFCxdi9OjR6NWrF/T19REbGws/Pz907dpV6GiUhenTp2PgwIG4efMmKlSooDIDvKaNyVF3JUuWxKNHj1SeO3vt2jWULl1aoFT0OWdnZ6V5Lr/kxIkT+ZxGvbB40yAlS5ZEfHw8ypUrh0qVKuH+/fsAAFNTU7x69UrgdJSZlpYW5s+fj3HjxiE4OBjbt2/XyPmMxCI0NBTR0dFYsWKFyjrO86Z+unbtiqlTpyrm53v06BHOnj2LJUuWoG/fvsKGIwCfurYzireEhARs2bIFrVq1gq2tLXR0dBRjtb28vAROWvA45k2DTJ48Gbdu3cLs2bPx5MkTzJw5E0uXLkVISAhOnjyJI0eOCB1R440fP15lmVwuR0hICMqUKaPUzTN79uyCjEZf4eTkhN69e6Nfv34wMDAQOg7lwMKFC7FhwwZIpVIAgI6ODrp164YJEyZk+exMEs7gwYNRv359lRtJNm3ahOPHj2PDhg0CJRMGizcN8v79e4wdOxZNmjTBL7/8Am9vb5w5cwY6OjqYO3cu3NzchI6o8Xr16pXjbTdt2pSPSSi3Mp4/W6FCBaGjUA5cvnwZtra2SE1NRWRkJORyOapWrcppeNRU/fr1sX//flSuXFlpeXR0NDp06KBxz6Jl8abhIiIiULJkSY7xUEOXL19G/fr1OSmlSPj5+aF8+fLw9vYWOgrlQKNGjRAYGIg6deoIHYVyoG3btujXrx+6d++utDwwMBB79uxBSEiIQMmEwTFvGio1NRXnz5+HXC5HtWrVhI5DWfDx8eEvFxEpUaIEli9fjmPHjqFKlSrQ0VH+8cpubvVSokQJvH//XugYlEP9+/fH9OnTcf36dVhbW0Mul+Pq1as4duyY0iMENQWLNw2wdetW7N27F8CnQbouLi745ZdfFDcsmJubY/369bCwsBAwJWXGXy7iEhYWhnr16gEAp5oQgaZNm8Lb2xstWrRA5cqVVSZW5lMW1EvXrl1hbGyMTZs24ejRo5BIJKhVqxZWrFiBFi1aCB2vwLHbtJALDAxEQEAAfvrpJxgaGiI4OBiVKlVCYmIipkyZArlcjtmzZ6NChQpYsmSJ0HHpM7Nnz8b27dv5y4UoHzg7O2e7TiKRaNzUEyQuLN4KuR9//BG+vr5wdXUFANy8eRM///wzVq5ciVatWgH41GIwbNgwnDt3TsiolAl/uYhPUlISDhw4gHv37kFHRwc1atSAq6srB8ET5YHbt28jMDBQcX1Vr14dffr00cgplFi8FXLW1tYIDQ1VesRS3bp1ceDAAVStWhUA8PLlS7Rs2RK3b98WKiaR6D1//hw9e/ZEfHw8qlSpgrS0NERHR6NEiRLYunUrzM3NhY5IWbhy5QoePnyI9u3b48WLF6hcuTJ0dXWFjkWZhIWFoV+/fqhZsybs7e2RlpaGa9eu4f79+9iwYQMaNGggdMQCxTFvhVxKSorKnFO6urpKP5wkEgnS09MLOhp9I5lMhps3b2b5aB8Szpw5c1C2bFns2rULZmZmAIBXr17B19cX/v7+WLBggcAJ6XMfPnxA//79cePGDUgkEjRp0gTz58/H48ePsX79ehbbambhwoXo0qUL/Pz8lJZPnToVixcv1ripkzgLIZGaioiIgIeHB+rUqYNatWop/mU885TUy/nz5zF27FhF4QZ8eqrJ2LFjOSRBDS1cuBASiQTHjh1T/IE7ZswYGBkZYd68eQKno8xu376Nnj17qizv2bMn/v33XwESCYstbxpg3bp1MDQ0VHydmpqKjRs3olixYgA+jdMh9TN79mzo6Ohg8uTJmDFjBsaNG4f//vsPW7Zs4S8XNaStrZ3lkxX09fUhk8kESERfcurUKSxYsEDp2aZVq1bF5MmTMXDgQAGTUVZMTU0RHx+vGO6TIT4+XiPnwmTxVsiVK1cOhw8fVlpWqlQplcHuZcuWLchYlAP//vsvNmzYABsbG+zZswc1a9bEL7/8AnNzc+zcuRMuLi5CR6TP2NnZYcWKFZg3b55iWEJKSgpWrlwJW1tbgdNRZq9fv0apUqVUlhsbGyM5OVmARPQlrVq1wvTp07Fo0SLF3KSRkZGYOXOm4uY7TcLirZA7efKk0BHoG6Wnpyt+uVSpUgX379+Hvb09WrdujdWrVwucjjIbNWoUunXrhjZt2qBu3bqQSCS4efMmPnz4oHHjccTA2toaISEhKk/E2LhxI2rXri1QKsrOsGHD0K9fP7Rv3x4mJiaQSCR49+4datasiTFjxggdr8CxeCNSU1WrVsWVK1fQoUMHVK5cGbdu3QLw6Rm17IZTP9WqVcP+/fuxZcsWPHjwAHK5HO3bt0e3bt2UuuZIPYwYMQL9+vVDeHg4UlNTsXLlSkRGRiIiIgKBgYFCx6NMihUrht27d+Ps2bOK66tmzZpo2rQptLW1hY5X4DhVCJGa2rNnD6ZMmYKZM2eiTp066NixI7p06YJr166hZMmS/AVD9J3u3r2LdevWISIiAunp6ahRowa8vLwUT8ogUlcs3ojU2PHjx1G8eHHY29vj0KFDWL16NcqWLYs//vgDFSpUEDoefSY5ORnr16/H1atXkZKSgsw/Wjdu3ChQMspKUFAQXF1dVQa7JyUlYefOnejbt68wwShL//77L6ZMmYIHDx5k2fNw584dAVIJh8UbEVEeGDduHEJCQtC8eXOYmJiorOeD6YX3+vVrfPz4EQDQunVr7N69G6ampkrbREREYMSIEbh586YQESkbHTt2hL6+Pjw8PFQeFQgA7u7uAqQSDse8EamRgICAHG/LZ5uql2PHjmHu3Lm8C1iNnTlzBuPGjYNEIoFcLoenp6fKNnK5XCMfdK7uHj9+jN27d6NGjRpCR1ELLN6I1EhAQAC0tLS+Oru7RCJh8aZmtLS0eJeimuvUqRPKly+P9PR09OnTB0uXLlXMdwl8uq6MjIxQs2ZNAVNSVqytrfHs2TMWb//DblMiNeLn54djx47B0NAQbm5ucHNzg5WVldCxKAcmTpyIUqVKYdiwYUJHoRy4fPky7OzsoKPDNgwxiIqKwsCBA+Hm5oYKFSpAS0v5AVGdOnUSJphAWLwRqZm0tDT8888/CAkJwfHjx2FmZob27dvDzc0NFhYWQsejbMydOxdbtmxBtWrVULVqVZWB8Bzzpn6uXr2a7Q0mbNlWLytXrsSSJUuyXCeRSHjDAhGpj5SUFJw7dw6HDx/GiRMnUKlSJbi6usLNzQ3lypUTOh595mvPm+VEverlzz//xMKFC1GsWDEUKVJEaZ1EIlF5Cg0Jy8nJCb1790a/fv2yfAydpmHxRiQSMpkMu3btwqJFi5CYmKhxf2kS5aXmzZujc+fO8PX1FToK5YCdnR0OHDjAKZL+h539RGouNjYWhw8fRmhoKG7cuIHKlSt/tZWHhJGUlIQDBw7g3r170NHRQY0aNeDq6gpjY2Oho1EmCQkJGjdOSszat2+PQ4cOqTzOTFOx5Y1IDWUu2CpWrAgXFxe4uLjwBgY19fz5c/Ts2RPx8fGoUqUK0tLSEB0djRIlSmDr1q1fvYOYCpaXlxc8PDzQvn17oaNQDixZsgSBgYGoWbMmqlSponKjiaaNKWXxRqRG1q9fj9DQUNy8eRPlypWDi4sL2rVrhzp16ggdjb7C19cX8fHxWLp0KczMzAAAr169gq+vL8zNzbFgwQKBE9Lndu3aBX9/f3h4eGR5gwlb5dQLx5QqY/FGpEasrKygq6uLxo0bw9ra+ovb8m449WJvb4+//vpL5bzdvHkTAwYMwKVLlwRKRln5Ugu2Jt69SOLCMW9EaiTjDtIHDx7gwYMH2W7HSXrVj7a2dpZ3wenr62f5LEYS1t27d4WOQLmUkJCA6OhoSKVSpeUSiQT29vYCpRIGW96IiPLA77//DgMDA8ybNw+6uroAPk31Mnr0aLx79w7r1q0TOCGReAUFBWHy5MmQyWQqc/JpYkspizciojzw8OFDdOvWDUWKFEHdunUhkUhw8+ZNfPjwAZs2beKjs9TA+PHjc7ytpg2AV3ctW7ZE8+bN0bdv3ywfTF++fHkBUgmH3aZERHmgWrVq2L9/P7Zu3Yr79+9DLpejffv26NatGypWrCh0PALw9OlToSPQN0pISICXlxefMvM/bHkjIsojDx8+RGJiImxsbAAA69atQ6tWrVClShWBkxGJ29ixY1G7dm306dNH6ChqgcUbEVEeOHv2LAYPHgwvLy/Fw+m7dOmCyMhIrFmzRuMGVBPlpVevXsHNzQ3Vq1dHxYoVIZFIlNZrWjc3izciojzQuXNntGjRAj4+PkrLFy1ahEuXLmH79u0CJSMSv5EjR+LIkSOoVatWlnd1c543IiLKtfr16yM4OFhlfNuTJ0/QoUMHhIeHC5SMSPxsbW2xePFitGjRQugoakFL6ABERIWBmZkZIiIiVJY/ePAARYsWFSARUeFRpEgRVKpUSegYaoN3mxIR5QF3d3dMnToV7969g42NDSQSCW7duoXFixfD3d1d6HhEoubt7Y3Fixdj5syZMDY2FjqO4NhtSkSUB9LS0jBz5kzs2rULqampkMvl0NHRQa9evTBixAjFxL2kHrKb800ikUBXVxfm5uZo164d7xRWE/369UNYWBjkcjlKlCih8mD6EydOCJRMGCzeiIjyUGJiIqKioqCjowMLC4ssB1eT8EaNGoVDhw6hVKlSiufRRkRE4MWLF6hXrx7evn2L58+fY926dWjQoIHAaSkgIOCL6zXtcYEs3oiIvlF8fDxKlCjxxW1kMhmOHz8OV1fXAkpFOTFp0iR8+PAB8+bNg56eHgAgNTUVkyZNgqGhISZPnoz58+fjxo0bGncnI6k/3rBARPSNmjZtivj4eKVlI0eOVFr27t07jBw5sqCj0VeEhoZiyJAhisINAHR0dDBgwAAcPHgQAODp6Ynbt28LFZEyuX37NkaMGAE3Nzd07NgRI0eOxM2bN4WOJQgWb0RE3yirjouTJ08iKSnpq9uRsHR0dPDq1SuV5XFxcYoJYNPS0lTGVpEwwsLC0K1bN0RHR6Np06ZwcHBAVFQUfvnlF1y9elXoeAWO35VERPks82zwJLwff/wRfn5+mDJlCurVqwe5XI7r169j+vTpaN26NZKSkrBy5UrFeDgS1sKFC9GlSxf4+fkpLZ86dSoWL16scV3bLN6IiEjjjB8/HmPGjIGXl5dSce3i4oKJEyfiwoULuHLlClavXi1gSspw+/ZtzJgxQ2V5z5494enpKUAiYbF4IyIijWNgYIClS5fiyZMnuHPnDrS1tWFpaYkKFSoAAJo3b46///5b4JSUwdTUFPHx8ahatarS8vj4eKVxi5qCY96IiL4Du0TFzdDQENbW1qhVqxa0tLQQExODmJgYjSwI1FmrVq0wffp0PHz4ULEsMjISM2fORKtWrQRMJgxOFUJE9I2srKzg6uoKfX19xbLg4GA4OzujSJEiAACpVIrDhw/jzp07QsWkLFy/fh1jx47Ff//9p7RcLpdDIpHwfKmZhIQE9OvXD3fu3IGJiQkkEgnevXuHmjVr4q+//oKZmZnQEQsUizciom/Uq1evHG+raQOq1Z2npye0tLTg7e0NExMTlfUNGzYUIBV9SXp6Os6ePYsHDx5ALpejZs2aaNq0KbS1tYWOVuBYvBERkcaxtrbGzp07UatWLaGj0FckJyfDwMBAaYjCgwcPUL58eRgZGQmYTDgc80ZERBqnbNmySElJEToGfUVQUBBatmyJf//9V2n5nDlz0Lx5cxw+fFigZMJi8UZERBpn0KBBmDVrFu7du8ciTk1dvHgREyZMQJs2bVC2bFmldX5+fmjXrh1GjRqFsLAwgRIKh92mRESkcZydnREXF4e0tLQs1/OGBeF5eXmhevXqmDBhQrbbTJw4EbGxsVi7dm0BJhMe53kjIiKNM3ToUKEj0FdERERg3LhxX9yme/fu+O233wookfpg8UZERBrH3d1d6Aj0FTKZDAYGBl/cplixYvj48WMBJVIfLN6IiEgjjB8/HhMnToSxsTHGjx+f7XYSiQSzZs0qwGSUlSpVqiA8PByVKlXKdptr166hfPnyBZhKPbB4IyIijfD06VOkp6cr/p/UW4cOHbB06VI4OTmhdOnSKuvj4uKwZMkSdO7cWYB0wuINC0RERKR20tLS0Lt3b9y/fx+enp6oX78+ihYtirdv3+L69evYu3cvKleujM2bN3+1e7WwYfFGREQaKSYmBkWLFoWxsTH++ecfHD16FHZ2dmjfvr3Q0eh/ZDIZli5dil27diEhIUGxvGTJkujcuTN+//13jSvcABZvRESkgY4dO4bhw4dj1apVqFy5MlxcXFCxYkU8f/4co0ePRo8ePYSOSJ9JTU3FkydPkJCQADMzM1SsWFHpiQuahsUbERFpHHd3dzRv3hy+vr5Ys2YN9uzZg9DQUBw+fBgBAQEaO3M/iQOfsEBERBrn4cOH+Pnnn6GlpYVz586hRYsW0NLSgq2tLZ49eyZ0PKIvYvFGREQap2jRonj//j0+fPiA69evo3HjxgCA//77D8WLFxc2HNFXcKoQIiLSOC1atICfnx+MjY1hbGyMJk2a4MKFC5gyZQpatmwpdDyiL2LLGxERaZw//vgDdnZ2MDQ0xMqVK6Gnp4erV6/CxsYGY8eOFToe0RfxhgUiIiIiEWG3KRERaYSAgAD0798fhoaGCAgI+OK2Q4YMKaBURLnHljciItIIzs7O2LNnD0xNTeHs7JztdhKJBCdOnCjAZES5w+KNiIiISER4wwIREdH/JCcnY9asWULHIPoiFm9ERKQRpFIpZsyYgUaNGqFJkyaYN28e0tPTFevPnTsHNzc3bN68WcCURF/HGxaIiEgjLFiwANu3b0eHDh2gp6eH7du3w8TEBN7e3pgxYwa2bduGSpUqYcOGDUJHJfoiFm9ERKQRjh8/jokTJ6J79+4AgJYtW2LmzJl4/vw5du/eDS8vL/j6+kJPT0/gpERfxhsWiIhII1hbWyMkJAQVK1YEAKSlpcHa2hrFihXD4sWL4ejoKHBCopzhmDciItIIKSkpMDIyUnytra0NfX19jB8/noUbiQqLNyIi0mj16tUTOgJRrrB4IyIijSGRSFSWaWnxVyGJC29YICIijTFjxgzo6+srvk5JSYG/vz+KFCmitN3s2bMLOhpRjrF4IyIijeDg4ICXL18qLbO1tcWbN2/w5s0bgVIR5R7vNiUiIiISEXb0ExEREYkIizciIiIiEWHxRkRERCQiLN6IiIiIRITFGxEREZGIsHgjokLv/v37GD58OJo0aYK6deuiadOmGDZsGCIiIoSOlitPnz6FpaUl9u7dK3QUIhIQpwohokLtwYMH+Pnnn2FjY4OuXbuiZMmSePHiBTZv3ow7d+5g06ZNqF+/vtAxc0QmkyEiIgKVKlWCmZmZ0HGISCAs3oioUJswYQIuXryIo0ePQldXV7E8KSkJLi4usLS0xJ9//ilgQiKi3GG3KREVaq9evQIAZP471cjICOPHj4eLi4ti2aFDh+Dh4YF69eqhZcuW8Pf3h0wmU6y/f/8+vL29YWdnBzs7OwwePBhPnjxRrL906RIsLS1x8eJFeHl5oV69emjcuDHmzp2L1NRUxXavX7/G1KlT0apVK9StWxcNGzbE4MGD8fTpU8U2vXr1wqhRo+Dj4wM7Ozv89ttvWXabPn78GD4+PmjSpAnq16+PXr164erVq3n3ARKR2mHxRkSFWsuWLRETE4Nu3bphy5YtePjwoaKQa9euHdzd3QEA27dvx4gRI1CrVi0EBATA29sbW7duxZQpUwAAUVFR6NatG+Lj4zFnzhzMnDkTT548Qffu3REfH690zFGjRqFBgwZYtWoVfvrpJ6xbtw67d+8G8KmI9Pb2xvnz5zFy5EgEBgZi0KBBuHDhAvz8/JT2c/jwYejq6mL58uXo3bu3ynuLjIyEh4cHnjx5gkmTJmH+/PmQSCTo06cPLl++nNcfJRGpCT7blIgKtV9++QUvX75EYGAgpk2bBgAwNTVF06ZN0atXL9SrVw/p6elYtmwZ2rRpg5kzZypeK5VKsW/fPshkMgQEBMDAwADr16+HsbExAMDJyQk//PAD1q5di7Fjxype16VLFwwePFixzfHjx3H69Gl069YNcXFxMDQ0xNixY2Fvbw8AcHR0xNOnT7F9+3al7FpaWpg+fTqMjIwAQKllDgACAgKgq6uLjRs3wsTEBMCnYrV9+/bw9/fHrl278vKjJCI1weKNiAo9X19f9O3bF2fPnsXFixdx6dIlBAcH4+DBgxg/fjyaNm2KV69e4YcfflB6Xd++fdG3b18AwD///ANHR0cYGBgoukCNjY1hb2+PCxcuKL3O1tZW6Wtzc3MkJSUBAMqUKYONGzcCAGJiYhAdHY2HDx/i2rVrSElJUXpdhQoVFIVbVi5fvoxWrVopCjcA0NHRgZubG5YvX47ExEQUKVIkF58UEYkBizci0gjFihVD+/bt0b59ewBAREQExowZg/nz56Nu3boAgBIlSmT7+rdv3yIkJAQhISEq6zLf+WlgYKD0tZaWltKYuwMHDmDhwoV4/vw5ihcvDisrK5XXAEDJkiW/+J4SEhKy3KZkyZKQy+X48OEDizeiQojFGxEVWrGxsejcuTN8fX3RpUsXpXW1a9fGsGHDMHjwYKSlpQH4dCPB596+fYvbt2+jfv36MDExQePGjdGvXz+V4+jo5PxHaVhYGMaOHYuePXuif//+MDc3BwDMmzcv1zcaFCtWTHFDxudevnwJ4FP3MBEVPrxhgYgKrZIlS0JHRwdbt26FVCpVWf/o0SPo6+ujRo0aMDU1xYkTJ5TWBwcHY8CAAZBKpWjYsCEiIyNRq1YtWFtbw9raGnXr1sX69etx7NixHGcKDw9Heno6fHx8FIVbWlqaous1PT09x/tycHDAqVOn8P79e8WytLQ0HDp0CNbW1tDT08vxvohIPNjyRkSFlra2NqZMmYLBgwejc+fO6NGjB6pVq4bk5GScP38eW7Zsga+vL0xNTTF06FBMmzYNU6ZMQZs2bfD48WMsXrwY3bt3h5mZGQYNGoRu3brB29sb3bt3h76+Pnbs2IHjx49j6dKlOc5kY2MDAJg2bRo6d+6Md+/eYfPmzbh79y6AT/PPZdwQ8TVDhgzBmTNn0Lt3b/z222/Q09PD5s2b8eTJE6xduzb3HxgRiQKLNyIq1Fq2bImdO3ciMDAQq1atwuvXr6Gnp4fatWtj0aJFaNu2LQCgR48eMDIyQmBgIHbv3o0yZcrAy8sLv/32GwDAysoKW7ZswaJFizBmzBjI5XLUrFkTy5cvR+vWrXOcx9HREX5+fvjrr78QGhqKkiVLwtHREQEBARg8eDCuXr2KFi1a5GhfNWrUwNatW7Fw4UJMmDABEokENjY22Lhxo+JOViIqfPiEBSIiIiIR4Zg3IiIiIhFh8UZEREQkIizeiIiIiESExRsRERGRiLB4IyIiIhIRFm9EREREIsLijYiIiEhEWLwRERERiQiLNyIiIiIRYfFGREREJCIs3oiIiIhEhMUbERERkYj8HxdaP5noHB94AAAAAElFTkSuQmCC",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "def scenario_analysis(weights, returns, scenarios):\n",
+ " # Calculate portfolio returns based on weights\n",
+ " portfolio_returns = np.sum(returns * weights, axis=1)\n",
+ " scenario_results = {}\n",
+ "\n",
+ " for scenario, shock in scenarios.items():\n",
+ " # Convert shock dictionary to a Series\n",
+ " shock_series = pd.Series(shock)\n",
+ " shocked_returns = returns + shock_series\n",
+ "\n",
+ " # Calculate scenario portfolio returns\n",
+ " scenario_portfolio_returns = np.sum(shocked_returns * weights, axis=1)\n",
+ "\n",
+ " # Store results for each scenario\n",
+ " scenario_results[scenario] = {\n",
+ " 'Mean Return': scenario_portfolio_returns.mean(),\n",
+ " 'Volatility': scenario_portfolio_returns.std(),\n",
+ " 'VaR 95%': np.percentile(scenario_portfolio_returns, 5),\n",
+ " 'CVaR 95%': scenario_portfolio_returns[scenario_portfolio_returns <= np.percentile(scenario_portfolio_returns, 5)].mean()\n",
+ " }\n",
+ "\n",
+ " return pd.DataFrame(scenario_results).T\n",
+ "\n",
+ "# Define scenarios\n",
+ "scenarios = {\n",
+ " 'Base Case': {asset: 0 for asset in returns.columns},\n",
+ " 'Market Crash': {asset: -0.3 for asset in returns.columns},\n",
+ " 'Economic Boom': {asset: 0.2 for asset in returns.columns},\n",
+ " 'Rising Interest Rates': {'US Aggregate Bonds': -0.1, 'US Treasury Bonds': -0.15},\n",
+ " 'Commodity Boom': {'Gold': 0.25, 'Commodities': 0.3}\n",
+ "}\n",
+ "\n",
+ "# Check if returns DataFrame is not empty before proceeding\n",
+ "if not returns.empty:\n",
+ " # Assuming optimal_weights is defined and returns is your DataFrame of returns\n",
+ " optimal_weights = [0.2] * len(returns.columns) # Example weights; adjust as necessary\n",
+ "\n",
+ " # Perform scenario analysis\n",
+ " scenario_results = scenario_analysis(optimal_weights, returns, scenarios)\n",
+ " print(scenario_results)\n",
+ "\n",
+ " # Visualize scenario analysis results\n",
+ " plt.figure(figsize=(12, 6))\n",
+ " scenario_results[['Mean Return', 'Volatility']].plot(kind='bar')\n",
+ " plt.title('Scenario Analysis: Mean Return and Volatility')\n",
+ " plt.xlabel('Scenario')\n",
+ " plt.ylabel('Value')\n",
+ " plt.legend(loc='best')\n",
+ " plt.tight_layout()\n",
+ " plt.show()\n",
+ "else:\n",
+ " print(\"Returns data is not available for calculations.\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 7. Performance Attribution\n",
+ "Performance attribution helps us understand which assets contributed most to our portfolio's performance. Let's implement a simple performance attribution analysis for our optimal portfolio."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " Weight Return Contribution Percent Contribution\n",
+ "Gold 0.2 0.327863 0.065573 0.180772\n",
+ "US Large Cap 0.2 0.324541 0.064908 0.178940\n",
+ "US Small Cap 0.2 0.314988 0.062998 0.173673\n",
+ "Real Estate 0.2 0.307817 0.061563 0.169719\n",
+ "Emerging Markets 0.2 0.223152 0.044630 0.123038\n",
+ "International Developed 0.2 0.208347 0.041669 0.114875\n",
+ "US Treasury Bonds 0.2 0.137840 0.027568 0.076000\n",
+ "US Aggregate Bonds 0.2 0.084281 0.016856 0.046469\n",
+ "Commodities 0.2 -0.115145 -0.023029 -0.063487\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Plot data (preview): \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAABLAAAAJYCAYAAABy5h8aAAAAOX...\"\n"
+ ]
+ }
+ ],
+ "source": [
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "import matplotlib.pyplot as plt\n",
+ "import io\n",
+ "import base64\n",
+ "from IPython.display import HTML\n",
+ "\n",
+ "def fig_to_base64(fig):\n",
+ " buf = io.BytesIO()\n",
+ " fig.savefig(buf, format='png')\n",
+ " buf.seek(0)\n",
+ " return base64.b64encode(buf.getvalue()).decode('utf-8')\n",
+ "\n",
+ "def performance_attribution(weights, returns):\n",
+ " # Calculate portfolio return\n",
+ " portfolio_return = np.sum(returns.mean() * weights) * 252 # Annualized return\n",
+ "\n",
+ " # Calculate asset contribution to returns\n",
+ " asset_contribution = returns.mean() * weights * 252 # Annualized contribution\n",
+ " percent_contribution = asset_contribution / portfolio_return # Contribution percentage\n",
+ "\n",
+ " # Create DataFrame for attribution data\n",
+ " attribution_data = pd.DataFrame({\n",
+ " 'Weight': weights,\n",
+ " 'Return': returns.mean() * 252,\n",
+ " 'Contribution': asset_contribution,\n",
+ " 'Percent Contribution': percent_contribution\n",
+ " })\n",
+ " return attribution_data.sort_values('Percent Contribution', ascending=False)\n",
+ "\n",
+ "# Check if returns DataFrame is not empty before proceeding\n",
+ "if not returns.empty and optimal_weights is not None:\n",
+ " # Calculate performance attribution\n",
+ " attribution = performance_attribution(optimal_weights, returns)\n",
+ " print(attribution)\n",
+ "\n",
+ " # Visualize performance attribution\n",
+ " fig, ax = plt.subplots(figsize=(12, 6))\n",
+ " attribution['Percent Contribution'].plot(kind='bar', ax=ax)\n",
+ " ax.set_title('Performance Attribution of Optimal Portfolio')\n",
+ " ax.set_xlabel('Asset')\n",
+ " ax.set_ylabel('Percent Contribution to Return')\n",
+ " plt.xticks(rotation=45, ha='right')\n",
+ " plt.tight_layout()\n",
+ "\n",
+ " # Convert plot to base64\n",
+ " plot_base64 = fig_to_base64(fig)\n",
+ " plt.close(fig)\n",
+ "\n",
+ " # Display the plot\n",
+ " display(HTML(f'"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "def detailed_scenario_analysis(weights, returns, scenarios):\n",
+ " portfolio_returns = np.sum(returns * weights, axis=1)\n",
+ " base_performance = {\n",
+ " 'Mean Return': portfolio_returns.mean() * 252,\n",
+ " 'Volatility': portfolio_returns.std() * np.sqrt(252),\n",
+ " 'Sharpe Ratio': (portfolio_returns.mean() * 252 - risk_free_rate) / (portfolio_returns.std() * np.sqrt(252)),\n",
+ " 'VaR 95%': np.percentile(portfolio_returns, 5) * np.sqrt(252),\n",
+ " 'CVaR 95%': portfolio_returns[portfolio_returns <= np.percentile(portfolio_returns, 5)].mean() * np.sqrt(252),\n",
+ " 'Max Drawdown': (portfolio_returns.cumsum() - portfolio_returns.cumsum().cummax()).min()\n",
+ " }\n",
+ "\n",
+ " scenario_results = {'Base Case': base_performance}\n",
+ "\n",
+ " for scenario, shocks in scenarios.items():\n",
+ " shocked_returns = returns.copy()\n",
+ " for asset, shock in shocks.items():\n",
+ " shocked_returns[asset] += shock\n",
+ "\n",
+ " scenario_portfolio_returns = np.sum(shocked_returns * weights, axis=1)\n",
+ " scenario_results[scenario] = {\n",
+ " 'Mean Return': scenario_portfolio_returns.mean() * 252,\n",
+ " 'Volatility': scenario_portfolio_returns.std() * np.sqrt(252),\n",
+ " 'Sharpe Ratio': (scenario_portfolio_returns.mean() * 252 - risk_free_rate) / (scenario_portfolio_returns.std() * np.sqrt(252)),\n",
+ " 'VaR 95%': np.percentile(scenario_portfolio_returns, 5) * np.sqrt(252),\n",
+ " 'CVaR 95%': scenario_portfolio_returns[scenario_portfolio_returns <= np.percentile(scenario_portfolio_returns, 5)].mean() * np.sqrt(252),\n",
+ " 'Max Drawdown': (scenario_portfolio_returns.cumsum() - scenario_portfolio_returns.cumsum().cummax()).min()\n",
+ " }\n",
+ "\n",
+ " return pd.DataFrame(scenario_results).T\n",
+ "\n",
+ "# Define more detailed scenarios\n",
+ "detailed_scenarios = {\n",
+ " 'Market Crash': {'US Large Cap': -0.4, 'US Small Cap': -0.5, 'International Developed': -0.35, 'Emerging Markets': -0.45, 'US Aggregate Bonds': 0.05, 'US Treasury Bonds': 0.1, 'Real Estate': -0.3, 'Gold': 0.15, 'Commodities': -0.25},\n",
+ " 'Economic Boom': {'US Large Cap': 0.25, 'US Small Cap': 0.3, 'International Developed': 0.2, 'Emerging Markets': 0.35, 'US Aggregate Bonds': -0.05, 'US Treasury Bonds': -0.1, 'Real Estate': 0.2, 'Gold': -0.1, 'Commodities': 0.15},\n",
+ " 'Rising Interest Rates': {'US Large Cap': -0.1, 'US Small Cap': -0.15, 'International Developed': -0.05, 'Emerging Markets': -0.1, 'US Aggregate Bonds': -0.2, 'US Treasury Bonds': -0.25, 'Real Estate': -0.15, 'Gold': -0.05, 'Commodities': 0.05},\n",
+ " 'Geopolitical Tension': {'US Large Cap': -0.15, 'US Small Cap': -0.2, 'International Developed': -0.25, 'Emerging Markets': -0.3, 'US Aggregate Bonds': 0.1, 'US Treasury Bonds': 0.15, 'Real Estate': -0.1, 'Gold': 0.25, 'Commodities': 0.2},\n",
+ " 'Tech Boom': {'US Large Cap': 0.3, 'US Small Cap': 0.35, 'International Developed': 0.2, 'Emerging Markets': 0.25, 'US Aggregate Bonds': -0.05, 'US Treasury Bonds': -0.1, 'Real Estate': 0.1, 'Gold': -0.05, 'Commodities': 0},\n",
+ "}\n",
+ "\n",
+ "# Run detailed scenario analysis\n",
+ "detailed_scenario_results = detailed_scenario_analysis(optimal_weights, returns, detailed_scenarios)\n",
+ "print(detailed_scenario_results)\n",
+ "\n",
+ "# Visualize scenario analysis results\n",
+ "plt.figure(figsize=(15, 10))\n",
+ "detailed_scenario_results[['Mean Return', 'Volatility', 'Sharpe Ratio', 'VaR 95%', 'CVaR 95%']].plot(kind='bar', subplots=True, layout=(3,2), sharex=False, figsize=(15,20))\n",
+ "plt.tight_layout()\n",
+ "plt.show()\n",
+ "\n",
+ "# Visualize max drawdown for each scenario\n",
+ "plt.figure(figsize=(12, 6))\n",
+ "detailed_scenario_results['Max Drawdown'].plot(kind='bar')\n",
+ "plt.title('Max Drawdown Under Different Scenarios')\n",
+ "plt.xlabel('Scenario')\n",
+ "plt.ylabel('Max Drawdown')\n",
+ "plt.xticks(rotation=45, ha='right')\n",
+ "plt.tight_layout()\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.12.3"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/examples/sectorRotationStrategy.ipynb b/examples/sectorRotationStrategy.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..37098028dd19e8071c6265b450c9c8ab6586c404
--- /dev/null
+++ b/examples/sectorRotationStrategy.ipynb
@@ -0,0 +1,1200 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 0,
+ "metadata": {
+ "colab": {
+ "provenance": []
+ },
+ "kernelspec": {
+ "name": "python3",
+ "display_name": "Python 3"
+ },
+ "language_info": {
+ "name": "python"
+ }
+ },
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "source": [
+ "# **Sector Rotation Strategy Analysis with OpenBB**\n",
+ "\n",
+ "Sector rotation involves shifting investments across different sectors in the stock market, based on economic cycles or market performance expectations. This strategy seeks to maximize returns by focusing on sectors that are expected to perform better in the current market environment while reducing exposure to underperforming sectors.\n",
+ "\n",
+ "Author:\n",
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " date \n",
+ " open \n",
+ " high \n",
+ " low \n",
+ " close \n",
+ " volume \n",
+ " split_ratio \n",
+ " dividend \n",
+ " capital_gains \n",
+ " Symbol \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 \n",
+ " 2015-01-02 \n",
+ " 20.194963 \n",
+ " 20.227457 \n",
+ " 19.935013 \n",
+ " 20.089357 \n",
+ " 40511471 \n",
+ " 0.0 \n",
+ " 0.0 \n",
+ " 0.0 \n",
+ " XLF \n",
+ " \n",
+ " \n",
+ " 1 \n",
+ " 2015-01-05 \n",
+ " 19.959383 \n",
+ " 20.000000 \n",
+ " 19.618196 \n",
+ " 19.666937 \n",
+ " 50770502 \n",
+ " 0.0 \n",
+ " 0.0 \n",
+ " 0.0 \n",
+ " XLF \n",
+ " \n",
+ " \n",
+ " 2 \n",
+ " 2015-01-06 \n",
+ " 19.666937 \n",
+ " 19.731924 \n",
+ " 19.277012 \n",
+ " 19.366369 \n",
+ " 57454463 \n",
+ " 0.0 \n",
+ " 0.0 \n",
+ " 0.0 \n",
+ " XLF \n",
+ " \n",
+ " \n",
+ " 3 \n",
+ " 2015-01-07 \n",
+ " 19.528837 \n",
+ " 19.618196 \n",
+ " 19.415110 \n",
+ " 19.569456 \n",
+ " 36287049 \n",
+ " 0.0 \n",
+ " 0.0 \n",
+ " 0.0 \n",
+ " XLF \n",
+ " \n",
+ " \n",
+ " 4 \n",
+ " 2015-01-08 \n",
+ " 19.796913 \n",
+ " 19.910643 \n",
+ " 19.756296 \n",
+ " 19.861900 \n",
+ " 37995923 \n",
+ " 0.0 \n",
+ " 0.0 \n",
+ " 0.0 \n",
+ " XLF \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " Symbol \n",
+ " XLE \n",
+ " XLF \n",
+ " XLI \n",
+ " XLK \n",
+ " XLU \n",
+ " XLV \n",
+ " XLY \n",
+ " \n",
+ " \n",
+ " date \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 2015-01-02 \n",
+ " 79.529999 \n",
+ " 20.089357 \n",
+ " 56.509998 \n",
+ " 41.270000 \n",
+ " 47.439999 \n",
+ " 68.629997 \n",
+ " 71.629997 \n",
+ " \n",
+ " \n",
+ " 2015-01-05 \n",
+ " 76.239998 \n",
+ " 19.666937 \n",
+ " 55.189999 \n",
+ " 40.639999 \n",
+ " 46.860001 \n",
+ " 68.279999 \n",
+ " 70.260002 \n",
+ " \n",
+ " \n",
+ " 2015-01-06 \n",
+ " 75.120003 \n",
+ " 19.366369 \n",
+ " 54.509998 \n",
+ " 40.150002 \n",
+ " 46.889999 \n",
+ " 68.050003 \n",
+ " 69.559998 \n",
+ " \n",
+ " \n",
+ " 2015-01-07 \n",
+ " 75.279999 \n",
+ " 19.569456 \n",
+ " 54.919998 \n",
+ " 40.490002 \n",
+ " 47.349998 \n",
+ " 69.650002 \n",
+ " 70.660004 \n",
+ " \n",
+ " \n",
+ " 2015-01-08 \n",
+ " 76.970001 \n",
+ " 19.861900 \n",
+ " 56.020000 \n",
+ " 41.380001 \n",
+ " 47.680000 \n",
+ " 70.839996 \n",
+ " 71.720001 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " Portfolio Return \n",
+ " \n",
+ " \n",
+ " date \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 2015-06-30 \n",
+ " -0.016801 \n",
+ " \n",
+ " \n",
+ " 2015-08-31 \n",
+ " -0.071857 \n",
+ " \n",
+ " \n",
+ " 2015-09-30 \n",
+ " -0.0343 \n",
+ " \n",
+ " \n",
+ " 2015-11-30 \n",
+ " -0.005501 \n",
+ " \n",
+ " \n",
+ " 2016-01-31 \n",
+ " -0.048637 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
\n",
+ "
"
+ ],
+ "image/png": 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\n"
+ },
+ "metadata": {}
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "final_strategy_return = portfolio_returns['Cumulative Return'].iloc[-1]\n",
+ "\n",
+ "final_market_return = market_cumulative_return.iloc[-1]\n",
+ "\n",
+ "print(f\"Final cumulative return of sector rotation strategy: {final_strategy_return:.2f}\")\n",
+ "print(f\"Final cumulative return of market average: {final_market_return:.2f}\")\n",
+ "\n",
+ "strategy_daily_returns = portfolio_returns['Portfolio Return'].dropna()\n",
+ "sharpe_ratio = (strategy_daily_returns.mean() / strategy_daily_returns.std()) * np.sqrt(12)\n",
+ "print(f\"Sharpe Ratio of the sector rotation strategy: {sharpe_ratio:.2f}\")"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "fyEmebirQVsc",
+ "outputId": "40355f7c-145c-44e3-a532-3e45b467301d"
+ },
+ "execution_count": 23,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Final cumulative return of sector rotation strategy: 1.49\n",
+ "Final cumulative return of market average: 2.49\n",
+ "Sharpe Ratio of the sector rotation strategy: 0.54\n"
+ ]
+ }
+ ]
+ }
+ ]
+}
\ No newline at end of file
diff --git a/examples/streamlit/news.py b/examples/streamlit/news.py
new file mode 100644
index 0000000000000000000000000000000000000000..324df305509bb1ff5e95c8fbf5e2ec2396b4566a
--- /dev/null
+++ b/examples/streamlit/news.py
@@ -0,0 +1,444 @@
+"""Streamlit News Page"""
+
+# flake8: noqa: I001
+
+from datetime import datetime, timedelta
+
+from openbb import obb
+from numpy import nan
+
+import streamlit as st
+
+st.set_page_config(
+ layout="wide",
+ page_title="News",
+ initial_sidebar_state="expanded",
+)
+
+st.sidebar.markdown(
+ """
+
+
+ Powered by Open Source
+
+""",
+ unsafe_allow_html=True,
+)
+
+
+button_pressed = False
+
+SUPPORTED_SOURCES = ["benzinga", "biztoc", "intrinio", "fmp", "tiingo"]
+
+providers = [
+ d
+ for d in list(obb.user.credentials.__dict__.keys()) # type: ignore
+ if obb.user.credentials.__dict__[d] is not None # type: ignore
+]
+providers = [d.split("_")[0] for d in providers if d.split("_")[0] in SUPPORTED_SOURCES]
+news_sources = [d.upper() if d == "fmp" else d.title() for d in providers]
+
+if "news" not in st.session_state:
+ st.session_state.news = None
+
+if "biztoc_sources" not in st.session_state:
+ st.session_state.biztoc_sources = []
+if "news_container" not in st.session_state:
+ st.session_state.news_container = st.empty()
+if "selected_limit" not in st.session_state:
+ st.session_state.selected_limit = 100
+if "selected_provider" not in st.session_state:
+ if len(news_sources) == 0:
+ st.error(
+ f"No news sources available. Please check your credentials for one of: {SUPPORTED_SOURCES}"
+ )
+ st.stop()
+ if len(news_sources) > 0:
+ st.session_state.selected_provider = (
+ "Biztoc" if "Biztoc" in news_sources else news_sources[0]
+ )
+if "selected_tags" not in st.session_state:
+ st.session_state.selected_tags = ""
+if "selected_term" not in st.session_state:
+ st.session_state.selected_term = ""
+if "news_start_date" not in st.session_state:
+ st.session_state.news_start_date = (datetime.now() - timedelta(days=2)).date()
+if "news_end_date" not in st.session_state:
+ st.session_state.news_end_date = datetime.now().date()
+if "selected_biztoc_source" not in st.session_state:
+ st.session_state.selected_biztoc_source = ""
+if "content_type" not in st.session_state:
+ st.session_state.content_type = "news"
+if "benzinga_tickers" not in st.session_state:
+ st.session_state.benzinga_tickers = ""
+if "selected_benzinga_channel" not in st.session_state:
+ st.session_state.selected_benzinga_channel = ""
+if "fmp_tickers" not in st.session_state:
+ st.session_state.fmp_tickers = ""
+if "intrinio_tickers" not in st.session_state:
+ st.session_state.intrinio_tickers = ""
+if "tiingo_tickers" not in st.session_state:
+ st.session_state.tiingo_tickers = ""
+if "tiingo_source" not in st.session_state:
+ st.session_state.tiingo_source = ""
+
+
+def fetch_openbb():
+ kwargs = {
+ "provider": st.session_state.selected_provider.lower(),
+ "limit": st.session_state.selected_limit,
+ }
+ if st.session_state.selected_provider == "Benzinga":
+ kwargs["start_date"] = st.session_state.news_start_date.strftime("%Y-%m-%d")
+ kwargs["end_date"] = st.session_state.news_end_date.strftime("%Y-%m-%d")
+ kwargs["topics"] = st.session_state.selected_tags
+ kwargs["display"] = "full"
+ kwargs["page_size"] = 100
+ kwargs["channels"] = (
+ st.session_state.selected_benzinga_channel.lower()
+ if st.session_state.selected_benzinga_channel
+ else None
+ )
+ kwargs["symbol"] = (
+ st.session_state.benzinga_tickers
+ if st.session_state.benzinga_tickers
+ else None
+ )
+
+ if st.session_state.selected_provider == "Biztoc":
+ kwargs["term"] = st.session_state.selected_term
+ kwargs["tag"] = st.session_state.selected_tags
+ kwargs["filter"] = "tag" if kwargs.get("tag") else None
+ if kwargs.get("filter") is None:
+ kwargs["filter"] = "latest"
+ kwargs["source"] = (
+ st.session_state.selected_biztoc_source
+ if st.session_state.selected_biztoc_source
+ else None
+ )
+ kwargs["filter"] = "source" if kwargs.get("source") else kwargs.get("filter")
+ if kwargs.get("filter") == "source":
+ kwargs.pop("tag")
+
+ if st.session_state.selected_provider == "FMP":
+ kwargs["symbol"] = (
+ st.session_state.fmp_tickers if st.session_state.fmp_tickers else None
+ )
+
+ if st.session_state.selected_provider == "Intrinio":
+ kwargs["symbol"] = (
+ st.session_state.intrinio_tickers
+ if st.session_state.intrinio_tickers
+ else None
+ )
+
+ if st.session_state.selected_provider == "Tiingo":
+ kwargs["start_date"] = st.session_state.news_start_date.strftime("%Y-%m-%d")
+ kwargs["end_date"] = st.session_state.news_end_date.strftime("%Y-%m-%d")
+ kwargs["symbol"] = (
+ st.session_state.tiingo_tickers if st.session_state.tiingo_tickers else None
+ )
+
+ kwargs = {key: value for key, value in kwargs.items() if value is not None}
+
+ data = (
+ obb.news.company(**kwargs) # type: ignore
+ if kwargs.get("symbol")
+ else obb.news.world(**kwargs) # type: ignore
+ )
+ if data.results != []:
+ return data.to_df().sort_index(ascending=False).reset_index()
+
+
+def update_data():
+ st.session_state.news = fetch_openbb()
+
+
+with st.sidebar:
+ c1, c2 = st.columns(2)
+ with c1:
+ old_start_date = st.session_state.news_start_date
+ old_provider = st.session_state.selected_provider
+ st.session_state.selected_provider = st.selectbox(
+ label="Provider",
+ options=news_sources,
+ index=news_sources.index(st.session_state.selected_provider),
+ )
+ old_tags = st.session_state.selected_tags
+ if st.session_state.selected_provider == "Benzinga":
+ st.session_state.news_start_date = st.date_input(
+ "Start Date", value=old_start_date
+ )
+ st.session_state.selected_tags = st.text_input(
+ label="Tag", value=st.session_state.selected_tags
+ )
+ old_biztoc_source = st.session_state.selected_biztoc_source
+ if st.session_state.selected_provider == "Biztoc":
+
+ st.session_state.selected_biztoc_source = st.text_input(label="Source")
+ old_benzinga_tickers = st.session_state.benzinga_tickers
+ if st.session_state.selected_provider == "Benzinga":
+ st.session_state.benzinga_tickers = st.text_input(
+ label="Tickers", value=old_benzinga_tickers
+ )
+ old_fmp_tickers = st.session_state.fmp_tickers
+ if st.session_state.selected_provider == "FMP":
+ st.session_state.fmp_tickers = st.text_input(
+ label="Tickers", value=old_fmp_tickers
+ )
+ old_intrinio_tickers = st.session_state.intrinio_tickers
+ if st.session_state.selected_provider == "Intrinio":
+ st.session_state.intrinio_tickers = st.text_input(
+ label="Tickers", value=old_intrinio_tickers
+ )
+ old_tiingo_tickers = st.session_state.tiingo_tickers
+ if st.session_state.selected_provider == "Tiingo":
+ st.session_state.news_start_date = st.date_input(
+ "Start Date", value=old_start_date
+ )
+ old_tiingo_tickers = st.session_state.tiingo_tickers
+ st.session_state.tiingo_tickers = st.text_input(
+ label="Tickers", value=st.session_state.tiingo_tickers
+ )
+ with c2:
+ old_limit = st.session_state.selected_limit
+ old_end_date = st.session_state.news_end_date
+ st.session_state.selected_limit = st.number_input(
+ "Number of Stories", min_value=1, value=100
+ )
+ old_channel = st.session_state.selected_benzinga_channel
+ if st.session_state.selected_provider == "Benzinga":
+ st.session_state.news_end_date = st.date_input(
+ "End Date", value=old_end_date
+ )
+ st.session_state.selected_benzinga_channel = st.text_input(
+ label="Feed Channel", value=old_channel
+ )
+ old_term = st.session_state.selected_term
+ if st.session_state.selected_provider == "Biztoc":
+ st.session_state.selected_term = st.text_input(
+ label="Search Term", value=old_term
+ )
+ if st.session_state.selected_provider == "Tiingo":
+ st.session_state.news_end_date = st.date_input(
+ "End Date", value=old_end_date
+ )
+
+ if any(
+ [
+ old_start_date != st.session_state.news_start_date,
+ old_end_date != st.session_state.news_end_date,
+ old_limit != st.session_state.selected_limit,
+ old_provider != st.session_state.selected_provider,
+ old_tags != st.session_state.selected_tags,
+ old_term != st.session_state.selected_term,
+ old_biztoc_source != st.session_state.selected_biztoc_source,
+ old_channel != st.session_state.selected_benzinga_channel,
+ old_benzinga_tickers != st.session_state.benzinga_tickers,
+ old_fmp_tickers != st.session_state.fmp_tickers,
+ old_intrinio_tickers != st.session_state.intrinio_tickers,
+ old_tiingo_tickers != st.session_state.tiingo_tickers,
+ ]
+ ):
+ update_data()
+
+ if st.button("Fetch Data"):
+ update_data()
+
+
+def main():
+ with st.session_state.news_container.container():
+ st.markdown(
+ " "
+ "Headlines and Stories ",
+ unsafe_allow_html=True,
+ )
+ if st.session_state.news is not None:
+ story = -1
+ expanded = False
+ for i in st.session_state.news.index:
+ story += 1
+ expanded = story == 0
+ text = (
+ st.session_state.news.loc[i].text
+ if "text" in st.session_state.news.loc[i]
+ else st.session_state.news.loc[i].get("title")
+ )
+ src = st.session_state.news.loc[i].url
+ date = str(st.session_state.news.loc[i].date)
+ title = st.session_state.news.loc[i].title
+ if text and text is not nan and text != "":
+ with st.expander(label=f"{date} - {title}", expanded=expanded):
+ st.markdown(
+ f"""
+
+
{title}
+
+ """,
+ unsafe_allow_html=True,
+ )
+
+ if st.session_state.selected_provider == "Benzinga":
+ _tags = (
+ st.session_state.news.loc[i].tags
+ if st.session_state.news.loc[i].get("tags")
+ else ""
+ )
+ _stocks = (
+ st.session_state.news.loc[i].stocks
+ if st.session_state.news.loc[i].get("stocks")
+ else ""
+ )
+ _channels = (
+ st.session_state.news.loc[i].channels
+ if st.session_state.news.loc[i].get("channels")
+ else ""
+ )
+ _images = (
+ st.session_state.news.loc[i].images
+ if st.session_state.news.loc[i].get("images")
+ else []
+ )
+ _url = st.session_state.news.loc[i].url
+ st.markdown(
+ """
+
+ """,
+ unsafe_allow_html=True,
+ )
+ if _images and _images is not nan:
+ img = _images[0].get("url")
+ if img is not None:
+ st.markdown(
+ f"
+
+
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " series_id \n",
+ " title \n",
+ " observation_start \n",
+ " observation_end \n",
+ " frequency \n",
+ " frequency_short \n",
+ " units \n",
+ " units_short \n",
+ " seasonal_adjustment \n",
+ " seasonal_adjustment_short \n",
+ " last_updated \n",
+ " popularity \n",
+ " group_popularity \n",
+ " notes \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 \n",
+ " WALCL \n",
+ " Assets: Total Assets: Total Assets (Less Elimi... \n",
+ " 2002-12-18 \n",
+ " 2024-08-14 \n",
+ " Weekly, As of Wednesday \n",
+ " W \n",
+ " Millions of U.S. Dollars \n",
+ " Mil. of U.S. $ \n",
+ " Not Seasonally Adjusted \n",
+ " NSA \n",
+ " 2024-08-15 15:37:22-05:00 \n",
+ " 94 \n",
+ " 94 \n",
+ " NaN \n",
+ " \n",
+ " \n",
+ " 1 \n",
+ " H41RESPPALDKNWW \n",
+ " Assets: Liquidity and Credit Facilities: Loans... \n",
+ " 2002-12-18 \n",
+ " 2024-08-14 \n",
+ " Weekly \n",
+ " W \n",
+ " Millions of U.S. Dollars \n",
+ " Mil. of U.S. $ \n",
+ " Not Seasonally Adjusted \n",
+ " NSA \n",
+ " 2024-08-15 15:37:01-05:00 \n",
+ " 76 \n",
+ " 76 \n",
+ " NaN \n",
+ " \n",
+ " \n",
+ " 2 \n",
+ " TREAST \n",
+ " Assets: Securities Held Outright: U.S. Treasur... \n",
+ " 2002-12-18 \n",
+ " 2024-08-14 \n",
+ " Weekly, As of Wednesday \n",
+ " W \n",
+ " Millions of U.S. Dollars \n",
+ " Mil. of U.S. $ \n",
+ " Not Seasonally Adjusted \n",
+ " NSA \n",
+ " 2024-08-15 15:36:54-05:00 \n",
+ " 71 \n",
+ " 71 \n",
+ " The total face value of U.S. Treasury securiti... \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " series_id \n",
+ " title \n",
+ " observation_start \n",
+ " observation_end \n",
+ " frequency \n",
+ " frequency_short \n",
+ " units \n",
+ " units_short \n",
+ " seasonal_adjustment \n",
+ " seasonal_adjustment_short \n",
+ " last_updated \n",
+ " notes \n",
+ " popularity \n",
+ " group_popularity \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 \n",
+ " WLRRAL \n",
+ " Liabilities and Capital: Liabilities: Reverse ... \n",
+ " 2002-12-18 \n",
+ " 2024-08-14 \n",
+ " Weekly, As of Wednesday \n",
+ " W \n",
+ " Millions of U.S. Dollars \n",
+ " Mil. of U.S. $ \n",
+ " Not Seasonally Adjusted \n",
+ " NSA \n",
+ " 2024-08-15 15:37:49-05:00 \n",
+ " Reverse repurchase agreements are transactions... \n",
+ " 63 \n",
+ " 63 \n",
+ " \n",
+ " \n",
+ " 1 \n",
+ " WLRRAFOIAL \n",
+ " Liabilities and Capital: Liabilities: Reverse ... \n",
+ " 2002-12-18 \n",
+ " 2024-08-14 \n",
+ " Weekly, As of Wednesday \n",
+ " W \n",
+ " Millions of U.S. Dollars \n",
+ " Mil. of U.S. $ \n",
+ " Not Seasonally Adjusted \n",
+ " NSA \n",
+ " 2024-08-15 15:37:36-05:00 \n",
+ " Reverse repurchase agreements are transactions... \n",
+ " 40 \n",
+ " 40 \n",
+ " \n",
+ " \n",
+ " 2 \n",
+ " WLRRAOL \n",
+ " Liabilities and Capital: Liabilities: Reverse ... \n",
+ " 2002-12-18 \n",
+ " 2024-08-14 \n",
+ " Weekly, As of Wednesday \n",
+ " W \n",
+ " Millions of U.S. Dollars \n",
+ " Mil. of U.S. $ \n",
+ " Not Seasonally Adjusted \n",
+ " NSA \n",
+ " 2024-08-15 15:37:40-05:00 \n",
+ " NaN \n",
+ " 29 \n",
+ " 29 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " series_id \n",
+ " title \n",
+ " observation_start \n",
+ " observation_end \n",
+ " frequency \n",
+ " frequency_short \n",
+ " units \n",
+ " units_short \n",
+ " seasonal_adjustment \n",
+ " seasonal_adjustment_short \n",
+ " last_updated \n",
+ " notes \n",
+ " popularity \n",
+ " group_popularity \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 \n",
+ " WDTGAL \n",
+ " Liabilities and Capital: Liabilities: Deposits... \n",
+ " 2002-12-18 \n",
+ " 2024-08-14 \n",
+ " Weekly, As of Wednesday \n",
+ " W \n",
+ " Millions of U.S. Dollars \n",
+ " Mil. of U.S. $ \n",
+ " Not Seasonally Adjusted \n",
+ " NSA \n",
+ " 2024-08-15 15:38:33-05:00 \n",
+ " This account is the primary operational accoun... \n",
+ " 64 \n",
+ " 64 \n",
+ " \n",
+ " \n",
+ " 1 \n",
+ " D2WLTGAL \n",
+ " Liabilities and Capital: Liabilities: Deposits... \n",
+ " 2002-12-18 \n",
+ " 2024-08-14 \n",
+ " Weekly, As of Wednesday \n",
+ " W \n",
+ " Millions of U.S. Dollars \n",
+ " Mil. of U.S. $ \n",
+ " Not Seasonally Adjusted \n",
+ " NSA \n",
+ " 2024-08-15 15:38:37-05:00 \n",
+ " NaN \n",
+ " 60 \n",
+ " 60 \n",
+ " \n",
+ " \n",
+ " 2 \n",
+ " WLDLCL \n",
+ " Liabilities and Capital: Liabilities: Deposits... \n",
+ " 2002-12-18 \n",
+ " 2024-08-14 \n",
+ " Weekly, As of Wednesday \n",
+ " W \n",
+ " Millions of U.S. Dollars \n",
+ " Mil. of U.S. $ \n",
+ " Not Seasonally Adjusted \n",
+ " NSA \n",
+ " 2024-08-15 15:36:57-05:00 \n",
+ " This item is the sum of \"Term deposits held by... \n",
+ " 27 \n",
+ " 27 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " series_id \n",
+ " title \n",
+ " observation_start \n",
+ " observation_end \n",
+ " frequency \n",
+ " frequency_short \n",
+ " units \n",
+ " units_short \n",
+ " seasonal_adjustment \n",
+ " seasonal_adjustment_short \n",
+ " last_updated \n",
+ " notes \n",
+ " popularity \n",
+ " group_popularity \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 \n",
+ " SP500 \n",
+ " S&P 500 \n",
+ " 2014-08-22 \n",
+ " 2024-08-21 \n",
+ " Daily, Close \n",
+ " D \n",
+ " Index \n",
+ " Index \n",
+ " Not Seasonally Adjusted \n",
+ " NSA \n",
+ " 2024-08-21 19:21:03-05:00 \n",
+ " The observations for the S&P 500 represent the... \n",
+ " 83 \n",
+ " 83 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " WALCL \n",
+ " WLRRAL \n",
+ " WDTGAL \n",
+ " SP500 \n",
+ " \n",
+ " \n",
+ " date \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 2002-12-18 \n",
+ " 719542.0 \n",
+ " 21905.0 \n",
+ " 6595.0 \n",
+ " NaN \n",
+ " \n",
+ " \n",
+ " 2002-12-25 \n",
+ " 732059.0 \n",
+ " 20396.0 \n",
+ " 4662.0 \n",
+ " NaN \n",
+ " \n",
+ " \n",
+ " 2003-01-01 \n",
+ " 730994.0 \n",
+ " 21091.0 \n",
+ " 4420.0 \n",
+ " NaN \n",
+ " \n",
+ " \n",
+ " 2003-01-08 \n",
+ " 723762.0 \n",
+ " 18709.0 \n",
+ " 5490.0 \n",
+ " NaN \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " WALCL \n",
+ " WLRRAL \n",
+ " WDTGAL \n",
+ " SP500 \n",
+ " \n",
+ " \n",
+ " date \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 2014-08-27 \n",
+ " 4413736.0 \n",
+ " 282002.0 \n",
+ " 29547.0 \n",
+ " 2000.12 \n",
+ " \n",
+ " \n",
+ " 2014-09-03 \n",
+ " 4415587.0 \n",
+ " 250306.0 \n",
+ " 21036.0 \n",
+ " 2000.72 \n",
+ " \n",
+ " \n",
+ " 2014-09-10 \n",
+ " 4421408.0 \n",
+ " 267602.0 \n",
+ " 31872.0 \n",
+ " 1995.69 \n",
+ " \n",
+ " \n",
+ " 2014-09-17 \n",
+ " 4449588.0 \n",
+ " 252224.0 \n",
+ " 123965.0 \n",
+ " 2001.57 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " WALCL \n",
+ " WLRRAL \n",
+ " WDTGAL \n",
+ " SP500 \n",
+ " USD Liquidity Index \n",
+ " \n",
+ " \n",
+ " date \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 2024-07-24 \n",
+ " 7205455.0 \n",
+ " 805967.0 \n",
+ " 767419.0 \n",
+ " 5427.13 \n",
+ " 5632069.0 \n",
+ " \n",
+ " \n",
+ " 2024-07-31 \n",
+ " 7178391.0 \n",
+ " 813261.0 \n",
+ " 854001.0 \n",
+ " 5522.30 \n",
+ " 5511129.0 \n",
+ " \n",
+ " \n",
+ " 2024-08-07 \n",
+ " 7175256.0 \n",
+ " 681881.0 \n",
+ " 785233.0 \n",
+ " 5199.50 \n",
+ " 5708142.0 \n",
+ " \n",
+ " \n",
+ " 2024-08-14 \n",
+ " 7177688.0 \n",
+ " 722198.0 \n",
+ " 788823.0 \n",
+ " 5455.21 \n",
+ " 5666667.0 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " USD Liquidity Index \n",
+ " SP500 \n",
+ " \n",
+ " \n",
+ " date \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 2024-07-31 \n",
+ " 0.643578 \n",
+ " 0.970490 \n",
+ " \n",
+ " \n",
+ " 2024-08-07 \n",
+ " 0.698835 \n",
+ " 0.885139 \n",
+ " \n",
+ " \n",
+ " 2024-08-14 \n",
+ " 0.687202 \n",
+ " 0.952750 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
OpenBB Interactive Charts
+
+
+
+
+
+
+
diff --git a/frontend-components/plotly/package-lock.json b/frontend-components/plotly/package-lock.json
new file mode 100644
index 0000000000000000000000000000000000000000..20a8cbc782b10920f379b6e30ed0f09229f15442
--- /dev/null
+++ b/frontend-components/plotly/package-lock.json
@@ -0,0 +1,7363 @@
+{
+ "name": "plotly",
+ "version": "0.1.0",
+ "lockfileVersion": 3,
+ "requires": true,
+ "packages": {
+ "": {
+ "name": "plotly",
+ "version": "0.1.0",
+ "dependencies": {
+ "@radix-ui/react-dialog": "^1.0.3",
+ "dom-to-image": "^2.6.0",
+ "esbuild": ">=0.25.0",
+ "lodash": "^4.17.21",
+ "plotly.js-dist-min": "^3.0.0",
+ "react": "^18.0.0",
+ "react-dom": "^18.0.0",
+ "react-plotly.js": "^2.6.0",
+ "rollup": ">=4.22.4"
+ },
+ "devDependencies": {
+ "@types/dom-to-image": "^2.6.4",
+ "@types/lodash": "^4.14.195",
+ "@types/node": "^18.16.3",
+ "@types/plotly.js-dist-min": "^2.3.4",
+ "@types/react": "^18.0.27",
+ "@types/react-dom": "^18.0.10",
+ "@types/react-plotly.js": "^2.6.3",
+ "@types/wicg-file-system-access": "^2020.9.6",
+ "@vitejs/plugin-react": "^4.2.1",
+ "autoprefixer": "^10.4.13",
+ "clsx": "^1.2.1",
+ "postcss": "^8.4.21",
+ "react-hotkeys-hook": "^4.4.0",
+ "tailwindcss": "^3.2.7",
+ "typescript": "^4.9.3",
+ "vite": ">=6.2.7",
+ "vite-plugin-singlefile": "^0.13.3"
+ }
+ },
+ "node_modules/@alloc/quick-lru": {
+ "version": "5.2.0",
+ "resolved": "https://registry.npmjs.org/@alloc/quick-lru/-/quick-lru-5.2.0.tgz",
+ "integrity": "sha512-UrcABB+4bUrFABwbluTIBErXwvbsU/V7TZWfmbgJfbkwiBuziS9gxdODUyuiecfdGQ85jglMW6juS3+z5TsKLw==",
+ "dev": true,
+ "license": "MIT",
+ "engines": {
+ "node": ">=10"
+ },
+ "funding": {
+ "url": "https://github.com/sponsors/sindresorhus"
+ }
+ },
+ "node_modules/@ampproject/remapping": {
+ "version": "2.3.0",
+ "resolved": "https://registry.npmjs.org/@ampproject/remapping/-/remapping-2.3.0.tgz",
+ "integrity": "sha512-30iZtAPgz+LTIYoeivqYo853f02jBYSd5uGnGpkFV0M3xOt9aN73erkgYAmZU43x4VfqcnLxW9Kpg3R5LC4YYw==",
+ "dev": true,
+ "license": "Apache-2.0",
+ "dependencies": {
+ "@jridgewell/gen-mapping": "^0.3.5",
+ "@jridgewell/trace-mapping": "^0.3.24"
+ },
+ "engines": {
+ "node": ">=6.0.0"
+ }
+ },
+ "node_modules/@babel/code-frame": {
+ "version": "7.27.1",
+ "resolved": "https://registry.npmjs.org/@babel/code-frame/-/code-frame-7.27.1.tgz",
+ "integrity": "sha512-cjQ7ZlQ0Mv3b47hABuTevyTuYN4i+loJKGeV9flcCgIK37cCXRh+L1bd3iBHlynerhQ7BhCkn2BPbQUL+rGqFg==",
+ "dev": true,
+ "license": "MIT",
+ "dependencies": {
+ "@babel/helper-validator-identifier": "^7.27.1",
+ "js-tokens": "^4.0.0",
+ "picocolors": "^1.1.1"
+ },
+ "engines": {
+ "node": ">=6.9.0"
+ }
+ },
+ "node_modules/@babel/compat-data": {
+ "version": "7.27.2",
+ "resolved": "https://registry.npmjs.org/@babel/compat-data/-/compat-data-7.27.2.tgz",
+ "integrity": "sha512-TUtMJYRPyUb/9aU8f3K0mjmjf6M9N5Woshn2CS6nqJSeJtTtQcpLUXjGt9vbF8ZGff0El99sWkLgzwW3VXnxZQ==",
+ "dev": true,
+ "license": "MIT",
+ "engines": {
+ "node": ">=6.9.0"
+ }
+ },
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diff --git a/frontend-components/plotly/package.json b/frontend-components/plotly/package.json
new file mode 100644
index 0000000000000000000000000000000000000000..74f2cd825e4f6544774fdad9fc15dca911233c62
--- /dev/null
+++ b/frontend-components/plotly/package.json
@@ -0,0 +1,42 @@
+{
+ "name": "plotly",
+ "version": "0.1.0",
+ "private": true,
+ "scripts": {
+ "dev": "vite",
+ "build": "vite build",
+ "build_tsc": "tsc && vite build",
+ "deploy": "npm run build && mv dist/index.html ../../openbb_platform/obbject_extensions/charting/openbb_charting/core/plotly.html",
+ "preview": "vite preview"
+ },
+ "dependencies": {
+ "@radix-ui/react-dialog": "^1.0.3",
+ "dom-to-image": "^2.6.0",
+ "esbuild": ">=0.25.0",
+ "lodash": "^4.17.21",
+ "plotly.js-dist-min": "^3.0.0",
+ "react": "^18.0.0",
+ "react-dom": "^18.0.0",
+ "react-plotly.js": "^2.6.0",
+ "rollup": ">=4.22.4"
+ },
+ "devDependencies": {
+ "@types/dom-to-image": "^2.6.4",
+ "@types/lodash": "^4.14.195",
+ "@types/node": "^18.16.3",
+ "@types/plotly.js-dist-min": "^2.3.4",
+ "@types/react": "^18.0.27",
+ "@types/react-dom": "^18.0.10",
+ "@types/react-plotly.js": "^2.6.3",
+ "@types/wicg-file-system-access": "^2020.9.6",
+ "@vitejs/plugin-react": "^4.2.1",
+ "autoprefixer": "^10.4.13",
+ "clsx": "^1.2.1",
+ "postcss": "^8.4.21",
+ "react-hotkeys-hook": "^4.4.0",
+ "tailwindcss": "^3.2.7",
+ "typescript": "^4.9.3",
+ "vite": ">=6.2.7",
+ "vite-plugin-singlefile": "^0.13.3"
+ }
+}
diff --git a/frontend-components/plotly/postcss.config.cjs b/frontend-components/plotly/postcss.config.cjs
new file mode 100644
index 0000000000000000000000000000000000000000..12a703d900da8159c30e75acbd2c4d87ae177f62
--- /dev/null
+++ b/frontend-components/plotly/postcss.config.cjs
@@ -0,0 +1,6 @@
+module.exports = {
+ plugins: {
+ tailwindcss: {},
+ autoprefixer: {},
+ },
+};
diff --git a/frontend-components/plotly/src/App.tsx b/frontend-components/plotly/src/App.tsx
new file mode 100644
index 0000000000000000000000000000000000000000..4360b6444c77c2eda4fba0680630dbf4a2bc41b8
--- /dev/null
+++ b/frontend-components/plotly/src/App.tsx
@@ -0,0 +1,142 @@
+//@ts-nocheck
+import { useEffect, useState } from "react";
+import Chart from "./components/Chart";
+import { candlestickMockup } from "./data/mockup";
+
+declare global {
+ [Exposed === Window, SecureContext];
+ interface Window {
+ json_data: any;
+ export_image: string;
+ save_image: boolean;
+ title: string;
+ Plotly: any;
+ MODEBAR: HTMLElement;
+ download_path: string;
+ pywry: any;
+ }
+}
+
+function App() {
+ const [json_data, setData] = useState(
+ process.env.NODE_ENV === "production" ? null : candlestickMockup,
+ );
+ const [options, setOptions] = useState({});
+
+ useEffect(() => {
+ if (process.env.NODE_ENV === "production") {
+ const interval = setInterval(() => {
+ if (window.json_data) {
+ const plotly_json = window.json_data;
+ console.log(plotly_json);
+ setData(plotly_json);
+ clearInterval(interval);
+ }
+ }, 100);
+ return () => clearInterval(interval);
+ }
+ }, []);
+
+ const transformData = (data: any) => {
+ if (!data) return null;
+ const globals = {
+ added_traces: [],
+ csv_yaxis_id: null,
+ cmd_src_idx: null,
+ cmd_idx: null,
+ cmd_src: "",
+ old_margin: null,
+ title: "",
+ };
+ const filename = data.layout?.title?.text
+ .replace(/ -/g, "")
+ .replace(/-/g, "")
+ .replace(/|<\/b>/g, "")
+ .replace(/ /g, "_");
+ const date = new Date().toISOString().slice(0, 10).replace(/-/g, "");
+ const time = new Date().toISOString().slice(11, 19).replace(/:/g, "");
+ window.title = `openbb_${filename}_${date}_${time}`.replace(/_{2,}/g, "_");
+
+ if (data.layout.annotations !== undefined) {
+ data.layout.annotations.forEach(function (annotation) {
+ if (annotation.text !== undefined)
+ if (annotation.text[0] === "/") {
+ globals.cmd_src = annotation.text;
+ globals.cmd_idx = data.layout.annotations.indexOf(annotation);
+ annotation.text = "";
+
+ const margin = data.layout.margin;
+ globals.old_margin = { ...margin };
+ if (margin.t !== undefined && margin.t > 40) margin.t = 40;
+
+ if (data.cmd === "/equity/price/historical") margin.r -= 50;
+ }
+ });
+ }
+
+ // We add spaces to all trace names, due to Fira Code font width issues
+ // to make sure that the legend is not cut off
+ data.data.forEach(function (trace) {
+ if (trace.name !== undefined) {
+ trace.hoverlabel = {
+ namelength: -1,
+ };
+ }
+ });
+
+ const title = data.layout?.title?.text || "OpenBB Platform";
+ globals.title = title;
+ return {
+ data: data,
+ date: new Date(),
+ globals: globals,
+ cmd: data.command_location,
+ python_version: data.python_version,
+ pywry_version: data.pywry_version,
+ terminal_version: data.terminal_version,
+ theme: data.theme,
+ title,
+ };
+ };
+
+ const transformedData = transformData(json_data);
+
+ if (transformedData) {
+ return (
+ ,
+ graphs: Figure,
+) {
+ try {
+ if (eventdata["xaxis.range[0]"] !== undefined) {
+ const x_min = eventdata["xaxis.range[0]"];
+ const x_max = eventdata["xaxis.range[1]"];
+ let x0_min = x_min;
+ let x1_max = x_max;
+
+ if (isoDateRegex.test(x_min.replace(" ", "T").split(".")[0])) {
+ x0_min = new Date(x_min.replace(" ", "T").split(".")[0]);
+ x1_max = new Date(x_max.replace(" ", "T").split(".")[0]);
+ }
+
+ const to_update = {};
+ const yaxis_fixedrange = [];
+ let y_min: number;
+ let y_max: number;
+ let min_xrange: any;
+
+ const get_all_yaxis_traces = {};
+ const get_all_yaxis_annotations = {};
+ let volumeTraceYaxis = null;
+
+ const yaxis_unique = [
+ ...new Set(
+ graphs.data.map((trace: Plotly.PlotData) => {
+ if (trace.y !== undefined || trace.type === "candlestick") {
+ if (
+ trace.yaxis === undefined &&
+ trace?.name?.trim() !== "Volume"
+ ) {
+ trace.yaxis = "y";
+ }
+ if (trace.type === "bar" && trace?.name?.trim() === "Volume") {
+ volumeTraceYaxis = `yaxis${trace.yaxis.replace("y", "")}`;
+ }
+ get_all_yaxis_traces[trace.yaxis] =
+ get_all_yaxis_traces[trace.yaxis] || [];
+ get_all_yaxis_traces[trace.yaxis].push(trace);
+ return trace.yaxis;
+ }
+ }),
+ ),
+ ];
+
+ graphs.layout.annotations.map((annotation: any, i: number) => {
+ if (annotation.yref !== undefined && annotation.yref !== "paper") {
+ annotation.index = i;
+ const yaxis = `yaxis${annotation.yref.replace("y", "")}`;
+ get_all_yaxis_annotations[yaxis] =
+ get_all_yaxis_annotations[yaxis] || [];
+ get_all_yaxis_annotations[yaxis].push(annotation);
+ }
+ });
+
+ yaxis_unique.map((unique) => {
+ if (typeof unique !== "string") {
+ return;
+ }
+ const yaxis = `yaxis${unique.replace("y", "")}`;
+ let y_candle = [];
+ let y_values = [];
+ let log_scale = graphs.layout[yaxis].type === "log";
+
+ get_all_yaxis_traces[unique].map((trace2) => {
+ const x = trace2.x;
+ log_scale = graphs.layout[yaxis].type === "log";
+
+ let y = trace2.y !== undefined ? trace2.y : [];
+ let y_low = trace2.type === "candlestick" ? trace2.low : [];
+ let y_high = trace2.type === "candlestick" ? trace2.high : [];
+
+ if (log_scale) {
+ y = y.map(Math.log10);
+ if (trace2.type === "candlestick") {
+ y_low = trace2.low.map(Math.log10);
+ y_high = trace2.high.map(Math.log10);
+ }
+ }
+
+ const yx_values = x.map(
+ (x: string | number | Date, i: string | number) => {
+ let out = null;
+
+ if (isoDateRegex.test(x.toString())) {
+ const x_time = new Date(x).getTime();
+ if (x_time >= x0_min.getTime() && x_time <= x1_max.getTime()) {
+ if (trace2.y !== undefined && y[i] !== undefined) {
+ out = y[i];
+ }
+ if (trace2.type === "candlestick") {
+ y_candle.push(y_low[i]);
+ y_candle.push(y_high[i]);
+ }
+ if (!min_xrange || x_time < min_xrange) {
+ min_xrange = x_time;
+ }
+ }
+ } else if (x >= x_min && x <= x_max) {
+ if (trace2.y !== undefined) {
+ out = y[i];
+ }
+ if (trace2.type === "candlestick") {
+ y_candle.push(y_low[i]);
+ y_candle.push(y_high[i]);
+ }
+ if (!min_xrange || x < min_xrange) {
+ min_xrange = x;
+ }
+ }
+ return out;
+ },
+ );
+
+ y_values = y_values.concat(yx_values);
+ });
+
+ y_values = y_values
+ .flat()
+ .filter((y2) => y2 !== undefined && y2 !== null);
+ y_min = Math.min(...y_values);
+ y_max = Math.max(...y_values);
+
+ if (y_candle.length > 0) {
+ y_candle = y_candle
+ .flat()
+ .filter((y2) => y2 !== undefined && y2 !== null);
+ y_min = Math.min(...y_candle);
+ y_max = Math.max(...y_candle);
+ }
+
+ const org_y_max = y_max;
+
+ if (y_min !== undefined && y_max !== undefined) {
+ const y_range = y_max - y_min;
+ let y_mult = 0.15;
+ if (y_candle.length > 0) {
+ y_mult = 0.3;
+ }
+
+ y_min -= y_range * y_mult;
+ y_max += y_range * y_mult;
+ if (to_update[yaxis] === undefined) {
+ to_update[yaxis] = {};
+ }
+
+ if (yaxis === volumeTraceYaxis) {
+ if (graphs.layout[yaxis].tickvals !== undefined) {
+ const range_x = 7;
+ const volume_ticks = org_y_max;
+ let round_digits = -3;
+ // @ts-ignore
+ let first_val = Math.round(volume_ticks * 0.2, round_digits);
+ const x_zipped = [2, 5, 6, 7, 8, 9, 10];
+ const y_zipped = [1, 4, 5, 6, 7, 8, 9];
+
+ for (let i = 0; i < x_zipped.length; i++) {
+ if (String(volume_ticks).length > x_zipped[i]) {
+ round_digits = -y_zipped[i];
+ // @ts-ignore
+ first_val = Math.round(volume_ticks * 0.2, round_digits);
+ }
+ }
+ const tickvals = [
+ Math.floor(first_val),
+ Math.floor(first_val * 2),
+ Math.floor(first_val * 3),
+ Math.floor(first_val * 4),
+ ];
+ const volume_range = [0, Math.floor(volume_ticks * range_x)];
+
+ to_update[yaxis].tickvals = tickvals;
+ to_update[yaxis].range = volume_range;
+ to_update[yaxis].tickformat = ".2s";
+ return;
+ }
+ y_min = 0;
+ y_max = graphs.layout[yaxis].range[1];
+ }
+ to_update[yaxis].range = [y_min, y_max];
+ to_update[yaxis].fixedrange = true;
+ yaxis_fixedrange.push(yaxis);
+
+ if (get_all_yaxis_annotations[yaxis] !== undefined) {
+ get_all_yaxis_annotations[yaxis].map((annotation) => {
+ if (annotation.ay !== undefined) {
+ const yshift = annotation.ay;
+ const yshift_new = Math.min(
+ Math.max(yshift, y_min + y_range * 0.2),
+ y_max - y_range * 0.2,
+ );
+
+ if (to_update.annotations === undefined) {
+ to_update.annotations = graphs.layout.annotations;
+ }
+
+ to_update.annotations[annotation.index].ay = yshift_new;
+ }
+ });
+ }
+ }
+ });
+
+ graphs.layout = merge(graphs.layout, to_update);
+
+ return { to_update: graphs.layout, yaxis_fixedrange };
+ }
+ } catch (e) {
+ console.log(`Error in AutoScaling: ${e}`);
+ }
+ return { to_update: {}, yaxis_fixedrange: [] };
+}
diff --git a/frontend-components/plotly/src/components/ChangeColor.tsx b/frontend-components/plotly/src/components/ChangeColor.tsx
new file mode 100644
index 0000000000000000000000000000000000000000..5f2b61a7bd8f5e5e20f1f81f60a4e1ec57d6ada1
--- /dev/null
+++ b/frontend-components/plotly/src/components/ChangeColor.tsx
@@ -0,0 +1,95 @@
+//@ts-nocheck
+import { useEffect, useState } from "react";
+
+export default function ChangeColor({
+ open,
+ onColorChange,
+}: {
+ open: boolean;
+ onColorChange: (color: string) => void;
+}) {
+ const [active, setActive] = useState(false);
+
+ function onChangeColor(color) {
+ onColorChange(color);
+ }
+
+ if (open && !active) {
+ setActive(true);
+ }
+ if (!open && active) {
+ setActive(false);
+ }
+
+ useEffect(() => {
+ if (active) {
+ let color_picker = document.getElementById("changecolor");
+ color_picker.style.display = "block";
+ color_picker.style.width = null;
+ dragElement(color_picker);
+
+ function dragElement(elmnt) {
+ let pos1 = 0,
+ pos2 = 0,
+ pos3 = 0,
+ pos4 = 0;
+ if (document.getElementById(elmnt.id + "_header")) {
+ // if present, the header is where you move the DIV from:
+ document.getElementById(elmnt.id + "_header").onmousedown =
+ dragMouseDown;
+ } else {
+ // otherwise, move the DIV from anywhere inside the DIV:
+ elmnt.onmousedown = dragMouseDown;
+ }
+
+ function dragMouseDown(e) {
+ e = e || window.event;
+ e.preventDefault();
+ // get the mouse cursor position at startup:
+ pos3 = e.clientX;
+ pos4 = e.clientY;
+ document.onmouseup = closeDragElement;
+ // call a function whenever the cursor moves:
+ document.onmousemove = elementDrag;
+ }
+
+ function elementDrag(e) {
+ e = e || window.event;
+ e.preventDefault();
+ // calculate the new cursor position:
+ pos1 = pos3 - e.clientX;
+ pos2 = pos4 - e.clientY;
+ pos3 = e.clientX;
+ pos4 = e.clientY;
+ // set the element's new position:
+ elmnt.style.top = elmnt.offsetTop - pos2 + "px";
+ elmnt.style.left = elmnt.offsetLeft - pos1 + "px";
+ }
+
+ function closeDragElement() {
+ // stop moving when mouse button is released:
+ document.onmouseup = null;
+ document.onmousemove = null;
+ }
+ }
+ } else {
+ document.getElementById("changecolor").style.display = "none";
+ }
+ }, [active]);
+
+ return (
+
+
+
+ );
+}
diff --git a/frontend-components/plotly/src/components/Chart.tsx b/frontend-components/plotly/src/components/Chart.tsx
new file mode 100644
index 0000000000000000000000000000000000000000..0b1e2473fa747f19136bd84c0082265882d0188b
--- /dev/null
+++ b/frontend-components/plotly/src/components/Chart.tsx
@@ -0,0 +1,902 @@
+// @ts-nocheck
+import clsx from "clsx";
+import { debounce } from "lodash";
+import * as Plotly from "plotly.js-dist-min";
+import { Icons as PlotlyIcons } from "plotly.js-dist-min";
+import React, { useCallback, useEffect, useMemo, useState } from "react";
+import createPlotlyComponent from "react-plotly.js/factory";
+import { init_annotation } from "../utils/addAnnotation";
+import { non_blocking } from "../utils/utils";
+import autoScaling, { isoDateRegex } from "./AutoScaling";
+import ChangeColor from "./ChangeColor";
+import { DARK_CHARTS_TEMPLATE, ICONS, LIGHT_CHARTS_TEMPLATE } from "./Config";
+import AlertDialog from "./Dialogs/AlertDialog";
+import OverlayChartDialog from "./Dialogs/OverlayChartDialog";
+import TextChartDialog from "./Dialogs/TextChartDialog";
+import TitleChartDialog from "./Dialogs/TitleChartDialog";
+import { PlotConfig, hideModebar, ChartHotkeys } from "./PlotlyConfig";
+import ResizeHandler from "./ResizeHandler";
+
+// Add logging to help debug why annotations aren't working
+console.log = ((oldLog) => {
+ return function(...args) {
+ if (args[0] === "plotly_click") {
+ console.trace("plotly_click called with:", args[1]);
+ }
+ return oldLog.apply(console, args);
+ };
+})(console.log);
+
+const Plot = createPlotlyComponent(Plotly);
+class PlotComponent extends React.Component {
+ constructor(props) {
+ super(props);
+ this.state = {
+ data: props.data,
+ layout: props.layout,
+ frames: props.frames,
+ config: props.config,
+ useResizeHandler: props.useResizeHandler,
+ style: props.style,
+ className: props.className,
+ divId: props.divId,
+ revision: props.revision,
+ graphDiv: props.graphDiv,
+ debug: props.debug,
+ onInitialized: props.onInitialized,
+ };
+ }
+
+ render() {
+ return (
+ this.setState(figure)}
+ onRelayout={(figure) => this.setState(figure)}
+ onPurge={(figure) => this.setState(figure)}
+ />
+ );
+ }
+}
+
+// Check if a chart is a scatter plot to handle annotations differently
+function isScatterPlot(data) {
+ if (!data || !data.data) return false;
+
+ // Check if chart is primarily scatter plots
+ return data.data.some(trace =>
+ (trace.type === 'scatter' || trace.mode === 'markers' || trace.mode === 'lines+markers') &&
+ !(trace.type === 'candlestick' || trace.type === 'ohlc')
+ );
+}
+
+// Debug function to check annotation structure
+function debugAnnotation(annotation, label = "Annotation Debug") {
+ console.log(`[${label}]`, {
+ text: annotation.text,
+ visible: annotation.visible,
+ x: annotation.x,
+ y: annotation.y,
+ layer: annotation.layer,
+ font: annotation.font,
+ arrowcolor: annotation.arrowcolor
+ });
+}
+
+// Exported for external use
+window.debugPlotlyAnnotations = function() {
+ if (window.Plotly && window.Plotly.d3.select('#plotlyChart').node()._fullLayout) {
+ const annotations = window.Plotly.d3.select('#plotlyChart').node()._fullLayout.annotations || [];
+ console.log("[All Annotations]", annotations);
+ annotations.forEach(a => debugAnnotation(a));
+ return annotations;
+ }
+ return [];
+}
+
+export const getXRange = (min: string, max: string) => {
+ if (isoDateRegex.test(min.replace(" ", "T").split(".")[0])) {
+ const check_min = new Date(min.replace(" ", "T").split(".")[0]);
+ const check_max = new Date(max.replace(" ", "T").split(".")[0]);
+ check_min.setSeconds(0);
+ check_max.setSeconds(0);
+ check_min.setMilliseconds(0);
+ check_max.setMilliseconds(0);
+
+ const multiplier =
+ [5, 0, 1].includes(check_min.getDay()) ||
+ [4, 5, 6].includes(check_max.getDay())
+ ? 2
+ : 0;
+
+ const x0_min = new Date(check_min.getTime() - 86400000 * multiplier);
+ const x1_max = new Date(check_max.getTime() + 86400000 * multiplier);
+
+ const xrange = [x0_min.toISOString(), x1_max.toISOString()];
+ return { x0_min, x1_max, xrange };
+ }
+
+ return { x0_min: min, x1_max: max, xrange: [min, max] };
+};
+
+function CreateDataXrange(figure: Figure, xrange?: any) {
+ if (figure.frames && figure.frames.length > 0) {
+ // Don't filter data for animated charts
+ return figure;
+ }
+ const new_figure = { ...figure };
+ const data = new_figure.data;
+ if (!xrange) {
+ xrange = [
+ data[0]?.x[data[0].x.length - 2000],
+ data[0]?.x[data[0].x.length - 1],
+ ];
+ }
+ const { x0_min, x1_max, range } = getXRange(xrange[0], xrange[1]);
+ xrange = range;
+
+ const new_data = [];
+ data.forEach((trace) => {
+ const new_trace = { ...trace };
+ const data_keys = [
+ "x",
+ "y",
+ "low",
+ "high",
+ "open",
+ "close",
+ "text",
+ "customdata",
+ ];
+ const xaxis: any[] = trace.x ? trace.x : [];
+ const chunks = [];
+ for (let i = 0; i < xaxis.length; i++) {
+ const xval = xaxis[i];
+
+ if (isoDateRegex.test(xval)) {
+ const x_time = new Date(xval).getTime();
+ if (x_time >= x0_min.getTime() && x_time <= x1_max.getTime()) {
+ chunks.push(i);
+ }
+ } else if (xval >= xrange[0] && xval <= xrange[1]) {
+ chunks.push(i);
+ }
+ }
+ data_keys.forEach((key) => {
+ if (trace[key] !== undefined && Array.isArray(trace[key])) {
+ new_trace[key] = trace[key].filter((_, i) => chunks.includes(i));
+ }
+ });
+ const color_keys = ["marker", "line"];
+ color_keys.forEach((key) => {
+ if (trace[key]?.color && Array.isArray(trace[key].color)) {
+ new_trace[key] = { ...trace[key] };
+ new_trace[key].color = trace[key].color.filter((_, i) =>
+ chunks.includes(i),
+ );
+ }
+ });
+
+ if (chunks.length > 0) new_data.push(new_trace);
+ });
+
+ if (new_data.length === 0)
+ return {
+ ...figure,
+ layout: {
+ ...figure.layout,
+ xaxis: { ...figure.layout.xaxis, range: xrange },
+ },
+ };
+
+ new_figure.layout.xaxis.range = xrange;
+ new_figure.data = new_data;
+ return new_figure;
+}
+
+async function DynamicLoad({
+ event,
+ figure,
+}: {
+ event?: any;
+ figure: any;
+}) {
+ if (figure.frames && figure.frames.length > 0) {
+ // Don't filter data for animated charts
+ return figure;
+ }
+ try {
+ const XDATA = figure.data.filter(
+ (trace) =>
+ trace.x !== undefined && trace.x.length > 0 && trace.x[0] !== undefined,
+ );
+
+ if (XDATA.length === 0) return figure;
+ // We get the xaxis range, if no event is passed, we get the last 1000 points
+ const xaxis_range = event
+ ? [event["xaxis.range[0]"], event["xaxis.range[1]"]]
+ : [
+ XDATA[0]?.x[XDATA[0].x.length - 1000],
+ XDATA[0]?.x[XDATA[0].x.length - 1],
+ ];
+
+ figure = CreateDataXrange(figure, xaxis_range);
+
+ return figure;
+ } catch (e) {
+ console.log("error", e);
+ }
+}
+
+function formatDate(date) {
+ const d = new Date(date);
+ const month = `${d.getMonth() + 1}`.padStart(2, "0");
+ const day = `${d.getDate()}`.padStart(2, "0");
+ const year = d.getFullYear();
+ const hour = `${d.getHours()}`.padStart(2, "0");
+ const minute = `${d.getMinutes()}`.padStart(2, "0");
+ const second = `${d.getSeconds()}`.padStart(2, "0");
+ return `${year}-${month}-${day} ${hour}:${minute}:${second}`;
+}
+
+function Chart({
+ json,
+ date,
+ cmd,
+ title,
+ globals,
+ theme,
+}: {
+ // @ts-ignore
+ json: Figure;
+ date: Date;
+ cmd: string;
+ title: string;
+ globals: any;
+ theme: string;
+}) {
+ json.layout.width = undefined;
+ json.layout.height = undefined;
+ if (json.layout?.title?.text) {
+ json.layout.title.text = "";
+ }
+
+ const [originalData, setOriginalData] = useState(json);
+ const [barButtons, setModeBarButtons] = useState({});
+ const [LogYaxis, setLogYaxis] = useState(false);
+ const [chartTitle, setChartTitle] = useState(title);
+ const [axesTitles, setAxesTitles] = useState({});
+ const [plotLoaded, setPlotLoaded] = useState(false);
+ const [modal, setModal] = useState({ name: "" });
+ const [loading, setLoading] = useState(false);
+ const [plotDiv, setPlotDiv] = useState(null);
+ const [volumeBars, setVolumeBars] = useState({ old_nticks: {} });
+ const [maximizePlot, setMaximizePlot] = useState(false);
+ const [dateSliced, setDateSliced] = useState(false);
+
+ const [plotData, setPlotDataState] = useState(originalData);
+ const [annotations, setAnnotations] = useState([]);
+ const [changeTheme, setChangeTheme] = useState(false);
+ const [darkMode, setDarkMode] = useState(true);
+ const [autoScale, setAutoScaling] = useState(false);
+ const [changeColor, setChangeColor] = useState(false);
+ const [colorActive, setColorActive] = useState(false);
+ const [onAnnotationClick, setOnAnnotationClick] = useState({});
+ const [ohlcAnnotation, setOhlcAnnotation] = useState([]);
+ const [yaxisFixedRange, setYaxisFixedRange] = useState([]); function setPlotData(data: any) {
+ data.layout.datarevision = data.layout.datarevision
+ ? data.layout.datarevision + 1
+ : 1;
+
+ setPlotDataState(data);
+ if (plotDiv && plotData) {
+ Plotly.react(plotDiv, data.data, data.layout);
+ }
+ }
+
+ const onClose = () => setModal({ name: "" });
+
+ // @ts-ignore
+ const onDeleteAnnotation = useCallback(
+ (annotation) => {
+ console.log("onDeleteAnnotation", annotation);
+ const index = plotData?.layout?.annotations?.findIndex(
+ (a: any) => a.text === annotation.text,
+ );
+ console.log("index", index);
+ if (index > -1) {
+ plotData?.layout?.annotations?.splice(index, 1);
+ setPlotData({ ...plotData });
+ setAnnotations(plotData?.layout?.annotations);
+ }
+ },
+ [plotData],
+ ); // @ts-ignore
+ const onAddAnnotation = useCallback(
+ (data) => {
+ console.log("onAddAnnotation being called with data:", data);
+
+ // Use the standard annotation flow
+ init_annotation({
+ plotData,
+ popupData: data,
+ setPlotData,
+ setModal,
+ setOnAnnotationClick,
+ setAnnotations,
+ onAnnotationClick,
+ ohlcAnnotation,
+ setOhlcAnnotation,
+ annotations,
+ plotDiv,
+ });
+ },
+ [plotData, onAnnotationClick, ohlcAnnotation, annotations, plotDiv],
+ ); useEffect(() => {
+ if (axesTitles && Object.keys(axesTitles).length > 0) {
+ const layoutUpdate = {};
+ // Update the layout with the new titles
+ Object.keys(axesTitles).forEach((k) => {
+ plotData.layout[k].title = {
+ ...(plotData.layout[k].title || {}),
+ text: axesTitles[k],
+ };
+ plotData.layout[k].showticklabels = true;
+ layoutUpdate[`${k}.title.text`] = axesTitles[k];
+ });
+
+ if (plotDiv && Object.keys(layoutUpdate).length > 0) {
+ Plotly.relayout(plotDiv, layoutUpdate);
+ }
+
+ setAxesTitles({});
+ }
+ }, [axesTitles, plotDiv]);
+
+ function onChangeColor(color) {
+ // updates the color of the last added shape
+ // this function is called when the color picker is used
+ // if there are no shapes, we remove the color picker
+ const shapes = plotDiv.layout.shapes;
+ if (!shapes || shapes.length === 0) {
+ return;
+ }
+ // we change last added shape color
+ const last_shape = shapes[shapes.length - 1];
+ last_shape.line.color = color;
+ Plotly.update(plotDiv, {}, { shapes: shapes });
+ }
+
+ function button_pressed(title, active = false) {
+ // changes the style of the button when it is pressed
+ // title is the title of the button
+ // active is true if the button is active, false otherwise
+
+ const button =
+ barButtons[title] || document.querySelector(`[data-title="${title}"]`);
+ if (!active) {
+ button.style.border = "1px solid rgba(0, 151, 222, 1.0)";
+ button.style.borderRadius = "5px";
+ button.style.borderpadding = "5px";
+ button.style.boxShadow = "0 0 5px rgba(0, 151, 222, 1.0)";
+ } else {
+ button.style.border = "transparent";
+ button.style.boxShadow = "none";
+ }
+ setModeBarButtons({ ...barButtons, [title]: button });
+ }
+
+ const debouncedDynamicLoad = async (eventData, figure) => {
+ if (dateSliced) {
+ const data = { ...figure };
+ DynamicLoad({
+ event: eventData,
+ figure: data,
+ }).then(async (toUpdate) => {
+ autoScaling(eventData, toUpdate).then((scaled) => {
+ if (!scaled.to_update) return;
+ setYaxisFixedRange(scaled.yaxis_fixedrange);
+ setPlotData({ ...toUpdate, layout: scaled.to_update });
+ });
+ });
+ } else {
+ const scaled = await autoScaling(eventData, figure);
+ if (!scaled.to_update) return;
+ setYaxisFixedRange(scaled.yaxis_fixedrange);
+ setPlotData({ ...figure, layout: scaled.to_update });
+ }
+ };
+
+ const autoscaleButton = useCallback(() => {
+ // We need to check if the button is active or not
+ const title = "Auto Scale (Ctrl+Shift+A)";
+ const button =
+ barButtons[title] || document.querySelector(`[data-title="${title}"]`);
+ let active = true;
+
+ if (button.style.border === "transparent") {
+ plotDiv.removeAllListeners("plotly_relayout");
+ active = false;
+ plotDiv.on("plotly_relayout", async (eventdata) => {
+ if (eventdata["xaxis.range[0]"] === undefined) return;
+ const debounceTimer = eventdata["relayout"] ? 0 : 300;
+ if (
+ !eventdata["relayout"] &&
+ isoDateRegex.test(
+ eventdata["xaxis.range[0]"].toString().replace(" ", "T"),
+ )
+ ) {
+ const date1 = new Date(eventdata["xaxis.range[0]"].replace(" ", "T"));
+ const date2 = new Date(eventdata["xaxis.range[1]"].replace(" ", "T"));
+
+ if (date2.getTime() - date1.getTime() < 3600000 * 2) {
+ const d1 = new Date(date1.getTime() - 3600000 * 2);
+ const d2 = new Date(date2.getTime() + 3600000 * 2);
+
+ eventdata["xaxis.range[0]"] = formatDate(d1);
+ eventdata["xaxis.range[1]"] = formatDate(d2);
+ eventdata["relayout"] = true;
+ return Plotly.relayout(plotDiv, eventdata);
+ }
+ }
+ debounce(async () => {
+ debouncedDynamicLoad(eventdata, originalData);
+ }, debounceTimer)();
+ });
+ }
+ // If the button isn't active, we remove the listener so
+ // the graphs don't autoscale anymore
+ else {
+ plotDiv.removeAllListeners("plotly_relayout");
+ yaxisFixedRange.forEach((yaxis) => {
+ plotDiv.layout[yaxis].fixedrange = false;
+ });
+ setYaxisFixedRange([]);
+ if (dateSliced) {
+ plotDiv.on(
+ "plotly_relayout",
+ debounce(async (eventdata) => {
+ if (eventdata["xaxis.range[0]"] === undefined) return;
+ debouncedDynamicLoad(eventdata, originalData);
+ }, 300),
+ );
+ }
+ }
+
+ button_pressed(title, active);
+ }, [
+ barButtons,
+ dateSliced,
+ debouncedDynamicLoad,
+ originalData,
+ plotDiv,
+ yaxisFixedRange,
+ ]);
+
+ function changecolorButton() {
+ // We need to check if the button is active or not
+ const title = "Edit Color (Ctrl+E)";
+ const button =
+ barButtons[title] || document.querySelector(`[data-title="${title}"]`);
+ let active = true;
+
+ if (button.style.border === "transparent") {
+ active = false;
+ }
+
+ setColorActive(!active);
+ button_pressed(title, active);
+ }
+
+ useEffect(() => {
+ if (autoScale) {
+ const scale = !autoScale;
+ console.log("activateAutoScale", scale);
+ autoscaleButton();
+ setAutoScaling(false);
+ }
+ }, [autoScale]);
+
+ useEffect(() => {
+ if (changeColor) {
+ changecolorButton();
+ setChangeColor(false);
+ }
+ }, [changeColor]);
+
+ useEffect(() => {
+ if (changeTheme) {
+ try {
+ console.log("changeTheme", changeTheme);
+ const TRACES = originalData?.data.filter(
+ (trace) => trace?.name?.trim() === "Volume",
+ );
+ const darkmode = !darkMode;
+
+ window.document.body.style.backgroundColor = darkmode ? "#000" : "#fff";
+
+ originalData.layout.font = {
+ ...(originalData.layout.font || {}),
+ color: darkmode ? "#fff" : "#000",
+ };
+
+ const changeIcon = darkmode ? ICONS.sunIcon : ICONS.moonIcon;
+
+ document
+ .querySelector('[data-title="Change Theme"]')
+ .getElementsByTagName("path")[0]
+ .setAttribute("d", changeIcon.path);
+
+ document
+ .querySelector('[data-title="Change Theme"]')
+ .getElementsByTagName("svg")[0]
+ .setAttribute("viewBox", changeIcon.viewBox);
+
+ const volumeColorsDark = {
+ "#00ACFF0": "#00ACFF",
+ "#e4003a": "#e4003a",
+ };
+ const volumeColorsLight = {
+ "#e4003a": "#e4003a",
+ "#00ACFF": "#00ACFF",
+ };
+
+ const volumeColors = darkmode ? volumeColorsDark : volumeColorsLight;
+
+ TRACES.forEach((trace) => {
+ if (trace.type === "bar" && Array.isArray(trace.marker.color))
+ trace.marker.color = trace.marker.color.map((color) => {
+ return volumeColors[color] || color;
+ });
+ });
+ originalData.layout.template = darkmode
+ ? DARK_CHARTS_TEMPLATE
+ : LIGHT_CHARTS_TEMPLATE;
+
+ // Preserve existing annotations as-is (no modifications)
+ if (plotData.layout.annotations && plotData.layout.annotations.length > 0) {
+ originalData.layout.annotations = [...plotData.layout.annotations];
+ }
+
+ setPlotData({ ...originalData });
+ setDarkMode(darkmode);
+ setChangeTheme(false);
+ } catch (e) {
+ console.log("error", e);
+ }
+ }
+ }, [changeTheme, plotData.layout.annotations]);
+
+ useEffect(() => {
+ if (plotLoaded) {
+ setDarkMode(true);
+ setAutoScaling(false);
+ const captureButtons = [
+ "Overlay chart from CSV",
+ "Add Text",
+ "Change Titles",
+ "Auto Scale (Ctrl+Shift+A)",
+ "Reset Axes",
+ ];
+ const autoscale = document.querySelector('[data-title="Autoscale"]');
+ if (autoscale) {
+ autoscale
+ .getElementsByTagName("path")[0]
+ .setAttribute("d", PlotlyIcons.home.path);
+ autoscale.setAttribute("data-title", "Reset Axes");
+ }
+
+ window.MODEBAR = document.getElementsByClassName(
+ "modebar-container",
+ )[0] as HTMLElement;
+ const modeBarButtons = window.MODEBAR.getElementsByClassName(
+ "modebar-btn",
+ ) as HTMLCollectionOf;
+
+ window.MODEBAR.style.cssText = `${window.MODEBAR.style.cssText}; display:flex;`;
+
+ // Add annotation click handler to ensure editing works on scatter plots
+ if (plotDiv) {
+ // When an annotation is clicked, open the edit dialog
+ plotDiv.on('plotly_clickannotation', function(data) {
+ console.log("Annotation clicked:", data);
+ if (data && data.annotation && data.annotation.text) {
+ setModal({
+ name: "textDialog",
+ data: {
+ annotation_dict: data.annotation,
+ mode: "edit"
+ }
+ });
+ }
+ });
+ }
+
+ if (modeBarButtons) {
+ const barbuttons: any = {};
+ for (let i = 0; i < modeBarButtons.length; i++) {
+ const btn = modeBarButtons[i];
+ if (captureButtons.includes(btn.getAttribute("data-title"))) {
+ btn.classList.add("ph-capture");
+ }
+ btn.style.border = "transparent";
+ barbuttons[btn.getAttribute("data-title")] = btn;
+ }
+ setModeBarButtons(barbuttons);
+ }
+
+ if (plotData?.layout?.yaxis?.type !== undefined) {
+ if (plotData.layout.yaxis.type === "log" && !LogYaxis) {
+ console.log("yaxis.type changed to log");
+ setLogYaxis(true);
+ }
+ if (plotData.layout.yaxis.type === "linear" && LogYaxis) {
+ console.log("yaxis.type changed to linear");
+ setLogYaxis(false);
+
+ // We update the yaxis exponent format to none,
+ // set the tickformat to null and the exponentbase to 10
+ const layout_update = {
+ "yaxis.exponentformat": "none",
+ "yaxis.tickformat": null,
+ "yaxis.exponentbase": 10,
+ };
+ Plotly.update(plotDiv, {}, layout_update);
+ }
+ }
+
+ window.addEventListener("resize", async function () {
+ const update = await ResizeHandler({
+ plotData,
+ volumeBars,
+ setMaximizePlot,
+ });
+ const layout_update = update.layout_update;
+ const newPlotData = update.plotData;
+ const volume_update = update.volume_update;
+
+ if (Object.keys(layout_update).length > 0) {
+ setPlotData(newPlotData);
+ setVolumeBars(volume_update);
+ Plotly.update(plotDiv, {}, layout_update);
+ }
+ });
+
+ if (theme !== "dark") {
+ setChangeTheme(true);
+ }
+ }
+ }, [plotLoaded]);
+
+ useEffect(() => {
+ // This effect ensures annotations appear correctly on all chart types
+ if (plotDiv && plotData?.layout?.annotations?.length > 0) {
+ Plotly.relayout(plotDiv, {'annotations': plotData.layout.annotations});
+ }
+ }, [plotData.layout.annotations, plotDiv]); const plotComponent = useMemo(
+ () => (
+ {
+ if (!plotDiv) {
+ if (graphDiv) {
+ graphDiv.globals = globals;
+ setPlotDiv(graphDiv);
+ graphDiv.on('plotly_clickannotation', function(data) {
+ if (data && data.annotation && data.annotation.text) {
+ setModal({
+ name: "textDialog",
+ data: {
+ annotation_dict: data.annotation,
+ mode: "edit"
+ }
+ });
+ }
+ });
+ }
+ }
+ if (!plotLoaded) setPlotLoaded(true);
+ }}
+ className="w-full h-full"
+ divId="plotlyChart"
+ data={plotData.data}
+ layout={plotData.layout}
+ frames={plotData.frames}
+ config={PlotConfig({
+ setModal: setModal,
+ changeTheme: setChangeTheme,
+ autoScaling: setAutoScaling,
+ Loading: setLoading,
+ changeColor: setChangeColor,
+ })}
+ />
+ ),
+ [
+ plotDiv,
+ originalData,
+ plotLoaded,
+ plotData,
+ globals,
+ setPlotDiv,
+ setPlotLoaded,
+ setModal,
+ setChangeTheme,
+ setAutoScaling,
+ setLoading,
+ onChangeColor,
+ ],
+ );
+
+ const memoizedAlertDialog = useMemo(() => {
+ return (
+ {
+ console.log(overlay);
+ overlay.layout.showlegend = true;
+ setOriginalData(overlay);
+ setPlotData(overlay);
+ }}
+ plotlyData={originalData}
+ setLoading={setLoading}
+ open={modal?.name === "overlayChart"}
+ close={onClose}
+ />
+ );
+ }, [modal, plotData, onClose, setPlotData, setLoading]);
+
+ const memoizedTitleChartDialog = useMemo(() => {
+ return (
+ setChartTitle(title)}
+ updateAxesTitles={(axesTitles) => setAxesTitles(axesTitles)}
+ defaultTitle={chartTitle}
+ plotlyData={plotData}
+ open={modal?.name === "titleDialog"}
+ close={onClose}
+ />
+ );
+ }, [modal, plotData, chartTitle, onClose]);
+
+ const memoizedTextChartDialog = useMemo(() => {
+ return (
+ onAddAnnotation(data)}
+ deleteAnnotation={(data) => onDeleteAnnotation(data)}
+ />
+ );
+ }, [
+ modal,
+ onAddAnnotation,
+ onDeleteAnnotation,
+ onClose,
+ plotData,
+ setPlotData,
+ ]);
+
+ const memoizedChangeColor = useMemo(() => {
+ return
+ {loading && (
+
+ )}
+
+ {memoizedAlertDialog}
+ {memoizedOverlayChartDialog}
+ {memoizedTitleChartDialog}
+ {memoizedTextChartDialog}
+ {memoizedChangeColor}
+ {memoizedChartHotkeys}
+
+
+
+
+
+ {chartTitle}
+ {/* {source && (
+ {`[${source}]`}
+ )} */}
+
+
+ {new Intl.DateTimeFormat("en-GB", {
+ dateStyle: "full",
+ timeStyle: "long",
+ })
+ .format(date)
+ .replace(/:\d\d /, " ")}
+ {cmd}
+
+ {/* {source && typeof source === "string" && source.includes("*") && (
+
+ *not affiliated
+
+ )} */}
+
+
+ {plotComponent}
+
+
+
+
+
+ );
+}
diff --git a/frontend-components/plotly/src/components/Dialogs/CommonDialog.tsx b/frontend-components/plotly/src/components/Dialogs/CommonDialog.tsx
new file mode 100644
index 0000000000000000000000000000000000000000..0ff023bb7c51f9ad5bbed6af28d54a8fb5672cab
--- /dev/null
+++ b/frontend-components/plotly/src/components/Dialogs/CommonDialog.tsx
@@ -0,0 +1,43 @@
+import * as DialogPrimitive from "@radix-ui/react-dialog";
+import CloseIcon from "../Icons/Close";
+import { ReactNode } from "react";
+
+export const styleDialog = {
+ margin: "2px 0px 2px 10px",
+ padding: "5px 2px 2px 5px",
+};
+
+export default function CommonDialog({
+ open,
+ close,
+ title,
+ description,
+ children,
+}: {
+ open: boolean;
+ close: () => void;
+ title: string;
+ description: string;
+ children: ReactNode;
+}) {
+ return (
+
+
+
+ {title}
+
+
+ {description}
+
+
+
+ {children}
+
+
+
+
+ );
+}
diff --git a/frontend-components/plotly/src/components/Dialogs/OverlayChartDialog.tsx b/frontend-components/plotly/src/components/Dialogs/OverlayChartDialog.tsx
new file mode 100644
index 0000000000000000000000000000000000000000..ca3da283aa5b1d0bf49ba2bd3a70de7e8da49da2
--- /dev/null
+++ b/frontend-components/plotly/src/components/Dialogs/OverlayChartDialog.tsx
@@ -0,0 +1,651 @@
+import { useState } from "react";
+import CommonDialog, { styleDialog } from "../Dialogs/CommonDialog";
+
+const reader = new FileReader();
+
+const layout_defaults = {
+ overlaying: "y",
+ side: "left",
+ tickfont: { size: 12 },
+ tickpadding: 5,
+ showgrid: false,
+ showline: false,
+ showticklabels: true,
+ showlegend: true,
+ zeroline: false,
+ anchor: "x",
+ type: "linear",
+ autorange: true,
+};
+
+export default function OverlayChartDialog({
+ open,
+ close,
+ setLoading,
+ addOverlay,
+ plotlyData,
+}: {
+ open: boolean;
+ close: () => void;
+ setLoading: (loading: boolean) => void;
+ addOverlay: (data: any) => void;
+ plotlyData: any;
+}) {
+ const [traceType, setTraceType] = useState("scatter");
+ const [traceColor, setTraceColor] = useState("#FFDD00");
+ const [increasingColor, setIncreasingColor] = useState("#00ACFF");
+ const [decreasingColor, setDecreasingColor] = useState("#FF0000");
+ const [traceName, setTraceName] = useState("");
+ const [csvData, setCsvData] = useState([]);
+ const [csvColumns, setCsvColumns] = useState([]);
+ const [yaxisOptions, setYaxisOptions] = useState({});
+ const optionIds = ["x", "open", "high", "low", "close"];
+
+ const traceTypes: any = {
+ scatter: "Scatter (Line)",
+ candlestick: "Candlestick",
+ bar: "Bar",
+ };
+
+ const [options, setOptions] = useState({});
+
+ function onClose() {
+ close();
+ setTraceType("scatter");
+ setTraceName("");
+ setCsvData([]);
+ setCsvColumns([]);
+ setOptions({});
+ }
+
+ function onSubmit() {
+ if (csvData.length === 0) {
+ document.getElementById("csv_file")?.focus();
+ document
+ .getElementById("csv_file")
+ ?.style.setProperty("border", "1px solid red");
+ document.getElementById("csv_file_warning")!.style.display = "block";
+ return;
+ }
+ const newPlotydata = CSVonSubmit({
+ csvData: csvData,
+ plotlyData: plotlyData,
+ yaxisOptions: yaxisOptions,
+ traceType: traceType,
+ traceColor: traceColor,
+ traceName: traceName,
+ options: options,
+ increasingColor: increasingColor,
+ decreasingColor: decreasingColor,
+ });
+ addOverlay(newPlotydata);
+ onClose();
+ }
+
+ return (
+
+
+
+
+
+ Cancel
+
+ {editMode && (
+
+ Delete
+
+ )}
+
+ Submit
+
+
+
+
+ );
+}
diff --git a/frontend-components/plotly/src/components/Dialogs/TitleChartDialog.tsx b/frontend-components/plotly/src/components/Dialogs/TitleChartDialog.tsx
new file mode 100644
index 0000000000000000000000000000000000000000..81722702c55fba4e1c57825c042324887125fb92
--- /dev/null
+++ b/frontend-components/plotly/src/components/Dialogs/TitleChartDialog.tsx
@@ -0,0 +1,157 @@
+import CommonDialog, { styleDialog } from "../Dialogs/CommonDialog";
+import { useState } from "react";
+
+export default function TitleChartDialog({
+ plotlyData,
+ open,
+ close,
+ defaultTitle,
+ updateTitle,
+ updateAxesTitles,
+}: {
+ plotlyData?: any;
+ open: boolean;
+ close: () => void;
+ defaultTitle: string;
+ updateTitle: (title: string) => void;
+ updateAxesTitles: (axesTitles: any) => void;
+}) {
+ const [title, setTitle] = useState(defaultTitle);
+
+ const yAxes = Object.keys(plotlyData.layout || {}).filter(
+ (k) => k.startsWith("yaxis") && plotlyData.layout[k].range != undefined
+ );
+ const xAxes = Object.keys(plotlyData.layout || {}).filter(
+ (k) =>
+ k.startsWith("xaxis") &&
+ plotlyData.layout[k].showticklabels != undefined &&
+ plotlyData.layout[k]?.anchor
+ );
+
+ const [axesTitles, setAxesTitles] = useState({});
+
+ return (
+
+
+
+ );
+}
diff --git a/frontend-components/plotly/src/components/Icons/Close.tsx b/frontend-components/plotly/src/components/Icons/Close.tsx
new file mode 100644
index 0000000000000000000000000000000000000000..bd5d2c1750bcb2950ca74a3ea71577f1b0112807
--- /dev/null
+++ b/frontend-components/plotly/src/components/Icons/Close.tsx
@@ -0,0 +1,28 @@
+import type { SVGProps } from "react";
+interface SVGRProps {
+ title?: string;
+ titleId?: string;
+}
+
+const CloseIcon = ({
+ title,
+ titleId,
+ ...props
+}: SVGProps & SVGRProps) => (
+
+
+);
+
+export default CloseIcon;
diff --git a/frontend-components/plotly/src/components/Icons/CloseCircle.tsx b/frontend-components/plotly/src/components/Icons/CloseCircle.tsx
new file mode 100644
index 0000000000000000000000000000000000000000..a6ab356aee1017dda8ab93f4d2ebf751c26a72ae
--- /dev/null
+++ b/frontend-components/plotly/src/components/Icons/CloseCircle.tsx
@@ -0,0 +1,32 @@
+import type { SVGProps } from "react";
+interface SVGRProps {
+ title?: string;
+ titleId?: string;
+}
+
+const CloseCircleIcon = ({
+ title,
+ titleId,
+ ...props
+}: SVGProps & SVGRProps) => (
+
+ {title ? {title} : null}
+
+);
+
+export default CloseCircleIcon;
diff --git a/frontend-components/plotly/src/components/Icons/Info.tsx b/frontend-components/plotly/src/components/Icons/Info.tsx
new file mode 100644
index 0000000000000000000000000000000000000000..1250d27f2cafbe386037986ac53f0fd2e53e6eb6
--- /dev/null
+++ b/frontend-components/plotly/src/components/Icons/Info.tsx
@@ -0,0 +1,32 @@
+import type { SVGProps } from "react";
+interface SVGRProps {
+ title?: string;
+ titleId?: string;
+}
+
+const InfoIcon = ({
+ title,
+ titleId,
+ ...props
+}: SVGProps & SVGRProps) => (
+
+ {title ? {title} : null}
+
+);
+
+export default InfoIcon;
diff --git a/frontend-components/plotly/src/components/Icons/Success.tsx b/frontend-components/plotly/src/components/Icons/Success.tsx
new file mode 100644
index 0000000000000000000000000000000000000000..e9271a10cb3dcff0b458c2ea9e4e3b56f6d8d107
--- /dev/null
+++ b/frontend-components/plotly/src/components/Icons/Success.tsx
@@ -0,0 +1,39 @@
+import type { SVGProps } from "react";
+interface SVGRProps {
+ title?: string;
+ titleId?: string;
+}
+
+const SuccessIcon = ({
+ title,
+ titleId,
+ ...props
+}: SVGProps & SVGRProps) => (
+
+ {title ? {title} : null}
+
+);
+
+export default SuccessIcon;
diff --git a/frontend-components/plotly/src/components/Icons/Warning.tsx b/frontend-components/plotly/src/components/Icons/Warning.tsx
new file mode 100644
index 0000000000000000000000000000000000000000..3a82f08ed91863ee1898517df9659d02f1cc6f19
--- /dev/null
+++ b/frontend-components/plotly/src/components/Icons/Warning.tsx
@@ -0,0 +1,32 @@
+import type { SVGProps } from "react";
+interface SVGRProps {
+ title?: string;
+ titleId?: string;
+}
+
+const WarningIcon = ({
+ title,
+ titleId,
+ ...props
+}: SVGProps & SVGRProps) => (
+
+ {title ? {title} : null}
+
+);
+
+export default WarningIcon;
diff --git a/frontend-components/plotly/src/components/PlotlyConfig.tsx b/frontend-components/plotly/src/components/PlotlyConfig.tsx
new file mode 100644
index 0000000000000000000000000000000000000000..ac46e4c32730c3c5c849cab981126ffb5f12d888
--- /dev/null
+++ b/frontend-components/plotly/src/components/PlotlyConfig.tsx
@@ -0,0 +1,222 @@
+import { Icons as PlotlyIcons } from "plotly.js-dist-min";
+import * as Plotly from "plotly.js-dist-min";
+import { useHotkeys } from "react-hotkeys-hook";
+import { ICONS } from "./Config";
+
+
+export function hideModebar(hide?: boolean) {
+ return new Promise((resolve) => {
+ if (!window.MODEBAR) {
+ window.MODEBAR = window.document.getElementsByClassName(
+ "modebar-container",
+ )[0] as HTMLElement;
+ window.MODEBAR.style.cssText = `${window.MODEBAR.style.cssText}; display:flex;`;
+ }
+
+ if (window.MODEBAR) {
+ if (hide) {
+ window.MODEBAR.style.cssText = `${window.MODEBAR.style.cssText}; display:none;`;
+ } else if (window.MODEBAR.style.cssText.includes("none")) {
+ window.MODEBAR.style.cssText = `${window.MODEBAR.style.cssText}; display:flex;`;
+ } else {
+ window.MODEBAR.style.cssText = `${window.MODEBAR.style.cssText}; display:none;`;
+ }
+ resolve(true);
+ }
+ });
+}
+
+export function PlotConfig({
+ setModal,
+ changeTheme,
+ autoScaling,
+ Loading,
+ changeColor,
+}: {
+ setModal: (modal: { name: string; data?: any }) => void;
+ changeTheme: (change: boolean) => void;
+ autoScaling: (change: boolean) => void;
+ Loading: (change: boolean) => void;
+ changeColor: (change: boolean) => void;
+}) {
+ const CONFIG = {
+ plotGlPixelRatio: 1,
+ scrollZoom: true,
+ responsive: true,
+ displaylogo: false,
+ displayModeBar: "hover",
+ edits: {
+ legendPosition: true,
+ legendText: true,
+ colorbarPosition: true,
+ annotationPosition: true,
+ annotationTail: true,
+ annotationText: true,
+ },
+ showTips: false,
+ setBackground: "transparent",
+ modeBarButtonsToRemove: ["lasso2d", "select2d", "saveImage"],
+ modeBarButtons: [
+ [
+ {
+ name: "Edit Color (Ctrl+E)",
+ icon: ICONS.changeColor,
+ click: function () {
+ changeColor(true);
+ },
+ },
+ "drawline",
+ "drawopenpath",
+ "drawcircle",
+ "drawrect",
+ "eraseshape",
+ ],
+ [
+ {
+ name: "Overlay chart from CSV (Ctrl+O)",
+ icon: ICONS.plotCsv,
+ click: function () {
+ setModal({ name: "overlayChart" });
+ },
+ },
+ {
+ name: "Add Text (Ctrl+T)",
+ icon: ICONS.addText,
+ click: function () {
+ setModal({ name: "textDialog", data: { text: "" } });
+ },
+ },
+ {
+ name: "Change Titles (Ctrl+Shift+T)",
+ icon: ICONS.changeTitle,
+ click: function () {
+ setModal({ name: "titleDialog" });
+ },
+ },
+ {
+ name: "Change Theme",
+ icon: ICONS.sunIcon,
+ click: function () {
+ changeTheme(true);
+ },
+ },
+ ],
+ ["hoverClosestCartesian", "hoverCompareCartesian", "toggleSpikelines"],
+ [
+ {
+ name: "Auto Scale (Ctrl+Shift+A)",
+ icon: PlotlyIcons.autoscale,
+ click: function () {
+ autoScaling(true);
+ },
+ },
+ "zoomIn2d",
+ "zoomOut2d",
+ "autoScale2d",
+ "zoom2d",
+ "pan2d",
+ ],
+ ],
+ };
+ return CONFIG;
+}
+
+
+export function ChartHotkeys({
+ setModal,
+ Loading,
+ changeColor,
+}: {
+ setModal: (modal: { name: string; data?: any }) => void;
+ Loading: (change: boolean) => void;
+ changeColor: (change: boolean) => void;
+}) {
+ useHotkeys(
+ "ctrl+shift+t",
+ () => {
+ setModal({ name: "titleDialog" });
+ },
+ { preventDefault: true },
+ );
+ useHotkeys(
+ "ctrl+t",
+ () => {
+ setModal({ name: "textDialog" });
+ },
+ { preventDefault: true },
+ );
+ useHotkeys(
+ "ctrl+o",
+ () => {
+ setModal({ name: "overlayChart" });
+ },
+ { preventDefault: true },
+ );
+ useHotkeys(
+ ["ctrl+shift+h", "ctrl+h"],
+ () => {
+ hideModebar();
+ },
+ { preventDefault: true },
+ ); useHotkeys(
+ "ctrl+l",
+ () => {
+ // Toggle log scale when Ctrl+L is pressed
+ const plotDiv = document.getElementById("plotlyChart") as any;
+ if (plotDiv && plotDiv._fullLayout) {
+ // Check if this is an OHLC/Candle chart or a time series Scatter
+ const isOHLCOrCandle = plotDiv._fullData.some((trace: any) =>
+ trace.type === 'ohlc' || trace.type === 'candlestick'
+ );
+
+ const isTimeSeriesScatter = plotDiv._fullData.some((trace: any) =>
+ trace.type === 'scatter' && (trace.mode === 'lines' || trace.mode === 'lines+markers') &&
+ trace.x && trace.x.length > 0 && (typeof trace.x[0] === 'string' || trace.x[0] instanceof Date)
+ );
+
+ if (isOHLCOrCandle || isTimeSeriesScatter) {
+ // Only toggle the main y-axis (yaxis or y1)
+ const currentType = plotDiv._fullLayout.yaxis?.type || 'linear';
+ const newType = currentType === 'linear' ? 'log' : 'linear';
+
+ // Only modify the main y-axis, leaving all others unchanged
+ const updateObj: any = {
+ 'yaxis.type': newType
+ };
+
+ // Apply change ONLY to main y-axis
+ console.log("Changing main y-axis scale to:", newType);
+ Plotly.relayout(plotDiv, updateObj as any);
+ } else {
+ console.log("Log scale toggle is only available for OHLC/Candle charts or time series Scatter plots");
+ }
+ }
+ },
+ { preventDefault: true },
+ );
+ useHotkeys(
+ "ctrl+e",
+ () => {
+ changeColor(true);
+ },
+ { preventDefault: true },
+ );
+
+ // Removed the ctrl+shift+s export shortcut
+
+ useHotkeys(
+ "ctrl+s",
+ () => {
+ // Download feature removed
+ },
+ { preventDefault: true },
+ );
+
+ useHotkeys(
+ "ctrl+w",
+ () => {
+ window.close();
+ },
+ { preventDefault: true },
+ );
+}
diff --git a/frontend-components/plotly/src/components/ResizeHandler.tsx b/frontend-components/plotly/src/components/ResizeHandler.tsx
new file mode 100644
index 0000000000000000000000000000000000000000..0db916ff3dfa3d361eafe4dbdf436c27659b2c4c
--- /dev/null
+++ b/frontend-components/plotly/src/components/ResizeHandler.tsx
@@ -0,0 +1,97 @@
+//@ts-nocheck
+import { Figure } from "react-plotly.js";
+import { hideModebar } from "./PlotlyConfig";
+
+export default async function ResizeHandler({
+ plotData,
+ volumeBars,
+ setMaximizePlot,
+}: {
+ plotData: Figure;
+ volumeBars: any;
+ setMaximizePlot: (value: boolean) => void;
+}) {
+ // We hide the modebar and set the number of ticks to 5
+ const XAXIS = Object.keys(plotData.layout)
+ .filter((x) => x.startsWith("xaxis"))
+ .filter(
+ (x) =>
+ plotData.layout[x].showticklabels ||
+ plotData.layout[x].matches === undefined,
+ );
+
+ const TRACES = plotData.data.filter(
+ (trace) => trace?.name?.trim() === "Volume",
+ );
+
+ const layout_update: any = {};
+ const volume: any = volumeBars || { old_nticks: {} };
+
+ const width = window.innerWidth;
+ const height = window.innerHeight;
+ const tick_size =
+ height > 420 && width < 920 ? 8 : height > 420 && width < 500 ? 9 : 7;
+
+ if (width < 850) {
+ // We hide the modebar and set the number of ticks to 6
+
+ TRACES.forEach((trace) => {
+ if (trace.type === "bar") {
+ trace.opacity = 1;
+ trace.marker.line.width = 0.09;
+ if (volumeBars.yaxis === undefined) {
+ volume.yaxis = `yaxis${trace.yaxis.replace("y", "")}`;
+ layout_update[`${volume.yaxis}.tickfont.size`] = tick_size;
+ volume.tickfont = plotData.layout[volume.yaxis].tickfont || {};
+
+ plotData.layout.margin.l -= 40;
+ }
+ }
+ });
+
+ XAXIS.forEach((x) => {
+ if (volumeBars.old_nticks?.[x] === undefined) {
+ layout_update[`${x}.nticks`] = 6;
+ volume.old_nticks[x] = plotData.layout[x].nticks || 10;
+ }
+ });
+ setMaximizePlot(true);
+
+ await hideModebar(true);
+ } else if (
+ width > 850 &&
+ window.MODEBAR.style.cssText.includes("display: none")
+ ) {
+ // We show the modebar
+ await hideModebar(false);
+ setMaximizePlot(false);
+
+ if (volumeBars.old_nticks !== undefined) {
+ XAXIS.forEach((x) => {
+ if (volumeBars.old_nticks[x] !== undefined) {
+ layout_update[`${x}.nticks`] = volume.old_nticks[x];
+ volume.old_nticks[x] = undefined;
+ }
+ });
+ }
+
+ if (volumeBars.yaxis !== undefined) {
+ TRACES.forEach((trace) => {
+ if (trace.type === "bar") {
+ trace.opacity = 0.5;
+ trace.marker.line.width = 0.2;
+ layout_update[`${volume.yaxis}.tickfont.size`] =
+ volume.tickfont.size + 3;
+ plotData.layout.margin.l += 40;
+ volume.yaxis = undefined;
+ }
+ });
+ }
+ }
+
+ return {
+ volume_update: volume,
+ layout_update: layout_update,
+ plotData: plotData,
+ };
+}
diff --git a/frontend-components/plotly/src/data/mockup.ts b/frontend-components/plotly/src/data/mockup.ts
new file mode 100644
index 0000000000000000000000000000000000000000..90075db4ce98754faf726e15da17bf54e0e06a70
--- /dev/null
+++ b/frontend-components/plotly/src/data/mockup.ts
@@ -0,0 +1,10553 @@
+export const plotlyMockup = {
+ collect_logs: false,
+ command_location: "/stocks/ta/bbands",
+ data: [
+ {
+ connectgaps: true,
+ hovertemplate: "%{y};
+ setPlotData: (plotData: Partial) => void;
+ setModal: (modal: { name: string; data?: any }) => void;
+ onAnnotationClick: any;
+ setOnAnnotationClick: (onAnnotationClick: any) => void;
+ setAnnotations: (annotations: Partial[]) => void;
+ ohlcAnnotation: any;
+ setOhlcAnnotation: (ohlcAnnotation: any) => void;
+ annotations: Annotations[];
+ plotDiv: PlotlyHTMLElement;
+}) {
+ if (popupData.text !== undefined && popupData.text !== "") {
+ popupData.text = popupData.text.replace(/\n/g, ";
+ let inOhlc = false;
+
+ if (popupData.annotation) {
+ console.log("data", popupData);
+ popup_data = {
+ x: popupData.annotation.x,
+ y: popupData.annotation.y,
+ yref: popupData.annotation.yref,
+ yanchor:
+ popupData.annotation.y < popupData.annotation.ay ? "above" : "below",
+ ...popupData,
+ };
+ if (popupData.annotation.high !== undefined) {
+ inOhlc = true;
+ }
+ console.log("popup_data", popup_data);
+ const to_update = plot_text({
+ plotData,
+ popup_data: popup_data as PopupData,
+ current_text: popupData.annotation.text,
+ });
+
+ if (inOhlc) {
+ // we update the ohlcAnnotation
+ const ohlcAnnotationIndex = ohlcAnnotation.findIndex(
+ (a) =>
+ a.x === popupData.annotation.x &&
+ a.y === popupData.annotation.y &&
+ a.yref === popupData.annotation.yref,
+ );
+ console.log("ohlcAnnotationIndex", ohlcAnnotationIndex);
+ if (ohlcAnnotationIndex === -1) {
+ // we add the annotation to the ohlcAnnotation array
+ setOhlcAnnotation([...ohlcAnnotation, to_update.annotation]);
+ } else {
+ // we replace the annotation in the ohlcAnnotation array
+ ohlcAnnotation[ohlcAnnotationIndex] = to_update.annotation;
+ setOhlcAnnotation(ohlcAnnotation);
+ }
+ }
+
+ setAnnotations(
+ [...annotations, to_update.annotation].filter((a) => a !== undefined),
+ );
+ plotData.layout.dragmode = "pan";
+ setPlotData({ ...plotData, ...to_update.update });
+ setOnAnnotationClick({});
+
+ return;
+ }
+
+ // First remove any existing click handlers to avoid duplicates
+ plotDiv.removeAllListeners("plotly_clickannotation");
+ plotDiv.removeAllListeners("plotly_click");
+
+ // Add handler for clicking on existing annotations
+ plotDiv.on("plotly_clickannotation", (eventData) => {
+ console.log("plotly_clickannotation", eventData);
+ const annotation = eventData.annotation;
+
+ if (annotation.text === undefined) {
+ console.log("annotation.text is undefined");
+ return;
+ }
+ console.log("annotation.text", annotation.text);
+ // we replace ,
+ handler: (event: MouseEvent | TouchEvent) => void
+) {
+ useEffect(() => {
+ const listener = (event: MouseEvent | TouchEvent) => {
+ // Do nothing if clicking ref's element or descendent elements
+ if (!ref.current || ref.current.contains(event.target as Node)) {
+ return;
+ }
+ handler(event);
+ };
+ document.addEventListener("mousedown", listener);
+ document.addEventListener("touchstart", listener);
+ return () => {
+ document.removeEventListener("mousedown", listener);
+ document.removeEventListener("touchstart", listener);
+ };
+ }, [ref, handler]);
+}
+
+export function useOnClickInside(
+ ref: RefObject,
+ handler: (event: MouseEvent | TouchEvent) => void
+) {
+ useEffect(() => {
+ const listener = (event: MouseEvent | TouchEvent) => {
+ // Do nothing if clicking ref's element or descendent elements
+ if (ref.current && ref.current.contains(event.target as Node)) {
+ handler(event);
+ }
+ };
+ document.addEventListener("mousedown", listener);
+ document.addEventListener("touchstart", listener);
+ return () => {
+ document.removeEventListener("mousedown", listener);
+ document.removeEventListener("touchstart", listener);
+ };
+ }, [ref, handler]);
+}
diff --git a/frontend-components/plotly/src/utils/utils.ts b/frontend-components/plotly/src/utils/utils.ts
new file mode 100644
index 0000000000000000000000000000000000000000..c7ca654b5fbe006beeb67c634260da0944d4457c
--- /dev/null
+++ b/frontend-components/plotly/src/utils/utils.ts
@@ -0,0 +1,13 @@
+// @ts-nocheck
+
+export const non_blocking = (func: Function, delay: number) => {
+ let timeout: number;
+ return function () {
+ // @ts-ignore
+ const context = this;
+ const args = arguments;
+ clearTimeout(timeout);
+ timeout = setTimeout(() => func.apply(context, args), delay);
+ };
+};
+
diff --git a/frontend-components/plotly/tailwind.config.cjs b/frontend-components/plotly/tailwind.config.cjs
new file mode 100644
index 0000000000000000000000000000000000000000..d47c187ebbccfe8d2c36c0b84f50dfa33ee54f94
--- /dev/null
+++ b/frontend-components/plotly/tailwind.config.cjs
@@ -0,0 +1,30 @@
+/** @type {import('tailwindcss').Config} */
+module.exports = {
+ darkMode: "class",
+ content: ["./index.html", "./src/**/*.{js,ts,jsx,tsx}", "../fonts"],
+ theme: {
+ extend: {
+ screens: {
+ smh: { raw: "(max-height: 450px)" },
+ },
+ colors: {
+ "grey-50": "#f6f6f6ff",
+ "grey-100": "#eaeaeaff",
+ "grey-200": "#dcdcdcff",
+ "grey-300": "#c8c8c8ff",
+ "grey-400": "#a2a2a2ff",
+ "grey-500": "#808080ff",
+ "grey-600": "#5a5a5aff",
+ "grey-700": "#474747ff",
+ "grey-800": "#2a2a2aff",
+ "grey-850": "#131313ff",
+ "grey-900": "#070707ff",
+ "burgundy-300": "#B47DA0",
+ "burgundy-400": "#9B5181",
+ "burgundy-500": "#822661",
+ "burgundy-900": "#340F27",
+ },
+ },
+ },
+ plugins: [],
+};
diff --git a/frontend-components/plotly/tsconfig.json b/frontend-components/plotly/tsconfig.json
new file mode 100644
index 0000000000000000000000000000000000000000..90fb89435edb6bb47101a6e2bf32f8a77454acf1
--- /dev/null
+++ b/frontend-components/plotly/tsconfig.json
@@ -0,0 +1,22 @@
+{
+ "compilerOptions": {
+ "target": "ESNext",
+ "typeRoots": ["node_modules/@types"],
+ "useDefineForClassFields": true,
+ "lib": ["DOM", "DOM.Iterable", "ESNext"],
+ "allowJs": false,
+ "skipLibCheck": true,
+ "esModuleInterop": false,
+ "allowSyntheticDefaultImports": true,
+ "strict": true,
+ "forceConsistentCasingInFileNames": true,
+ "module": "ESNext",
+ "moduleResolution": "Node",
+ "resolveJsonModule": true,
+ "isolatedModules": true,
+ "noEmit": true,
+ "jsx": "react-jsx"
+ },
+ "include": ["src", "../fonts"],
+ "references": [{ "path": "./tsconfig.node.json" }]
+}
diff --git a/frontend-components/plotly/tsconfig.node.json b/frontend-components/plotly/tsconfig.node.json
new file mode 100644
index 0000000000000000000000000000000000000000..9d31e2aed93c876bc048cf2f863cb2a847c901e8
--- /dev/null
+++ b/frontend-components/plotly/tsconfig.node.json
@@ -0,0 +1,9 @@
+{
+ "compilerOptions": {
+ "composite": true,
+ "module": "ESNext",
+ "moduleResolution": "Node",
+ "allowSyntheticDefaultImports": true
+ },
+ "include": ["vite.config.ts"]
+}
diff --git a/frontend-components/plotly/vite.config.ts b/frontend-components/plotly/vite.config.ts
new file mode 100644
index 0000000000000000000000000000000000000000..73c7ce35834bebebba2a2917df3bb1b1e448e434
--- /dev/null
+++ b/frontend-components/plotly/vite.config.ts
@@ -0,0 +1,22 @@
+import react from "@vitejs/plugin-react";
+import { defineConfig } from "vite";
+import { viteSingleFile } from "vite-plugin-singlefile";
+
+const stripUseClientDirective = () => {
+ return {
+ name: 'strip-use-client',
+ transform(code) {
+ if (code.includes('use client')) {
+ return {
+ code: code.replace(/"use client"/, ''),
+ map: null
+ }
+ }
+ }
+ }
+}
+
+// https://vitejs.dev/config/
+export default defineConfig({
+ plugins: [react(), stripUseClientDirective(), viteSingleFile()],
+});
diff --git a/frontend-components/tables/.gitignore b/frontend-components/tables/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..a547bf36d8d11a4f89c59c144f24795749086dd1
--- /dev/null
+++ b/frontend-components/tables/.gitignore
@@ -0,0 +1,24 @@
+# Logs
+logs
+*.log
+npm-debug.log*
+yarn-debug.log*
+yarn-error.log*
+pnpm-debug.log*
+lerna-debug.log*
+
+node_modules
+dist
+dist-ssr
+*.local
+
+# Editor directories and files
+.vscode/*
+!.vscode/extensions.json
+.idea
+.DS_Store
+*.suo
+*.ntvs*
+*.njsproj
+*.sln
+*.sw?
diff --git a/frontend-components/tables/index.html b/frontend-components/tables/index.html
new file mode 100644
index 0000000000000000000000000000000000000000..a0d02f39cebe31afcd0b9d6354e35b77c8fceb1e
--- /dev/null
+++ b/frontend-components/tables/index.html
@@ -0,0 +1,39 @@
+
+
+
+ OpenBB Interactive Tables
+
+
+
+
+
+
+
diff --git a/frontend-components/tables/package-lock.json b/frontend-components/tables/package-lock.json
new file mode 100644
index 0000000000000000000000000000000000000000..b46e4e583098eb7d103383a252df3dd619bcb2ee
--- /dev/null
+++ b/frontend-components/tables/package-lock.json
@@ -0,0 +1,8333 @@
+{
+ "name": "tables",
+ "version": "0.0.0",
+ "lockfileVersion": 3,
+ "requires": true,
+ "packages": {
+ "": {
+ "name": "tables",
+ "version": "0.0.0",
+ "dependencies": {
+ "@radix-ui/react-checkbox": "^1.0.3",
+ "@radix-ui/react-context-menu": "^2.1.3",
+ "@radix-ui/react-dialog": "^1.0.3",
+ "@radix-ui/react-dropdown-menu": "^2.0.4",
+ "@radix-ui/react-icons": "^1.2.0",
+ "@radix-ui/react-radio-group": "^1.1.2",
+ "@radix-ui/react-select": "^1.2.1",
+ "@radix-ui/react-toast": "^1.1.3",
+ "@tanstack/match-sorter-utils": "^8.7.6",
+ "@tanstack/react-table": "^8.7.9",
+ "@tanstack/react-virtual": "^3.13.9",
+ "dom-to-image": "^2.6.0",
+ "esbuild": ">=0.25.0",
+ "nanoid": ">=3.3.8",
+ "plotly.js": "^3.0.0",
+ "react": "^18.0.0",
+ "react-dnd": "^16.0.1",
+ "react-dnd-html5-backend": "^16.0.1",
+ "react-dom": "^18.0.0",
+ "react-plotly.js": "^2.6.0",
+ "react-table": "^7.8.0",
+ "rollup": ">=4.22.4",
+ "xss": "^1.0.14"
+ },
+ "devDependencies": {
+ "@types/dom-to-image": "^2.6.4",
+ "@types/react": "^18.0.27",
+ "@types/react-dom": "^18.0.10",
+ "@types/react-table": "^7.7.14",
+ "@types/wicg-file-system-access": "^2020.9.6",
+ "@vitejs/plugin-react": "^4.2.1",
+ "autoprefixer": "^10.4.13",
+ "clsx": "^1.2.1",
+ "postcss": "^8.4.21",
+ "tailwindcss": "^3.2.7",
+ "typescript": "^4.9.3",
+ "vite": ">=6.2.7",
+ "vite-plugin-singlefile": "^0.13.3"
+ }
+ },
+ "node_modules/@alloc/quick-lru": {
+ "version": "5.2.0",
+ "resolved": "https://registry.npmjs.org/@alloc/quick-lru/-/quick-lru-5.2.0.tgz",
+ "integrity": "sha512-UrcABB+4bUrFABwbluTIBErXwvbsU/V7TZWfmbgJfbkwiBuziS9gxdODUyuiecfdGQ85jglMW6juS3+z5TsKLw==",
+ "dev": true,
+ "license": "MIT",
+ "engines": {
+ "node": ">=10"
+ },
+ "funding": {
+ "url": "https://github.com/sponsors/sindresorhus"
+ }
+ },
+ "node_modules/@ampproject/remapping": {
+ "version": "2.3.0",
+ "resolved": "https://registry.npmjs.org/@ampproject/remapping/-/remapping-2.3.0.tgz",
+ "integrity": "sha512-30iZtAPgz+LTIYoeivqYo853f02jBYSd5uGnGpkFV0M3xOt9aN73erkgYAmZU43x4VfqcnLxW9Kpg3R5LC4YYw==",
+ "dev": true,
+ "license": "Apache-2.0",
+ "dependencies": {
+ "@jridgewell/gen-mapping": "^0.3.5",
+ "@jridgewell/trace-mapping": "^0.3.24"
+ },
+ "engines": {
+ "node": ">=6.0.0"
+ }
+ },
+ "node_modules/@babel/code-frame": {
+ "version": "7.27.1",
+ "resolved": "https://registry.npmjs.org/@babel/code-frame/-/code-frame-7.27.1.tgz",
+ "integrity": "sha512-cjQ7ZlQ0Mv3b47hABuTevyTuYN4i+loJKGeV9flcCgIK37cCXRh+L1bd3iBHlynerhQ7BhCkn2BPbQUL+rGqFg==",
+ "dev": true,
+ "license": "MIT",
+ "dependencies": {
+ "@babel/helper-validator-identifier": "^7.27.1",
+ "js-tokens": "^4.0.0",
+ "picocolors": "^1.1.1"
+ },
+ "engines": {
+ "node": ">=6.9.0"
+ }
+ },
+ "node_modules/@babel/compat-data": {
+ "version": "7.27.2",
+ "resolved": "https://registry.npmjs.org/@babel/compat-data/-/compat-data-7.27.2.tgz",
+ "integrity": "sha512-TUtMJYRPyUb/9aU8f3K0mjmjf6M9N5Woshn2CS6nqJSeJtTtQcpLUXjGt9vbF8ZGff0El99sWkLgzwW3VXnxZQ==",
+ "dev": true,
+ "license": "MIT",
+ "engines": {
+ "node": ">=6.9.0"
+ }
+ },
+ "node_modules/@babel/core": {
+ "version": "7.27.1",
+ "resolved": "https://registry.npmjs.org/@babel/core/-/core-7.27.1.tgz",
+ "integrity": "sha512-IaaGWsQqfsQWVLqMn9OB92MNN7zukfVA4s7KKAI0KfrrDsZ0yhi5uV4baBuLuN7n3vsZpwP8asPPcVwApxvjBQ==",
+ "dev": true,
+ "license": "MIT",
+ "dependencies": {
+ "@ampproject/remapping": "^2.2.0",
+ "@babel/code-frame": "^7.27.1",
+ "@babel/generator": "^7.27.1",
+ "@babel/helper-compilation-targets": "^7.27.1",
+ "@babel/helper-module-transforms": "^7.27.1",
+ "@babel/helpers": "^7.27.1",
+ "@babel/parser": "^7.27.1",
+ "@babel/template": "^7.27.1",
+ "@babel/traverse": "^7.27.1",
+ "@babel/types": "^7.27.1",
+ "convert-source-map": "^2.0.0",
+ "debug": "^4.1.0",
+ "gensync": "^1.0.0-beta.2",
+ "json5": "^2.2.3",
+ "semver": "^6.3.1"
+ },
+ "engines": {
+ "node": ">=6.9.0"
+ },
+ "funding": {
+ "type": "opencollective",
+ "url": "https://opencollective.com/babel"
+ }
+ },
+ "node_modules/@babel/generator": {
+ "version": "7.27.1",
+ "resolved": "https://registry.npmjs.org/@babel/generator/-/generator-7.27.1.tgz",
+ "integrity": "sha512-UnJfnIpc/+JO0/+KRVQNGU+y5taA5vCbwN8+azkX6beii/ZF+enZJSOKo11ZSzGJjlNfJHfQtmQT8H+9TXPG2w==",
+ "dev": true,
+ "license": "MIT",
+ "dependencies": {
+ "@babel/parser": "^7.27.1",
+ "@babel/types": "^7.27.1",
+ "@jridgewell/gen-mapping": "^0.3.5",
+ "@jridgewell/trace-mapping": "^0.3.25",
+ "jsesc": "^3.0.2"
+ },
+ "engines": {
+ "node": ">=6.9.0"
+ }
+ },
+ "node_modules/@babel/helper-compilation-targets": {
+ "version": "7.27.2",
+ "resolved": "https://registry.npmjs.org/@babel/helper-compilation-targets/-/helper-compilation-targets-7.27.2.tgz",
+ "integrity": "sha512-2+1thGUUWWjLTYTHZWK1n8Yga0ijBz1XAhUXcKy81rd5g6yh7hGqMp45v7cadSbEHc9G3OTv45SyneRN3ps4DQ==",
+ "dev": true,
+ "license": "MIT",
+ "dependencies": {
+ "@babel/compat-data": "^7.27.2",
+ "@babel/helper-validator-option": "^7.27.1",
+ "browserslist": "^4.24.0",
+ "lru-cache": "^5.1.1",
+ "semver": "^6.3.1"
+ },
+ "engines": {
+ "node": ">=6.9.0"
+ }
+ },
+ "node_modules/@babel/helper-module-imports": {
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diff --git a/frontend-components/tables/package.json b/frontend-components/tables/package.json
new file mode 100644
index 0000000000000000000000000000000000000000..57bd453b59f6fca06b47cb06630fd61dbaa7d0b4
--- /dev/null
+++ b/frontend-components/tables/package.json
@@ -0,0 +1,53 @@
+{
+ "name": "tables",
+ "private": true,
+ "version": "0.0.0",
+ "type": "module",
+ "scripts": {
+ "dev": "vite",
+ "build": "vite build",
+ "build_tsc": "tsc && vite build",
+ "deploy": "npm run build && mv dist/index.html ../../openbb_platform/obbject_extensions/charting/openbb_charting/core/table.html",
+ "preview": "vite preview"
+ },
+ "dependencies": {
+ "@radix-ui/react-checkbox": "^1.0.3",
+ "@radix-ui/react-context-menu": "^2.1.3",
+ "@radix-ui/react-dialog": "^1.0.3",
+ "@radix-ui/react-dropdown-menu": "^2.0.4",
+ "@radix-ui/react-icons": "^1.2.0",
+ "@radix-ui/react-radio-group": "^1.1.2",
+ "@radix-ui/react-select": "^1.2.1",
+ "@radix-ui/react-toast": "^1.1.3",
+ "@tanstack/match-sorter-utils": "^8.7.6",
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+ "react-table": "^7.8.0",
+ "rollup": ">=4.22.4",
+ "xss": "^1.0.14"
+ },
+ "devDependencies": {
+ "@types/dom-to-image": "^2.6.4",
+ "@types/react": "^18.0.27",
+ "@types/react-dom": "^18.0.10",
+ "@types/react-table": "^7.7.14",
+ "@types/wicg-file-system-access": "^2020.9.6",
+ "@vitejs/plugin-react": "^4.2.1",
+ "autoprefixer": "^10.4.13",
+ "clsx": "^1.2.1",
+ "postcss": "^8.4.21",
+ "tailwindcss": "^3.2.7",
+ "typescript": "^4.9.3",
+ "vite": ">=6.2.7",
+ "vite-plugin-singlefile": "^0.13.3"
+ }
+}
diff --git a/frontend-components/tables/postcss.config.cjs b/frontend-components/tables/postcss.config.cjs
new file mode 100644
index 0000000000000000000000000000000000000000..33ad091d26d8a9dc95ebdf616e217d985ec215b8
--- /dev/null
+++ b/frontend-components/tables/postcss.config.cjs
@@ -0,0 +1,6 @@
+module.exports = {
+ plugins: {
+ tailwindcss: {},
+ autoprefixer: {},
+ },
+}
diff --git a/frontend-components/tables/src/App.tsx b/frontend-components/tables/src/App.tsx
new file mode 100644
index 0000000000000000000000000000000000000000..75c92ee6c2a7bce02109574e78f00303d8eb6c7b
--- /dev/null
+++ b/frontend-components/tables/src/App.tsx
@@ -0,0 +1,101 @@
+//@ts-nocheck
+import { useEffect, useState } from "react";
+import Table from "./components/Table";
+import { DndProvider } from "react-dnd";
+import { HTML5Backend } from "react-dnd-html5-backend";
+import {
+ cryptoData,
+ incomeData,
+ longIncomeData,
+ performanceData,
+} from "./data/mockup";
+
+declare global {
+ [(Exposed = Window), SecureContext];
+ interface Window {
+ json_data: any;
+ title: string;
+ download_path: string;
+ pywry: any;
+ }
+}
+
+function App() {
+ const [data, setData] = useState(
+ process.env.NODE_ENV === "production" ? null : JSON.parse(cryptoData)
+ );
+ const [title, setTitle] = useState("Interactive Table");
+ // const [source, setSource] = useState("");
+
+ if (process.env.NODE_ENV === "production") {
+ useEffect(() => {
+ const interval = setInterval(() => {
+ if (window.json_data) {
+ const data = JSON.parse(window.json_data);
+ console.log(data);
+ setData(data);
+ if (data.title && typeof data.title === "string") {
+ setTitle(data.title);
+ }
+ // if (data.source && typeof data.source === "string") {
+ // setSource(data.source);
+ // }
+ clearInterval(interval);
+ }
+ }, 100);
+ return () => clearInterval(interval);
+ }, []);
+ }
+
+ const transformData = (data: any) => {
+ if (!data) return null;
+
+ const filename = data.title?.replace(/|<\/b>/g, "").replace(/ /g, "_");
+ const date = new Date().toISOString().slice(0, 10).replace(/-/g, "");
+ const time = new Date().toISOString().slice(11, 19).replace(/:/g, "");
+ window.title = `openbb_${filename}_${date}_${time}`;
+
+ const columns = data.columns;
+ const index = data.index;
+ const newData = data.data;
+ const transformedData = newData.map((row: any, index: number) => {
+ const transformedRow = {};
+ row.forEach((value: any, index: number) => {
+ //@ts-ignore
+ transformedRow[columns[index]] = value ? value : value === 0 ? 0 : "";
+ });
+ return transformedRow;
+ });
+ return {
+ columns,
+ data: transformedData,
+ };
+ };
+
+ const transformedData = transformData(data);
+
+ return (
+
+
+ {transformedData && (
+
+ )}
+
+
& SVGRProps) => (
+
+
+);
+
+export default CloseIcon;
diff --git a/frontend-components/tables/src/components/Icons/CloseCircle.tsx b/frontend-components/tables/src/components/Icons/CloseCircle.tsx
new file mode 100644
index 0000000000000000000000000000000000000000..a6ab356aee1017dda8ab93f4d2ebf751c26a72ae
--- /dev/null
+++ b/frontend-components/tables/src/components/Icons/CloseCircle.tsx
@@ -0,0 +1,32 @@
+import type { SVGProps } from "react";
+interface SVGRProps {
+ title?: string;
+ titleId?: string;
+}
+
+const CloseCircleIcon = ({
+ title,
+ titleId,
+ ...props
+}: SVGProps & SVGRProps) => (
+
+ {title ? {title} : null}
+
+);
+
+export default CloseCircleIcon;
diff --git a/frontend-components/tables/src/components/Icons/Info.tsx b/frontend-components/tables/src/components/Icons/Info.tsx
new file mode 100644
index 0000000000000000000000000000000000000000..1250d27f2cafbe386037986ac53f0fd2e53e6eb6
--- /dev/null
+++ b/frontend-components/tables/src/components/Icons/Info.tsx
@@ -0,0 +1,32 @@
+import type { SVGProps } from "react";
+interface SVGRProps {
+ title?: string;
+ titleId?: string;
+}
+
+const InfoIcon = ({
+ title,
+ titleId,
+ ...props
+}: SVGProps & SVGRProps) => (
+
+ {title ? {title} : null}
+
+);
+
+export default InfoIcon;
diff --git a/frontend-components/tables/src/components/Icons/Success.tsx b/frontend-components/tables/src/components/Icons/Success.tsx
new file mode 100644
index 0000000000000000000000000000000000000000..e9271a10cb3dcff0b458c2ea9e4e3b56f6d8d107
--- /dev/null
+++ b/frontend-components/tables/src/components/Icons/Success.tsx
@@ -0,0 +1,39 @@
+import type { SVGProps } from "react";
+interface SVGRProps {
+ title?: string;
+ titleId?: string;
+}
+
+const SuccessIcon = ({
+ title,
+ titleId,
+ ...props
+}: SVGProps & SVGRProps) => (
+
+ {title ? {title} : null}
+
+);
+
+export default SuccessIcon;
diff --git a/frontend-components/tables/src/components/Icons/Warning.tsx b/frontend-components/tables/src/components/Icons/Warning.tsx
new file mode 100644
index 0000000000000000000000000000000000000000..3a82f08ed91863ee1898517df9659d02f1cc6f19
--- /dev/null
+++ b/frontend-components/tables/src/components/Icons/Warning.tsx
@@ -0,0 +1,32 @@
+import type { SVGProps } from "react";
+interface SVGRProps {
+ title?: string;
+ titleId?: string;
+}
+
+const WarningIcon = ({
+ title,
+ titleId,
+ ...props
+}: SVGProps & SVGRProps) => (
+
+ {title ? {title} : null}
+
+);
+
+export default WarningIcon;
diff --git a/frontend-components/tables/src/components/Select.tsx b/frontend-components/tables/src/components/Select.tsx
new file mode 100644
index 0000000000000000000000000000000000000000..5693ea3d781a1a1adfce0fed47076bd54e4b0493
--- /dev/null
+++ b/frontend-components/tables/src/components/Select.tsx
@@ -0,0 +1,120 @@
+import * as SelectPrimitive from "@radix-ui/react-select";
+import {
+ CheckIcon,
+ ChevronDownIcon,
+ ChevronUpIcon,
+} from "@radix-ui/react-icons";
+import { forwardRef } from "react";
+import clsx from "clsx";
+
+const Select = ({
+ value,
+ onChange,
+ label = "Select",
+ placeholder = "Select a fruit…",
+ groups,
+ labelType = "col",
+}: {
+ value: string;
+ onChange: (value: string) => void;
+ label?: string;
+ placeholder?: string;
+ labelType?: "col" | "row";
+ groups: {
+ label: string;
+ items: {
+ label: string;
+ value: string | number;
+ disabled?: boolean;
+ }[];
+ }[];
+}) => {
+ const onlyOneGroup = groups?.length === 1;
+ return (
+
+
+
+ {label}
+
+
+
+
+
+
+
+
+
+
+
+ {onlyOneGroup ? (
+
+ {groups[0].items.map((item) => (
+ //@ts-ignore
+
+ {item.label}
+
+ ))}
+
+ ) : (
+ groups.map((group, idx) => (
+
+
+ {group.label}
+
+ {group.items.map((item) => (
+ //@ts-ignore
+
+ {item.label}
+
+ ))}
+
+ ))
+ )}
+
+
+
+
+
+
+ );
+};
+
+const SelectItem = forwardRef(
+ //@ts-ignore
+ ({ children, className, ...props }, forwardedRef) => {
+ return (
+
+ {children}
+
+
+
+ );
+ }
+);
+
+export default Select;
diff --git a/frontend-components/tables/src/components/Table/ColumnHeader.tsx b/frontend-components/tables/src/components/Table/ColumnHeader.tsx
new file mode 100644
index 0000000000000000000000000000000000000000..f49d5e742c4752bd86c59d6d7ce55b4250933455
--- /dev/null
+++ b/frontend-components/tables/src/components/Table/ColumnHeader.tsx
@@ -0,0 +1,382 @@
+import * as ContextMenuPrimitive from "@radix-ui/react-context-menu";
+import { Table, flexRender } from "@tanstack/react-table";
+import clsx from "clsx";
+import { FC } from "react";
+import { useDrag, useDrop } from "react-dnd";
+import { includesDateNames } from "../../utils/utils";
+
+export const magnitudeRegex = new RegExp("^([0-9]+)(\\s)([kKmMbBtT])$");
+export const isoYearRegex = new RegExp("^\\d{4}$");
+export const isoDateRegex = new RegExp(
+ "^\\d{4}-\\d{2}-\\d{2}T\\d{2}:\\d{2}:\\d{2}|\\d{4}-\\d{2}-\\d{2}$",
+);
+
+function Filter({
+ column,
+ table,
+ numberOfColumns,
+}: {
+ column: any;
+ table: Table;
+ numberOfColumns: number;
+}) {
+ function getTime(value: string | number | Date) {
+ if (!value) return null;
+ const datetime = new Date(value);
+ const date = datetime.toISOString().split("T")[0];
+ const time = datetime.toTimeString().split(" ")[0];
+ return `${date} ${time}`;
+ }
+
+ const values = table.getPreFilteredRowModel().flatRows.map(
+ (row: { getValue: (arg0: any) => any }) =>
+ // @ts-ignore
+ row.original[column.id],
+ );
+
+ const areAllValuesString = values.every(
+ (value: null) => typeof value === "string" || value === null,
+ );
+
+ const areAllValuesNumber = values.every(
+ (value: null | number | string) =>
+ typeof value === "number" ||
+ magnitudeRegex.test(value as string) ||
+ value === null ||
+ value === "",
+ );
+
+ const valuesContainStringWithSpaces = values.some(
+ (value: string | string[]) =>
+ typeof value === "string" && value.includes(" "),
+ );
+
+ const columnFilterValue = column.getFilterValue();
+
+ let dateType = "date";
+
+ const isProbablyDate = values.every((value: string) => {
+ const only_numbers = value?.toString().replace(/[^0-9]/g, "").trim();
+ if (isoDateRegex.test(value?.toString())) {
+ dateType = "datetime-local";
+ }
+ if (isoYearRegex.test(value?.toString())) {
+ dateType = "number";
+ }
+ return (
+ only_numbers?.length >= 4 &&
+ (includesDateNames(column.id) ||
+ (column.id.toLowerCase() === "index" && !valuesContainStringWithSpaces))
+ );
+ });
+
+ if (isProbablyDate && dateType === "number") {
+ return (
+
+
+ );
+ }
+
+ if (isProbablyDate && dateType !== "number") {
+ return (
+
+
+ );
+ }
+
+ if (areAllValuesNumber) {
+ return (
+
+
+ );
+ }
+ if (areAllValuesString) {
+ return (
+
+
+ );
+ }
+ return
;
+}
+
+const reorderColumn = (
+ draggedColumnId: string,
+ targetColumnId: string,
+ columnOrder: string[],
+) => {
+ columnOrder.splice(
+ columnOrder.indexOf(targetColumnId),
+ 0,
+ columnOrder.splice(columnOrder.indexOf(draggedColumnId), 1)[0] as string,
+ );
+ return [...columnOrder];
+};
+
+const DraggableColumnHeader: FC<{
+ header: any;
+ table: any;
+ advanced: boolean;
+ idx: number;
+ lockFirstColumn: boolean;
+ setLockFirstColumn: (value: boolean) => void;
+}> = ({
+ header,
+ table,
+ advanced,
+ idx,
+ lockFirstColumn,
+ setLockFirstColumn,
+}) => {
+ const { getState, setColumnOrder } = table;
+ const { columnOrder } = getState();
+ const { column } = header;
+
+ const [, dropRef] = useDrop({
+ accept: "column",
+ drop: (draggedColumn: any) => {
+ const newColumnOrder = reorderColumn(
+ draggedColumn.id,
+ column.id,
+ columnOrder,
+ );
+ setColumnOrder(newColumnOrder);
+ },
+ });
+
+ const [{ isDragging }, dragRef, previewRef] = useDrag({
+ collect: (monitor) => ({
+ isDragging: monitor.isDragging(),
+ }),
+ item: () => column,
+ type: "column",
+ });
+
+ const renderField = () => (
+
+ {header.isPlaceholder ? null : (
+ <>
+
+
+ {flexRender(column.columnDef.header, header.getContext())}
+ {column.getCanSort() && (
+
+ )}
+
+ {advanced && column.id !== "select" && (
+
+
+
+
+ )}
+
+ {advanced && column.getCanFilter() ? (
+
+
+ ) : null}
+
+ )}
+
+ {idx === 0 ? (
+
+
+ {renderField()}
+
+
+
+
+ {
+ setLockFirstColumn(!lockFirstColumn);
+ }}
+ className="hover:bg-grey-300 dark:hover:bg-grey-800 rounded-md p-2"
+ >
+ {lockFirstColumn ? "Unlock" : "Lock"} first column
+
+
+
+
+
+ ) : (
+ renderField()
+ )}
+
+ );
+};
+
+export default DraggableColumnHeader;
diff --git a/frontend-components/tables/src/components/Table/DebouncedInput.tsx b/frontend-components/tables/src/components/Table/DebouncedInput.tsx
new file mode 100644
index 0000000000000000000000000000000000000000..7029bea9cfe6823c7b20fd22746393cd9be43b82
--- /dev/null
+++ b/frontend-components/tables/src/components/Table/DebouncedInput.tsx
@@ -0,0 +1,61 @@
+import { FC, useEffect, useState } from "react";
+
+type Props = {
+ value: string | number;
+ onChange: (value: string | number) => void;
+ debounce?: number;
+} & Omit, "onChange">;
+
+const DebouncedInput: FC = ({
+ value: initialValue,
+ onChange,
+ debounce = 500,
+ ...props
+}) => {
+ const [value, setValue] = useState(initialValue);
+
+ const handleInputChange = (event: React.ChangeEvent) =>
+ setValue(event.target.value);
+
+ useEffect(() => {
+ setValue(initialValue);
+ }, [initialValue]);
+
+ useEffect(() => {
+ const timeout = setTimeout(() => {
+ onChange(value);
+ }, debounce);
+
+ return () => clearTimeout(timeout);
+ }, [value]);
+
+ return (
+
+ );
+};
+
+export default DebouncedInput;
diff --git a/frontend-components/tables/src/components/Table/DownloadFinishedDialog.tsx b/frontend-components/tables/src/components/Table/DownloadFinishedDialog.tsx
new file mode 100644
index 0000000000000000000000000000000000000000..d0207bfb2e844798abbb36a68dae162f0572a216
--- /dev/null
+++ b/frontend-components/tables/src/components/Table/DownloadFinishedDialog.tsx
@@ -0,0 +1,80 @@
+import * as DialogPrimitive from "@radix-ui/react-dialog";
+import CloseIcon from "../Icons/Close";
+
+export default function DownloadFinishedDialog({
+ open,
+ close,
+}: {
+ open: boolean;
+ close: () => void;
+}) {
+ const userHomeDir = window.download_path || "~/OpenBBUserData/exports";
+ return (
+
+
+
+
+
+
+
+
+ Success
+
+
+
+
+ );
+}
diff --git a/frontend-components/tables/src/components/Table/Export.tsx b/frontend-components/tables/src/components/Table/Export.tsx
new file mode 100644
index 0000000000000000000000000000000000000000..c3084f0d84c39e8f6de6846e8ff1718ea57457b0
--- /dev/null
+++ b/frontend-components/tables/src/components/Table/Export.tsx
@@ -0,0 +1,56 @@
+import { useState } from "react";
+import { downloadData, downloadImage } from "../../utils/utils";
+import * as RadioGroup from "@radix-ui/react-radio-group";
+import useLocalStorage from "../../utils/useLocalStorage";
+import { EXPORT_TYPES } from ".";
+import Select from "../Select";
+
+export default function Export({
+ columns,
+ data,
+ type,
+ setType,
+ downloadFinished,
+}: {
+ columns: any;
+ data: any;
+ type: any;
+ setType: any;
+ downloadFinished: (change: boolean) => void;
+}) {
+ const onExport = () => {
+ switch (type) {
+ case "csv":
+ downloadData("csv", columns, data, downloadFinished);
+ break;
+ case "png":
+ downloadImage("table", downloadFinished);
+ break;
+ }
+ };
+ return (
+
+ {
+ setType(value);
+ }}
+ label="Type"
+ placeholder="Select type"
+ groups={[
+ {
+ label: "Type",
+ items: EXPORT_TYPES.map((type) => ({
+ label: type,
+ value: type,
+ })),
+ },
+ ]}
+ />
+
+ Export
+
+
+ );
+}
diff --git a/frontend-components/tables/src/components/Table/FilterColumns.tsx b/frontend-components/tables/src/components/Table/FilterColumns.tsx
new file mode 100644
index 0000000000000000000000000000000000000000..f12e16be051948a47841b4c69b72977a83f57810
--- /dev/null
+++ b/frontend-components/tables/src/components/Table/FilterColumns.tsx
@@ -0,0 +1,151 @@
+import * as DropdownMenuPrimitive from "@radix-ui/react-dropdown-menu";
+import { CheckIcon, ChevronDownIcon } from "@radix-ui/react-icons";
+import * as CheckboxPrimitive from "@radix-ui/react-checkbox";
+import { useEffect, useRef, useState } from "react";
+import useOnClickOutside from "../../utils/useClickOutside";
+import clsx from "clsx";
+
+export default function FilterColumns({
+ label,
+ table,
+ onlyIconTrigger = false,
+}: {
+ label: string;
+ table: any;
+ onlyIconTrigger?: boolean;
+}) {
+ const [open, setOpen] = useState(false);
+ const ref = useRef(null);
+
+ useOnClickOutside(ref, () => setOpen(false));
+
+ useEffect(() => {
+ const handleKeyDown = (event: KeyboardEvent) => {
+ if (event.key === "Escape") {
+ setOpen(false);
+ }
+ };
+ document.addEventListener("keydown", handleKeyDown);
+ return () => document.removeEventListener("keydown", handleKeyDown);
+ }, []);
+
+ function clearFilters() {
+ table.resetColumnFilters();
+ setOpen(false);
+ }
+
+ return (
+
+ {onlyIconTrigger ? (
+ setOpen(!open)}
+ >
+
+
+
+ ) : (
+
+
+ {label}
+
+ setOpen(!open)}
+ className="bg-white text-black dark:bg-grey-900 dark:text-white whitespace-nowrap h-[36px] border-[1.5px] border-grey-700 rounded p-3 inline-flex items-center justify-center leading-none gap-[5px] shadow-[0_2px_10px] shadow-black/10 focus:shadow-[0_0_0_2px] focus:shadow-black data-[placeholder]:text-white outline-none"
+ aria-label={label}
+ >
+ Filter columns
+
+
+ )}
+
+
+
+
+ Clear Filters
+
+
+
+
+
+
+ {table
+ .getAllLeafColumns()
+ .filter((column: any) => column.id !== "select")
+ .map((column: any) => {
+ return (
+
+
+
+
+ );
+ })}
+
+
+
+ );
+}
+
+/*
+
+ */
diff --git a/frontend-components/tables/src/components/Table/InderterminateCheckbox.tsx b/frontend-components/tables/src/components/Table/InderterminateCheckbox.tsx
new file mode 100644
index 0000000000000000000000000000000000000000..64b954061150de066c80cccfbc9988d7db1be641
--- /dev/null
+++ b/frontend-components/tables/src/components/Table/InderterminateCheckbox.tsx
@@ -0,0 +1,26 @@
+import { HTMLProps, useEffect, useRef } from "react";
+
+function IndeterminateCheckbox({
+ indeterminate,
+ className = "",
+ ...rest
+}: { indeterminate?: boolean } & HTMLProps) {
+ const ref = useRef(null!);
+
+ useEffect(() => {
+ if (typeof indeterminate === "boolean") {
+ ref.current.indeterminate = !rest.checked && indeterminate;
+ }
+ }, [ref, indeterminate]);
+
+ return (
+
+
{
+ const newValue = validatePageSize(value);
+ setCurrentPage(newValue);
+ if (newValue.toString().includes("All")) table.setPageSize(totalRows);
+ else table.setPageSize(newValue);
+ }}
+ labelType="row"
+ label="Rows per page"
+ placeholder="Select rows per page"
+ groups={[
+ {
+ label: "Rows per page", // TODO: generate number automatically
+ items: [10, 20, 30, 40, 50, `All (${totalRows})`].map(
+ (pageSize) => ({
+ label: `${pageSize}`,
+ value: pageSize,
+ })
+ ),
+ },
+ ]}
+ />
+
+ {table.getState().pagination.pageIndex + 1}
+ of
+ {table.getPageCount()}
+
+ {/*
+ | Go to page:
+ */}
+
+ table.setPageIndex(0)}
+ disabled={!table.getCanPreviousPage()}
+ >
+ {"<<"}
+
+ table.previousPage()}
+ disabled={!table.getCanPreviousPage()}
+ >
+ {"<"}
+
+ table.nextPage()}
+ disabled={!table.getCanNextPage()}
+ >
+ {">"}
+
+ table.setPageIndex(table.getPageCount() - 1)}
+ disabled={!table.getCanNextPage()}
+ >
+ {">>"}
+
+
+
+
+
+
+
+
+
+
+
+
+ {minutesPassed > 0 ? `${minutesPassed} min ago` : "Just now"}
+
+
([]);
+ const [globalFilter, setGlobalFilter] = useState("");
+ const [fontSize, setFontSize] = useLocalStorage("fontSize", "1");
+ const [open, setOpen] = useState(false);
+ const defaultVisibleColumns = columns.reduce((acc, cur, idx) => {
+ acc[cur] = idx < MAX_COLUMNS ? true : false;
+ return acc;
+ }, {});
+ const [columnVisibility, setColumnVisibility] = useState(
+ defaultVisibleColumns,
+ );
+
+ //@ts-ignore
+ const getColumnWidth = (rows, accessor, headerText) => {
+ const maxWidth = 200;
+ const magicSpacing = 12;
+ const cellLength = Math.max(
+ //@ts-ignore
+ ...rows.map((row) => getCellWidth(row, accessor)),
+ headerText?.length ? headerText?.length + 8 : 0,
+ );
+ return Math.min(maxWidth, cellLength * magicSpacing);
+ };
+
+ const rtColumns = useMemo(
+ () => [
+ ...columns.map((column: any, index: number) => ({
+ accessorKey: column,
+ accessorFn: (row: any) => {
+ const indexLabel = row.hasOwnProperty("index")
+ ? "index"
+ : row.hasOwnProperty("Index")
+ ? "Index"
+ : columns[0];
+ const indexValue = indexLabel ? row[indexLabel] : null;
+ const value = row[column];
+ const only_numbers =
+ value?.toString()?.split(".")?.[0]?.replace(/[^0-9]/g, "") ?? "";
+ const probablyDate =
+ only_numbers?.length >= 4 &&
+ (includesDateNames(column) ||
+ column.toLowerCase() === "index" ||
+ (indexValue &&
+ typeof indexValue === "string" &&
+ (indexValue.toLowerCase().includes("date") ||
+ indexValue.toLowerCase().includes("time") ||
+ indexValue.toLowerCase().includes("timestamp") ||
+ indexValue.toLowerCase().includes("year") ||
+ indexValue.toLowerCase().includes("month") ||
+ indexValue.toLowerCase().includes("week") ||
+ indexValue.toLowerCase().includes("hour") ||
+ indexValue.toLowerCase().includes("minute"))));
+
+ if (
+ probablyDate &&
+ value?.length === 4 &&
+ isoYearRegex.test(value?.toString())
+ )
+ return value;
+
+ if (probablyDate) {
+ if (typeof value === "number") return value;
+ return new Date(value).getTime();
+ }
+ return value;
+ },
+ id: column,
+ header: column,
+ size: getColumnWidth(data, column, column),
+ footer: column,
+ cell: ({ row }: any) => {
+ const indexLabel = row.original.hasOwnProperty("index")
+ ? "index"
+ : row.original.hasOwnProperty("Index")
+ ? "Index"
+ : columns[0];
+ const indexValue = indexLabel ? row.original[indexLabel] : null;
+ const value = row.original[column];
+ const valueType = typeof value;
+ const only_numbers =
+ value?.toString()?.split(".")?.[0]?.replace(/[^0-9]/g, "") ?? "";
+ const probablyDate =
+ only_numbers?.length >= 4 &&
+ (includesDateNames(column) ||
+ column.toLowerCase() === "index" ||
+ (indexValue &&
+ typeof indexValue === "string" &&
+ (indexValue.toLowerCase().includes("date") ||
+ indexValue.toLowerCase().includes("time") ||
+ indexValue.toLowerCase().includes("timestamp") ||
+ indexValue.toLowerCase().includes("year"))));
+
+ const probablyLink =
+ valueType === "string" && value.startsWith("http");
+
+ if (probablyLink) {
+ return (
+
+ {value?.length > 25 ? `${value.substring(0, 25)}...` : value}
+
+ );
+ }
+
+ if (
+ probablyDate &&
+ value?.length === 4 &&
+ isoYearRegex.test(value?.toString())
+ ) {
+ return {value}
;
+ }
+ if (probablyDate && !isNaN(new Date(value).getTime())) {
+ if (typeof value === "string") {
+ const date = value.split("T")[0];
+ const time = value.split("T")[1]?.split(".")[0];
+ if (time === "00:00:00") {
+ return {date}
;
+ }
+ return (
+
+ {date} {time}
+
+ );
+ }
+ try {
+ const date = new Date(value);
+ let dateFormatted = "";
+ if (
+ date.getUTCHours() === 0 &&
+ date.getUTCMinutes() === 0 &&
+ date.getUTCSeconds() === 0 &&
+ date.getMilliseconds() === 0
+ ) {
+ dateFormatted = date.toISOString().split("T")[0];
+ } else {
+ dateFormatted = date.toISOString();
+ dateFormatted = `${dateFormatted.split("T")[0]} ${
+ dateFormatted.split("T")[1].split(".")[0]
+ }`;
+ }
+
+ return {dateFormatted}
;
+ } catch (e) {
+ return {value}
;
+ }
+ }
+ if (
+ valueType === "number" ||
+ magnitudeRegex.test(value?.toString())
+ ) {
+ let valueFormatted = formatNumberMagnitude(value, column);
+ const valueFormattedNoMagnitude = Number(
+ formatNumberNoMagnitude(value),
+ );
+
+ if (
+ typeof indexValue === "string" &&
+ includesPriceNames(indexValue)
+ ) {
+ valueFormatted = Number(formatNumberNoMagnitude(value));
+ const maxFixed = valueFormatted < 2 ? 4 : 2;
+ valueFormatted = valueFormatted.toLocaleString("en-US", {
+ maximumFractionDigits: maxFixed,
+ minimumFractionDigits: 2,
+ });
+ }
+
+ return (
+ 0 && colors,
+ "text-[#F87171]": valueFormattedNoMagnitude < 0 && colors,
+ "text-[#404040]": valueFormattedNoMagnitude === 0 && colors,
+ })}
+ title={formatNumber(value).toString() ?? ""}
+ >
+ {valueFormattedNoMagnitude !== 0
+ ? valueFormattedNoMagnitude > 0
+ ? `${valueFormatted}`
+ : `${valueFormatted}`
+ : valueFormatted}
+
+ );
+ } else if (valueType === "string") {
+ return
;
+ }
+ return {value}
;
+ },
+ })),
+ ],
+ [advanced, colors],
+ );
+
+ const [lockFirstColumn, setLockFirstColumn] = useState(false);
+
+ const [columnOrder, setColumnOrder] = useState(
+ rtColumns.map((column) => column.id as string),
+ );
+
+ const resetOrder = () =>
+ setColumnOrder(columns.map((column) => column.id as string));
+
+ const needsReorder = useMemo(() => {
+ const currentOrder = columnOrder.map((columnId) => columnId);
+ const defaultOrder = rtColumns.map((column) => column.id as string);
+ return !isEqual(currentOrder, defaultOrder);
+ }, [columnOrder, rtColumns]);
+
+ const table = useReactTable({
+ data,
+ columns: rtColumns,
+ getCoreRowModel: getCoreRowModel(),
+ getSortedRowModel: getSortedRowModel(),
+ getFilteredRowModel: getFilteredRowModel(),
+ getPaginationRowModel: getPaginationRowModel(),
+ columnResizeMode: "onChange",
+ onColumnVisibilityChange: setColumnVisibility,
+ onColumnOrderChange: setColumnOrder,
+ onSortingChange: setSorting,
+ onGlobalFilterChange: setGlobalFilter,
+ globalFilterFn: fuzzyFilter,
+ state: {
+ sorting,
+ globalFilter,
+ columnOrder,
+ columnVisibility,
+ },
+ initialState: {
+ pagination: {
+ pageIndex: 0,
+ pageSize:
+ typeof currentPage === "string"
+ ? currentPage.includes("All")
+ ? data?.length
+ : parseInt(currentPage)
+ : currentPage,
+ },
+ },
+ });
+
+ const tableContainerRef = useRef(null);
+ const { rows } = table.getRowModel();
+ const visibleColumns = table.getVisibleFlatColumns();
+
+ const [downloadFinishedDialogOpen, setDownloadFinishedDialogOpen] =
+ useState(false);
+
+ useEffect(() => {
+ if (downloadFinished) {
+ setDownloadFinished(false);
+ setDownloadFinishedDialogOpen(true);
+ }
+ }, [downloadFinished]);
+
+ return (
+ <>
+ setDownloadFinishedDialogOpen(false)}
+ />
+
+
+
+
+
+
+
+
+ {title}
+ {/* {source && (
+ {`[${source}]`}
+ )} */}
+
+
+ {new Intl.DateTimeFormat("en-GB", {
+ dateStyle: "full",
+ timeStyle: "long",
+ })
+ .format(date)
+ .replace(/:\d\d /, " ")}
+ {cmd}
+
+ {/* {source && typeof source === "string" && source.includes("*") && (
+
+ *not affiliated
+
+ )} */}
+
+
+
+
+ {table.getHeaderGroups().map((headerGroup, idx) => (
+
+ {headerGroup.headers.map((header, idx2) => {
+ return (
+
+ ))}
+
+
+ {table.getRowModel().rows.map((row, idx) => {
+ return (
+
+ {row.getVisibleCells().map((cell, idx2) => {
+ return (
+
+ {flexRender(
+ cell.column.columnDef.cell,
+ cell.getContext(),
+ )}
+
+ );
+ })}
+
+ );
+ })}
+
+ {rows?.length > 30 && visibleColumns?.length > 4 && (
+
+ {table.getFooterGroups().map((footerGroup) => (
+
+ {footerGroup.headers.map((header) => (
+
+ {header.isPlaceholder
+ ? null
+ : flexRender(
+ header.column.columnDef.footer,
+ header.getContext(),
+ )}
+
+ ))}
+
+ ))}
+
+ )}
+
+
+
+
+
+
+
+
+ Settings
+
+
+
+
+
+
+ Settings
+
+
+ {needsReorder && (
+
resetOrder()} className="_btn h-9">
+ Reset Order
+
+ )}
+
{
+ toggleDarkMode(value !== "dark");
+ }}
+ label="Theme"
+ placeholder="Select theme"
+ groups={[
+ {
+ label: "Theme",
+ items: [
+ {
+ label: "Dark",
+ value: "dark",
+ },
+ {
+ label: "Light",
+ value: "light",
+ },
+ ],
+ },
+ ]}
+ />
+ {
+ setType(value);
+ }}
+ label="Export type"
+ placeholder="Select export type"
+ groups={[
+ {
+ label: "Export type",
+ items: EXPORT_TYPES.map((type) => ({
+ label: type,
+ value: type,
+ })),
+ },
+ ]}
+ />
+
+ {
+ setAdvanced(value === "advanced");
+ }}
+ label="Type"
+ placeholder="Select type"
+ groups={[
+ {
+ label: "Type",
+ items: [
+ {
+ label: "Simple",
+ value: "simple",
+ },
+ {
+ label: "Advanced",
+ value: "advanced",
+ },
+ ],
+ },
+ ]}
+ />
+
+
+ Colors
+
+
+
+
+
+
+
+
+
+
+ {
+ if (!toast.preventClose) {
+ setOpen(open);
+ }
+ }}
+ className={clsx(
+ "z-50 fixed bottom-4 md:left-1/2 md:-translate-x-[50%] inset-x-4 w-auto shadow-lg md:max-w-[658px] duration-300",
+ "radix-state-open:animate-fade-in",
+ "radix-state-closed:animate-toast-hide",
+ "radix-swipe-end:animate-toast-swipe-out",
+ "translate-x-radix-toast-swipe-move-x",
+ "radix-swipe-cancel:translate-x-0 radix-swipe-cancel:duration-200 radix-swipe-cancel:ease-[ease]",
+ "px-[40px] md:px-[58px] py-6 flex flex-col border rounded-[4px]",
+ {
+ "bg-green-100 text-green-600 border-green-600":
+ toast.status === "success",
+ "bg-red-200 text-red-600 border-red-600": toast.status === "error",
+ "bg-blue-100 text-blue-700 border-blue-600":
+ toast.status === "info",
+ "bg-orange-200 text-orange-600 border-orange-600":
+ toast.status === "warning",
+ },
+ {
+ "h-[72px]": !toast.description,
+ }
+ /*"focus:outline-none focus-visible:ring focus-visible:ring-purple-500 focus-visible:ring-opacity-75"*/
+ )}
+ >
+ {toast.status === "success" ? (
+
+ {toast.title}
+
+ {toast.description && (
+
+ {toast.description}
+
+ )}
+ {/*action && (
+ {
+ e.preventDefault();
+ action();
+ }}
+ >
+ {actionLabel}
+
+ )*/}
+
+
+
+
+ );
+};
+
+export default Toast;
diff --git a/frontend-components/tables/src/data/mockup.ts b/frontend-components/tables/src/data/mockup.ts
new file mode 100644
index 0000000000000000000000000000000000000000..236ea6c6cee5e9da267e73efab6b24c44a3c4f74
--- /dev/null
+++ b/frontend-components/tables/src/data/mockup.ts
@@ -0,0 +1,10836 @@
+export const longIncomeData = `{
+ "columns": [
+ "Index",
+ "1993",
+ "1994",
+ "1995",
+ "1996",
+ "1997",
+ "1998",
+ "1999",
+ "2000",
+ "2001",
+ "2002",
+ "2003",
+ "2004",
+ "2005",
+ "2006",
+ "2007",
+ "2008",
+ "2009",
+ "2010",
+ "2011",
+ "2012",
+ "2013",
+ "2014",
+ "2015",
+ "2016",
+ "2017",
+ "2018",
+ "2019",
+ "2020",
+ "2021",
+ "2022"
+ ],
+ "index": [
+ 0,
+ 1,
+ 2,
+ 3,
+ 4,
+ 5,
+ 6,
+ 7,
+ 8,
+ 9,
+ 10,
+ 11,
+ 12,
+ 13,
+ 14,
+ 15,
+ 16,
+ 17,
+ 18,
+ 19,
+ 20,
+ 21,
+ 22,
+ 23,
+ 24,
+ 25,
+ 26,
+ 27,
+ 28,
+ 29,
+ 30,
+ 31,
+ 32,
+ 33
+ ],
+ "data": [
+ [
+ "Reported Currency",
+ "USD",
+ "USD",
+ "USD",
+ "USD",
+ "USD",
+ "USD",
+ "USD",
+ "USD",
+ "USD",
+ "USD",
+ "USD",
+ "USD",
+ "USD",
+ "USD",
+ "USD",
+ "USD",
+ "USD",
+ "USD",
+ "USD",
+ "USD",
+ "USD",
+ "USD",
+ "USD",
+ "USD",
+ "USD",
+ "USD",
+ "USD",
+ "USD",
+ "USD",
+ "USD"
+ ],
+ [
+ "Cik",
+ "0000320193",
+ "0000320193",
+ "0000320193",
+ "0000320193",
+ "0000320193",
+ "0000320193",
+ "0000320193",
+ "0000320193",
+ "0000320193",
+ "0000320193",
+ "0000320193",
+ "0000320193",
+ "0000320193",
+ "0000320193",
+ "0000320193",
+ "0000320193",
+ "0000320193",
+ "0000320193",
+ "0000320193",
+ "0000320193",
+ "0000320193",
+ "0000320193",
+ "0000320193",
+ "0000320193",
+ "0000320193",
+ "0000320193",
+ "0000320193",
+ "0000320193",
+ "0000320193",
+ "0000320193"
+ ],
+ [
+ "Filling Date",
+ 749347200000,
+ 787276800000,
+ 819331200000,
+ 850953600000,
+ 881280000000,
+ 906681600000,
+ 945820800000,
+ 976752000000,
+ 1008892800000,
+ 1040256000000,
+ 1071792000000,
+ 1102032000000,
+ 1133395200000,
+ 1167350400000,
+ 1195084800000,
+ 1225843200000,
+ 1256601600000,
+ 1288137600000,
+ 1319587200000,
+ 1351641600000,
+ 1383091200000,
+ 1414368000000,
+ 1445990400000,
+ 1477440000000,
+ 1509667200000,
+ 1541376000000,
+ 1572480000000,
+ 1604016000000,
+ 1635465600000,
+ 1666828800000
+ ],
+ [
+ "Accepted Date",
+ 749332800000,
+ 787276800000,
+ 819331200000,
+ 850953600000,
+ 881280000000,
+ 906667200000,
+ 945820800000,
+ 976752000000,
+ 1008892800000,
+ 1040318421000,
+ 1071854745000,
+ 1102010749000,
+ 1133385768000,
+ 1167372358000,
+ 1195145377000,
+ 1225865783000,
+ 1256660309000,
+ 1288197381000,
+ 1319646925000,
+ 1351703239000,
+ 1383079108000,
+ 1414429915000,
+ 1446049869000,
+ 1477500136000,
+ 1509696097000,
+ 1541404900000,
+ 1572459156000,
+ 1603994785000,
+ 1635444268000,
+ 1666893674000
+ ],
+ [
+ "Calendar Year",
+ 725846400000,
+ 757382400000,
+ 788918400000,
+ 820454400000,
+ 852076800000,
+ 883612800000,
+ 915148800000,
+ 946684800000,
+ 978307200000,
+ 1009843200000,
+ 1041379200000,
+ 1072915200000,
+ 1104537600000,
+ 1136073600000,
+ 1167609600000,
+ 1199145600000,
+ 1230768000000,
+ 1262304000000,
+ 1293840000000,
+ 1325376000000,
+ 1356998400000,
+ 1388534400000,
+ 1420070400000,
+ 1451606400000,
+ 1483228800000,
+ 1514764800000,
+ 1546300800000,
+ 1577836800000,
+ 1609459200000,
+ 1640995200000
+ ],
+ [
+ "Period",
+ "FY",
+ "FY",
+ "FY",
+ "FY",
+ "FY",
+ "FY",
+ "FY",
+ "FY",
+ "FY",
+ "FY",
+ "FY",
+ "FY",
+ "FY",
+ "FY",
+ "FY",
+ "FY",
+ "FY",
+ "FY",
+ "FY",
+ "FY",
+ "FY",
+ "FY",
+ "FY",
+ "FY",
+ "FY",
+ "FY",
+ "FY",
+ "FY",
+ "FY",
+ "FY"
+ ],
+ [
+ "Revenue",
+ "7.977 B",
+ "9.189 B",
+ "11.062 B",
+ "9.833 B",
+ "7.081 B",
+ "5.941 B",
+ "6.134 B",
+ "7.983 B",
+ "5.363 B",
+ "5.742 B",
+ "6.207 B",
+ "8.279 B",
+ "13.931 B",
+ "19.315 B",
+ "24.006 B",
+ "32.479 B",
+ "36.537 B",
+ "65.225 B",
+ "108.249 B",
+ "156.508 B",
+ "170.910 B",
+ "182.795 B",
+ "233.715 B",
+ "215.639 B",
+ "229.234 B",
+ "265.595 B",
+ "260.174 B",
+ "274.515 B",
+ "365.817 B",
+ "394.328 B"
+ ],
+ [
+ "Cost Of Revenue",
+ "5.083 B",
+ "6.845 B",
+ "8.204 B",
+ "8.865 B",
+ "5.713 B",
+ "4.462 B",
+ "4.438 B",
+ "5.817 B",
+ "4.128 B",
+ "4.139 B",
+ "4.499 B",
+ "6.020 B",
+ "9.888 B",
+ "13.717 B",
+ "15.852 B",
+ "21.334 B",
+ "23.397 B",
+ "39.541 B",
+ "64.431 B",
+ "87.846 B",
+ "106.606 B",
+ "112.258 B",
+ "140.089 B",
+ "131.376 B",
+ "141.048 B",
+ "163.756 B",
+ "161.782 B",
+ "169.559 B",
+ "212.981 B",
+ "223.546 B"
+ ],
+ [
+ "Gross Profit",
+ "2.894 B",
+ "2.344 B",
+ "2.858 B",
+ "968 M",
+ "1.368 B",
+ "1.479 B",
+ "1.696 B",
+ "2.166 B",
+ "1.235 B",
+ "1.603 B",
+ "1.708 B",
+ "2.259 B",
+ "4.043 B",
+ "5.598 B",
+ "8.154 B",
+ "11.145 B",
+ "13.140 B",
+ "25.684 B",
+ "43.818 B",
+ "68.662 B",
+ "64.304 B",
+ "70.537 B",
+ "93.626 B",
+ "84.263 B",
+ "88.186 B",
+ "101.839 B",
+ "98.392 B",
+ "104.956 B",
+ "152.836 B",
+ "170.782 B"
+ ],
+ [
+ "Gross Profit Ratio",
+ "0.363",
+ "0.255",
+ "0.258",
+ "0.098",
+ "0.193",
+ "0.249",
+ "0.276",
+ "0.271",
+ "0.230",
+ "0.279",
+ "0.275",
+ "0.273",
+ "0.290",
+ "0.290",
+ "0.340",
+ "0.343",
+ "0.360",
+ "0.394",
+ "0.405",
+ "0.439",
+ "0.376",
+ "0.386",
+ "0.401",
+ "0.391",
+ "0.385",
+ "0.383",
+ "0.378",
+ "0.382",
+ "0.418",
+ "0.433"
+ ],
+ [
+ "Research And Development Expenses",
+ "0",
+ "564.303 M",
+ "614 M",
+ "604 M",
+ "860 M",
+ "310 M",
+ "314 M",
+ "380 M",
+ "441 M",
+ "447 M",
+ "471 M",
+ "489 M",
+ "534 M",
+ "712 M",
+ "782 M",
+ "1.109 B",
+ "1.333 B",
+ "1.782 B",
+ "2.429 B",
+ "3.381 B",
+ "4.475 B",
+ "6.041 B",
+ "8.067 B",
+ "10.045 B",
+ "11.581 B",
+ "14.236 B",
+ "16.217 B",
+ "18.752 B",
+ "21.914 B",
+ "26.251 B"
+ ],
+ [
+ "General And Administrative Expenses",
+ "0",
+ "0",
+ "0",
+ "0",
+ "0",
+ "0",
+ "0",
+ "0",
+ "0",
+ "0",
+ "0",
+ "0",
+ "0",
+ "0",
+ "0",
+ "0",
+ "0",
+ "0",
+ "0",
+ "0",
+ "0",
+ "0",
+ "0",
+ "0",
+ "0",
+ "0",
+ "0",
+ "0",
+ "0",
+ "0"
+ ],
+ [
+ "Selling And Marketing Expenses",
+ "0",
+ "0",
+ "0",
+ "0",
+ "0",
+ "0",
+ "0",
+ "0",
+ "0",
+ "0",
+ "0",
+ "0",
+ "0",
+ "0",
+ "0",
+ "0",
+ "0",
+ "0",
+ "0",
+ "0",
+ "0",
+ "0",
+ "0",
+ "0",
+ "0",
+ "0",
+ "0",
+ "0",
+ "0",
+ "0"
+ ],
+ [
+ "Selling General And Administrative",
+ "2.618 B",
+ "1.384 B",
+ "1.583 B",
+ "1.568 B",
+ "1.286 B",
+ "908 M",
+ "996 M",
+ "1.166 B",
+ "1.138 B",
+ "1.111 B",
+ "1.212 B",
+ "1.421 B",
+ "1.859 B",
+ "2.433 B",
+ "2.963 B",
+ "3.761 B",
+ "4.149 B",
+ "5.517 B",
+ "7.599 B",
+ "10.040 B",
+ "10.830 B",
+ "11.993 B",
+ "14.329 B",
+ "14.194 B",
+ "15.261 B",
+ "16.705 B",
+ "18.245 B",
+ "19.916 B",
+ "21.973 B",
+ "25.094 B"
+ ],
+ [
+ "Other Expenses",
+ "166.100 M",
+ "0",
+ "0",
+ "0",
+ "0",
+ "0",
+ "0",
+ "0",
+ "0",
+ "0",
+ "0",
+ "0",
+ "0",
+ "0",
+ "0",
+ "0",
+ "0",
+ "0",
+ "0",
+ "0",
+ "0",
+ "0",
+ "0",
+ "0",
+ "0",
+ "0",
+ "0",
+ "0",
+ "0",
+ "0"
+ ],
+ [
+ "Operating Expenses",
+ "2.784 B",
+ "1.948 B",
+ "2.197 B",
+ "2.172 B",
+ "2.146 B",
+ "1.218 B",
+ "1.310 B",
+ "1.546 B",
+ "1.579 B",
+ "1.558 B",
+ "1.683 B",
+ "1.910 B",
+ "2.393 B",
+ "3.145 B",
+ "3.745 B",
+ "4.870 B",
+ "5.482 B",
+ "7.299 B",
+ "10.028 B",
+ "13.421 B",
+ "15.305 B",
+ "18.034 B",
+ "22.396 B",
+ "24.239 B",
+ "26.842 B",
+ "30.941 B",
+ "34.462 B",
+ "38.668 B",
+ "43.887 B",
+ "51.345 B"
+ ],
+ [
+ "Costs And Expenses",
+ "7.867 B",
+ "8.793 B",
+ "10.401 B",
+ "11.037 B",
+ "7.859 B",
+ "5.680 B",
+ "5.748 B",
+ "7.363 B",
+ "5.707 B",
+ "5.697 B",
+ "6.182 B",
+ "7.930 B",
+ "12.281 B",
+ "16.862 B",
+ "19.597 B",
+ "26.204 B",
+ "28.879 B",
+ "46.840 B",
+ "74.459 B",
+ "101.267 B",
+ "121.911 B",
+ "130.292 B",
+ "162.485 B",
+ "155.615 B",
+ "167.890 B",
+ "194.697 B",
+ "196.244 B",
+ "208.227 B",
+ "256.868 B",
+ "274.891 B"
+ ],
+ [
+ "Interest Income",
+ "0",
+ "43.284 M",
+ "100 M",
+ "60 M",
+ "82 M",
+ "100 M",
+ "144 M",
+ "210 M",
+ "218 M",
+ "118 M",
+ "69 M",
+ "64 M",
+ "0",
+ "394 M",
+ "647 M",
+ "653 M",
+ "407 M",
+ "311 M",
+ "519 M",
+ "1.088 B",
+ "1.616 B",
+ "1.795 B",
+ "2.921 B",
+ "3.999 B",
+ "5.201 B",
+ "5.686 B",
+ "4.961 B",
+ "3.763 B",
+ "2.843 B",
+ "2.825 B"
+ ],
+ [
+ "Interest Expense",
+ "0",
+ "39.653 M",
+ "48 M",
+ "60 M",
+ "71 M",
+ "62 M",
+ "47 M",
+ "21 M",
+ "16 M",
+ "11 M",
+ "8 M",
+ "3 M",
+ "0",
+ "0",
+ "0",
+ "0",
+ "0",
+ "0",
+ "0",
+ "0",
+ "136 M",
+ "384 M",
+ "733 M",
+ "1.456 B",
+ "2.323 B",
+ "3.240 B",
+ "3.576 B",
+ "2.873 B",
+ "2.645 B",
+ "2.931 B"
+ ],
+ [
+ "Depreciation And Amortization",
+ "166.100 M",
+ "167.958 M",
+ "127 M",
+ "156 M",
+ "118 M",
+ "111 M",
+ "85 M",
+ "84 M",
+ "102 M",
+ "118 M",
+ "113 M",
+ "150 M",
+ "179 M",
+ "225 M",
+ "317 M",
+ "473 M",
+ "703 M",
+ "1.027 B",
+ "1.814 B",
+ "3.277 B",
+ "6.757 B",
+ "7.946 B",
+ "11.257 B",
+ "10.505 B",
+ "10.157 B",
+ "10.903 B",
+ "12.547 B",
+ "11.056 B",
+ "11.284 B",
+ "11.104 B"
+ ],
+ [
+ "Ebitda",
+ "305.800 M",
+ "707.897 M",
+ "849 M",
+ "-1.079 B",
+ "-856 M",
+ "502 M",
+ "808 M",
+ "1.197 B",
+ "78 M",
+ "216 M",
+ "214 M",
+ "536 M",
+ "1.994 B",
+ "3.043 B",
+ "5.325 B",
+ "7.368 B",
+ "8.687 B",
+ "19.567 B",
+ "36.019 B",
+ "59.040 B",
+ "57.048 B",
+ "61.813 B",
+ "84.505 B",
+ "73.333 B",
+ "76.569 B",
+ "87.046 B",
+ "81.860 B",
+ "81.020 B",
+ "123.136 B",
+ "133.138 B"
+ ],
+ [
+ "Ebitda Ratio",
+ "0.038",
+ "0.077",
+ "0.077",
+ "-0.110",
+ "-0.121",
+ "0.084",
+ "0.132",
+ "0.150",
+ "0.015",
+ "0.038",
+ "0.034",
+ "0.065",
+ "0.143",
+ "0.158",
+ "0.222",
+ "0.227",
+ "0.238",
+ "0.300",
+ "0.333",
+ "0.377",
+ "0.334",
+ "0.338",
+ "0.362",
+ "0.340",
+ "0.334",
+ "0.328",
+ "0.315",
+ "0.295",
+ "0.337",
+ "0.338"
+ ],
+ [
+ "Operating Income",
+ "110.400 M",
+ "522.274 M",
+ "684 M",
+ "-1.383 B",
+ "-1.070 B",
+ "261 M",
+ "359 M",
+ "522 M",
+ "-344 M",
+ "17 M",
+ "-1 M",
+ "326 M",
+ "1.650 B",
+ "2.453 B",
+ "4.409 B",
+ "6.275 B",
+ "7.658 B",
+ "18.385 B",
+ "33.790 B",
+ "55.241 B",
+ "48.999 B",
+ "52.503 B",
+ "71.230 B",
+ "60.024 B",
+ "61.344 B",
+ "70.898 B",
+ "63.930 B",
+ "66.288 B",
+ "108.949 B",
+ "119.437 B"
+ ],
+ [
+ "Operating Income Ratio",
+ "0.014",
+ "0.057",
+ "0.062",
+ "-0.141",
+ "-0.151",
+ "0.044",
+ "0.059",
+ "0.065",
+ "-0.064",
+ "0.003",
+ "-0.000",
+ "0.039",
+ "0.118",
+ "0.127",
+ "0.184",
+ "0.193",
+ "0.210",
+ "0.282",
+ "0.312",
+ "0.353",
+ "0.287",
+ "0.287",
+ "0.305",
+ "0.278",
+ "0.268",
+ "0.267",
+ "0.246",
+ "0.241",
+ "0.298",
+ "0.303"
+ ],
+ [
+ "Non Operating Income Loss",
+ "29.300 M",
+ "-21.988 M",
+ "-10 M",
+ "88 M",
+ "25 M",
+ "68 M",
+ "317 M",
+ "570 M",
+ "292 M",
+ "70 M",
+ "93 M",
+ "57 M",
+ "165 M",
+ "365 M",
+ "599 M",
+ "620 M",
+ "326 M",
+ "155 M",
+ "415 M",
+ "522 M",
+ "1.156 B",
+ "980 M",
+ "1.285 B",
+ "1.348 B",
+ "2.745 B",
+ "2.005 B",
+ "1.807 B",
+ "803 M",
+ "258 M",
+ "-334 M"
+ ],
+ [
+ "Income Before Tax",
+ "139.700 M",
+ "500.286 M",
+ "674 M",
+ "-1.295 B",
+ "-1.045 B",
+ "329 M",
+ "676 M",
+ "1.092 B",
+ "-52 M",
+ "87 M",
+ "92 M",
+ "383 M",
+ "1.815 B",
+ "2.818 B",
+ "5.008 B",
+ "6.895 B",
+ "7.984 B",
+ "18.540 B",
+ "34.205 B",
+ "55.763 B",
+ "50.155 B",
+ "53.483 B",
+ "72.515 B",
+ "61.372 B",
+ "64.089 B",
+ "72.903 B",
+ "65.737 B",
+ "67.091 B",
+ "109.207 B",
+ "119.103 B"
+ ],
+ [
+ "Income Before Tax Ratio",
+ "0.018",
+ "0.054",
+ "0.061",
+ "-0.132",
+ "-0.148",
+ "0.055",
+ "0.110",
+ "0.137",
+ "-0.010",
+ "0.015",
+ "0.015",
+ "0.046",
+ "0.130",
+ "0.146",
+ "0.209",
+ "0.212",
+ "0.219",
+ "0.284",
+ "0.316",
+ "0.356",
+ "0.293",
+ "0.293",
+ "0.310",
+ "0.285",
+ "0.280",
+ "0.274",
+ "0.253",
+ "0.244",
+ "0.299",
+ "0.302"
+ ],
+ [
+ "Income Tax Expense",
+ "53.100 M",
+ "190.108 M",
+ "250 M",
+ "-479 M",
+ "0",
+ "20 M",
+ "75 M",
+ "306 M",
+ "-15 M",
+ "22 M",
+ "24 M",
+ "107 M",
+ "480 M",
+ "829 M",
+ "1.512 B",
+ "2.061 B",
+ "2.280 B",
+ "4.527 B",
+ "8.283 B",
+ "14.030 B",
+ "13.118 B",
+ "13.973 B",
+ "19.121 B",
+ "15.685 B",
+ "15.738 B",
+ "13.372 B",
+ "10.481 B",
+ "9.680 B",
+ "14.527 B",
+ "19.300 B"
+ ],
+ [
+ "Net Income",
+ "86.600 M",
+ "310.178 M",
+ "424 M",
+ "-816 M",
+ "-1.045 B",
+ "309 M",
+ "601 M",
+ "786 M",
+ "-25 M",
+ "65 M",
+ "69 M",
+ "276 M",
+ "1.335 B",
+ "1.989 B",
+ "3.496 B",
+ "4.834 B",
+ "5.704 B",
+ "14.013 B",
+ "25.922 B",
+ "41.733 B",
+ "37.037 B",
+ "39.510 B",
+ "53.394 B",
+ "45.687 B",
+ "48.351 B",
+ "59.531 B",
+ "55.256 B",
+ "57.411 B",
+ "94.680 B",
+ "99.803 B"
+ ],
+ [
+ "Net Income Ratio",
+ "0.011",
+ "0.034",
+ "0.038",
+ "-0.083",
+ "-0.148",
+ "0.052",
+ "0.098",
+ "0.098",
+ "-0.005",
+ "0.011",
+ "0.011",
+ "0.033",
+ "0.096",
+ "0.103",
+ "0.146",
+ "0.149",
+ "0.156",
+ "0.215",
+ "0.239",
+ "0.267",
+ "0.217",
+ "0.216",
+ "0.228",
+ "0.212",
+ "0.211",
+ "0.224",
+ "0.212",
+ "0.209",
+ "0.259",
+ "0.253"
+ ],
+ [
+ "Basic Earnings Per Share",
+ "0.007",
+ "0.023",
+ "0.031",
+ "-0.059",
+ "-0.074",
+ "0.021",
+ "0.037",
+ "0.043",
+ "-0.001",
+ "0.003",
+ "0.003",
+ "0.013",
+ "0.059",
+ "0.084",
+ "0.144",
+ "0.196",
+ "0.228",
+ "0.550",
+ "1.002",
+ "1.594",
+ "1.430",
+ "1.623",
+ "2.320",
+ "2.087",
+ "2.317",
+ "3.002",
+ "2.993",
+ "3.310",
+ "5.670",
+ "6.150"
+ ],
+ [
+ "Diluted Earnings Per Share",
+ "0.007",
+ "0.023",
+ "0.031",
+ "-0.059",
+ "-0.074",
+ "0.019",
+ "0.032",
+ "0.039",
+ "-0.001",
+ "0.003",
+ "0.003",
+ "0.013",
+ "0.056",
+ "0.081",
+ "0.140",
+ "0.191",
+ "0.225",
+ "0.541",
+ "0.989",
+ "1.577",
+ "1.420",
+ "1.613",
+ "2.305",
+ "2.078",
+ "2.303",
+ "2.978",
+ "2.973",
+ "3.280",
+ "5.610",
+ "6.110"
+ ],
+ [
+ "Basic Average Shares",
+ "13.107 B",
+ "13.298 B",
+ "13.781 B",
+ "13.858 B",
+ "14.119 B",
+ "14.781 B",
+ "16.034 B",
+ "18.176 B",
+ "19.354 B",
+ "19.881 B",
+ "20.195 B",
+ "20.809 B",
+ "22.636 B",
+ "23.634 B",
+ "24.209 B",
+ "24.685 B",
+ "25.004 B",
+ "25.465 B",
+ "25.879 B",
+ "26.175 B",
+ "25.909 B",
+ "24.342 B",
+ "23.014 B",
+ "21.883 B",
+ "20.869 B",
+ "19.822 B",
+ "18.471 B",
+ "17.352 B",
+ "16.701 B",
+ "16.216 B"
+ ],
+ [
+ "Diluted Average Shares",
+ "13.107 B",
+ "13.307 B",
+ "13.781 B",
+ "13.858 B",
+ "14.119 B",
+ "18.807 B",
+ "19.506 B",
+ "20.178 B",
+ "19.354 B",
+ "20.260 B",
+ "20.354 B",
+ "21.689 B",
+ "23.990 B",
+ "24.571 B",
+ "24.900 B",
+ "25.260 B",
+ "25.396 B",
+ "25.892 B",
+ "26.226 B",
+ "26.470 B",
+ "26.087 B",
+ "24.491 B",
+ "23.172 B",
+ "22.001 B",
+ "21.007 B",
+ "20.000 B",
+ "18.596 B",
+ "17.528 B",
+ "16.865 B",
+ "16.326 B"
+ ]
+ ],
+ "title": "AAPL Income Statement"
+}`;
+
+export const performanceData = `{
+ "columns": [
+ "Name",
+ "Week",
+ "Month",
+ "3Month",
+ "6Month",
+ "1Year",
+ "YTD",
+ "Recom",
+ "AvgVolume",
+ "RelVolume",
+ "Change",
+ "Volume"
+ ],
+ "index": [
+ 10,
+ 9,
+ 8,
+ 7,
+ 6,
+ 5,
+ 4,
+ 3,
+ 2,
+ 1,
+ 0
+ ],
+ "data": [
+ [
+ "Utilities",
+ -0.0298,
+ -0.0415,
+ -0.0684,
+ -0.1207,
+ -0.0974,
+ -0.0785,
+ 2.36,
+ 147490000,
+ 0.97,
+ -0.0184,
+ 142500000
+ ],
+ [
+ "Technology",
+ 0.0287,
+ 0.0396,
+ 0.1421,
+ 0.0994,
+ -0.1228,
+ 0.1521,
+ 2.1,
+ 1530000000,
+ 0.97,
+ -0.0117,
+ 1480000000
+ ],
+ [
+ "Real Estate",
+ -0.051,
+ -0.1131,
+ -0.057,
+ -0.1125,
+ -0.263,
+ -0.059,
+ 2.21,
+ 330350000,
+ 1.19,
+ -0.0364,
+ 392410000
+ ],
+ [
+ "Industrials",
+ 0.0019,
+ -0.0412,
+ 0.0092,
+ 0.0684,
+ -0.0731,
+ -0.0003,
+ 2.34,
+ 577490000,
+ 1.06,
+ -0.0183,
+ 611230000
+ ],
+ [
+ "Healthcare",
+ 0.0009,
+ -0.0339,
+ -0.0446,
+ 0.022,
+ -0.0889,
+ -0.0566,
+ 2.14,
+ 1170000000,
+ 0.86,
+ -0.0152,
+ 1000000000
+ ],
+ [
+ "Financial",
+ 0.0051,
+ -0.0968,
+ -0.025,
+ -0.015,
+ -0.1652,
+ -0.0484,
+ 2.27,
+ 1050000000,
+ 1.39,
+ -0.0207,
+ 1460000000
+ ],
+ [
+ "Energy",
+ 0.0218,
+ -0.0657,
+ -0.044,
+ -0.0068,
+ -0.0031,
+ -0.083,
+ 2.24,
+ 570550000,
+ 0.91,
+ -0.017,
+ 518000000
+ ],
+ [
+ "Consumer Defensive",
+ 0.0001,
+ -0.0215,
+ -0.0222,
+ 0.0219,
+ -0.0362,
+ -0.0244,
+ 2.31,
+ 328290000,
+ 0.84,
+ -0.0076,
+ 276400000
+ ],
+ [
+ "Consumer Cyclical",
+ 0.0137,
+ -0.0334,
+ 0.0679,
+ -0.0736,
+ -0.2161,
+ 0.0854,
+ 2.16,
+ 1750000000,
+ 1.08,
+ -0.019,
+ 1890000000
+ ],
+ [
+ "Communication Services",
+ 0.0407,
+ 0.049,
+ 0.1583,
+ 0.0416,
+ -0.2122,
+ 0.143,
+ 1.95,
+ 770570000,
+ 0.79,
+ -0.0151,
+ 608400000
+ ],
+ [
+ "Basic Materials",
+ 0.0143,
+ -0.0525,
+ 0.0078,
+ 0.0988,
+ -0.1447,
+ -0.0048,
+ 2.25,
+ 393000000,
+ 1.01,
+ -0.0123,
+ 395770000
+ ]
+ ],
+ "title": "Group Performance Data"
+}`;
+
+export const candleData = `{
+ "columns": [
+ "Index",
+ "Open",
+ "High",
+ "Low",
+ "Close",
+ "Adj Close",
+ "Volume",
+ "Dividends",
+ "Stock Splits"
+ ],
+ "index": [
+ 0,
+ 1,
+ 2,
+ 3,
+ 4,
+ 5,
+ 6,
+ 7,
+ 8,
+ 9,
+ 10,
+ 11,
+ 12,
+ 13,
+ 14,
+ 15,
+ 16,
+ 17,
+ 18,
+ 19,
+ 20,
+ 21,
+ 22,
+ 23,
+ 24,
+ 25,
+ 26,
+ 27,
+ 28,
+ 29,
+ 30,
+ 31,
+ 32,
+ 33,
+ 34,
+ 35,
+ 36,
+ 37,
+ 38,
+ 39,
+ 40,
+ 41,
+ 42,
+ 43,
+ 44,
+ 45,
+ 46,
+ 47,
+ 48,
+ 49,
+ 50,
+ 51,
+ 52,
+ 53,
+ 54,
+ 55,
+ 56,
+ 57,
+ 58,
+ 59,
+ 60,
+ 61,
+ 62,
+ 63,
+ 64,
+ 65,
+ 66,
+ 67,
+ 68,
+ 69,
+ 70,
+ 71,
+ 72,
+ 73,
+ 74,
+ 75,
+ 76,
+ 77,
+ 78,
+ 79,
+ 80,
+ 81,
+ 82,
+ 83,
+ 84,
+ 85,
+ 86,
+ 87,
+ 88,
+ 89,
+ 90,
+ 91,
+ 92,
+ 93,
+ 94,
+ 95,
+ 96,
+ 97,
+ 98,
+ 99,
+ 100,
+ 101,
+ 102,
+ 103,
+ 104,
+ 105,
+ 106,
+ 107,
+ 108,
+ 109,
+ 110,
+ 111,
+ 112,
+ 113,
+ 114,
+ 115,
+ 116,
+ 117,
+ 118,
+ 119,
+ 120,
+ 121,
+ 122,
+ 123,
+ 124,
+ 125,
+ 126,
+ 127,
+ 128,
+ 129,
+ 130,
+ 131,
+ 132,
+ 133,
+ 134,
+ 135,
+ 136,
+ 137,
+ 138,
+ 139,
+ 140,
+ 141,
+ 142,
+ 143,
+ 144,
+ 145,
+ 146,
+ 147,
+ 148,
+ 149,
+ 150,
+ 151,
+ 152,
+ 153,
+ 154,
+ 155,
+ 156,
+ 157,
+ 158,
+ 159,
+ 160,
+ 161,
+ 162,
+ 163,
+ 164,
+ 165,
+ 166,
+ 167,
+ 168,
+ 169,
+ 170,
+ 171,
+ 172,
+ 173,
+ 174,
+ 175,
+ 176,
+ 177,
+ 178,
+ 179,
+ 180,
+ 181,
+ 182,
+ 183,
+ 184,
+ 185,
+ 186,
+ 187,
+ 188,
+ 189,
+ 190,
+ 191,
+ 192,
+ 193,
+ 194,
+ 195,
+ 196,
+ 197,
+ 198,
+ 199,
+ 200,
+ 201,
+ 202,
+ 203,
+ 204,
+ 205,
+ 206,
+ 207,
+ 208,
+ 209,
+ 210,
+ 211,
+ 212,
+ 213,
+ 214,
+ 215,
+ 216,
+ 217,
+ 218,
+ 219,
+ 220,
+ 221,
+ 222,
+ 223,
+ 224,
+ 225,
+ 226,
+ 227,
+ 228,
+ 229,
+ 230,
+ 231,
+ 232,
+ 233,
+ 234,
+ 235,
+ 236,
+ 237,
+ 238,
+ 239,
+ 240,
+ 241,
+ 242,
+ 243,
+ 244,
+ 245,
+ 246,
+ 247,
+ 248,
+ 249,
+ 250,
+ 251,
+ 252,
+ 253,
+ 254,
+ 255,
+ 256,
+ 257,
+ 258,
+ 259,
+ 260,
+ 261,
+ 262,
+ 263,
+ 264,
+ 265,
+ 266,
+ 267,
+ 268,
+ 269,
+ 270,
+ 271,
+ 272,
+ 273,
+ 274,
+ 275,
+ 276,
+ 277,
+ 278,
+ 279,
+ 280,
+ 281,
+ 282,
+ 283,
+ 284,
+ 285,
+ 286,
+ 287,
+ 288,
+ 289,
+ 290,
+ 291,
+ 292,
+ 293,
+ 294,
+ 295,
+ 296,
+ 297,
+ 298,
+ 299,
+ 300,
+ 301,
+ 302,
+ 303,
+ 304,
+ 305,
+ 306,
+ 307,
+ 308,
+ 309,
+ 310,
+ 311,
+ 312,
+ 313,
+ 314,
+ 315,
+ 316,
+ 317,
+ 318,
+ 319,
+ 320,
+ 321,
+ 322,
+ 323,
+ 324,
+ 325,
+ 326,
+ 327,
+ 328,
+ 329,
+ 330,
+ 331,
+ 332,
+ 333,
+ 334,
+ 335,
+ 336,
+ 337,
+ 338,
+ 339,
+ 340,
+ 341,
+ 342,
+ 343,
+ 344,
+ 345,
+ 346,
+ 347,
+ 348,
+ 349,
+ 350,
+ 351,
+ 352,
+ 353,
+ 354,
+ 355,
+ 356,
+ 357,
+ 358,
+ 359,
+ 360,
+ 361,
+ 362,
+ 363,
+ 364,
+ 365,
+ 366,
+ 367,
+ 368,
+ 369,
+ 370,
+ 371,
+ 372,
+ 373,
+ 374,
+ 375,
+ 376,
+ 377,
+ 378,
+ 379,
+ 380,
+ 381,
+ 382,
+ 383,
+ 384,
+ 385,
+ 386,
+ 387,
+ 388,
+ 389,
+ 390,
+ 391,
+ 392,
+ 393,
+ 394,
+ 395,
+ 396,
+ 397,
+ 398,
+ 399,
+ 400,
+ 401,
+ 402,
+ 403,
+ 404,
+ 405,
+ 406,
+ 407,
+ 408,
+ 409,
+ 410,
+ 411,
+ 412,
+ 413,
+ 414,
+ 415,
+ 416,
+ 417,
+ 418,
+ 419,
+ 420,
+ 421,
+ 422,
+ 423,
+ 424,
+ 425,
+ 426,
+ 427,
+ 428,
+ 429,
+ 430,
+ 431,
+ 432,
+ 433,
+ 434,
+ 435,
+ 436,
+ 437,
+ 438,
+ 439,
+ 440,
+ 441,
+ 442,
+ 443,
+ 444,
+ 445,
+ 446,
+ 447,
+ 448,
+ 449,
+ 450,
+ 451,
+ 452,
+ 453,
+ 454,
+ 455,
+ 456,
+ 457,
+ 458,
+ 459,
+ 460,
+ 461,
+ 462,
+ 463,
+ 464,
+ 465,
+ 466,
+ 467,
+ 468,
+ 469,
+ 470,
+ 471,
+ 472,
+ 473,
+ 474,
+ 475,
+ 476,
+ 477,
+ 478,
+ 479,
+ 480,
+ 481,
+ 482,
+ 483,
+ 484,
+ 485,
+ 486,
+ 487,
+ 488,
+ 489,
+ 490,
+ 491,
+ 492,
+ 493,
+ 494,
+ 495,
+ 496,
+ 497,
+ 498,
+ 499,
+ 500,
+ 501,
+ 502,
+ 503,
+ 504,
+ 505,
+ 506,
+ 507,
+ 508,
+ 509,
+ 510,
+ 511,
+ 512,
+ 513,
+ 514,
+ 515,
+ 516,
+ 517,
+ 518,
+ 519,
+ 520,
+ 521,
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+ 68.6354022633,
+ 70.6576953657,
+ 68.1574027195,
+ 70.3635406494,
+ 70.3635406494,
+ 194994800,
+ 0,
+ 0
+ ],
+ [
+ "2020-04-13",
+ 65.7698491802,
+ 67.0910837736,
+ 65.1619331502,
+ 66.9807739258,
+ 66.9807739258,
+ 131022800,
+ 0,
+ 0
+ ],
+ [
+ "2020-04-09",
+ 65.8654589892,
+ 66.2012809211,
+ 64.8849535035,
+ 65.6914138794,
+ 65.6914138794,
+ 161834800,
+ 0,
+ 0
+ ],
+ [
+ "2020-04-08",
+ 64.4045043631,
+ 65.539440729,
+ 64.0343686066,
+ 65.2207794189,
+ 65.2207794189,
+ 168895200,
+ 0,
+ 0
+ ],
+ [
+ "2020-04-07",
+ 66.3802216154,
+ 66.6008413449,
+ 63.4877332843,
+ 63.5931358337,
+ 63.5931358337,
+ 202887200,
+ 0,
+ 0
+ ],
+ [
+ "2020-04-06",
+ 61.5021967153,
+ 64.4951872081,
+ 61.1296073746,
+ 64.3383102417,
+ 64.3383102417,
+ 201820400,
+ 0,
+ 0
+ ],
+ [
+ "2020-04-03",
+ 59.5166783875,
+ 60.2275433046,
+ 58.5778440203,
+ 59.1759529114,
+ 59.1759529114,
+ 129880000,
+ 0,
+ 0
+ ],
+ [
+ "2020-04-02",
+ 58.9136701588,
+ 60.0927273859,
+ 58.0704348577,
+ 60.0387992859,
+ 60.0387992859,
+ 165934000,
+ 0,
+ 0
+ ],
+ [
+ "2020-04-01",
+ 60.4236537227,
+ 60.967834595,
+ 58.6170734675,
+ 59.0533981323,
+ 59.0533981323,
+ 176218400,
+ 0,
+ 0
+ ],
+ [
+ "2020-03-31",
+ 62.6543058111,
+ 64.3432227221,
+ 61.7718493246,
+ 62.3331871033,
+ 62.3331871033,
+ 197002000,
+ 0,
+ 0
+ ],
+ [
+ "2020-03-30",
+ 61.4629964979,
+ 62.6347004214,
+ 61.1345242706,
+ 62.4606590271,
+ 62.4606590271,
+ 167976400,
+ 0,
+ 0
+ ],
+ [
+ "2020-03-27",
+ 61.9556855918,
+ 62.7204786147,
+ 60.5584661307,
+ 60.7276039124,
+ 60.7276039124,
+ 204216800,
+ 0,
+ 0
+ ],
+ [
+ "2020-03-26",
+ 60.4285687079,
+ 63.4093032608,
+ 60.3893475813,
+ 63.3504753113,
+ 63.3504753113,
+ 252087200,
+ 0,
+ 0
+ ],
+ [
+ "2020-03-25",
+ 61.4654318431,
+ 63.3038794555,
+ 59.8843676444,
+ 60.1834220886,
+ 60.1834220886,
+ 303602000,
+ 0,
+ 0
+ ],
+ [
+ "2020-03-24",
+ 57.9380767499,
+ 60.7153593441,
+ 57.4331169583,
+ 60.5168075562,
+ 60.5168075562,
+ 287531200,
+ 0,
+ 0
+ ],
+ [
+ "2020-03-23",
+ 55.9084209965,
+ 56.0113736195,
+ 52.1163158837,
+ 54.9990005493,
+ 54.9990005493,
+ 336752800,
+ 0,
+ 0
+ ],
+ [
+ "2020-03-20",
+ 60.5903359654,
+ 61.7301758607,
+ 55.8888138582,
+ 56.1927719116,
+ 56.1927719116,
+ 401693200,
+ 0,
+ 0
+ ],
+ [
+ "2020-03-19",
+ 60.6418093609,
+ 61.9777472596,
+ 59.4701056726,
+ 60.0020294189,
+ 60.0020294189,
+ 271857200,
+ 0,
+ 0
+ ],
+ [
+ "2020-03-18",
+ 58.7739501485,
+ 61.2815918393,
+ 58.1243630308,
+ 60.4653205872,
+ 60.4653205872,
+ 300233600,
+ 0,
+ 0
+ ],
+ [
+ "2020-03-17",
+ 60.6712269852,
+ 63.1470010982,
+ 58.4381255868,
+ 61.9826545715,
+ 61.9826545715,
+ 324056000,
+ 0,
+ 0
+ ]
+ ],
+ "title": "Raw Data"
+}`;
+
+export const incomeData = `{
+ "columns": [
+ "Index",
+ "2018",
+ "2019",
+ "2020",
+ "2021",
+ "2022"
+ ],
+ "index": [
+ 0,
+ 1,
+ 2,
+ 3,
+ 4,
+ 5,
+ 6,
+ 7,
+ 8,
+ 9,
+ 10,
+ 11,
+ 12,
+ 13,
+ 14,
+ 15,
+ 16,
+ 17,
+ 18,
+ 19,
+ 20,
+ 21,
+ 22,
+ 23,
+ 24,
+ 25,
+ 26,
+ 27,
+ 28,
+ 29,
+ 30,
+ 31,
+ 32,
+ 33
+ ],
+ "data": [
+ [
+ "Reported Currency",
+ "USD",
+ "USD",
+ "USD",
+ "USD",
+ "USD"
+ ],
+ [
+ "Cik",
+ "0000320193",
+ "0000320193",
+ "0000320193",
+ "0000320193",
+ "0000320193"
+ ],
+ [
+ "Filling Date",
+ 1541376000000,
+ 1572480000000,
+ 1604016000000,
+ 1635465600000,
+ 1666828800000
+ ],
+ [
+ "Accepted Date",
+ 1541404900000,
+ 1572459156000,
+ 1603994785000,
+ 1635444268000,
+ 1666893674000
+ ],
+ [
+ "Calendar Year",
+ 1514764800000,
+ 1546300800000,
+ 1577836800000,
+ 1609459200000,
+ 1640995200000
+ ],
+ [
+ "Period",
+ "FY",
+ "FY",
+ "FY",
+ "FY",
+ "FY"
+ ],
+ [
+ "Revenue",
+ "265.595 B",
+ "260.174 B",
+ "274.515 B",
+ "365.817 B",
+ "394.328 B"
+ ],
+ [
+ "Cost Of Revenue",
+ "163.756 B",
+ "161.782 B",
+ "169.559 B",
+ "212.981 B",
+ "223.546 B"
+ ],
+ [
+ "Gross Profit",
+ "101.839 B",
+ "98.392 B",
+ "104.956 B",
+ "152.836 B",
+ "170.782 B"
+ ],
+ [
+ "Gross Profit Ratio",
+ "0.383",
+ "0.378",
+ "0.382",
+ "0.418",
+ "0.433"
+ ],
+ [
+ "Research And Development Expenses",
+ "14.236 B",
+ "16.217 B",
+ "18.752 B",
+ "21.914 B",
+ "26.251 B"
+ ],
+ [
+ "General And Administrative Expenses",
+ "0",
+ "0",
+ "0",
+ "0",
+ "0"
+ ],
+ [
+ "Selling And Marketing Expenses",
+ "0",
+ "0",
+ "0",
+ "0",
+ "0"
+ ],
+ [
+ "Selling General And Administrative",
+ "16.705 B",
+ "18.245 B",
+ "19.916 B",
+ "21.973 B",
+ "25.094 B"
+ ],
+ [
+ "Other Expenses",
+ "0",
+ "0",
+ "0",
+ "0",
+ "0"
+ ],
+ [
+ "Operating Expenses",
+ "30.941 B",
+ "34.462 B",
+ "38.668 B",
+ "43.887 B",
+ "51.345 B"
+ ],
+ [
+ "Costs And Expenses",
+ "194.697 B",
+ "196.244 B",
+ "208.227 B",
+ "256.868 B",
+ "274.891 B"
+ ],
+ [
+ "Interest Income",
+ "5.686 B",
+ "4.961 B",
+ "3.763 B",
+ "2.843 B",
+ "2.825 B"
+ ],
+ [
+ "Interest Expense",
+ "3.240 B",
+ "3.576 B",
+ "2.873 B",
+ "2.645 B",
+ "2.931 B"
+ ],
+ [
+ "Depreciation And Amortization",
+ "10.903 B",
+ "12.547 B",
+ "11.056 B",
+ "11.284 B",
+ "11.104 B"
+ ],
+ [
+ "Ebitda",
+ "87.046 B",
+ "81.860 B",
+ "81.020 B",
+ "123.136 B",
+ "133.138 B"
+ ],
+ [
+ "Ebitda Ratio",
+ "0.328",
+ "0.315",
+ "0.295",
+ "0.337",
+ "0.338"
+ ],
+ [
+ "Operating Income",
+ "70.898 B",
+ "63.930 B",
+ "66.288 B",
+ "108.949 B",
+ "119.437 B"
+ ],
+ [
+ "Operating Income Ratio",
+ "0.267",
+ "0.246",
+ "0.241",
+ "0.298",
+ "0.303"
+ ],
+ [
+ "Non Operating Income Loss",
+ "2.005 B",
+ "1.807 B",
+ "803 M",
+ "258 M",
+ "-334 M"
+ ],
+ [
+ "Income Before Tax",
+ "72.903 B",
+ "65.737 B",
+ "67.091 B",
+ "109.207 B",
+ "119.103 B"
+ ],
+ [
+ "Income Before Tax Ratio",
+ "0.274",
+ "0.253",
+ "0.244",
+ "0.299",
+ "0.302"
+ ],
+ [
+ "Income Tax Expense",
+ "13.372 B",
+ "10.481 B",
+ "9.680 B",
+ "14.527 B",
+ "19.300 B"
+ ],
+ [
+ "Net Income",
+ "59.531 B",
+ "55.256 B",
+ "57.411 B",
+ "94.680 B",
+ "99.803 B"
+ ],
+ [
+ "Net Income Ratio",
+ "0.224",
+ "0.212",
+ "0.209",
+ "0.259",
+ "0.253"
+ ],
+ [
+ "Basic Earnings Per Share",
+ "3.002",
+ "2.993",
+ "3.310",
+ "5.670",
+ "6.150"
+ ],
+ [
+ "Diluted Earnings Per Share",
+ "2.978",
+ "2.973",
+ "3.280",
+ "5.610",
+ "6.110"
+ ],
+ [
+ "Basic Average Shares",
+ "19.822 B",
+ "18.471 B",
+ "17.352 B",
+ "16.701 B",
+ "16.216 B"
+ ],
+ [
+ "Diluted Average Shares",
+ "20.000 B",
+ "18.596 B",
+ "17.528 B",
+ "16.865 B",
+ "16.326 B"
+ ]
+ ],
+ "title": "AAPL Income Statement"
+}`;
+
+export const rekNewsData = `{"columns":["Platform","Date","Amount [$]","Audit","Slug","URL"],"index":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123],"data":[["Ronin Network - REKT",1647993600000,"624 M","Unaudited","ronin-rekt","https:\/\/rekt.news\/ronin-rekt\/"],["Poly Network - REKT",1628553600000,"611 M","Unaudited","polynetwork-rekt","https:\/\/rekt.news\/polynetwork-rekt\/"],["BNB Bridge - REKT",1665014400000,"586 M","Unaudited","bnb-bridge-rekt","https:\/\/rekt.news\/bnb-bridge-rekt\/"],["SBF - MASK OFF",1668211200000,"477 M","N\/A","sbf-mask-off","https:\/\/rekt.news\/sbf-mask-off\/"],["Wormhole - REKT",1643760000000,"326 M","Neodyme","wormhole-rekt","https:\/\/rekt.news\/wormhole-rekt\/"],["BitMart - REKT",1638576000000,"196 M","N\/A","bitmart-rekt","https:\/\/rekt.news\/bitmart-rekt\/"],["Nomad Bridge - REKT",1659312000000,"190 M","N\/A","nomad-rekt","https:\/\/rekt.news\/nomad-rekt\/"],["Beanstalk - REKT",1650153600000,"181 M","Unaudited","beanstalk-rekt","https:\/\/rekt.news\/beanstalk-rekt\/"],["Wintermute - REKT 2",1663632000000,"162.300 M","N\/A","wintermute-rekt-2","https:\/\/rekt.news\/wintermute-rekt-2\/"],["Compound - REKT",1632873600000,"147 M","Unaudited","compound-rekt","https:\/\/rekt.news\/compound-rekt\/"],["Vulcan Forged - REKT",1639353600000,"140 M","Unaudited","vulcan-forged-rekt","https:\/\/rekt.news\/vulcan-forged-rekt\/"],["Cream Finance - REKT 2",1635292800000,"130 M","Unaudited","cream-rekt-2","https:\/\/rekt.news\/cream-rekt-2\/"],["BonqDAO - REKT",1675209600000,"120 M","Out of scope","bonq-rekt","https:\/\/rekt.news\/bonq-rekt\/"],["Badger - REKT",1638403200000,"120 M","Unaudited","badger-rekt","https:\/\/rekt.news\/badger-rekt\/"],["Mango Markets - REKT",1665446400000,"115 M","Out of Scope","mango-markets-rekt","https:\/\/rekt.news\/mango-markets-rekt\/"],["Harmony Bridge - REKT",1655942400000,"100 M","N\/A","harmony-rekt","https:\/\/rekt.news\/harmony-rekt\/"],["Mirror Protocol - REKT",1633651200000,"92 M","Unaudited","mirror-rekt","https:\/\/rekt.news\/mirror-rekt\/"],["Fei Rari - REKT 2",1651363200000,"80 M","Unaudited","fei-rari-rekt","https:\/\/rekt.news\/fei-rari-rekt\/"],["Qubit Finance - REKT",1643328000000,"80 M","Unaudited","qubit-rekt","https:\/\/rekt.news\/qubit-rekt\/"],["Ascendex - REKT",1639267200000,"77.700 M","Unaudited","ascendex-rekt","https:\/\/rekt.news\/ascendex-rekt\/"],["EasyFi - REKT",1618790400000,"59 M","Unaudited","easyfi-rekt","https:\/\/rekt.news\/easyfi-rekt\/"],["Uranium Finance - REKT",1619568000000,"57.200 M","Unaudited","uranium-rekt","https:\/\/rekt.news\/uranium-rekt\/"],["bZx - REKT",1636070400000,"55 M","Unaudited","bzx-rekt","https:\/\/rekt.news\/bzx-rekt\/"],["Cashio - REKT",1647993600000,"48 M","Unaudited","cashio-rekt","https:\/\/rekt.news\/cashio-rekt\/"],["PancakeBunny - REKT",1621382400000,"45 M","Unaudited","pancakebunny-rekt","https:\/\/rekt.news\/pancakebunny-rekt\/"],["Kucoin - REKT",1601337600000,"45 M","Internal audit","epic-hack-homie","https:\/\/rekt.news\/epic-hack-homie\/"],["Alpha Finance - REKT",1613174400000,"37.500 M","Quantstamp, Peckshield","alpha-finance-rekt","https:\/\/rekt.news\/alpha-finance-rekt\/"],["Vee Finance - REKT",1632182400000,"34 M","Slowmist","veefinance-rekt","https:\/\/rekt.news\/veefinance-rekt\/"],["Crypto.com - REKT",1642464000000,"33.700 M","Deloitte","cryptocom-rekt","https:\/\/rekt.news\/cryptocom-rekt\/"],["Meerkat Finance - BSC - REKT",1614816000000,"32 M","Unaudited","meerkat-finance-bsc-rekt","https:\/\/rekt.news\/meerkat-finance-bsc-rekt\/"],["MonoX - REKT",1638230400000,"31.400 M","Halborn, Peckshield","monox-rekt","https:\/\/rekt.news\/monox-rekt\/"],["Spartan Protocol - REKT",1619913600000,"30.500 M","N\/A","spartan-rekt","https:\/\/rekt.news\/spartan-rekt\/"],["Grim Finance - REKT",1639785600000,"30 M","Solidity Finance","grim-finance-rekt","https:\/\/rekt.news\/grim-finance-rekt\/"],["Deribit - REKT",1667260800000,"28 M","N\/A","deribit-rekt","https:\/\/rekt.news\/deribit-rekt\/"],["Wintermute - REKT",1654387200000,"27.600 M","N\/A","wintermute-rekt","https:\/\/rekt.news\/wintermute-rekt\/"],["StableMagnet - REKT",1624406400000,"27 M","Techrate","stablemagnet-rekt","https:\/\/rekt.news\/stablemagnet-rekt\/"],["Paid Network - REKT",1614902400000,"27 M","Unaudited","paid-rekt","https:\/\/rekt.news\/paid-rekt\/"],["Harvest Finance - REKT",1603670400000,"25 M","Haechi, Peckshield","harvest-finance-rekt","https:\/\/rekt.news\/harvest-finance-rekt\/"],["Ankr & Helio - REKT",1669939200000,"24 M","N\/A","ankr-helio-rekt","https:\/\/rekt.news\/ankr-helio-rekt\/"],["XToken - REKT",1620777600000,"24 M","Peckshield","xtoken-rekt","https:\/\/rekt.news\/xtoken-rekt\/"],["Elephant Money - REKT",1618185600000,"22.200 M","Solidity Finance","elephant-money-rekt","https:\/\/rekt.news\/elephant-money-rekt\/"],["Blizz Finance, Venus Protocol - REKT",1652400000000,"21.800 M","n\/a","venus-blizz-rekt","https:\/\/rekt.news\/venus-blizz-rekt\/"],["Transit Swap - REKT",1664668800000,"21.200 M","Out of scope","transit-swap-rekt","https:\/\/rekt.news\/transit-swap-rekt\/"],["Popsicle Finance - REKT",1627948800000,"20 M","Peckshield","popsicle-rekt","https:\/\/rekt.news\/popsicle-rekt\/"],["Pickle Finance - REKT",1606003200000,"19.700 M","Unaudited","pickle-finance-rekt","https:\/\/rekt.news\/pickle-finance-rekt\/"],["Cream Finance - REKT",1630281600000,"18.800 M","Unaudited","cream-rekt","https:\/\/rekt.news\/cream-rekt\/"],["Snowdog - REKT",1637798400000,"18.100 M","Unaudited","snowdog-rekt","https:\/\/rekt.news\/snowdog-rekt\/"],["bEarn - REKT",1621209600000,"18 M","Unaudited","bearn-rekt","https:\/\/rekt.news\/bearn-rekt\/"],["Indexed Finance - REKT",1634169600000,"16 M","Unaudited","indexed-finance-rekt","https:\/\/rekt.news\/indexed-finance-rekt\/"],["Team Finance - REKT",1666828800000,"15.800 M","Zokyo Security","teamfinance-rekt","https:\/\/rekt.news\/teamfinance-rekt\/"],["Inverse Finance - REKT",1648857600000,"15.600 M","Unaudited","inverse-finance-rekt","https:\/\/rekt.news\/inverse-finance-rekt\/"],["Eminence - Rekt in prod",1601251200000,"15 M","Unaudited","eminence-rekt-in-prod","https:\/\/rekt.news\/eminence-rekt-in-prod\/"],["Furucombo - REKT",1614384000000,"14 M","Unaudited","furucombo-rekt","https:\/\/rekt.news\/furucombo-rekt\/"],["Deus DAO - REKT 2",1651104000000,"13.400 M","Armor Labs","deus-dao-rekt-2","https:\/\/rekt.news\/deus-dao-rekt-2\/"],["Compounder Finance - REKT",1606867200000,"12 M","out of scope","deathbed-confessions-c3pr","https:\/\/rekt.news\/deathbed-confessions-c3pr\/"],["Agave DAO, Hundred Finance - REKT",1647302400000,"11.700 M","Unaudited","agave-hundred-rekt","https:\/\/rekt.news\/agave-hundred-rekt\/"],["Saddle Finance - REKT 2",1638403200000,"11 M","Unaudited","saddle-finance-rekt2","https:\/\/rekt.news\/saddle-finance-rekt2\/"],["Value DeFi - REKT 3",1620345600000,"11 M","Unaudited","value-rekt3","https:\/\/rekt.news\/value-rekt3\/"],["Yearn - REKT",1612483200000,"11 M","Unaudited","yearn-rekt","https:\/\/rekt.news\/yearn-rekt\/"],["Dego Finance - REKT",1644451200000,"10 M","Peckshield","dego-finance-rekt","https:\/\/rekt.news\/dego-finance-rekt\/"],["Arbix Finance - REKT",1641254400000,"10 M","Certik","arbix-rekt","https:\/\/rekt.news\/arbix-rekt\/"],["Rari Capital - REKT",1620432000000,"10 M","Quantstamp","rari-capital-rekt","https:\/\/rekt.news\/rari-capital-rekt\/"],["Value DeFi - REKT 2",1620172800000,"10 M","Unaudited","value-rekt2","https:\/\/rekt.news\/value-rekt2\/"],["Cover - REKT",1609200000000,"9.400 M","Arcadia Group","cover-rekt","https:\/\/rekt.news\/cover-rekt\/"],["Punk Protocol - REKT",1628553600000,"8.950 M","Unaudited","punkprotocol-rekt","https:\/\/rekt.news\/punkprotocol-rekt\/"],["Crema Finance - REKT",1656720000000,"8.800 M","Bramah Systems","crema-finance-rekt","https:\/\/rekt.news\/crema-finance-rekt\/"],["Superfluid - REKT",1644278400000,"8.700 M","Peckshield","superfluid-rekt","https:\/\/rekt.news\/superfluid-rekt\/"],["Platypus Finance - REKT",1676592000000,"8.500 M","Unaudited","platypus-finance-rekt","https:\/\/rekt.news\/platypus-finance-rekt\/"],["Moola Market - REKT",1666137600000,"8.400 M","N\/A","moola-markets-rekt","https:\/\/rekt.news\/moola-markets-rekt\/"],["Visor Finance - REKT",1640044800000,"8.200 M","Unaudited","visor-finance-rekt","https:\/\/rekt.news\/visor-finance-rekt\/"],["THORChain - REKT 2",1626912000000,"8 M","THORChain","thorchain-rekt2","https:\/\/rekt.news\/thorchain-rekt2\/"],["Hack Epidemic (Origin Protocol - REKT)",1605571200000,"8 M","Unaudited","hack-epidemic","https:\/\/rekt.news\/hack-epidemic\/"],["LCX - REKT",1641600000000,"7.940 M","Unaudited","lcx-rekt","https:\/\/rekt.news\/lcx-rekt\/"],["Anyswap - REKT",1625875200000,"7.900 M","Unaudited","anyswap-rekt","https:\/\/rekt.news\/anyswap-rekt\/"],["Warp Finance - REKT",1608249600000,"7.800 M","Hacken","warp-finance-rekt","https:\/\/rekt.news\/warp-finance-rekt\/"],["Meter - REKT",1644105600000,"7.700 M","Unaudited","meter-rekt","https:\/\/rekt.news\/meter-rekt\/"],["BurgerSwap - REKT",1622160000000,"7.200 M","Unaudited","burgerswap-rekt","https:\/\/rekt.news\/burgerswap-rekt\/"],["Value DeFi - REKT",1605312000000,"7 M","Unaudited","value-defi-rekt","https:\/\/rekt.news\/value-defi-rekt\/"],["Lodestar Finance - REKT",1670630400000,"6.500 M","Unaudited","lodestar-rekt","https:\/\/rekt.news\/lodestar-rekt\/"],["Alchemix - REKT",1623801600000,"6.500 M","Unaudited","alchemix-rekt","https:\/\/rekt.news\/alchemix-rekt\/"],["Belt - REKT",1622246400000,"6.300 M","Haechi","belt-rekt","https:\/\/rekt.news\/belt-rekt\/"],["Audius - REKT",1658534400000,"6 M","Kudelski, OpenZeppelin","audius-rekt","https:\/\/rekt.news\/audius-rekt\/"],["Bondly - REKT",1626307200000,"5.900 M","Unaudited","bondly-rekt","https:\/\/rekt.news\/bondly-rekt\/"],["Inverse Finance - REKT 2",1655337600000,"5.800 M","Unaudited","inverse-rekt2","https:\/\/rekt.news\/inverse-rekt2\/"],["Roll - REKT",1615680000000,"5.700 M","Unaudited","roll-rekt","https:\/\/rekt.news\/roll-rekt\/"],["An Un-SOL-ved Mystery",1659398400000,"5.300 M","N\/A","unsolved-mystery","https:\/\/rekt.news\/unsolved-mystery\/"],["THORChain - REKT",1626307200000,"5 M","Unaudited","thorchain-rekt","https:\/\/rekt.news\/thorchain-rekt\/"],["X-Token - REKT X2",1630195200000,"4.500 M","Unaudited","xtoken-rekt-x2","https:\/\/rekt.news\/xtoken-rekt-x2\/"],["Eleven Finance - REKT",1624320000000,"4.500 M","Unaudited","11-rekt","https:\/\/rekt.news\/11-rekt\/"],["Raydium - REKT",1671148800000,"4.400 M","N\/A","raydium-rekt","https:\/\/rekt.news\/raydium-rekt\/"],["ChainSwap - REKT",1625961600000,"4.400 M","Unaudited","chainswap-rekt","https:\/\/rekt.news\/chainswap-rekt\/"],["Voltage Finance - REKT",1648684800000,"4 M","Unaudited","voltage-finance-rekt","https:\/\/rekt.news\/voltage-finance-rekt\/"],["DAO Maker - REKT",1630713600000,"4 M","TBC","daomaker-rekt","https:\/\/rekt.news\/daomaker-rekt\/"],["dForce Network - REKT",1675900800000,"3.650 M","Out of scope","dforce-network-rekt","https:\/\/rekt.news\/dforce-network-rekt\/"],["Nirvana Finance - REKT",1658966400000,"3.500 M","Sec3 Auto Audit Software","nirvana-rekt","https:\/\/rekt.news\/nirvana-rekt\/"],["Skyward Finance - REKT",1667347200000,"3.200 M","Unaudited","skyward-rekt","https:\/\/rekt.news\/skyward-rekt\/"],["JayPegs Automart - REKT",1631836800000,"3.100 M","Unaudited","jaypegs-automart-rekt","https:\/\/rekt.news\/jaypegs-automart-rekt\/"],["Orion Protocol - REKT",1675296000000,"3 M","Unaudited","orion-protocol-rekt","https:\/\/rekt.news\/orion-protocol-rekt\/"],["Fortress Protocol - REKT",1651968000000,"3 M","Hash0x, EtherAuthority","fortress-rekt","https:\/\/rekt.news\/fortress-rekt\/"],["Deus DAO - REKT",1615766400000,"3 M","Unaudited","deus-dao-rekt","https:\/\/rekt.news\/deus-dao-rekt\/"],["PancakeBunny - REKT 2",1626393600000,"2.400 M","Unaudited","pancakebunny2-rekt","https:\/\/rekt.news\/pancakebunny2-rekt\/"],["TempleDAO - REKT",1665446400000,"2.300 M","Unaudited","templedao-rekt","https:\/\/rekt.news\/templedao-rekt\/"],["Gym Network - REKT",1654646400000,"2.100 M","Out of scope","gymnet-rekt","https:\/\/rekt.news\/gymnet-rekt\/"],["Revest Finance - REKT",1648339200000,"2.010 M","Solidity Finance","revest-finance-rekt","https:\/\/rekt.news\/revest-finance-rekt\/"],["Dexible - REKT",1676592000000,"2 M","Unaudited","dexible-rekt","https:\/\/rekt.news\/dexible-rekt\/"],["MM Finance - REKT",1651622400000,"2 M","Unaudited","madmeerkat-finance-rekt","https:\/\/rekt.news\/madmeerkat-finance-rekt\/"],["DODO - REKT",1615248000000,"2 M","Unaudited","au-dodo-rekt","https:\/\/rekt.news\/au-dodo-rekt\/"],["Akropolis - REKT",1605139200000,"2 M","CertiK, SmartDec","akropolis-rekt","https:\/\/rekt.news\/akropolis-rekt\/"],["Hope Finance - REKT",1676851200000,"1.860 M","AuditRateTech, Cognitos","hope-finance-rekt","https:\/\/rekt.news\/hope-finance-rekt\/"],["Bent Finance - REKT",1640044800000,"1.750 M","Unaudited","bent-finance","https:\/\/rekt.news\/bent-finance\/"],["8ight Finance - REKT",1638921600000,"1.750 M","Unaudited","8ight-finance-rekt","https:\/\/rekt.news\/8ight-finance-rekt\/"],["Acala Network - REKT",1628812800000,"1.600 M","Out of scope","acala-network-rekt","https:\/\/rekt.news\/acala-network-rekt\/"],["Levyathan - REKT",1627603200000,"1.500 M","Unaudited","levyathan-rekt","https:\/\/rekt.news\/levyathan-rekt\/"],["Treasure DAO - REKT",1646265600000,"1.400 M","Unaudited","treasure-dao-rekt","https:\/\/rekt.news\/treasure-dao-rekt\/"],["The Big Combo (Growth DeFi - REKT)",1612828800000,"1.300 M","Consensys Diligence","the-big-combo","https:\/\/rekt.news\/the-big-combo\/"],["Sovryn - REKT",1664841600000,"1.111 M","Unaudited","sovryn-rekt","https:\/\/rekt.news\/sovryn-rekt\/"],["Autoshark - REKT",1621814400000,"745 K","Techrate","autoshark-rekt","https:\/\/rekt.news\/autoshark-rekt\/"],["Merlin Labs - REKT",1621987200000,"680 K","Hacken","merlinlabs-rekt","https:\/\/rekt.news\/merlinlabs-rekt\/"],["Midas Capital - REKT",1673740800000,"660 K","Out of scope","midas-capital-rekt","https:\/\/rekt.news\/midas-capital-rekt\/"],["Curve Finance - REKT",1660003200000,"575 K","N\/A","curve-finance-rekt","https:\/\/rekt.news\/curve-finance-rekt\/"],["Merlin Labs - REKT 2",1621987200000,"550 K","Unaudited","merlin2-rekt","https:\/\/rekt.news\/merlin2-rekt\/"],["Merlin Labs - R3KT",1624924800000,"330 K","Unaudited","merlin3-rekt","https:\/\/rekt.news\/merlin3-rekt\/"],["Saddle Finance - REKT",1611100800000,"275.735 K","Openzeppelin, Certik, Quantstamp","saddle-finance-rekt","https:\/\/rekt.news\/saddle-finance-rekt\/"],["SafeDollar - REKT",1624838400000,"248 K","Unaudited","safedollar-rekt","https:\/\/rekt.news\/safedollar-rekt\/"]]}`;
+
+export const cryptoData = `{"columns":["Symbol","Name","Volume [$]","Market Cap","Market Cap Rank","7D Change [%]","24H Change [%]"],"index":[499,498,497,496,495,494,493,492,491,490,489,479,488,487,486,483,485,481,480,482,484,478,477,476,475,474,473,472,471,469,470,468,467,460,465,466,464,462,461,463,459,457,458,456,453,455,454,451,452,450,449,448,447,446,445,444,443,442,441,440,438,439,437,436,435,434,433,431,432,430,429,428,427,425,426,424,423,422,420,421,419,418,411,417,415,414,413,416,412,410,409,408,407,406,405,402,404,403,401,400,399,398,397,396,395,394,393,392,391,390,389,388,387,386,385,384,383,382,381,380,379,378,377,376,375,374,372,373,371,370,369,368,367,366,365,364,361,363,362,360,358,359,356,355,353,354,352,351,350,357,349,348,347,346,344,343,345,342,341,339,340,338,337,331,333,336,335,334,332,330,329,328,327,326,325,324,323,322,321,320,318,317,319,316,315,314,313,312,311,309,310,308,307,305,306,304,303,302,301,300,299,298,297,296,295,294,293,288,290,291,289,292,286,287,285,284,282,283,281,280,279,278,277,276,275,274,273,272,271,269,268,267,270,266,265,262,264,263,261,260,259,258,256,257,255,253,254,252,251,250,249,248,246,247,245,244,243,242,241,240,239,238,237,236,235,234,233,231,232,230,229,227,228,226,225,224,223,222,221,220,219,218,217,216,215,214,213,212,210,209,211,208,207,206,205,203,204,202,201,200,199,198,196,197,195,194,193,192,190,191,189,187,188,185,186,184,183,181,182,180,179,178,177,176,175,174,173,172,171,170,168,169,167,164,165,166,163,162,161,160,159,157,158,156,155,154,153,152,151,149,150,148,147,146,145,144,143,142,141,140,139,138,137,136,135,134,133,132,131,130,129,128,127,126,125,124,123,122,121,120,119,118,117,116,115,113,114,112,111,110,109,108,107,106,105,104,103,102,100,101,99,98,97,96,95,94,93,92,91,90,89,88,87,86,85,84,83,82,81,80,79,78,77,76,75,74,73,72,71,70,69,68,67,66,65,64,63,62,61,60,59,58,57,56,55,54,53,52,51,50,49,48,47,46,45,44,43,42,41,40,39,38,37,36,35,34,33,32,31,30,29,28,27,26,25,24,23,22,21,20,19,18,17,16,15,14,13,12,11,10,9,8,7,6,5,4,3,2,1,0],"data":[["cre","Carry","5.2M","43.5M",501,-0.8424177903,-3.3009760144],["idex","IDEX","11M","43.7M",499,-5.4559449493,1.240113672],["quick","Quickswap [OLD]","5M","43.7M",498,-8.018028025,-2.795598108],["sweat","Sweatcoin (Sweat Economy)","839.8K","44.2M",497,-8.9515803275,-3.3416593468],["itamcube","CUBE","0","44.2M",496,23.7121984017,-0.1741412228],["etn","Electroneum","32.6K","44.3M",495,-10.8816668796,-4.1242543652],["ntx","NuNet","816.4K","44.6M",494,11.7859963823,-2.6336202899],["ufo","UFO Gaming","538.5K","44.9M",493,-5.9910130879,-2.7268239207],["xeta","XANA","392.3K","45.1M",492,-5.470390737,-5.7061485822],["mimatic","MAI","3.4M","45.2M",491,0.561481042,-0.1047229837],["lyra","Lyra Finance","984.5K","45.4M",490,-12.098217852,-5.8781732257],["idia","Impossible Finance Launchpad","77.4K","47.1M",489,-5.6403961751,9.2680943991],["bel","Bella Protocol","4.4M","45.6M",488,-14.8272842675,-3.550469001],["gmm","Gamium","17.1M","45.8M",487,281.2045758462,-6.0630196265],["lit","Litentry","6M","46.5M",486,-20.1750587117,-2.8750285947],["qlc","Kepple","1.2M","46.9M",485,-13.2426814764,-3.203515984],["alpaca","Alpaca Finance","1.8M","46.8M",484,-2.6642850668,-3.8981973354],["gas","Gas","10.7M","46.9M",483,-10.7939432682,-4.4469502033],["lina","Linear","10.6M","46.9M",482,-25.2051467069,-4.2609613292],["hifi","Hifi Finance","2.3M","46.9M",481,-3.3213558533,-1.6961048241],["vela","Vela Token","4.6M","46.8M",480,14.6907207382,7.5525624641],["loka","League of Kingdoms","4.4M","47.2M",479,-11.8983136717,-3.3555343776],["zcx","Unizen","633.6K","47.7M",478,3.2866895817,-1.4944833082],["mtrg","Meter Governance","346.5K","47.7M",477,-11.2848954835,-1.2060121189],["hi","hi Dollar","357.7K","47.7M",476,-21.8808896788,-6.5238729873],["caps","Ternoa","8.1M","47.9M",475,114.9571800081,-14.2310073566],["gmt","GMT Token","590.2K","47.9M",474,-3.0395263527,0.250389437],["iris","IRISnet","3.6M","48M",473,-3.3391741155,-7.6035909619],["loomold","Loom Network (OLD)","566.7K","48.4M",472,4.7898911205,-4.806436383],["plex","PLEX","5M","49.1M",471,-4.2367190298,-2.1357240063],["flm","Flamingo Finance","3.3M","48.7M",470,-12.9658236867,-2.0144674043],["sudo","sudoswap","3.6M","49.3M",469,-14.642097903,-6.2324277726],["ixt","IX","187.7K","49.4M",468,-5.952468485,1.2334527228],["velo","Velo","17.8M","50.5M",467,-3.477476871,-4.4343221367],["aurabal","Aura BAL","291.4K","49.9M",466,0.9712931494,-1.0740149829],["rev","Revain","224.3K","49.9M",465,-9.6627376753,-2.0936231012],["susd","sUSD","3.8M","49.9M",464,0.3068911644,0.0517358683],["koda","Koda Cryptocurrency","32","50.2M",463,-8.6012830143,-1.6067844178],["xcad","XCAD Network","2.1M","50.4M",462,5.0351403751,-2.433718922],["col","Clash of Lilliput","68.4K","50.2M",461,0.712888138,-6.0900409194],["lto","LTO Network","3.7M","50.8M",460,-0.2625277363,-3.1523877648],["bwo","Battle World","1.4M","51.1M",459,-7.5110878435,-0.3815669074],["pols","Polkastarter","2.8M","51.1M",458,-4.6707088648,-2.5983303032],["yfii","DFI.money","12.6M","51.5M",457,-6.2411449932,-2.2644301876],["gbex","Globiance Exchange","497.4K","51.9M",456,23.5489218223,-3.8786859983],["premia","Premia","92.3K","51.7M",455,-3.3094611056,-0.3951670703],["tt","ThunderCore","2.7M","51.8M",454,-2.4988319304,-3.6278672634],["qom","Shiba Predator","1.4M","51.9M",453,6.0580233256,-5.5203626939],["wan","Wanchain","1.5M","51.9M",452,-17.9390140694,-5.2160169051],["dfg","Defigram","183.1K","52M",451,4.2849672947,-4.2515441247],["krd","Krypton DAO","9.1K","52.4M",450,-0.5233374441,-0.8246037126],["vlx","Velas","1.2M","52.6M",449,-9.2201268038,-2.4680317184],["dero","Dero","68.5K","53.2M",448,-4.1921014737,-1.1908408884],["btrfly","Redacted","398.8K","53.3M",447,-21.1535383293,-8.1311035212],["utk","Utrust","2.7M","53.5M",446,-5.6640237127,-2.8576094798],["wnxm","Wrapped NXM","1.1M","53.7M",445,-1.5146393466,-3.4230125953],["phb","Phoenix Global","6.7M","53.9M",444,-5.0240324673,-4.8333634578],["mbx","Marblex","1.2M","54M",443,-9.8490115963,-4.4545138389],["bor","BoringDAO [OLD]","633","54.2M",442,-20.6372891922,-3.4626125015],["sx","SX Network","465.4K","54.3M",441,2.3916811879,-0.1802506011],["xvg","Verge","629.2K","54.8M",440,-3.1052190783,-1.3351224955],["kilt","KILT Protocol","57.3K","54.6M",439,-10.5717005268,-5.0847675384],["ctxc","Cortex","8.1M","54.8M",438,-6.9997713923,-4.975057969],["sfund","Seedify.fund","3.2M","55M",437,5.9760325599,0.2660415409],["flexusd","flexUSD","14","55.4M",436,-5.017416515,-1.5072206138],["efi","Efinity","966.6K","55.7M",435,-5.8491258285,-1.8475872653],["maticx","Stader MaticX","171.6K","55.8M",434,-12.6901332046,-1.783012869],["polis","Star Atlas DAO","341.4K","56.2M",433,-9.8476221987,-0.3247639535],["strk","Strike","4.2M","56.2M",432,-0.6103928247,-2.7437400849],["gods","Gods Unchained","1.4M","56.2M",431,-6.3937944891,-2.9193641455],["tlos","Telos","1.7M","56.4M",430,-21.6772616736,-4.6421583264],["temple","TempleDAO","10K","56.6M",429,null,null],["quack","Rich Quack","1M","56.6M",428,-7.7311778603,0.2961857888],["super","SuperVerse","3M","57.2M",427,-11.8907351187,-5.0374214992],["ray","Raydium","29M","56.9M",426,8.1037316483,7.450675177],["ygg","Yield Guild Games","9.9M","57.3M",425,-8.6388903349,-3.52993184],["meta","Metadium","4.8M","57.8M",424,-2.9645855065,-3.8270751689],["ssx","SOMESING Exchange","5.9M","58.7M",423,-4.00359604,-4.5136519849],["sdao","SingularityDAO","8.1M","58.9M",422,14.4469673238,-1.4839340699],["dxp","Vela Exchange","45.6K","58.7M",421,22.6756731164,12.9372107736],["rdpx","Dopex Rebate","1.4M","59M",420,4.8975147923,0.3256111877],["aergo","Aergo","5.8M","59.6M",419,-2.5537799145,-3.1869987871],["oxt","Orchid Protocol","16.4M","60.5M",418,0.1564469851,6.8243386098],["phb","Phoenix Global [OLD]","4","59.8M",417,-15.2172426841,0.1589768051],["xsgd","XSGD","3.9M","59.8M",416,-0.9458620052,-0.4809389517],["kishu","Kishu Inu","1.8M","59.8M",415,-16.0155741862,-4.6835869167],["hxro","Hxro","22.9K","60.1M",414,2.9270278183,5.8904950438],["leash","Doge Killer","2.2M","59.8M",413,-16.4243590024,-4.2781255137],["mxc","MXC","2.1M","60.3M",412,-9.7314124523,-4.5595371304],["ousd","Origin Dollar","393.7K","60.5M",411,-0.081750727,-0.1103856021],["mbl","MovieBloc","11.7M","60.5M",410,1.3629712628,-1.1286551398],["kuji","Kujira","87K","60.7M",409,-3.5858956554,-1.618052907],["storj","Storj","11.9M","61.1M",408,-6.9622887815,-4.3359526622],["bmex","BitMEX","105K","61.2M",407,-10.7120715581,-0.8043483247],["ata","Automata","2.8M","61.3M",406,-14.5319379664,-3.2378784238],["uos","Ultra","2.3M","62.8M",405,-1.2778233401,-1.0866222801],["sun","Sun Token","9.7M","61.9M",404,-2.6903340486,-2.4201318172],["dawn","Dawn Protocol","37.9M","62.7M",403,-1.0866707222,-1.3082229643],["btm","Bytom","2.2M","63M",402,41.5050515552,3.2585390885],["grv","GroveCoin","7.5M","63.1M",401,-4.0910579701,0.5391020662],["movr","Moonriver","2.5M","63.6M",400,-8.8620002387,-3.1174317681],["key","SelfKey","17.6M","64.5M",399,4.2258257741,-9.6663679437],["rdnt","Radiant Capital","9.9M","64.8M",398,7.8287388407,-1.8733011197],["xsushi","xSUSHI","11.2K","65M",397,-5.1155155549,-3.2834653257],["ark","ARK","3.3M","65.3M",396,-2.1841714032,-3.7653230195],["exrd","e-Radix","47.6K","66M",395,-10.3199820301,-3.4214980082],["vega","Vega Protocol","506.7K","66M",394,20.1105566779,-1.8910817204],["xyo","XYO Network","1.4M","66.4M",393,-14.4174577711,-4.4958424274],["mnw","Morpheus Network","531.8K","67.2M",392,-4.3626215619,-5.14048466],["gft","Gifto","14.6M","67.4M",391,-15.7612649079,-4.4930183613],["bitci","Bitcicoin","1.3M","67.7M",390,20.6021652373,-2.4870050185],["ogn","Origin 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Framework","20.8M","175.2M",213,23.5771103942,-1.9013330091],["sxp","SXP","13.5M","177.2M",212,-5.5607618033,-3.6459127176],["frxeth","Frax Ether","1.9M","178.7M",211,0.1109305689,-0.5846687524],["flex","FLEX Coin","71.6K","176.9M",210,28.8170279099,-6.7277500707],["dgb","DigiByte","3M","179.6M",209,-8.7770628128,-1.9638466309],["core","Core","47.8M","184M",208,-2.9971978218,-3.9560027655],["poly","Polymath","1.4M","185.3M",207,0.0525542592,1.5420168086],["tel","Telcoin","1.5M","189.7M",206,8.1651522265,-1.5580121111],["sfp","SafePal","4.1M","190.6M",205,-8.6118610593,-1.3279478667],["lpt","Livepeer","10.7M","190.5M",204,-16.7367877815,-3.3365108728],["ilv","Illuvium","28.5M","194.4M",203,8.2852771978,-0.6379917523],["ewt","Energy Web","3.6M","196.2M",202,-5.5122909562,-7.5070816738],["bora","BORA","12.4M","196.6M",201,-0.4794441083,-4.5079169253],["skl","SKALE","16.3M","197.7M",200,-13.5307156504,-3.6081309621],["cel","Celsius Network","8.7M","198.1M",199,-0.8889850573,-5.4449459404],["waxp","WAX","16.8M","200M",198,-0.2372179925,-5.2820182319],["multi","Multichain","1.5M","199.8M",197,-4.0390539385,-1.9178108328],["btc.b","Bitcoin Avalanche Bridged (BTC.b)","3.7M","200.9M",196,-2.780152915,-0.4837483403],["chsb","SwissBorg","303.9K","204.5M",195,-3.5689392385,-1.5631440262],["brise","Bitgert","2.2M","208.6M",194,10.5059112295,-4.2325970816],["hbtc","Huobi BTC","58.6K","210.3M",193,-3.5064158029,0.2323105966],["flux","Flux","7.8M","217.7M",192,-5.9514218919,-4.2265992349],["ont","Ontology","14.9M","217.6M",191,-10.9022034984,-5.6452382559],["eurt","Euro Tether","1.2M","218.2M",190,0.0411970314,-0.773997133],["ali","Artificial Liquid Intelligence","3.8M","218.5M",189,4.3675043181,-15.3844566953],["xcn","Onyxcoin","8.7M","218.3M",188,-19.6020360615,-0.8509198799],["sc","Siacoin","11.2M","221.1M",187,-3.6238197639,-5.5144279806],["bico","Biconomy","6.5M","220.6M",186,6.6556305693,-5.9173592756],["gns","Gains Network","14.1M","222.5M",185,-14.3350125427,-8.6756704408],["syn","Synapse","20M","225.9M",184,-25.223861428,-8.4938940557],["elon","Dogelon Mars","3.7M","227.6M",183,-5.245280282,-0.8941374684],["icx","ICON","14.4M","226.8M",182,1.5502590295,-3.9053438087],["iost","IOST","92.1M","228.5M",181,0.3382766536,4.3584663033],["elg","Escoin","154.4K","228.8M",180,-4.2153110709,-1.107465184],["rsr","Reserve Rights","12M","231.1M",179,-1.6380405796,-3.7016202681],["lusd","Liquity USD","3.3M","231.2M",178,0.1106644203,-0.0342556818],["omg","OMG Network","18M","234.5M",177,-9.4911110878,-2.6341661027],["zrx","0x","69M","235.2M",176,5.8759808667,-10.5372908715],["dao","DAO Maker","4.9M","236.3M",175,12.5478340043,7.4637351668],["cvxcrv","Convex CRV","848.6K","236.8M",174,-15.3199711914,-5.1108934684],["band","Band Protocol","10.4M","243.8M",173,-6.2780750349,-3.7653582323],["kas","Kaspa","4.5M","244M",172,103.7702462192,11.5778969446],["kda","Kadena","8.2M","245.8M",171,-12.0354426563,-0.5091760814],["glmr","Moonbeam","14.9M","251.8M",170,-13.8227187103,0.1551713313],["sushi","Sushi","47.8M","251.7M",169,-6.3765661373,-3.0803466934],["gmt","STEPN","244.6M","254.9M",168,-2.4465873575,5.635719645],["mask","Mask Network","58.1M","257M",167,-1.7522889457,-8.3713391572],["waves","Waves","75.3M","257M",166,-8.8171452922,0.5371417701],["iotx","IoTeX","14.7M","256.9M",165,-10.9145862649,-2.4616183677],["jst","JUST","17M","258.2M",164,-4.8302763358,-2.8141717988],["xch","Chia","6.2M","260.9M",163,-7.8650627865,1.0254258105],["ustc","TerraClassicUSD","19.3M","264.4M",162,0.0983795845,-0.1035689438],["one","Harmony","16.6M","264.8M",161,-12.4805135839,-2.5544740859],["gfarm2","Gains Farm","8K","267M",160,-11.4896166336,-4.5301093798],["glm","Golem","13.3M","268.6M",159,-2.8680685308,-1.4644673035],["omi","ECOMI","905K","268.4M",158,-14.8709203339,-2.5529243555],["bal","Balancer","9.4M","276.5M",157,-2.3779569601,-3.1730285389],["safemoon","SafeMoon [OLD]","0","277.8M",156,-3.9373790541,-0.8435813084],["gno","Gnosis","2.3M","279.4M",155,0.6636997342,-0.6913523475],["jasmy","JasmyCoin","44.4M","280.4M",154,-12.0293537769,-1.9214666867],["ln","LINK","798.5K","283.8M",153,-19.2495791165,-2.6954842494],["ohm","Olympus","660.9K","284.8M",152,-1.7920359034,-0.2999481889],["ssv","SSV Network","43.7M","293.4M",151,13.5133989341,-3.0754709714],["inj","Injective","23.1M","291.4M",150,-2.1860486625,-0.0115571163],["ecoin","Ecoin","88","295.1M",149,62.6060484568,27.7749639178],["astrafer","Astrafer","52K","296.3M",148,-3.1605878885,0.1975087547],["cdt","Blox","104.4K","296.8M",147,52.1414225522,2.3559701935],["audio","Audius","19.1M","300.3M",146,-3.7332121599,-5.4654779796],["gala","GALA","102.1M","301.9M",145,-11.3809446202,-2.7575408813],["btg","Bitcoin Gold","14.6M","302.2M",144,-7.5371547558,0.3549950056],["amp","Amp","14.2M","306.1M",143,-11.8184627892,0.4802793357],["ankr","Ankr Network","59.6M","307.4M",142,-23.0470712896,-4.7565883449],["nu","NuCypher","450.7M","315.4M",141,59.6063378427,58.3692821424],["astr","Astar","12.2M","316.9M",140,-14.7617260532,-5.2861351512],["azero","Aleph Zero","1.9M","318.4M",139,-10.9001881893,-2.6246984758],["nxm","Nexus Mutual","6.3K","319.8M",138,0.5599532324,-0.8915661385],["blur","Blur","196.5M","320.8M",137,-23.8845459588,-6.9860533963],["ksm","Kusama","18.6M","326M",136,-12.0088253029,-3.4365304424],["rose","Oasis Network","25.7M","327.4M",135,-10.4861334138,-1.2608741126],["magic","Magic","43.6M","330.3M",134,-14.0664929896,-3.2718151254],["qtum","Qtum","27.3M","339.9M",133,-11.4626207077,-3.6625492537],["comp","Compound","21.1M","341M",132,-8.2903316267,-2.923983633],["yfi","yearn.finance","178.7M","345.1M",131,23.7967552956,1.360271028],["dcr","Decred","1M","347.2M",130,-6.2297305733,-1.218962729],["woo","WOO Network","17.2M","348.9M",129,-8.3244191294,-3.3184512853],["rvn","Ravencoin","14.1M","351.9M",128,-12.4325583166,-4.4748070299],["tfuel","Theta Fuel","18.4M","353.1M",127,0.8227132266,-3.5188965462],["hot","Holo","26.5M","360.8M",126,-8.8606644663,-2.7350163861],["reth","Rocket Pool ETH","2.9M","363.5M",125,-0.3632175168,-0.500094323],["dfi","DeFiChain","2.7M","366M",124,-7.984840013,-0.4182501507],["celo","Celo","17.1M","368.8M",123,-9.1103441071,-3.2243758666],["hnt","Helium","1.7M","369.1M",122,-10.5602955076,-2.7056093255],["kava","Kava","16.3M","370.5M",121,-4.6488263294,-2.9646588535],["nexo","NEXO","7.5M","372.7M",120,-10.477846452,-5.5423760234],["babydoge","Baby Doge Coin","7.8M","378.7M",119,-19.3587168261,-6.5908896315],["bone","Bone ShibaSwap","11.8M","383.4M",118,-11.9436296932,-2.5856515061],["luna","Terra","52.5M","390.9M",117,-3.3446501229,-0.2392622682],["ethw","EthereumPoW","14.8M","396.8M",116,-6.7657373039,-1.7250977063],["xdc","XDC Network","3.9M","403.5M",115,7.5174828157,5.2423226855],["ens","Ethereum Name Service","36.9M","400.6M",114,-4.1711514433,-2.4217411794],["flr","Flare","8.6M","418.6M",113,-11.3722168262,-2.6341376467],["btse","BTSE Token","169.2K","422.3M",112,10.5217623246,-1.8683297771],["xem","NEM","28.2M","428.8M",111,10.1831386101,-5.805122748],["bat","Basic Attention","42.1M","435.7M",110,-11.3353847729,-2.6832862782],["floki","FLOKI","40.6M","437.4M",109,-15.4152639923,-4.2912356217],["xrd","Radix","601.1K","442M",108,-7.2882830682,-1.6659353531],["lrc","Loopring","42.7M","443.1M",107,-13.5604604329,-1.6197835718],["rndr","Render","54.2M","451.5M",106,-6.6000230372,-0.0206253681],["xaut","Tether Gold","608.6K","453.7M",105,0.2057186927,0.1715074295],["cvx","Convex Finance","8.3M","457.3M",104,-1.7806637367,-4.2307909908],["dydx","dYdX","216.1M","458.3M",103,13.2595741618,-6.5451300442],["ceth","cETH","5.3K","459.4M",102,1.6089817405,-0.9047237704],["paxg","PAX Gold","7.6M","459.1M",101,0.7312569459,-0.3772773898],["enj","Enjin Coin","22.8M","463.2M",100,-4.6851628043,-3.0851646202],["cspr","Casper Network","20M","466.9M",99,9.1386627353,6.8997775386],["1inch","1inch","24.5M","468.5M",98,-12.2324998515,-3.6776469923],["cfx","Conflux","200.2M","468.8M",97,-20.4007523337,-7.0553733522],["ar","Arweave","45.4M","484.1M",96,-13.6954179518,-3.3568500981],["fet","Fetch.ai","123.3M","484.2M",95,5.6965458715,-4.8805547543],["rune","THORChain","60.6M","491.7M",94,-4.4516081218,-1.8069985219],["osmo","Osmosis","12.8M","492.2M",93,-9.8598463596,-3.8072597329],["zil","Zilliqa","34.6M","498M",92,-13.0123057366,-3.9744531705],["tkx","Tokenize Xchange","6.4M","508.3M",91,-0.4521480556,-1.1005438988],["zec","Zcash","33.8M","524.3M",90,-9.29966044,-1.0185326678],["wemix","WEMIX","7.3M","525.2M",89,-16.2874812088,-5.3343454529],["edgt","Edgecoin","49.9M","525.3M",88,0.2763449575,-0.1668267112],["okt","OKC","10.1M","531M",87,-3.426809894,-4.8656753375],["twt","Trust Wallet","21.4M","538.2M",86,-9.9338328996,-3.3291919632],["cdai","cDAI","254","544.1M",85,-0.1370074086,-0.0564508695],["wbt","WhiteBIT Token","1.1M","558M",84,-0.5297448072,-0.1230729428],["gusd","Gemini Dollar","987K","561.3M",83,-0.049033567,-0.1777205048],["op","Optimism","259.9M","582.9M",82,0.5213764185,-0.8014935808],["cusdc","cUSDC","2","599.8M",81,-0.0959118713,-0.039731295],["bgb","Bitget Token","15.1M","602.1M",80,10.7787724352,0.1932974056],["gmx","GMX","47.2M","615.2M",79,-3.029160538,-4.9885349404],["agix","SingularityNET","175.8M","632.1M",78,31.7303750079,-3.8036511638],["xec","eCash","6.7M","662.5M",77,-8.6215713126,-1.7806497236],["miota","IOTA","15.8M","663M",76,-6.5404130391,-4.7325435787],["btt","BitTorrent","13M","693.1M",75,-0.0065756281,-1.1318488357],["bit","BitDAO","6.9M","708M",74,-4.1047294415,-1.9148227017],["chz","Chiliz","79.8M","717.1M",73,-3.4648391686,-1.9004874555],["usdd","USDD","36.3M","722.3M",72,-0.0750251361,-0.0784111916],["gt","Gate","744K","729.1M",71,1.9289909356,-2.5935389539],["cake","PancakeSwap","21.7M","737.5M",70,-4.8260097473,-0.9363889594],["crv","Curve DAO","63.8M","744.5M",69,-10.7259716471,-3.5718991542],["usdp","Pax Dollar","531.1K","774.9M",68,0.4596523316,-0.2752969024],["klay","Klaytn","106.1M","785.7M",67,-16.5439538852,-7.7490600601],["mkr","Maker","66.3M","795.5M",66,16.3210528117,-5.985258982],["bsv","Bitcoin SV","38.1M","806.7M",65,-4.6817741382,0.1556958186],["dash","Dash","108.5M","807.4M",64,-1.6479257911,-1.7221316177],["fxs","Frax Share","54.9M","829.1M",63,6.3040552286,-7.8386386041],["mina","Mina Protocol","96.1M","831.6M",62,-1.3039405032,0.3767275484],["imx","ImmutableX","31.9M","839.3M",61,-2.9386278786,-6.541131374],["ht","Huobi","11.9M","840.4M",60,-3.9150142129,-1.0396398312],["rpl","Rocket Pool","10.5M","845.3M",59,-11.0031946514,-5.5638969691],["kcs","KuCoin","1.3M","862.6M",58,1.2359602196,0.4403053069],["neo","NEO","61.1M","876M",57,-8.5528705703,-2.3530457387],["snx","Synthetix Network","90.3M","909.5M",56,15.7352387909,-4.4799521775],["lunc","Terra Luna Classic","149.2M","996.8M",55,2.9956746548,2.2559161703],["frax","Frax","13.7M","1B",54,0.1083604397,-0.1589488194],["xtz","Tezos","30.8M","1.1B",53,-18.4815577865,-1.7242562195],["axs","Axie Infinity","46.7M","1.1B",52,-8.162939169,-2.6031743386],["aave","Aave","56.1M","1.1B",51,-6.0794201139,-3.2103576814],["egld","MultiversX","46.1M","1.2B",50,-6.4668590185,-4.3832685028],["tusd","TrueUSD","47.2M","1.2B",49,0.0462047673,-0.1769450643],["mana","Decentraland","71.9M","1.2B",48,-8.6684443646,-2.0066674275],["theta","Theta Network","24.2M","1.2B",47,-1.4133365333,-1.980935975],["flow","Flow","23M","1.2B",46,-8.3803049619,-3.872499026],["ftm","Fantom","212.1M","1.2B",45,-12.4653613699,-4.5373516452],["sand","The Sandbox","127.8M","1.2B",44,-9.8711399666,-1.5519980279],["stx","Stacks","246.3M","1.3B",43,26.5044052349,-5.3819983122],["eos","EOS","193M","1.4B",42,2.6326803264,3.1137261133],["grt","The Graph","71.7M","1.4B",41,-5.7119746801,-3.6316269373],["icp","Internet Computer","46M","1.7B",40,-11.9409433472,-3.3273753139],["algo","Algorand","56.7M","1.7B",39,-10.6849660757,-3.2550140642],["ape","ApeCoin","105.1M","1.8B",38,-9.4447956436,-1.5702201862],["qnt","Quant","19M","1.8B",37,-7.4392504723,-0.6558199668],["hbar","Hedera","42.1M","1.9B",36,-17.2144810363,-2.975174227],["near","NEAR Protocol","89.4M","1.9B",35,-10.4694331963,-2.6571305507],["cro","Cronos","21.3M","1.9B",34,-8.3458211347,-2.9114430229],["vet","VeChain","69.8M","2B",33,-8.6132829352,-4.0529853862],["apt","Aptos","330.2M","2.2B",32,-7.6839261394,-2.8988748444],["xlm","Stellar","50.9M","2.3B",31,-5.2409253358,-0.2119946396],["bch","Bitcoin Cash","457.8M","2.5B",30,-7.3065446317,-1.8064940376],["ldo","Lido DAO","168.6M","2.6B",29,3.6191517731,-0.2769401965],["fil","Filecoin","344.9M","2.7B",28,-14.7825129502,-4.855004928],["xmr","Monero","94.5M","2.8B",27,-3.8204951204,-0.3901958735],["etc","Ethereum Classic","120.1M","2.9B",26,-6.4045575132,-0.8052142074],["leo","LEO Token","276.5K","3.1B",25,-1.3482603128,-0.3620006703],["link","Chainlink","283.4M","3.6B",24,-6.0468220884,-3.2946660036],["ton","Toncoin","30.5M","3.6B",23,-1.659421446,-3.7301247133],["wbtc","Wrapped Bitcoin","161.1M","3.6B",22,-2.9022313213,-0.403233771],["atom","Cosmos Hub","110M","3.6B",21,-8.5445030077,-2.0526394974],["uni","Uniswap","66.8M","5B",20,-4.4619549027,-2.87056613],["dai","Dai","195.7M","5B",19,0.0857478297,0.0837932365],["avax","Avalanche","151.1M","5.6B",18,-14.2256787613,-1.294928427],["trx","TRON","260M","6.4B",17,0.7375527667,0.075379056],["ltc","Litecoin","477.5M","6.9B",16,0.170831318,-2.3645069682],["shib","Shiba Inu","218.7M","7.1B",15,-7.7576763537,-1.0314842763],["dot","Polkadot","237.7M","7.6B",14,-12.2172781032,-2.8497593086],["sol","Solana","329.3M","8.4B",13,-8.7090333176,-1.4022402134],["steth","Lido Staked Ether","20.9M","9.4B",12,0.2428514566,-0.5583503266],["busd","Binance USD","6.4B","9.7B",11,-0.0410055134,0.1065032089],["matic","Polygon","472.8M","11B",10,-12.7810020966,-1.6290017724],["doge","Dogecoin","285.4M","11.1B",9,-5.7004583609,-1.7422482843],["ada","Cardano","244.4M","12.3B",8,-9.8597080601,-2.6763866835],["okb","OKB","52M","12.4B",7,-3.5306618155,-1.1849951354],["xrp","XRP","918.6M","19.3B",6,-4.1737202076,-1.1968855613],["usdc","USD Coin","3.4B","43.1B",5,0.0072426471,-0.0868131801],["bnb","BNB","372.6M","47.3B",4,-4.0847854279,-0.8259215573],["usdt","Tether","34.5B","71.1B",3,0.0733324279,-0.0371107676],["eth","Ethereum","8B","198.2B",2,0.1514006534,-0.5090095311],["btc","Bitcoin","27.7B","452.8B",1,-2.8048043155,-0.143585142]]}`;
diff --git a/frontend-components/tables/src/index.css b/frontend-components/tables/src/index.css
new file mode 100644
index 0000000000000000000000000000000000000000..16e09d8f2957287371a14a38f7bb6c19e73be9d5
--- /dev/null
+++ b/frontend-components/tables/src/index.css
@@ -0,0 +1,176 @@
+@tailwind base;
+@tailwind components;
+@tailwind utilities;
+
+@layer components {
+ ._input {
+ @apply w-full text-xs px-2 py-1 bg-grey-50 dark:bg-grey-900 h-[25px] border-[1.5px] border-grey-300 dark:border-grey-700 rounded outline-none dark:outline-1 focus:border-grey-500 active:border-grey-500 dark:text-white dark:focus:text-white dark:focus:border-white dark:active:border-white dark:hover:border-white disabled:border-grey-600 font-normal;
+ }
+ ._btn-common {
+ @apply whitespace-nowrap inline-flex w-full items-center justify-center gap-2 rounded py-2 px-6 text-center !no-underline transition duration-150 ease-out md:w-fit;
+ }
+ ._btn {
+ @apply _btn-common text-sm bg-grey-800 text-white hover:bg-grey-600 dark:bg-grey-100 dark:text-grey-800 dark:hover:bg-white focus:outline focus:outline-2 focus:outline-grey-500 dark:active:bg-grey-200 dark:hover:active:text-grey-900 disabled:bg-grey-300 disabled:text-grey-500 active:disabled:bg-grey-300 active:disabled:text-grey-500;
+ }
+ ._btn-secondary {
+ @apply _btn-common border border-grey-300 text-grey-300 outline-offset-0 hover:border-white hover:text-white focus:outline focus:outline-2 focus:outline-grey-500 active:border-white active:bg-grey-800 active:text-white disabled:border-grey-500 disabled:bg-transparent disabled:text-grey-500;
+ }
+ ._btn-tertiary {
+ @apply _btn-common bg-grey-900 text-grey-50 hover:bg-grey-800 hover:text-white focus:bg-grey-900 focus:outline focus:outline-2 focus:outline-grey-500 active:bg-grey-700 active:text-white disabled:bg-grey-500 disabled:text-grey-700;
+ }
+ ._icon-btn {
+ @apply _btn-common bg-grey-900 text-grey-50 hover:bg-grey-800 hover:text-white focus:bg-grey-900 focus:outline focus:outline-2 focus:outline-grey-500 active:bg-grey-700 active:text-white disabled:bg-grey-500 disabled:text-grey-700;
+ }
+ ._hyper-link {
+ @apply underline text-blue-300 hover:text-blue-400 active:text-blue-500 disabled:text-grey-600;
+ }
+ ._modal {
+ @apply card fixed top-1/2 left-1/2 z-50 max-h-[608px] w-[90%] max-w-[568px] -translate-x-1/2 -translate-y-1/2 overflow-auto text-white bg-grey-900 p-2 duration-300 focus:outline-none md:p-10;
+ }
+ ._modal-overlay {
+ @apply bg-grey-900 opacity-60 z-40 inset-0 fixed;
+ }
+ ._modal-title {
+ @apply text-white uppercase tracking-widest text-lg font-bold;
+ }
+ .card {
+ @apply w-full rounded border border-grey-600 p-6 shadow bg-grey-850;
+ }
+}
+
+._header {
+ background: #062d48;
+ background: linear-gradient(
+ 90deg,
+ #062d48,
+ #0b3054 15%,
+ #0e386c 45%,
+ #0b203d 64%,
+ #06101a 82%,
+ #060709
+ );
+}
+
+body {
+ font-family: "Arial", monospace;
+}
+
+table {
+ width: 100%;
+ table-layout: auto;
+}
+
+table thead,
+table tfoot,
+table tbody tr {
+ display: table;
+ width: 100%;
+ table-layout: fixed;
+ white-space: nowrap;
+ overflow: hidden;
+ text-overflow: ellipsis;
+}
+
+th, td {
+ word-wrap: break-word;
+ overflow: hidden;
+ text-overflow: ellipsis;
+}
+
+.resizer {
+ cursor: col-resize;
+ user-select: none;
+ touch-action: none;
+ transition: opacity 0.2s ease-in-out;
+}
+
+.resizer.isResizing {
+ background: white;
+ opacity: 1;
+}
+
+@media (hover: hover) {
+ .resizer {
+ opacity: 0;
+ }
+
+ *:hover > .resizer {
+ opacity: 1;
+ }
+}
+
+/* custom scrollbar */
+::-webkit-scrollbar {
+ width: 20px;
+}
+
+::-webkit-scrollbar-track {
+ background-color: transparent;
+}
+
+::-webkit-scrollbar-thumb {
+ background-color: #d6dee1;
+ border-radius: 20px;
+ border: 6px solid transparent;
+ background-clip: content-box;
+}
+
+::-webkit-scrollbar-thumb:hover {
+ background-color: #a8bbbf;
+}
+
+::-webkit-scrollbar-corner {
+ background: rgba(0, 0, 0, 0);
+}
+
+table thead th {
+ position: -webkit-sticky;
+ position: sticky;
+ top: 0;
+}
+
+.saving {
+ position: absolute;
+ z-index: 9999999;
+ top: 50%;
+ left: 50%;
+ transform: translate(-50%, -50%);
+ height: 100px;
+ width: 200px;
+ display: none;
+ justify-content: center;
+ align-items: center;
+ background-color: rgba(0, 0, 0, 0.7);
+ color: white;
+ box-shadow: 0 0 10px 0 rgba(0, 0, 0, 0.5);
+ border: 1px solid rgba(250, 250, 250, 0.5);
+ font-size: 0.9em;
+ border-radius: 20px;
+ animation: popup 0.3s ease-in-out;
+}
+
+.saving.show {
+ display: flex;
+}
+.loader {
+ position: relative;
+ bottom: 0px;
+ right: -5px;
+ border: 4px solid #f3f3f3;
+ border-radius: 50%;
+ border-top: 4px solid #00acff;
+ width: 20px;
+ height: 20px;
+ animation: spin 1.5s linear infinite;
+ opacity: 1;
+}
+
+@keyframes spin {
+ 0% {
+ transform: rotate(0deg);
+ }
+
+ 100% {
+ transform: rotate(360deg);
+ }
+}
diff --git a/frontend-components/tables/src/main.tsx b/frontend-components/tables/src/main.tsx
new file mode 100644
index 0000000000000000000000000000000000000000..6da14a4ab64ff22e8e48679de3e2f384d65a6017
--- /dev/null
+++ b/frontend-components/tables/src/main.tsx
@@ -0,0 +1,14 @@
+import React from "react";
+import ReactDOM from "react-dom/client";
+import App from "./App";
+import "./index.css";
+
+const root = ReactDOM.createRoot(
+ document.getElementById("root") as HTMLElement
+);
+
+root.render(
+
+
+);
diff --git a/frontend-components/tables/src/utils/useClickOutside.tsx b/frontend-components/tables/src/utils/useClickOutside.tsx
new file mode 100644
index 0000000000000000000000000000000000000000..1fa5f62a95d790785e1144242c0ad9beef2e7744
--- /dev/null
+++ b/frontend-components/tables/src/utils/useClickOutside.tsx
@@ -0,0 +1,22 @@
+import { RefObject, useEffect } from "react";
+
+export default function useOnClickOutside(
+ ref: RefObject,
+ handler: (event: MouseEvent | TouchEvent) => void
+) {
+ useEffect(() => {
+ const listener = (event: MouseEvent | TouchEvent) => {
+ // Do nothing if clicking ref's element or descendent elements
+ if (!ref.current || ref.current.contains(event.target as Node)) {
+ return;
+ }
+ handler(event);
+ };
+ document.addEventListener("mousedown", listener);
+ document.addEventListener("touchstart", listener);
+ return () => {
+ document.removeEventListener("mousedown", listener);
+ document.removeEventListener("touchstart", listener);
+ };
+ }, [ref, handler]);
+}
diff --git a/frontend-components/tables/src/utils/useDarkMode.tsx b/frontend-components/tables/src/utils/useDarkMode.tsx
new file mode 100644
index 0000000000000000000000000000000000000000..9a631e2f6eea350aeaa57de12d2fa6745ceb37f8
--- /dev/null
+++ b/frontend-components/tables/src/utils/useDarkMode.tsx
@@ -0,0 +1,14 @@
+import { useState, useEffect } from "react";
+
+export default function useDarkMode(initialTheme: "dark" | "light") {
+ const [theme, setTheme] = useState(initialTheme);
+ const colorTheme = theme === "dark" ? "light" : "dark";
+
+ useEffect(() => {
+ const root = window.document.documentElement;
+ root.classList.remove(colorTheme);
+ root.classList.add(theme);
+ }, [theme, colorTheme]);
+
+ return [colorTheme, setTheme];
+}
diff --git a/frontend-components/tables/src/utils/useLocalStorage.tsx b/frontend-components/tables/src/utils/useLocalStorage.tsx
new file mode 100644
index 0000000000000000000000000000000000000000..b3ec493eb10c714c9ac10c94697bd9891b5ac5e3
--- /dev/null
+++ b/frontend-components/tables/src/utils/useLocalStorage.tsx
@@ -0,0 +1,42 @@
+import { useState } from "react";
+
+export default function useLocalStorage(key: string, initialValue: any, validateFn?: (value: any) => any) {
+ // State to store our value
+ // Pass initial state function to useState so logic is only executed once
+ const [storedValue, setStoredValue] = useState(() => {
+ if (typeof window === "undefined") {
+ return initialValue;
+ }
+ try {
+ // Get from local storage by key
+ const item = window.localStorage.getItem(key);
+ // Parse stored json or if none return initialValue
+ return item ?
+ validateFn ? validateFn(JSON.parse(item)) :
+ JSON.parse(item) : initialValue;
+ } catch (error) {
+ // If error also return initialValue
+ console.log(error);
+ return initialValue;
+ }
+ });
+ // Return a wrapped version of useState's setter function that ...
+ // ... persists the new value to localStorage.
+ const setValue = (value: any) => {
+ try {
+ // Allow value to be a function so we have same API as useState
+ const valueToStore =
+ value instanceof Function ? value(storedValue) : value;
+ // Save state
+ setStoredValue(valueToStore);
+ // Save to local storage
+ if (typeof window !== "undefined") {
+ window.localStorage.setItem(key, JSON.stringify(valueToStore));
+ }
+ } catch (error) {
+ // A more advanced implementation would handle the error case
+ console.log(error);
+ }
+ };
+ return [storedValue, setValue];
+}
diff --git a/frontend-components/tables/src/utils/utils.ts b/frontend-components/tables/src/utils/utils.ts
new file mode 100644
index 0000000000000000000000000000000000000000..42f8171e19795a741f83c14d0c80b81278870537
--- /dev/null
+++ b/frontend-components/tables/src/utils/utils.ts
@@ -0,0 +1,320 @@
+import { rankItem } from "@tanstack/match-sorter-utils";
+import domtoimage from "dom-to-image";
+
+
+export function formatNumberNoMagnitude(value: number | string) {
+ if (typeof value === "string") {
+ const suffix = value.replace(/[^a-zA-Z]/g, "").trim();
+ const magnitude = ["", "K", "M", "B", "T"].indexOf(
+ suffix.replace(/\s/g, ""),
+ );
+ value =
+ Number(value.replace(/[^0-9.]/g, "").trim()) *
+ Math.pow(10, magnitude * 3);
+ }
+
+ return value;
+}
+
+export function formatNumberMagnitude(value: number | string, column?: string) {
+ if (typeof value === "string") {
+ value = Number(formatNumberNoMagnitude(value));
+ }
+
+ if (value % 1 !== 0) {
+ const decimalPlaces = Math.max(
+ 2,
+ value.toString().split(".")[1]?.length || 0,
+ );
+ const toFixed = Math.min(4, decimalPlaces);
+ if (value < 5) {
+ return value.toFixed(toFixed) || 0;
+ }
+ value = Number(value.toFixed(2));
+ }
+
+ if (
+ (value > 100_000 || value < -100_000) &&
+ !includesPriceNames(column || "")
+ ) {
+ const magnitude = Math.min(4, Math.floor(Math.log10(Math.abs(value)) / 3));
+ const suffix = ["", "K", "M", "B", "T"][magnitude];
+ const formatted = (value / 10 ** (magnitude * 3)).toFixed(3);
+ return `${formatted.replace(/\.?0+$/, "")} ${suffix}`;
+ }
+
+ if (value > 1000 || value < -1000) return formatNumber(value);
+
+ return value;
+}
+
+export function formatNumber(value: number) {
+ if (value > 1000 || value < -1000) {
+ const parts = value.toString().split(".");
+ const integerPart = parts[0].replace(/\B(?=(\d{3})+(?!\d))/g, ",");
+ const decimalPart = parts[1] ? `.${parts[1]}` : "";
+ return `${integerPart}${decimalPart}`;
+ }
+
+ return value;
+}
+
+export function includesDateNames(column: string) {
+ return ["date", "day", "time", "timestamp", "year"].some((dateName) =>
+ column?.toLowerCase().includes(dateName),
+ );
+}
+
+export function includesPriceNames(column: string) {
+ return ["price", "open", "close"].some((priceName) =>
+ column?.toLowerCase().includes(priceName),
+ );
+}
+
+function loadingOverlay(message?: string, is_close?: boolean) {
+ const loading = window.document.getElementById("loading") as HTMLElement;
+ const loading_text = window.document.getElementById(
+ "loading_text",
+ ) as HTMLElement;
+ return new Promise((resolve) => {
+ if (is_close) {
+ loading.classList.remove("show");
+ } else {
+ // @ts-ignore
+ loading_text.innerHTML = message;
+ loading.classList.add("show");
+ }
+
+ const is_loaded = setInterval(function () {
+ if (
+ is_close
+ ? !loading.classList.contains("show")
+ : loading.classList.contains("show")
+ ) {
+ clearInterval(is_loaded);
+ resolve(true);
+ }
+ }, 0.01);
+ });
+}
+
+export function isEqual(a: any, b: any) {
+ if (a === b) return true;
+ if (a == null || b == null) return false;
+ if (a?.length !== b?.length) return false;
+
+ for (let i = 0; i < a?.length; ++i) {
+ if (a[i] !== b[i]) return false;
+ }
+ return true;
+}
+
+export const fuzzyFilter = (
+ row: any,
+ columnId: string,
+ value: string,
+ addMeta: any,
+): any => {
+ const itemRank = rankItem(row.getValue(columnId), value);
+ addMeta(itemRank);
+ return itemRank;
+};
+
+const exportNativeFileSystem = async ({
+ fileHandle,
+ blob,
+}: {
+ fileHandle?: FileSystemFileHandle | null;
+ blob: Blob;
+}) => {
+ if (!fileHandle) {
+ return;
+ }
+
+ await writeFileHandler({ fileHandle, blob });
+};
+
+const writeFileHandler = async ({
+ fileHandle,
+ blob,
+}: {
+ fileHandle: FileSystemFileHandle;
+ blob: Blob;
+}) => {
+ const writer = await fileHandle.createWritable();
+ await writer.write(blob);
+ await writer.close();
+};
+
+const IMAGE_TYPE: FilePickerAcceptType[] = [
+ {
+ description: "PNG Image",
+ accept: {
+ "image/png": [".png"],
+ },
+ },
+ {
+ description: "JPEG Image",
+ accept: {
+ "image/jpeg": [".jpeg"],
+ },
+ },
+];
+
+const getNewFileHandle = ({
+ filename,
+ is_image,
+}: {
+ filename: string;
+ is_image?: boolean;
+}): Promise => {
+ try {
+ if ("showSaveFilePicker" in window) {
+ const opts: SaveFilePickerOptions = {
+ suggestedName: filename,
+ types: is_image
+ ? IMAGE_TYPE
+ : [
+ {
+ description: "CSV File",
+ accept: {
+ "image/csv": [".csv"],
+ },
+ },
+ ],
+ excludeAcceptAllOption: true,
+ };
+
+ return showSaveFilePicker(opts);
+ }
+ } catch (error) {
+ console.error(error);
+ }
+
+ return new Promise((resolve) => {
+ resolve(null);
+ });
+};
+
+export const saveToFile = (
+ blob: Blob,
+ fileName: string,
+ fileHandle?: FileSystemFileHandle,
+) => {
+ try {
+ if (fileHandle === null) {
+ throw new Error("Cannot access filesystem");
+ }
+ exportNativeFileSystem({ fileHandle, blob });
+ } catch (error) {
+ console.error("oops, something went wrong!", error);
+ const url = URL.createObjectURL(blob);
+ const link = document.createElement("a");
+ link.setAttribute("href", url);
+ link.setAttribute("download", fileName);
+ link.style.visibility = "hidden";
+ document.body.appendChild(link);
+ link.click();
+ document.body.removeChild(link);
+ }
+
+ return new Promise((resolve) => {
+ resolve(true);
+ });
+};
+
+export async function downloadData(
+ type: "csv",
+ columns: any,
+ data: any,
+ downloadFinished: (changed: boolean) => void,
+) {
+ const headers = columns;
+ const rows = data.map((row: any) =>
+ headers.map((column: any) => row[column]),
+ );
+ const csvData = [headers, ...rows];
+
+ if (type === "csv") {
+ const csvContent = csvData.map((e) => e.join(",")).join("\n");
+ const blob = new Blob([csvContent], { type: "text/csv;charset=utf-8;" });
+ const filename = `${window.title}.csv`;
+
+ try {
+ const fileHandle = await getNewFileHandle({
+ filename: filename,
+ });
+ let ext = "csv";
+
+ if (fileHandle !== null) {
+ // @ts-ignore
+ ext = fileHandle.name.split(".").pop();
+ }
+
+ await loadingOverlay(`Saving ${ext.toUpperCase()}`);
+
+ // @ts-ignore
+ non_blocking(async function () {
+ // @ts-ignore
+ saveToFile(blob, filename, fileHandle).then(async function () {
+ await new Promise((resolve) => setTimeout(resolve, 1500));
+ await loadingOverlay("", true);
+ if (!fileHandle) {
+ downloadFinished(true);
+ }
+ });
+ }, 2)();
+ } catch (error) {
+ console.error(error);
+ }
+
+ return;
+ }
+}
+
+export async function downloadImage(
+ id: string,
+ downloadFinished: (change: boolean) => void,
+) {
+ const table = document.getElementById(id);
+ const filename = `${window.title}.png`;
+ try {
+ const fileHandle = await getNewFileHandle({
+ filename: filename,
+ is_image: true,
+ });
+ let extension = "png";
+ if (fileHandle !== null) {
+ // @ts-ignore
+ extension = fileHandle.name.split(".").pop();
+ }
+ await loadingOverlay(`Saving ${extension.toUpperCase()}`);
+
+ non_blocking(async function () {
+ // @ts-ignore
+ domtoimage.toBlob(table).then(function (blob: Blob) {
+ // @ts-ignore
+ saveToFile(blob, filename, fileHandle).then(async function () {
+ await new Promise((resolve) => setTimeout(resolve, 1500));
+ await loadingOverlay("", true);
+ if (!fileHandle) {
+ downloadFinished(true);
+ }
+ });
+ });
+ }, 2)();
+ } catch (error) {
+ console.error(error);
+ }
+}
+
+export const non_blocking = (func: Function, delay: number) => {
+ let timeout: number;
+ return function () {
+ // @ts-ignore
+ const context = this;
+ const args = arguments;
+ clearTimeout(timeout);
+ timeout = setTimeout(() => func.apply(context, args), delay);
+ };
+};
diff --git a/frontend-components/tables/src/vite-env.d.ts b/frontend-components/tables/src/vite-env.d.ts
new file mode 100644
index 0000000000000000000000000000000000000000..11f02fe2a0061d6e6e1f271b21da95423b448b32
--- /dev/null
+++ b/frontend-components/tables/src/vite-env.d.ts
@@ -0,0 +1 @@
+/// 
+