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Add Polygon python library.
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@@ -252,6 +252,7 @@ A curated list of insanely awesome libraries, packages and resources for Quants
- [investpy](https://github.com/alvarobartt/investpy) - Financial Data Extraction from Investing.com with Python! <https://investpy.readthedocs.io/>
- [yliveticker](https://github.com/yahoofinancelive/yliveticker) - Live stream of market data from Yahoo Finance websocket.
- [bbgbridge](https://github.com/ran404/bbgbridge) - Easy to use Bloomberg Desktop API wrapper for Python.
- [polygon.io](https://github.com/polygon-io/client-python) - A python library for Polygon.io financial data APIs.
- [alpha_vantage](https://github.com/RomelTorres/alpha_vantage) - A python wrapper for Alpha Vantage API for financial data.
- [FinanceDataReader](https://github.com/FinanceData/FinanceDataReader) - Open Source Financial data reader for U.S, Korean, Japanese, Chinese, Vietnamese Stocks
- [pystlouisfed](https://github.com/TomasKoutek/pystlouisfed) - Python client for Federal Reserve Bank of St. Louis API - FRED, ALFRED, GeoFRED and FRASER.
@@ -567,4 +568,4 @@ date conversion, scaling factor values, and filtering by the specified date.
- [101_formulaic_alphas](https://github.com/ram-ki/101_formulaic_alphas) - Implementation of [101 formulaic alphas](https://arxiv.org/ftp/arxiv/papers/1601/1601.00991.pdf) using qstrader.
- [Tidy Finance](https://www.tidy-finance.org/) - An opinionated approach to empirical research in financial economics - a fully transparent, open-source code base in multiple programming languages (Python and R) to enable the reproducible implementation of financial research projects for students and practitioners.
- [RoughVolatilityWorkshop](https://github.com/jgatheral/RoughVolatilityWorkshop) - 2024 QuantMind's Rough Volatility Workshop lectures.
- [AFML](https://github.com/boyboi86/AFML) - All the answers for exercises from Advances in Financial Machine Learning by Dr Marco Lopez de Parodo.
- [AFML](https://github.com/boyboi86/AFML) - All the answers for exercises from Advances in Financial Machine Learning by Dr Marco Lopez de Parodo.