My curated list of Python resources on Quantitative Finance

• Aurelien Geron (2019), Hands–On Machine Learning with Scikit–Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems

• François Chollet (2017), Deep Learning with Python

• Jake VanderPlas (2016), Data Science Handbook: Essential Tools for Working with Data

• Marcos M. Lopez de Prado (2020), Machine Learning for Asset Managers (Elements in Quantitative Finance)

• Tony Guida (2019), Big Data and Machine Learning in Quantitative Investment

• Wes McKinney (2018), Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython

• Yves Hilpisch (2015), Derivatives Analytics with Python: Data Analysis, Models, Simulation, Calibration and Hedging

• Yves Hilpisch (2016), Listed Volatility and Variance Derivatives: A Python–based Guide

• Yves Hilpisch (2018), Python for Finance

• Yves Hilpisch (2020), Artificial Intelligence in Finance: A Python–based Guide

• Alphavantage - Python module to get stock data from the Alpha Vantage API.

• Bloomberg Open API - Bloomberg Open API with pandas.

• Eikon Data API - Python package for retrieving Eikon data.

• IEX - Python module to get stock data from IEX Cloud and IEX API 1.0.

• TWS API - Interactive Brokers interface to retrieve data and automate trading strategies.

• YFinance - Yahoo! Finance market data downloader.

• QuantLib-Python - Backward-compatible meta-package for the QuantLib module.

• Keras - Keras is a high-level neural networks API for Python.

• NumPy - Fundamental package for array computing with Python.

• Pandas - Powerful data structures for data analysis, time series, and statistics.

• Scikit-Learn - Python modules for machine learning and data mining.

• SciPy - Scientific Library for Python.

• TensorFlow - Open source library for high performance numerical computation.

• Python Wheels - New standard of Python distribution.

• TA-Lib - Python wrapper for TA-Lib

• Backtrader - A feature-rich Python framework for backtesting and trading.

• Bokeh - Interactive visualization library for modern web browsers.

• BQPlot - Interactive plotting for the Jupyter notebook, using D3.js and IPywidgets.

• Cufflinks -  Productivity Tools for Plotly + Pandas.

• IPywidgets - Interactive HTML widgets for Jupyter notebooks and the IPython kernel.

• Matplotlib - Comprehensive library for creating static, animated, and interactive visualizations in Python.

• Plotly - Open-source, interactive data visualization library for Python

• Seaborn - Library for making statistical graphics in Python.