Python for Algorithmic Trading Cookbook. Recipes for designing, building, and deploying algorithmic trading strategies with Python - Second Edition

Get Python code for algorithmic trading along with practical guidance from Jason Strimpel, founder of PyQuant News and a veteran of global trading and risk management. This highly practical book takes you from core algorithmic trading concepts and modern data acquisition to rigorous backtesting and strategy execution. Detailed recipes show you how to use the OpenBB Platform to source free equities, options, and futures data. Using that data, accelerate research with Parquet, Polars, DuckDB, and ArcticDB. You’ll engineer alpha factors with SciPy and statsmodels, using PCA to find latent factors, regression to hedge beta, and measure Fama-French exposures. Then optimize backtests with walk-forward analysis using VectorBT and build production-grade backtests with Zipline Reloaded. You’ll evaluate alpha with pro tools like Alphalens Reloaded and PyFolio and apply agentic AI workflows to automate research and code generation. For execution, you’ll connect to Interactive Brokers’ API to stream ticks, place and manage orders, retrieve portfolio state, and deploy strategies with monitoring and risk KPIs suitable for live trading. By the end of this book, you’ll not only understand the essentials, but you’ll also have the code templates and patterns to implement, evaluate, and operate Python-based algorithmic trading strategies.