Python for Algorithmic Trading Cookbook: Recipes for designing, building, and deploying algorithmic trading strategies with Python

Transform financial market data into algorithmic trading strategies and deploy them into a live trading environment with recipes leveraging modern Python libraries like pandas, Polars, and DuckDB Key Features Follow practical, production-grade Python recipes to acquire, visualize, and store financial market data Design, backtest, and evaluate the performance of trading strategies using professional techniques Deploy trading strategies built in Python to a live trading environment with API connectivity Book DescriptionGet 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.What you will learn Acquire equities, futures, and options data using OpenBB and FMP Process and analyze time series data efficiently with pandas and Polars Store and query massive datasets with ArcticDB, DuckDB, and Parquet Visualize trading data using Matplotlib, Seaborn, and Plotly Dash Engineer alpha factors using PCA, regression, and Fama-French models Backtest strategies with VectorBT and Zipline Reloaded frameworks Evaluate performance and risk using Alphalens Reloaded and PyFolio Deploy and automate live trades using the Interactive Brokers API Who this book is forThis book is for traders, investors, and Python enthusiasts who need practical code to acquire, analyze, and automate algorithmic trading strategies using modern, high-performance Python tools. Readers should have some exposure to investing or trading, a basic familiarity with Python syntax, and a basic knowledge of libraries such as Pandas and NumPy. This book is ideal for discretionary traders who want to adopt a systematic approach and apply professional techniques, such as factor modeling, backtesting, and execution automation, to trading workflows using Python.

Table of Contents:
Table of Contents

1. Acquire Free Financial Market Data with Cutting-Edge Python Libraries
2. Analyze and Transforming Financial Market Data with pandas
3. Accelerate Financial Market Data Analysis with Parquet, DuckDB, and Polars
4. Visualize Financial Market Data with Matplotlib, Plotly, and Streamlit
5. Build a Quantamental Research Database with ArcticDB
6. Conduct Market Research with Advanced AI and Agentic Workflows
7. Build Alpha Factors for Stock Portfolios
8. Event-Based Backtesting Factor Portfolios with Zipline Reloaded
9. Vector-Based Backtesting with VectorBT
10. Evaluate Factor Risk and Performance With Alphalens
11. Assess Backtest Risk and Performance Metrics with Pyfolio
12. Set Up the Interactive Brokers Python API
13. Manage Orders, Positions, and Portfolios with the IB API
14. Deploy Strategies to a Live Environment
15. Advanced Recipes for Market Data and Strategy Management