Python for Algorithmic Trading Cookbook: Recipes for designing, building, and deploying algorithmic trading strategies with Python, 2nd Edition

article المحتوى

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

Backtest Python trading strategies with VectorBT and Zipline Reloaded using walk-forward analysis
Measure risk, performance, and alpha quality with Alphalens Reloaded and PyFolio
Automate strategy execution with the Interactive Brokers API for live trading
Get practical Python code for algorithmic trading from Jason Strimpel, founder of PyQuant News and a veteran of global trading, risk management, and machine learning. This hands-on guide shows you how to turn market data into tested, automated trading strategies using modern Python tools.

You’ll source equities, options, and futures data with OpenBB and FMP, then accelerate Python for data analysis workflows with Pandas, Polars, Parquet, DuckDB, and ArcticDB. You’ll visualize market data with Matplotlib, Seaborn, and Plotly Dash before moving into alpha research and quantitative trading techniques.

Detailed recipes help you engineer alpha factors with PCA, regression, Fama-French models, SciPy, and statsmodels. You’ll design and evaluate quantitative trading strategies using VectorBT, Zipline Reloaded, Alphalens Reloaded, and PyFolio, including walk-forward analysis and risk-aware performance review.

For execution, you’ll connect to the Interactive Brokers API to stream ticks, manage orders, retrieve portfolio state, and monitor live trading workflows. By the end, you’ll have reusable Python templates for researching, backtesting, evaluating, and operating 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