Python for Algorithmic Trading Cookbook

Table of Contents

1. Acquire Free Financial Market Data with Cutting-edge Python Libraries
2. Analyze and Transform Financial Market Data with pandas
3. Visualize Financial Market Data with Matplotlib, Seaborn, and Plotly Dash
4. Store Financial Market Data on Your Computer
5. Build Alpha Factors for Stock Portfolios
6. Vector-Based Backtesting with VectorBT
7. Event-Based Backtesting Factor Portfolios with Zipline Reloaded
8. Evaluate Factor Risk and Performance with Alphalens Reloaded
9. Assess Backtest Risk and Performance Metrics with Pyfolio
10. Set Up the Interactive Brokers Python API
11. Manage Orders, Positions, and Portfolios with the IB API
12. Deploy Strategies to a Live Environment
13. Advanced Recipes for Market Data and Strategy Management