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Implementing AI Quant Trading with Python
HMMs, Kalman Filters, Statistical Arbitrage, Reinforcement Learning, Backtesting, and Risk Management
This book is 100% completeLast updated on 2026-10-13
Learn to question AI-generated trading code. Follow the data, verify backtest accounting, and understand regime models, position sizing and risk controls.
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Build a clearer understanding of AI-assisted trading systems.
A practical Python guide to data pipelines, backtest accounting, hidden Markov models, Kalman filtering, pairs trading, position sizing, reinforcement learning and system operations. For readers comfortable with basic Python who want to understand and review quantitative trading software.
October 2026 revision: 436 pages in the full PDF; 27 pages in the free sample. Every Python listing comes from an executable repository implementation, with source file and callable links. Download the public repository to run the exercises and behavioral tests.
English PDF edition. Access to private production repositories or complete commercial application source code is not included. AI was used extensively in preparing the manuscript. The author personally checked important code and validation work. Adapt and test examples in your own environment. This is educational material about software and verification; it does not guarantee live trading profitability.
Examples are synchronized from https://github.com/tedpark/stock-trading-ai-examples. Full-file exercises and excerpts of actual functions/classes are distinguished.
The book focuses on explaining concepts and code behavior, with key excerpts in the text. Complete chapter exercise code, including imports, initialization, shared modules, and tests, is provided separately in the public repository. Use manifest.json to find each chapter’s files and README.md to run them.
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