Machine learning is not just an advantage; it is becoming standard practice among top-performing trading firms. As traditional strategies struggle to navigate noise, complexity, and speed, ML-powered systems extract alpha by identifying transient patterns beyond human reach. This shift is transforming how hedge funds, quant teams, and algorithmic platforms operate, and now, these same capabilities are available to advanced practitioners.

This book is a practitioner’s blueprint for building production-grade ML trading systems from scratch. It goes far beyond basic return-sign classification tasks, which often fail in live markets, and delivers field-tested techniques used inside elite quant desks. It covers everything from the fundamentals of systematic trading and ML's role in detecting patterns to data preparation, backtesting, and model lifecycle management using Python libraries. You will learn to implement supervised learning for advanced feature engineering and sophisticated ML models. You will also learn to use unsupervised learning for pattern detection, apply ultra-fast pattern matching to chartist strategies, and extract crucial trading signals from unstructured news and financial reports. Finally, you will be able to implement anomaly detection and association rules for comprehensive insights.

By the end of this book, you will be ready to design, test, and deploy intelligent trading strategies to institutional standards.

What you will learn

● Build end-to-end machine learning pipelines for trading systems.

● Apply unsupervised learning to detect anomalies and regime shifts.

● Extract alpha signals from financial text using modern NLP.

● Use AutoML to optimize features, models, and parameters.

● Design fast pattern detectors from signal processing techniques.

● Backtest event-driven strategies using professional-grade tools.

● Interpret ML results with clear visualizations and plots.

Who this book is for

This book is for robo traders, algorithmic traders, hedge fund managers, portfolio managers, Python developers, engineers, and analysts who want to understand, master, and integrate machine learning into trading strategies. Readers should understand basic automated trading concepts and have some beginner experience writing Python code.

Table of Contents

1. Algorithmic Trading and Machine Learning in a Nutshell

2. Data Feed, Backtests, and Forward Testing

3. Optimizing Trading Systems, Metrics, and Automated Reporting

4. Implement Trading Strategies

5. Supervised Learning for Trading Systems

6. Improving Model Capability with Features

7. Advanced Machine Learning Models for Trading

8. AutoML and Low-Code for Trading Strategies

9. Unsupervised Learning Methods for Trading

10. Unsupervised Learning with Pattern Matching

11. Trading Signals from Reports and News

12. Advanced Unsupervised Learning, Anomaly Detection, and Association Rules

Appendix: APIs and Libraries for each chapter

About the Author

Franck Bardol is an AI professor, senior consultant, and expert advisor in generative AI and machine learning with over two decades of experience in the intersection of quantitative finance, applied research, and data science.

"About this title" may belong to another edition of this title.

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