2nd Edition

Machine Learning for Algorithmic Trading

Predictive models to extract signals from market and alternative data to design and backtest systematic trading strategies with Python.

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Machine Learning for Algorithmic Trading - 2nd Edition

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Book Contents

Part 1: From Data to Strategy Development

Part 1 provides a framework for the trading strategy development process from ideation, data sourcing, alpha factor research, to portfolio optimization and performance evaluation.

1. Machine Learning for Trading: From Idea to Execution 2. Market and Fundamental Data: Sources and Techniques 3. Alternative Data for Finance: Categories and Use Cases 4. Financial Feature Engineering: How to research Alpha Factors 5. Portfolio Optimization and Performance Evaluation

Part 2: Machine Learning Fundamentals

Part 2 covers the foundational supervised and unsupervised machine learning models and how to apply them to trading, including linear models, time-series models, decision trees, and unsupervised learning.

6. The Machine Learning Process 7. Linear Models: From Risk Factors to Return Forecasts 8. The ML4T Workflow: From Model to Strategy Backtesting 9. Time Series Models for Volatility Forecasts and Statistical Arbitrage 10. Bayesian ML: Dynamic Sharpe Ratios and Pairs Trading 11. Random Forests: A Long-Short Strategy for Japanese Stocks 12. Boosting your Trading Strategy 13. Data-Driven Risk Factors and Asset Allocation with Unsupervised Learning

Part 3: Natural Language Processing

Part 3 demonstrates how to extract trading signals from text data, covering word embeddings, topic modeling, sentiment analysis, and modern deep learning approaches for NLP.

14. Text Data for Trading: Sentiment Analysis 15. Topic Modeling: Summarizing Financial News 16. Word embeddings for Earnings Calls and SEC Filings

Part 4: Deep & Reinforcement Learning

Part 4 introduces deep learning architectures and reinforcement learning for trading applications, including CNNs, RNNs, autoencoders, GANs, and deep Q-learning.

17. Deep Learning for Trading 18. CNN for Financial Time Series and Satellite Images 19. RNN for Multivariate Time Series and Sentiment Analysis 20. Autoencoders for Conditional Risk Factors and Asset Pricing 21. Generative Adversarial Nets for Synthetic Time Series Data 22. Deep Reinforcement Learning: Building a Trading Agent 23. Conclusions and Next Steps 24. Appendix - Alpha Factor Library

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3rd Edition

27 chapters, 5 integrated Python libraries, GenAI, causal inference, and reinforcement learning for real-world trading systems.

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