Book Content
chapters • 27h24m total length
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
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
14. Text Data for Trading – Sentiment Analysis
15. Topic Modeling – Summarizing Financial News
16. Word Embeddings for Earnings Calls and SEC Filings
17. Deep Learning for Trading
18. CNNs for Financial Time Series and Satellite Images
19. RNNs for Multivariate Time Series and Sentiment Analysis
20. Autoencoders for Conditional Risk Factors and Asset Pricing
21. Generative Adversarial Networks for Synthetic Time-Series Data
22. Deep Reinforcement Learning – Building a Trading Agent
23. Conclusions and Next Steps
24. Appendix – Alpha Factor Library
