Machine Learning for Algorithmic Trading: Predictive models to extract signals from market and alternative data for systematic trading strategies with Python

Table of Contents

1. Machine Learning for Trading
2. Market and Fundamental Data
3. Alternative Data for Finance
4. Financial Feature Engineering
5. Portfolio Optimization and Performance Evaluation
6. The Machine Learning Process
7. Linear Models
8. The ML4T Workflow
9. Time-Series Models for Volatility Forecasts and Statistical Arbitrage
10. Bayesian ML
11. Random Forests
12. Boosting Your Trading Strategy
13. Data-Driven Risk Factors and Asset Allocation with Unsupervised Learning
14. Text Data for Trading
15. Topic Modeling
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
23. Conclusions and Next Steps
24. Appendix