Ebook: Advances in Financial Machine Learning

1. Financial machine learning as a distinct subject -- 2. Financial data structures -- 3. Labeling -- 4. Sample weights -- 5. Fractionally differentiated features -- 6. Ensemble methods -- 7. Cross-validation in finance -- 8. Feature importance -- 9. Hyper-parameter tuning with cross-validation -- 10. Bet sizing -- 11. The dangers of backtesting -- 12. Backtesting through cross-validation -- 13. Backtesting on synthetic data -- 14. Backtest statistics -- 15. Understanding strategy risk -- 16. Machine learning asset allocation -- 17. Structural breaks -- 18. Entropy features -- 19. Microstructural features -- 20. Multiprocessing and vectorization -- 21. Brute force and quantum computers -- 22. High-performance computational intelligence and forecasting technologies / Kesheng Wu and Horst D. Simon.