Advances in Financial Machine Learning (Hardcover)

Preamble

Foreword by Paul Tudor Jones, II,. Tudor Investment Corporation.

Foreword by Professor. Horst Simon,. Lawrence Berkeley National Laboratory.

1. Financial ML as a distinct subject.

Section 1: Data Analysis

2. Financial data structures.

3. Labeling.

4. Sample weights.

5. Fractional differentiation.

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Section 2: Modelling

6. Ensemble methods.

7. Cross validation in finance.

8. Feature importance.

9. Hyper-parameter tuning with CV.

Section 3: Backtesting

10. Bet sizing.

11. The dangers of backtesting.

12. Backtesting through CV.

13. Backtesting on synthetic data.

14. Backtest statistics.

15. Understanding strategy risk.

16. ML asset allocation.

Section 4: Useful Financial Features

17. Structural breaks.

18. Entropy features.

19. Microstructural features.

Section 5: HPC Recipes

20. Multiprocessing and vectorization.

21. Brute force and quantum computers.

Epilogue

22. Partnering with Berkeley Lab (by Dr. John Wu).

23. ML at University of California, Berkeley (by TBD).