Prado Marcos Lopez de  <br/><br/>
Advances in Financial Machine Learning  

 - New Jersey  John Wiley& Sons  2018 
<br/><br/> Includes index. 
<br/><br/>Content 1. Financial Machine Learning as a Distinct Subject <br/>Part 1: Data Analysis<br/> 2. Financial Data Structures <br/>3. Labeling <br/>4. Sample Weights<br/> 5. Fractionally Differentiated Features <br/>Part 2: Modelling <br/>6. Ensemble Methods <br/>7. Cross-validation in Finance<br/> 8. Feature Importance 9. Hyper-parameter Tuning with Cross-Validation <br/>Part 3: Backtesting <br/>10. Bet Sizing <br/>11. The Dangers of Backtesting <br/>12. Backtesting through Cross-Validation<br/>13. Backtesting on Synthetic Data<br/>14. Backtest Statistics <br/>15. Understanding Strategy Risk <br/>16. Machine Learning Asset Allocation <br/>Part 4: Useful Financial Features <br/>17. Structural Breaks <br/>18. Entropy Features <br/>19. Microstructural Features <br/>Part 5: High-Performance Computing Recipes <br/>20. Multiprocessing and Vectorization <br/>21. Brute Force and Quantum Computers <br/>22. High-Performance Computational Intelligence and Forecasting Technologies  
<br/><br/><label>ISBN: </label>9781119482086 
<br/><br/><label>Subjects--Topical Terms: </label><br/>Finance<br/>Machine learning.
<br/><br/><label>Dewey Class. No.: </label>006.31  / PRA/A