Advances in Financial Machine Learning

by Marcos López de Prado

Table of Contents

Preamble

• Chapter 1: Financial Machine Learning as a Distinct Subject

Part I: Data Analysis

• Chapter 2: Financial Data Structures
• Chapter 3: Labeling
• Chapter 4: Sample Weights
• Chapter 5: Fractionally Differentiated Features

Part II: Modeling

• Chapter 6: Ensemble Methods
• Chapter 7: Cross-Validation in Finance
• Chapter 8: Feature Importance
• Chapter 9: Hyper-Parameter Tuning with Cross-Validation

Part III: Backtesting

• Chapter 10: Bet Sizing
• Chapter 11: The Dangers of Backtesting
• Chapter 12: Backtesting through Cross-Validation
• Chapter 13: Backtesting on Synthetic Data
• Chapter 14: Backtest Statistics
• Chapter 15: Understanding Strategy Risk
• Chapter 16: Machine Learning Asset Allocation

Part IV: Useful Financial Features

• Chapter 17: Structural Breaks
• Chapter 18: Entropy Features
• Chapter 19: Microstructural Features

Part V: High-Performance Computing Recipes

• Chapter 20: Multiprocessing and Vectorization
• Chapter 21: Brute Force and Quantum Computers
• Chapter 22: High-Performance Computational Intelligence and Forecasting Technologies