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• About the Author
• PREAMBLE
• Chapter 1 Financial Machine Learning as a Distinct Subject
• 1.1 Motivation
• 1.2 The Main Reason Financial Machine Learning Projects Usually Fail
• 1.3 Book Structure
• 1.4 Target Audience
• 1.5 Requisites
• 1.6 FAQs
• 1.7 Acknowledgments
• Exercises
• References
• Bibliography
• Notes
• PART 1 DATA ANALYSIS
• Chapter 2 Financial Data Structures
• 2.1 Motivation
• 2.2 Essential Types of Financial Data
• 2.3 Bars
• 2.4 Dealing with Multi-Product Series
• 2.5 Sampling Features
• Exercises
• References
• Chapter 3 Labeling
• 3.1 Motivation
• 3.2 The Fixed-Time Horizon Method
• 3.3 Computing Dynamic Thresholds
• 3.4 The Triple-Barrier Method
• 3.5 Learning Side and Size
• 3.6 Meta-Labeling
• 3.7 How to Use Meta-Labeling
• 3.8 The Quantamental Way
• 3.9 Dropping Unnecessary Labels
• Exercises
• Bibliography
• Note
• Chapter 4 Sample Weights
• 4.1 Motivation
• 4.2 Overlapping Outcomes
• 4.3 Number of Concurrent Labels
• 4.4 Average Uniqueness of a Label
• 4.5 Bagging Classifiers and Uniqueness
• 4.6 Return Attribution
• 4.7 Time Decay
• 4.8 Class Weights
• Exercises
• References
• Bibliography
• Chapter 5 Fractionally Differentiated Features
• 5.1 Motivation
• 5.2 The Stationarity vs. Memory Dilemma
• 5.3 Literature Review
• 5.4 The Method
• 5.5 Implementation
• 5.6 Stationarity with Maximum Memory Preservation
• 5.7 Conclusion
• Exercises
• References
• Bibliography
• PART 2 MODELLING
• Chapter 6 Ensemble Methods
• 6.1 Motivation
• 6.2 The Three Sources of Errors
• 6.3 Bootstrap Aggregation
• 6.4 Random Forest
• 6.5 Boosting
• 6.6 Bagging vs. Boosting in Finance
• 6.7 Bagging for Scalability
• Exercises
• References
• Bibliography
• Notes
• Chapter 7 Cross-Validation in Finance
• 7.1 Motivation
• 7.2 The Goal of Cross-Validation
• 7.3 Why K-Fold CV Fails in Finance
• 7.4 A Solution: Purged K-Fold CV
• 7.5 Bugs in Sklearn’s Cross-Validation
• Exercises
• Bibliography
• Chapter 8 Feature Importance
• 8.1 Motivation
• 8.2 The Importance of Feature Importance
• 8.3 Feature Importance with Substitution Effects
• 8.4 Feature Importance without Substitution Effects
• 8.5 Parallelized vs. Stacked Feature Importance
• 8.6 Experiments with Synthetic Data
• Exercises
• References
• Note
• Chapter 9 Hyper-Parameter Tuning with Cross-Validation
• 9.1 Motivation
• 9.2 Grid Search Cross-Validation
• 9.3 Randomized Search Cross-Validation
• 9.4 Scoring and Hyper-parameter Tuning
• Exercises
• References
• Bibliography
• Notes
• PART 3 BACKTESTING
• Chapter 10 Bet Sizing
• 10.1 Motivation
• 10.2 Strategy-Independent Bet Sizing Approaches
• 10.3 Bet Sizing from Predicted Probabilities
• 10.4 Averaging Active Bets
• 10.5 Size Discretization
• 10.6 Dynamic Bet Sizes and Limit Prices
• Exercises
• References
• Bibliography
• Notes
• Chapter 11 The Dangers of Backtesting
• 11.1 Motivation
• 11.2 Mission Impossible: The Flawless Backtest
• 11.3 Even If Your Backtest Is Flawless, It Is Probably Wrong
• 11.4 Backtesting Is Not a Research Tool
• 11.5 A Few General Recommendations
• 11.6 Strategy Selection
• Exercises
• References
• Bibliography
• Note
• Chapter 12 Backtesting through Cross-Validation
• 12.1 Motivation
• 12.2 The Walk-Forward Method
• 12.3 The Cross-Validation Method
• 12.4 The Combinatorial Purged Cross-Validation Method
• 12.5 How Combinatorial Purged Cross-Validation Addresses Backtest Overfitting
• Exercises
• References
• Chapter 13 Backtesting on Synthetic Data
• 13.1 Motivation
• 13.2 Trading Rules
• 13.3 The Problem
• 13.4 Our Framework
• 13.5 Numerical Determination of Optimal Trading Rules
• 13.6 Experimental Results
• 13.7 Conclusion
• Exercises
• References
• Notes
• Chapter 14 Backtest Statistics
• 14.1 Motivation
• 14.2 Types of Backtest Statistics
• 14.3 General Characteristics
• 14.4 Performance
• 14.5 Runs
• 14.6 Implementation Shortfall
• 14.7 Efficiency
• 14.8 Classification Scores
• 14.9 Attribution
• Exercises
• References
• Bibliography
• Notes
• Chapter 15 Understanding Strategy Risk
• 15.1 Motivation
• 15.2 Symmetric Payouts
• 15.3 Asymmetric Payouts
• 15.4 The Probability of Strategy Failure
• Exercises
• References
• Chapter 16 Machine Learning Asset Allocation
• 16.1 Motivation
• 16.2 The Problem with Convex Portfolio Optimization
• 16.3 Markowitz’s Curse
• 16.4 From Geometric to Hierarchical Relationships
• 16.5 A Numerical Example
• 16.6 Out-of-Sample Monte Carlo Simulations
• 16.7 Further Research
• 16.8 Conclusion
• APPENDICES
• 16.A.1 Correlation-based Metric
• 16.A.2 Inverse Variance Allocation
• 16.A.3 Reproducing the Numerical Example
• 16.A.4 Reproducing the Monte Carlo Experiment
• Exercises
• References
• Notes
• PART 4 USEFUL FINANCIAL FEATURES
• Chapter 17 Structural Breaks
• 17.1 Motivation
• 17.2 Types of Structural Break Tests
• 17.3 CUSUM Tests
• 17.4 Explosiveness Tests
• Exercises
• References
• Chapter 18 Entropy Features
• 18.1 Motivation
• 18.2 Shannon’s Entropy
• 18.3 The Plug-in (or Maximum Likelihood) Estimator
• 18.4 Lempel-Ziv Estimators
• 18.5 Encoding Schemes
• 18.6 Entropy of a Gaussian Process
• 18.7 Entropy and the Generalized Mean
• 18.8 A Few Financial Applications of Entropy
• Exercises
• References
• Bibliography
• Note
• Chapter 19 Microstructural Features
• 19.1 Motivation
• 19.2 Review of the Literature
• 19.3 First Generation: Price Sequences
• 19.4 Second Generation: Strategic Trade Models
• 19.5 Third Generation: Sequential Trade Models
• 19.6 Additional Features from Microstructural Datasets
• 19.7 What Is Microstructural Information?
• Exercises
• References
• PART 5 HIGH-PERFORMANCE COMPUTING RECIPES
• Chapter 20 Multiprocessing and Vectorization
• 20.1 Motivation
• 20.2 Vectorization Example
• 20.3 Single-Thread vs. Multithreading vs. Multiprocessing
• 20.4 Atoms and Molecules
• 20.5 Multiprocessing Engines
• 20.6 Multiprocessing Example
• Exercises
• Reference
• Bibliography
• Notes
• Chapter 21 Brute Force and Quantum Computers
• 21.1 Motivation
• 21.2 Combinatorial Optimization
• 21.3 The Objective Function
• 21.4 The Problem
• 21.5 An Integer Optimization Approach
• 21.6 A Numerical Example
• Exercises
• References
• Chapter 22 High-Performance Computational Intelligence and Forecasting Technologies
• 22.1 Motivation
• 22.2 Regulatory Response to the Flash Crash of 2010
• 22.3 Background
• 22.4 HPC Hardware
• 22.5 HPC Software
• 22.6 Use Cases
• 22.7 Summary and Call for Participation
• 22.8 Acknowledgments
• References
• Notes
• Index
• EULA