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

Learn to understand and implement the latest machine learning innovations to improve your investment performance Machine learning (ML) is changing virtually every aspect of our lives. Today, ML algorithms accomplish tasks that – until recently – only expert humans could perform. And finance is ripe for disruptive innovations that will transform how the following generations understand money and invest. In the book, readers will learn how to: Structure big data in a way that is amenable to ML algorithms Conduct research with ML algorithms on big data Use supercomputing methods and back test their discoveries while avoiding false positives Advances in Financial Machine Learning addresses real life problems faced by practitioners every day, and explains scientifically sound solutions using math, supported by code and examples. Readers become active users who can test the proposed solutions in their individual setting. Written by a recognized expert and portfolio manager, this book will equip investment professionals with the groundbreaking tools needed to succeed in modern finance.