Advances in financial machine learning

Cover -- Title Page -- Copyright -- Contents -- About the Author -- Preamble -- 1 Financial Machine Learning as a Distinct Subject -- 1.1 Motivation -- 1.2 The Main Reason Financial Machine Learning Projects Usually Fail -- 1.2.1 The Sisyphus Paradigm -- 1.2.2 The Meta-Strategy Paradigm -- 1.3 Book Structure -- 1.3.1 Structure by Production Chain -- 1.3.2 Structure by Strategy Component -- 1.3.3 Structure by Common Pitfall -- 1.4 Target Audience -- 1.5 Requisites -- 1.6 FAQs -- 1.7 Acknowledgments -- Exercises -- References -- Bibliography -- PART 1 Data Analysis -- 2 Financial Data Structures -- 2.1 Motivation -- 2.2 Essential Types of Financial Data -- 2.2.1 Fundamental Data -- 2.2.2 Market Data -- 2.2.3 Analytics -- 2.2.4 Alternative Data -- 2.3 Bars -- 2.3.1 Standard Bars -- 2.3.2 Information-Driven Bars -- 2.4 Dealing with Multi-Product Series -- 2.4.1 The ETF Trick -- 2.4.2 PCA Weights -- 2.4.3 Single Future Roll -- 2.5 Sampling Features -- 2.5.1 Sampling for Reduction -- 2.5.2 Event-Based Sampling -- Exercises -- References -- 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 -- 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 -- 4.5.1 Sequential Bootstrap -- 4.5.2 Implementation of Sequential Bootstrap -- 4.5.3 A Numerical Example -- 4.5.4 Monte Carlo Experiments -- 4.6 Return Attribution -- 4.7 Time Decay -- 4.8 Class Weights -- Exercises -- References -- Bibliography -- 5 Fractionally Differentiated Features -- 5.1 Motivation.