Advances In Financial Machine Learning

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.

About the Author xxi

PREAMBLE 1

1 Financial Machine Learning as a Distinct Subject 3

1.1 Motivation, 3

1.2 The Main Reason Financial Machine Learning Projects Usually Fail, 4

1.2.1 The Sisyphus Paradigm, 4

1.2.2 The Meta-Strategy Paradigm, 5

1.3 Book Structure, 6

1.3.1 Structure by Production Chain, 6

1.3.2 Structure by Strategy Component, 9

1.3.3 Structure by Common Pitfall, 12

1.4 Target Audience, 12

1.5 Requisites, 13

1.6 FAQs, 14

1.7 Acknowledgments, 18

Exercises, 19

References, 20

Bibliography, 20

Part 1 Data Analysis 21

2 Financial Data Structures 23

2.1 Motivation, 23

2.2 Essential Types of Financial Data, 23

2.2.1 Fundamental Data, 23

2.2.2 Market Data, 24

2.2.3 Analytics, 25

2.2.4 Alternative Data, 25

2.3 Bars, 25

2.3.1 Standard Bars, 26

2.3.2 Information-Driven Bars, 29

2.4 Dealing with Multi-Product Series, 32

2.4.1 The ETF Trick, 33

2.4.2 PCA Weights, 35

2.4.3 Single Future Roll, 36

2.5 Sampling Features, 38

2.5.1 Sampling for Reduction, 38

2.5.2 Event-Based Sampling, 38

Exercises, 40

References, 41

3 Labeling 43

3.1 Motivation, 43

3.2 The Fixed-Time Horizon Method, 43

3.3 Computing Dynamic Thresholds, 44

3.4 The Triple-Barrier Method, 45

3.5 Learning Side and Size, 48

3.6 Meta-Labeling, 50

3.7 How to Use Meta-Labeling, 51

3.8 The Quantamental Way, 53

3.9 Dropping Unnecessary Labels, 54

Exercises, 55

Bibliography, 56

4 Sample Weights 59

4.1 Motivation, 59

4.2 Overlapping Outcomes, 59

4.3 Number of Concurrent Labels, 60

4.4 Average Uniqueness of a Label, 61

4.5 Bagging Classifiers and Uniqueness, 62

4.5.1 Sequential Bootstrap, 63

4.5.2 Implementation of Sequential Bootstrap, 64

4.5.3 A Numerical Example, 65

4.5.4 Monte Carlo Experiments, 66

4.6 Return Attribution, 68

4.7 Time Decay, 70

4.8 Class Weights, 71

Exercises, 72

References, 73

Bibliography, 73

5 Fractionally Differentiated Features 75

5.1 Motivation, 75

5.2 The Stationarity vs. Memory Dilemma, 75

5.3 Literature Review, 76

5.4 The Method, 77

5.4.1 Long Memory, 77

5.4.2 Iterative Estimation, 78

5.4.3 Convergence, 80

5.5 Implementation, 80

5.5.1 Expanding Window, 80

5.5.2 Fixed-Width Window Fracdiff, 82

5.6 Stationarity with Maximum M