Librería Portfolio

Machine learning (ML) is changing virtually every aspect of our lives. Today ML algorithms accomplish tasks that until recently only expert humans could perform. As it relates to finance, this is the most exciting time to adopt a disruptive technology that will transform how everyone invests for generations. Readers will learn how to structure Big data in a way that is amenable to ML algorithms; how to conduct research with ML algorithms on that data; how to use supercomputing methods; how to backtest your discoveries while avoiding false positives. The book addresses real-life problems faced by practitioners on a daily basis, and explains scientifically sound solutions using math, supported by code and examples. Readers become active users who can test the proposed solutions in their particular 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.



TABLE OF CONTENTS
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 Memory Preservation, 84

5.7 Conclusion, 88

Exercises, 88

References, 89

Bibliography, 89

PART 2 MODELLING 91

6 Ensemble Methods 93

6.1 Motivation, 93

6.2 The Three Sources of Errors, 93

6.3 Bootstrap Aggregation, 94

6.3.1 Variance Reduction, 94

6.3.2 Improved Accuracy, 96

6.3.3 Observation Redundancy, 97

6.4 Random Forest, 98

6.5 Boosting, 99

6.6 Bagging vs. Boosting in Finance, 100

6.7 Bagging for Scalability, 101

Exercises, 101

References, 102

Bibliography, 102

7 Cross-Validation in Finance 103

7.1 Motivation, 103

7.2 The Goal of Cross-Validation, 103

7.3 Why K-Fold CV Fails in Finance, 104

7.4 A Solution: Purged K-Fold CV, 105

7.4.1 Purging the Training Set, 105

7.4.2 Embargo, 107

7.4.3 The Purged K-Fold Class, 108

7.5 Bugs in Sklearn's Cross-Validation, 109

Exercises, 110

Bibliography, 111

8 Feature Importance 113

8.1 Motivation, 113

8.2 The Importance of Feature Importance, 113

8.3 Feature Importance with Substitution Effects, 114

8.3.1 Mean Decrease Impurity, 114

8.3.2 Mean Decrease Accuracy, 116

8.4 Feature Importance without Substitution Effects, 117

8.4.1 Single Feature Importance, 117

8.4.2 Orthogonal Features, 118

8.5 Parallelized vs. Stacked Feature Importance, 121

8.6 Experiments with Synthetic Data, 122

Exercises, 127

References, 127

9 Hyper-Parameter Tuning with Cross-Validation 129

9.1 Motivation, 129

9.2 Grid Search Cross-Validation, 129

9.3 Randomized Search Cross-Validation, 131

9.3.1 Log-Uniform Distribution, 132

9.4 Scoring and Hyper-parameter Tuning, 134

Exercises, 135

References, 136

Bibliography, 137

PART 3 BACKTESTING 139

10 Bet Sizing 141

10.1 Motivation, 141

10.2 Strategy-Independent Bet Sizing Approaches, 141

10.3 Bet Sizing from Predicted Probabilities, 142

10.4 Averaging Active Bets, 144

10.5 Size Discretization, 144

10.6 Dynamic Bet Sizes and Limit Prices, 145 Exercises, 148

References, 149

Bibliography, 149

11 The Dangers of Backtesting 151

11.1 Motivation, 151

11.2 Mission Impossible: The Flawless Backtest, 151

11.3 Even If Your Backtest Is Flawless, It Is Probably Wrong, 152

11.4 Backtesting Is Not a Research Tool, 153

11.5 A Few General Recommendations, 153

11.6 Strategy Selection, 155

Exercises, 158

References, 158

Bibliography, 159

12 Backtesting through Cross-Validation 161

12.1 Motivation, 161

12.2 The Walk-Forward Method, 161

12.2.1 Pitfalls of the Walk-Forward Method, 162

12.3 The Cross-Validation Method, 162

12.4 The Combinatorial Purged Cross-Validation Method, 163

12.4.1 Combinatorial Splits, 164

12.4.2 The Combinatorial Purged Cross-Validation Backtesting Algorithm, 165

12.4.3 A Few Examples, 165

12.5 How Combinatorial Purged Cross-Validation Addresses Backtest Overfitting, 166

Exercises, 167

References, 168

13 Backtesting on Synthetic Data 169

13.1 Motivation, 169

13.2 Trading Rules, 169

13.3 The Problem, 170

13.4 Our Framework, 172

13.5 Numerical Determination of Optimal Trading Rules, 173

13.5.1 The Algorithm, 173

13.5.2 Implementation, 174

13.6 Experimental Results, 176

13.6.1 Cases with Zero Long-Run Equilibrium, 177

13.6.2 Cases with Positive Long-Run Equilibrium, 180

13.6.3 Cases with Negative Long-Run Equilibrium, 182

13.7 Conclusion, 192

Exercises, 192

References, 193

14 Backtest Statistics 195

14.1 Motivation, 195

14.2 Types of Backtest Statistics, 195

14.3 General Characteristics, 196

14.4 Perfor