Advances in Financial Machine Learning by Prado, Marcos Lopez de -- Read -- Imperial Library of Trantor

Index

Cover Praise Title page Copyright Dedication 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

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