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. INDICE: 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 Performance, 198 .14.4.1 Time–Weighted Rate of Return, 198 .14.5 Runs, 199 .14.5.1 Returns Concentration, 199 .14.5.2 Drawdown and Time under Water, 201 .14.5.3 Runs Statistics for Performance Evaluation, 201 .14.6 Implementation Shortfall, 202 .14.7 Efficiency, 203 .14.7.1 The Sharpe Ratio, 203 .14.7.2 The Probabilistic Sharpe Ratio, 203 .14.7.3 The Deflated Sharpe Ratio, 204 .14.7.4 Efficiency Statistics, 205 .14.8 Classification Scores, 206 .14.9 Attribution, 207 .Exercises, 208 .References, 209 .Bibliography, 209 .15 Understanding Strategy Risk 211 .15.1 Motivation, 211 .15.2 Symmetric Payouts, 211 .15.3 Asymmetric Payouts, 213 .15.4 The Probability of Strategy Failure, 216 .15.4.1 Algorithm, 217 .15.4.2 Implementation, 217 .Exercises, 219 .References, 220 .16 Machine Learning Asset Allocation 221 .16.1 Motivation, 221 .16.2 The Problem with Convex Portfolio Optimization, 221 .16.3 Markowitz s Curse, 222 .16.4 From Geometric to Hierarchical Relationships, 223 .16.4.1 Tree Clustering, 224 .16.4.2 Quasi–Diagonalization, 229 .16.4.3 Recursive Bisection, 229 .16.5 A Numerical Example, 231 .16.6 Out–of–Sample Monte Carlo Simulations, 234 .16.7 Further Research, 236 .16.8 Conclusion, 238 .Appendices, 239 .16.A.1 Correlation–based Metric, 239 .16.A.2 Inverse Variance Allocation, 239 .16.A.3 Reproducing the Numerical Example, 240 .16.A.4 Reproducing the Monte Carlo Experiment, 242 .Exercises, 244 .References, 245 .PART 4 USEFUL FINANCIAL FEATURES 247 .17 Structural Breaks 249 .17.1 Motivation, 249 .17.2 Types of Structural Break Tests, 249 .17.3 CUSUM Tests, 250 .17.3.1 Brown–Durbin–Evans CUSUM Test on Recursive Residuals, 250 .17.3.2 Chu–Stinchcombe–White CUSUM Test on Levels, 251 .17.4 Explosiveness Tests, 251 .17.4.1 Chow–Type Dickey–Fuller Test, 251 .17.4.2 Supremum Augmented Dickey–Fuller, 252 .17.4.3 Sub– and Super–Martingale Tests, 259 .Exercises, 261 .References, 261 .18 Entropy Features 263 .18.1 Motivation, 263 .18.2 Shannon s Entropy, 263 .18.3 The Plug–in (or Maximum Likelihood) Estimator, 264 .18.4 Lempel–Ziv Estimators, 265 .18.5 Encoding Schemes, 269 .18.5.1 Binary Encoding, 270 .18.5.2 Quantile Encoding, 270 .18.5.3 Sigma Encoding, 270 .18.6 Entropy of a Gaussian Process, 271 .18.7 Entropy and the Generalized Mean, 271 .18.8 A Few Financial Applications of Entropy, 275 .18.8.1 Market Efficiency, 275 .18.8.2 Maximum Entropy Generation, 275 .18.8.3 Portfolio Concentration, 275 .18.8.4 Market Microstructure, 276 .Exercises, 277 .References, 278 .Bibliography, 279 .19 Microstructural Features 281 .19.1 Motivation, 281 .19.2 Review of the Literature, 281 .19.3 First Generation: Price Sequences, 282 .19.3.1 The Tick Rule, 282 .19.3.2 The Roll Model, 282 .19.3.3 High–Low Volatility Estimator, 283 .19.3.4 Corwin and Schultz, 284 .19.4 Second Generation: Strategic Trade Models, 286 .19.4.1 Kyle s Lambda, 286 .19.4.2 Amihud s Lambda, 288 .19.4.3 Hasbrouck s Lambda, 289 .19.5 Third Generation: Sequential Trade Models, 290 .19.5.1 Probability of Information–based Trading, 290 .19.5.2 Volume–Synchronized Probability of Informed Trading, 292 .19.6 Additional Features from Microstructural Datasets, 293 .19.6.1 Distibution of Order Sizes, 293 .19.6.2 Cancellation Rates, Limit Orders, Market Orders, 293 .19.6.3 Time–Weighted Average Price Execution Algorithms, 294 .19.6.4 Options Markets, 295 .19.6.5 Serial Correlation of Signed Order Flow, 295 .19.7 What Is Microstructural Information?, 295 .Exercises, 296 .References, 298 .PART 5 HIGH–PERFORMANCE COMPUTING RECIPES 301 .20 Multiprocessing and Vectorization 303 .20.1 Motivation, 303 .20.2 Vectorization Example, 303 .20.3 Single–Thread vs. Multithreading vs. Multiprocessing, 304 .20.4 Atoms and Molecules, 306 .20.4.1 Linear Partitions, 306 .20.4.2 Two–Nested Loops Partitions, 307 .20.5 Multiprocessing Engines, 309 .20.5.1 Preparing the Jobs, 309 .20.5.2 Asynchronous Calls, 311 .20.5.3 Unwrapping the Callback, 312 .20.5.4 Pickle/Unpickle Objects, 313 .20.5.5 Output Reduction, 313 .20.6 Multiprocessing Example, 315 .Reference, 317 .Bibliography, 317 .21 Brute Force and Quantum Computers 319 .21.1 Motivation, 319 .21.2 Combinatorial Optimization, 319 .21.3 The Objective Function, 320 .21.4 The Problem, 321 .21.5 An Integer Optimization Approach, 321 .21.5.1 Pigeonhole Partitions, 321 .21.5.2 Feasible Static Solutions, 323 .21.5.3 Evaluating Trajectories, 323 .21.6 A Numerical Example, 325 .21.6.1 Random Matrices, 325 .21.6.2 Static Solution, 326 .21.6.3 Dynamic Solution, 327 .Exercises, 327 .References, 328 .22 High–Performance Computational Intelligence and Forecasting Technologies 329Kesheng Wu and Horst D. Simon .22.1 Motivation, 329 .22.2 Regulatory Response to the Flash Crash of 2010, 329 .22.3 Background, 330 .22.4 HPC Hardware, 331 .22.5 HPC Software, 335 .22.5.1 Message Passing Interface, 335 .22.5.2 Hierarchical Data Format 5, 336 .22.5.3 In Situ Processing, 336 .22.5.4 Convergence, 337 .22.6 Use Cases, 337 .22.6.1 Supernova Hunting, 337 .22.6.2 Blobs in Fusion Plasma, 338 .22.6.3 Intraday Peak Electricity Usage, 340 .22.6.4 The Flash Crash of 2010, 341 .22.6.5 Volume–synchronized Probability of Informed Trading .22.6.6 Revealing High Frequency Events with Non–uniform Fast Fourier Transform, 347 .22.7 Summary and Call for Participation, 349 .22.8 Acknowledgments, 350 .References, 350 .Index 353
- ISBN: 978-1-119-48208-6
- Editorial: John Wiley & Sons
- Encuadernacion: Cartoné
- Páginas: 400
- Fecha Publicación: 04/05/2018
- Nº Volúmenes: 1
- Idioma: Inglés