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【实例简介】OReilly.Artificial.Intelligence.in.Finance.pdf
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Table of Contents Preface. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix Part I. Machine Intelligence 1. Artificial Intelligence. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Algorithms 3 Types of Data 4 Types of Learning 4 Types of Tasks 8 Types of Approaches 8 Neural Networks 9 OLS Regression 9 Estimation with Neural Networks 13 Classification with Neural Networks 20 Importance of Data 22 Small Data Set 23 Larger Data Set 26 Big Data 28 Conclusions 29 References 30 2. Superintelligence. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 Success Stories 32 Atari 32 Go 38 Chess 40 Importance of Hardware 42 iii Forms of Intelligence 44 Paths to Superintelligence 45 Networks and Organizations 46 Biological Enhancements 46 Brain-Machine Hybrids 47 Whole Brain Emulation 48 Artificial Intelligence 49 Intelligence Explosion 50 Goals and Control 50 Superintelligence and Goals 51 Superintelligence and Control 53 Potential Outcomes 54 Conclusions 56 References 56 Part II. Finance and Machine Learning 3. Normative Finance. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 Uncertainty and Risk 62 Definitions 62 Numerical Example 63 Expected Utility Theory 66 Assumptions and Results 66 Numerical Example 69 Mean-Variance Portfolio Theory 72 Assumptions and Results 72 Numerical Example 75 Capital Asset Pricing Model 82 Assumptions and Results 83 Numerical Example 85 Arbitrage Pricing Theory 90 Assumptions and Results 91 Numerical Example 93 Conclusions 95 References 96 4. Data-Driven Finance. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 Scientific Method 100 Financial Econometrics and Regression 101 Data Availability 104 Programmatic APIs 105 iv | Table of Contents Structured Historical Data 105 Structured Streaming Data 108 Unstructured Historical Data 110 Unstructured Streaming Data 112 Alternative Data 113 Normative Theories Revisited 117 Expected Utility and Reality 118 Mean-Variance Portfolio Theory 123 Capital Asset Pricing Model 130 Arbitrage Pricing Theory 134 Debunking Central Assumptions 143 Normally Distributed Returns 143 Linear Relationships 153 Conclusions 155 References 156 Python Code 156 5. Machine Learning. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161 Learning 162 Data 162 Success 165 Capacity 169 Evaluation 172 Bias and Variance 178 Cross-Validation 180 Conclusions 183 References 183 6. AI-First Finance. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 185 Efficient Markets 186 Market Prediction Based on Returns Data 192 Market Prediction with More Features 199 Market Prediction Intraday 204 Conclusions 205 References 207 Part III. Statistical Inefficiencies 7. Dense Neural Networks. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 211 The Data 212 Baseline Prediction 214 Table of Contents | v Normalization 218 Dropout 220 Regularization 222 Bagging 225 Optimizers 227 Conclusions 228 References 228 8. Recurrent Neural Networks. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 229 First Example 230 Second Example 234 Financial Price Series 237 Financial Return Series 240 Financial Features 242 Estimation 243 Classification 244 Deep RNNs 245 Conclusions 246 References 247 9. Reinforcement Learning. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 249 Fundamental Notions 250 OpenAI Gym 251 Monte Carlo Agent 255 Neural Network Agent 257 DQL Agent 260 Simple Finance Gym 264 Better Finance Gym 268 FQL Agent 271 Conclusions 277 References 278 Part IV. Algorithmic Trading 10. Vectorized Backtesting. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 281 Backtesting an SMA-Based Strategy 282 Backtesting a Daily DNN-Based Strategy 289 Backtesting an Intraday DNN-Based Strategy 295 Conclusions 301 References 301 vi | Table of Contents 11. Risk Management. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 303 Trading Bot 304 Vectorized Backtesting 308 Event-Based Backtesting 311 Assessing Risk 318 Backtesting Risk Measures 322 Stop Loss 324 Trailing Stop Loss 326 Take Profit 328 Conclusions 332 References 332 Python Code 333 Finance Environment 333 Trading Bot 335 Backtesting Base Class 339 Backtesting Class 342 12. Execution and Deployment. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 345 Oanda Account 346 Data Retrieval 347 Order Execution 351 Trading Bot 357 Deployment 364 Conclusions 368 References 369 Python Code 369 Oanda Environment 369 Vectorized Backtesting 372 Oanda Trading Bot 373 Part V. Outlook 13. AI-Based Competition. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 379 AI and Finance 380 Lack of Standardization 382 Education and Training 383 Fight for Resources 385 Market Impact 386 Competitive Scenarios 387 Risks, Regulation, and Oversight 388 Table of Contents | vii Conclusions 391 References 392 14. Financial Singularity. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 395 Notions and Definitions 396 What Is at Stake? 396 Paths to Financial Singularity 400 Orthogonal Skills and Resources 401 Scenarios Before and After 402 Star Trek or Star Wars 403 Conclusions 404 References 404 Part VI. Appendixes A. Interactive Neural Networks. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 407 B. Neural Network Classes. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 425 C. Convolutional Neural Networks. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 439 Index. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 447
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