Strategic article

Chapter 65438 +0 Quantitative Investment Concept

1. 1 What is quantitative investment 2?

1. 1. 1 quantitative investment definition 2

1. 1.2 Misunderstanding of Quantitative Investment 3

1.2 Comparison between quantitative investment and traditional investment 6

1.2. 1 disadvantages of traditional investment strategy 6

1.2.2 Advantages of quantitative investment strategy 7

1.2.3 Comparison between quantitative investment and traditional investment strategies 8

1.3 Quantifying investment history 10

1.3. 1 Development of quantitative investment theory 10

1.3.2 developing overseas quantitative funds 12

1.3.3 China Quantitative Investment 15

1.4 Main contents of quantitative investment 16

1.5 Main methods of quantifying investment 2 1

The second chapter quantitative stock selection 25 pages

2. 1 multifactor 26

2. 1. 1 Basic concepts 27

2. 1.2 strategy model 27

2. 1.3 empirical case: multi-factor stock selection model 30

2.2 Style rotates 35.

2.2. 1 Basic concepts 35

2.2.2 Expected life cycle model of profit 38

2.2.3 Policy model 40

2.2.4 Empirical case: CITIC Standard & Poor's Style 4 1

2.2.5 Empirical Case: Style of Large and Small Disks 44

2.3 Industry rotation 47

2.3. 1 Basic concepts 47

2.3.2 m2 Industry Rotation Strategy 50

2.3.3 Market sentiment rotation strategy 52

2.4 Capital flows 56

2.4. 1 Basic concepts 56

2.4.2 Policy model 59

2.4.3 Empirical case: the stock selection strategy of capital flow 60 pages

2.5 momentum reversal 63

2.5. 1 basic concepts 63

2.5.2 Policy model 67

2.5.3 Empirical case: momentum stock selection strategy and reversal stock selection strategy 70

2.6 Consensus expectations 73

2.6. 1 Basic concepts 74

2.6.2 Policy model 76

2.6.3 Empirical Case: Consistent Expectation Model Case 78

2.7 Trend Tracking 84

2.7. 1 Basic concepts 84

2.7.2 Policy model 86

2.7.3 Empirical Case: Trend Tracking Stock Selection Model 92

2.8 chip stock selection 94

2.8. 1 Basic concepts

2.8.2 Policy model 97

2.8.3 Empirical Case: Chip Stock Selection Model 99

2.9 performance evaluation 104

2.9. 1 yield index 104

Risk index 105

Chapter 3 Quantization Timing 1 1 1

3. 1 trend tracking 1 12

3. 1. 1 Basic concepts 1 12

3. 1.2 traditional trend indicator 1 13

3. 1.3 adaptive average 12 1

3.2 Market sentiment 125

3.2. 1 basic concepts 126

3.2.2 sentiment index 128

3.2.3 Empirical Case: Timing Strategy of Emotional Indicators 129

3.3 Effective funds 133

3.3. 1 basic concepts 133

3.3.2 Policy model 134

3.3.3 Empirical Case: Effective Capital Timing Model 137

3.4 Niuxiong Line 14 1

3.4. 1 Basic concepts 14 1

3.4.2 Policy model 143

3.4.3 Empirical Case: Bull-Bear Line Timing Model 144

3.5 husrt index 146

3.5. 1 basic concepts 146

3.5.2 Policy Model 148

3.5.3 Experience Case 149

3.6 Support Vector Machine 152

3.6. 1 basic concepts 152

3.6.2 Policy Model 153

3.6.3 Empirical case: svm timing model 155

3.7 Swartz model 160

3.7. 1 basic concepts 160

3.7.2 Policy model 16 1

3.7.3 Empirical Case: Schwartz Model 164

3.8 Abnormal indicator 168

Market noise 168

3.8.2 Industry concentration 170

Hindenburg omen 172

Chapter IV Stock Index Futures Arbitrage 180

4. 1 basic concepts 18 1

4. 1. 1 introduction to arbitrage 18 1

4. 1.2 arbitrage strategy 183

4.2 spot arbitrage 185

4.2. 1 pricing model 185

4.2.2 spot index replication 186

4.2.3 Positive Arbitrage Case 190

4.2.4 Settlement date arbitrage 192

4.3 Intertemporal arbitrage 195

4.3. 1 intertemporal arbitrage principle 195

4.3.2 No arbitrage interval 196

4.3.3 Intertemporal Arbitrage Trigger and Termination 197

4.3.4 Empirical Case: Intertemporal Arbitrage Strategy 199

4.3.5 Major arbitrage opportunities 200

4.4 Impact cost 203

4.4. 1 main indicators 204

4.4.2 Empirical Case: Impact Costs 205

4.5 Profit Management 208

4.5. 1 var method 208

4.5.2 Calculation Method of VaR 209

4.5.3 Empirical Case 2 1 1

Chapter V Commodity Futures Arbitrage 2 14

5. 1 basic concept 2 15

5. 1. 1 arbitrage condition 2 16

5. 1.2 basic arbitrage mode 2 17

5. 1.3 arbitrage reserve 2 19

5. 1.4 general arbitrage portfolio 22 1

5.2 Spot arbitrage 225

Basic principles 225

5.2.2 Operation Flow 226

Vat risk 230

5.3 Intertemporal Arbitrage 23 1

5.3. 1 arbitrage strategy 23 1

5.3.2 Empirical case: pvc intertemporal arbitrage strategy 233

5.4 Cross-market arbitrage 234

5.4. 1 arbitrage strategy 234

5.4.2 Empirical Case: Cross-market Arbitrage of Luntong-Shanghai Copper 235

5.5 Cross-variety arbitrage 236

5.5. 1 arbitrage strategy 237

5.5.2 Empirical Cases 238

5.6 Abnormal State Handling 240

Chapter VI Statistical Arbitrage 242

6. 1 Basic concepts 243

6. 1. 1 statistical arbitrage definition 243

6. 1.2 Pairing transaction 244

6.2 Matching transactions 247

6.2. 1 cointegration strategy 247

6.2.2 Principal Component Strategy 254

6.2.3 Performance evaluation 256

6.2.4 Empirical Case: Paired Transaction 258

6.3 Stock index arbitrage 26 1

6.3. 1 Industry index arbitrage 26 1

6.3.2 National index arbitrage 263

6.3.3 Mainland index arbitrage 264

6.3.4 Global index arbitrage 266

6.4 Margin arbitrage 267

6.4. 1 stock margin arbitrage 267

6.4.2 Convertible bonds-margin arbitrage 268

6.4.3 Stock Index Futures-Margin Arbitrage 269

6.4.4 Closed-end fund-margin arbitrage 27 1

6.5 foreign exchange arbitrage 272

6.5. 1 spread arbitrage 273

6.5.2 Currency pair arbitrage 275

Chapter VII Option Arbitrage 277

7. 1 Basic concepts 278

7. 1. 1 option introduction 278

7. 1.2 option trading 279

7. 1.3 Bull-Bear Syndrome 280

7.2 Stock/option arbitrage 283

7.2. 1 stock-stock option arbitrage 283

7.2.2 Stock index option arbitrage 284

7.3 conversion arbitrage 285

7.3. 1 conversion arbitrage 285

7.3.2 Reverse conversion arbitrage 287

7.4 Intertemporal Arbitrage 288

7.4. 1 Buy Cross Arbitrage 289

7.4.2 Sell long and short arbitrage 29 1

7.5 Long-span arbitrage 293

7.5. 1 Buy long-span arbitrage 293

7.5.2 Selling Long Span Arbitrage 294

7.6 Butterfly Arbitrage 296

7.6. 1 Buy Butterfly Arbitrage 296

7.6.2 Selling Butterfly Arbitrage 298

7.7 Eagle Arbitrage 299

7.7. 1 Buy Eagle Arbitrage 300

7.7.2 Selling Eagle Arbitrage 30 1

Chapter 8 Algorithm Trading 304

8. 1 Basic concepts 305

8. 1. 1 algorithmic transaction definition 305

8. 1.2 algorithm transaction classification 306

8. 1.3 algorithm transaction design 308

8.2 Passive Trading Algorithm 309

8.2. 1 impact cost 3 10

8.2.2 Waiting Risk 3 12

8.2.3 Common passive trading strategies 3 14

8.3 vwap algorithm 3 16

Standard vwap algorithm 3 16

8.3.2 Improved vwap Algorithm 3 19

Chapter 9 Other Strategies 323

9. 1 event arbitrage 324

9. 1. 1 M&A arbitrage strategy 324

9. 1.2 private arbitrage 325

9. 1.3 Heavy Arbitrage Suspension Stock Portfolio 326

9. 1.4 closed portfolio arbitrage 327

9.2 etf arbitrage 328

9.2. 1 Basic concepts 328

9.2.2 Risk-free arbitrage 330

9.2.3 Other arbitrage

9.3 lof arbitrage 335

9.3. 1 Basic concepts

9.3.2 Model Strategy 336

9.3.3 Empirical Case: lof Arbitrage 337

9.4 high frequency trading 34 1

9.4. 1 Liquidity rebate transaction 34 1

9.4.2 Game Algorithm Trading 342

9.4.3 Automatic Market Maker Strategy 343

9.4.4 Programmatic trading 343

Theoretical article

Chapter 10 artificial intelligence 346

10. 1 main content 347

10. 1. 1 machine learning 347

10. 1.2 automatic reasoning 350

10. 1.3 expert system

10. 1.4 pattern recognition 356

10. 1.5 artificial neural network358

10. 1.6 genetic algorithm 362

10.2 application of artificial intelligence in quantitative investment 366

10.2. 1 pattern recognition short-term timekeeping 366

10.2.2 rbf neural network stock price forecast 370

10.2.3 IPO prediction based on genetic algorithm 375

Chapter 1 1 Data Mining 38 1

1 1. 1 basic concepts 382

11.1.1main model 382

1 1. 1.2 typical method 384

1 1.2 Main contents 385

1 1.2. 1 classification and prediction 385

1 1.2.2 association rules 39 1

1 1.2.3 cluster analysis 397

1 1.3 application of data mining in quantitative investment 400

1 1.3. 1 stock clustering analysis method based on som network 400

1 1.3.2 disk rotation based on association rules 403

Chapter 12 Wavelet analysis 407

Basic concepts 408

12.2 wavelet transform main content 409

12.2. 1 continuous wavelet transform 409

12.2.2 discretization of continuous wavelet transform 4 10

12.2.3 multiresolution analysis and mallat algorithm 4 1 1

Application of 12.3 wavelet analysis in quantitative investment

12.3. 1 k-line wavelet denoising 4 14

12.3.2 financial time series data forecast 420

Chapter 13 Support Vector Machine 429

Basic concepts 430

13. 1. 1 linear svm 430

13. 1.2 nonlinear support vector machine

13. 1.3 svm classifier parameter selection 435

13.10.4 generalization of support vector machine classifier from two classes to multi-class10000.000000000003

13.2 fuzzy support vector machine 437

Added 13.2. 1 svm 437 with fuzzy post-processing.

Support vector machine training algorithm with fuzzy factors 13.2.2/666/kloc-0 /5666.66666666666666

13.3 the application of support vector machine in quantitative investment120666.1066868666666

13.3. 1 complex financial time series data forecast 440

13.3.2 trend inflection point forecast 445

Chapter 14 Fractal theory 452

Basic concepts 453

14. 1. 1 fractal definition 453

Several typical fractals (14.10.2) 50000.000000000505

Study on the application of the fractal theory of14.1.3.50000.000000000605

14.2 main contents 457

14.2. 1 fractal dimension 457

14.2.2 l system 458

14.2.3 ifs system 460

Application of Fractal Theory in Quantifying Investment

14.3. 1Megatrend forecast 46 1

14.3.2 exchange rate forecast 466

Chapter 15 stochastic process 473

Basic concepts 473

15.2 Main contents 476

15.2. 1 distribution function of stochastic process

Digital characteristics of 15.2.2 stochastic processes

15.2.3 Several Common Stochastic Processes 477

Stationary stochastic process 479

15.3 Grey Markov Chain Stock Market Forecast 480

Chapter 16 it technology 486

16. 1 data warehouse technology

16. 1. 1 from database to data warehouse 487

16. 1.2 data organization in data warehouse 489

16.10.3 key technologies of data warehouse+0.50000.00000000606

16.2 programming language 493

16.2. 1 GPU algorithm transaction 493

MATLAB language 497

16.2.3 c# language

Chapter 17 main data and tools 509

17. 1 multivariate analysis system 509

17.2 Multiple parts: Programmatic trading platform 5 1 1

17.3 trading pioneer: futures automatic trading platform 5 14

17.4 Dalian stock exchange arbitrage instruction 5 18

17.5 mt5: automatic foreign exchange trading platform 522

Chapter 18 Quantitative Hedging Trading System: D- Alpha 528

18. 1 system architecture 528

18.2 policy analysis process 530

18.3 core algorithm 532

18.4 verification result 534

Table directory index

Table 1 Comparison of Different Investment Strategies 7

Table 2 1 Candidate factors of multi-factor stock selection model 30

Preliminary test of 3 1 candidate factors in multi-factor model

Table 2 3 Effective factors of multi-factor model test 32

Table 2 4 Factors after Eliminating Redundancy in Multi-factor Model 33

Table 2 5 Multi-factor model portfolio segment return rate 33

Table 2 6 Morningstar Market Style Discrimination Method 36

Table 2 7 Identification of basic investment style, sharp rate of return 37

Table 2 8 CITIC S&P Style Index 4 1

Table 2 9 Monthly Return of Style Momentum Strategy Combination 43

Table 2 10 Monthly average rate of return of large and small disk style rotation strategy 46

Table 2 1 1 China Currency Cycle Segment (2000-2009) 49

Table 2 12 Shanghai and Shenzhen 300 Industry Index Statistics 50

Table 2 13 Yield of different industries in different currency stages 5 1

Table 2 14 Calculation Method of China Merchants' Capital Flow Model (cmsmf) 58

Table 2 15 Definition of Stock Selection Index of China Merchants Capital Flow Model (cmsmf) 59

Table 2 16 Capital Flow Model Strategy-CSI 300 6 1

Table 2 17 Capital Flow Model Strategy-Whole Market 62

Table 2 18 Relative benchmark of average annualized excess return of momentum combination (partial) 68

Table 2 19 Average annualized excess return of reverse portfolio relative benchmark (partial) 69

Table 2 20 Risk-return Analysis of Momentum Strategy 7 1

Table 2 2 1 risk-return analysis of reverse strategy 73

Table 2 22 Rate of Return of Trend Tracking Technology 93

Table 2 23 Comparison of returns of various indicators in chip stock selection model 99

Table 3120 groups of parameters and their performance of the best timing test of ma indicator 1 17

Table 3 Performance Comparison of Independent Timing Trading under the Optimal Parameters of 24 Trend Indicators 120

Table 3 3 The comprehensive timing strategies under different signal numbers include transaction cost 120.

Table 3-4 Rate of Return Analysis of Adaptive Moving Average Timing Strategy 124

Table 3 5 Market Emotion Category 126

Table 3 6 Comparison of Monthly Returns of Shanghai and Shenzhen 300 Index in Different Emotional Regions 128

Table 3 7 Comparison of Monthly Returns of Shanghai and Shenzhen 300 Index in Different Emotional Change Regions 129

Table 3 8 Comparison of Monthly Returns of Shanghai and Shenzhen 300 Index in Different Emotional Regions 130

Table 3 9 Comparison of Monthly Returns of Shanghai and Shenzhen 300 Index in Different Emotional Change Regions 130

Table 3 10 Statistics of sentiment index's Timing Rate of Return 132

Table 3 1 1 SVM timing model 156 indicators

Table 3 12 svm Forecast Results Index Summary CSI 300 Index 156

Table 313 Performance of SVM Timing Model in the Overall Market 156

Table 314 Performance of SVM Timing Model in Unilateral Rising Market 157

Table 315 Performance of SVM Timing Model in Unilateral Down Market 158

Table 316 Performance of SVM Timing Model in Shock City 159

Table 3 17 The yield of bear market timing noise trading is 170.

Table 4 1 tracking errors of various methods under different stock numbers (annualized) 190

Table 4-2 Analysis of Intertemporal Arbitrage Long Process of Stock Index Futures 199

Table 4.3 Market Fluctuations Covered by Different Margin Levels and Their Probability 2 1 1

Table 4 4 Margin Coverage Ratio under Different Warehouse Receipt Holding Periods 2 12

Table 1 50 most relevant portfolios of the underlying stock in the sample period (partial) 248

Table 6 2 Test of residual stationarity and autocorrelation 249

Table 6.3 The average profit of opening and closing positions under different thresholds is 25 1.

Table 6-4 Yield and optimal threshold 252 obtained by different models in samples.

Table 6 5 Yield (%) obtained outside the sample by different models and different extrapolation methods 253

Table 6 6 Yield and Optimal Threshold 255 of Principal Component Pairing Transactions in Samples

Table 6 7 Influence of Out-of-Sample Principal Component Pairing on Trading 255

Table 6-8 Statistical Arbitrage Results under Different Models 256

Table 6 9 Empirical Results of Delayed Opening+Early Closing Strategy 260

Table 6 10 Matchmaking Transaction Results of Various Industries 26 1

Table 7 1 Comprehensive Analysis of Long Stock Option Arbitrage Table 283

Table 7 2 Profit and Loss Analysis of Long Stock-Stock Option Arbitrage Case Table 284

Table 7 3 Profit and Loss Analysis of Stock Index Option Arbitrage Case Table 285

Table 7-4 Conversion Arbitrage Analysis Flow 286

Table 7-5 Comprehensive Analysis of Buying Cross Arbitrage Table 289

Table 7 6 Buying Cross Arbitrage Transaction Details 289

Table 7 7 Comprehensive Analysis of Selling Cross Arbitrage Table 29 1

Table 7-8 Selling Cross Arbitrage Transaction Details 292

Table 7-9 Comprehensive Analysis of Buying Long Span Arbitrage Table 293

Table 7 10 Comprehensive Analysis of Selling Long Span Arbitrage Table 294

Table 7 Comprehensive Analysis 1 1 Buy Butterfly Arbitrage Table 296

Table 7 12 Comprehensive Analysis of Selling Butterfly Arbitrage Table 298

Table 7 13 Buy Eagle Arbitrage Analysis Table 300

Table 7 14 Comprehensive Analysis of Selling Eagle Arbitrage Table 30 1

Table 9 1 Main M&A Methods 324

Table 9-2 M&A Arbitrage Process 325

Table 9 3 Two Forward Arbitrages of Penghua 300 lof 339

Table 9 4 Two Reverse Arbitrages of Penghua 300 lof 340

Table 10 1 conjunction system in automatic reasoning 352

Table 10 2 pattern recognition short-term timing sample data classification 369

Table 10 3 rbf neural network stock price forecast results 375

Table 10 4 parameter setting of genetic algorithm for new stock forecasting 379

Table 10 5 genetic algorithm for prediction results of new shares 380

Table 1 1 1 decision tree data table 389

Table 1 1 2 Association Rule Case Data Table 392

Table 1 1 3 som stock cluster analysis results 403

Table 1 1 4 2 1 Stock Sector Index 404 Boolean Relation Table Data Fragment

Table 12 1 Comparison between the predicted and actual daily closing price of SDB A by wavelet analysis method 427

Table 12 2 root mean square error values of different decomposition layers 428

Table 13 1 svm CSI 300 Index Prediction Error 445

Table132 Comparison between SVM index prediction and neural network prediction 445

Table 13 3 Definition and Morphology of Technology Reversal Point 448

Table 13 4 svm trend inflection point prediction results 450

Table 14 1 Main parameter values of fractal before and after continuous surge 463

Table 14 2 Fractal main parameter values before and after the continuous plunge of 465

Table 14 3 foreign exchange r/ s analysis indicators 469

Table 14 4 v(r/s) curve regression test 470

Table 15 1 Prediction of Grey Markov Chain in Shenzhen Component Index Sample (2005/ 1—2006/8) 484

Table 15 2 Prediction of Shenzhen Stock Exchange Index by Grey Markov Chain (2006/9—2006/ 12) 484

Table 16- 12 data type 0 VBA 499

Table 18- 1 d-alpha system global market yield analysis 534