![Piecewise Linear Representation
Derive the trading signal
Tradition
Up Down : 1 [sell]
Down UP : 0 [buy]
Not quite related to the
price variation](https://image.slidesharecdn.com/integratingapiecewiselinearrepresentationmethodandaneuralnetworkmodelforstocktradingpointsprediction-111229074746-phpapp02/85/Integrating-a-piecewise-linear-representation-method-and-a-neural-network-model-for-stock-trading-points-prediction-10-320.jpg)
![Experimental results
S&P500 : four years data [2000-2003]](https://image.slidesharecdn.com/integratingapiecewiselinearrepresentationmethodandaneuralnetworkmodelforstocktradingpointsprediction-111229074746-phpapp02/85/Integrating-a-piecewise-linear-representation-method-and-a-neural-network-model-for-stock-trading-points-prediction-22-320.jpg)
Integrating a piecewise linear representation method and a neural network model for stock trading points prediction
- 1. Integrating a Piecewise Linear Representation Method and a Neural Network Model for Stock Trading Points Prediction Pei-Chann Chang, Chin-Yuan Fan, and Chen-Hao Liu TSMCC.2008 Presenter: Yu Hsiang Huang Date: 2011-12-30
- 2. Outline • Introduction • IPLR Model – Piecewise Linear Representation – Stepwise Regression Algorithm – Genetic Algorithm – Back-propagation Network • Experimental results • Conclusion
- 3. Introduction • Stock market – Highly nonlinear dynamic system • Interest rates, inflation rate, economic environments, political issues… • Most resent research – Derive accurate models – Predict the future price of stock movement • In this paper – Trading decision • Buy/Sell points – Critical role to make a profit
- 4. IPLR Model Candidate Stocks Screening Selected stock GA Technical indexes SRA Related input variable no PLR Turning point Trading signal Reach Expect output input number of generation ? Calculate profit Test BP(train) yes End Trading decision Related input variables BP Buy/sell Related input variables
- 5. Genetic Algorithm Initialization Randomly generate initial population 50 1 0 … 0 1 10 0.8 0.1 Selection Fitness function roulette-wheel selection Tournament selection Reproduction Crossover Two-point Mutation genetic diversity Termination # of generation , reach the best fitness value , …
- 6. IPLR Model Candidate Stocks Screening Selected stock GA PLR Turning point Trading signal
- 7. Piecewise Linear Representation Stock Turning price point Turning point t1 t2 t3 t4 t5 date segment1
- 9. Piecewise Linear Representation Get trend of time series data Calculate trend Only in turning point
- 10. Piecewise Linear Representation Derive the trading signal Tradition Up Down : 1 [sell] Down UP : 0 [buy] Not quite related to the price variation
- 11. Piecewise Linear Representation Derive the trading signal Redefine the trading signals
- 12. IPLR Model Candidate Stocks Screening Selected stock GA Technical indexes SRA Related input variable PLR Turning point Trading signal
- 14. Stepwise Regression Algorithm Apply by SPSS (Statistic Package for Social Science) Calculate the significant value S yes no X1 X2 Y X3 X4 no Last X ? X5 Xp yes Output
- 15. IPLR Model Candidate Stocks Screening Selected stock GA Technical indexes SRA Related input variable PLR Turning point Trading signal Expect output input BP(train)
- 17. IPLR Model Candidate Stocks Screening Selected stock GA Technical indexes SRA Related input variable PLR Turning point Trading signal Expect output input Test BP(train) Trading decision Related input variables BP Buy/sell
- 18. Back-propagation Network Trading decision Test data input to BP Change of the trading signal pass through the boundary value: Change is upward sell Change is downward buy Boundary value : 0.508
- 19. IPLR Model Candidate Stocks Screening Selected stock GA Technical indexes SRA Related input variable no PLR Turning point Trading signal Reach Expect output input number of generation ? Calculate profit Test BP(train) yes End Trading decision Related input variables BP Buy/sell Related input variables
- 20. Experimental results Historic data : from 2004/01/02 to 2006/04/12 Training data : 2004/01/02 to 2005/09/30 Testing data : 2005/10/1 to 2006/04/12 Up-trend : 30-day moving average cross over 90-day moving average Down-trend : 30-day moving average cross down 90-day moving average Steady : no major tendency of 30-day moving average with 90-day moving average
- 22. Experimental results S&P500 : four years data [2000-2003]
- 23. Conclusion • Trading decision > determine stock price itself • IPLR – PLR : find turning point – GA : improve the threshold value for PLR – BPN : train the connection of the model – Significant amount of profit • Clustering of financial time series data • A different forecasting model – SVM , FNN,… • A similar training pattern