A Collaborative Trading Model by Support Vector Regression and Ts Fuzzy Rule for Daily Stock Turning Points Detection
نویسندگان
چکیده
The daily stock turning point detection problems are investigated in this study. The Support Vector Regression model has been applied in various forecasting applications and proved to be with stable performances. In this research, SVR has been used to predict the trading signal since it could handle overall information effectively even under the complex environment of stock price variations. The trading signals from the historic database is derived from the application of piecewise linear representation of stock price. Therefore, the temporary bottoms and peaks of stock price within the studied period are identified by PLR. TS fuzzy rules were applied to calculate the dynamic threshold which intersects the trading signal and provides the trading points. The fuzzy rules were trained and obtained from the trading signals generated by PLR during the training period. A collaborative trading model of SVR and TS fuzzy rule is used to detect the trading points for various stocks of Taiwanese and America under different trend tendencies. The experimental results show our system is more profitable and can be implemented in real time trading system.
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