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首页> 外文会议>International Conference on Computer Engineering and Technology >A COLLABORATIVE TRADING MODEL BY SUPPORT VECTOR REGRESSION AND TS FUZZY RULE FOR DAILY STOCK TURNING POINTS DETECTION
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A COLLABORATIVE TRADING MODEL BY SUPPORT VECTOR REGRESSION AND TS FUZZY RULE FOR DAILY STOCK TURNING POINTS DETECTION

机译：通过支持向量回归和日常股票转折点检测的TS模糊规则的协作交易模型

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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.
机译：本研究调查了每日股票转折点检测问题。支持向量回归模型已应用于各种预测应用，并证明具有稳定的性能。在本研究中，SVR已被用于预测交易信号，因为它即使在股票价格变化的复杂环境下也可以有效地处理整体信息。来自历史数据库的交易信号是从分段线性表示的应用程序源自股价。因此，研究期内的股票价格临时底部和股价均由PLR识别。应用模糊规则来计算与交易信号相交的动态阈值并提供交易点。模糊规则培训并从培训期间从PLR产生的交易信号中获得。 SVR和TS模糊规则的协作交易模式用于检测不同趋势趋势下的各种股票的交易点。实验结果表明我们的系统更有利可图，可以在实时交易系统中实施。

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