Buy-Sell Strategy Model Construction with Hybrid XCS
Many financial time series forecasting techniques have been so far developed for predicting stock prices. However, only with the forecasting value of the next time, it is difficult to determine the optimal buy-sell strategy and get benefit. On the other hand, because the financial time series change severely, it can hardly be identified by a single global model. One model will just be suitable for some kinds of changing patterns, but fail on other patterns. Then, in this paper, we proposed a Hybrid XCS (eXtended Classifier System) learning method by adopting multiple local models. Each local model is called Slaver-Agent and trained with XCS method. A unique Master-Agent chooses which Slaver-Agent is the most effectively for a given changing pattern. With the hybrid learning structure, multiple Slaver-Agents work alternately, and the limitation of learning by one single agent can be overcome. Their learning objective is to obtain profitable transaction decisions directly and get maximum return benefit after several transactions. Experiments have been performed on several well known securities, and the results have been compared with a single agent and some traditional Technical Analysis strategies.
XCS Hybrid XCS Buy-Sell Strategy Financial Time Series
Shumei Liu Feng You
College of Information Science & Technology, Beijing University of Chemical Technology, BUCT Beijing, China
国际会议
第四届国际计算机新科技与教育学术会议(2009 4th International Conference on Computer Science & Education)
南京
英文
148-153
2009-07-25（万方平台首次上网日期，不代表论文的发表时间）