Applying genetic programming to analyze moving average and long & mid-term trends of stock prices
遗传程序设计分析股价移动平均及中长期走势*

This paper employed genetic programming (GP) to analyze stock price. The task tried to find out how far it could go if used only one element, which was the price, to predict the stock market, based on the understanding that it was impossible to distinguish all the interactions between various elements in the stock market. Our work proposed two multi-scale approaches trying to predict stock prices. One was to use GP to form empirical formulas to predict the moving average lines of stock prices; the other was to use GP to do long & mid-term predictions on pre-processed data. The aim was to find empirical laws for specific enterprises stock prices based on previous stock price data. Simulations show that the method to predict the moving average and long & mid-term trends of stock prices is effective.