In this thesis, we propose a model which applies the genetic programming (GP) to train the profitable and stable trading strategy in the training period, and then the strategy is applied to trade stocks in the testing period. The variables for GP in our models include 6 basic information and 25 technical indicators. We perform our models on Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) from 2000/9/14 to 2010/5/21, approximately ten years. We conduct five experiments. In these experiments, we find that the trading strategies generated by GP with two arithmetic trees have more stable returns. In addition, if we obtain the trading strategies in three historical periods which are the most similar to the current training period, we earn higher return in the testing periods. In each experiment, 24 cases are considered, with training periods of 90, 180, 270, 365, 455, 545, 635 and 730 days, and testing periods of 90, 180 and 365 days, respectively. The testing period is rolling updated until the end of the experiment period. The best cumulative return 165.30\% occurs when 730-day training period pairs with 365-day testing period, which is much higher than the return of the buy-and-hold strategy 1.19\%.