Real Time Updating Genetic Network Programming for adapting to the change of stock prices

The key in stock trading model is to take the right actions for trading at the right time, primarily based on accurate forecast of future stock trends. Since an effective trading with given information of stock prices needs an intelligent strategy for the decision making, we applied genetic network programming (GNP) to creat a stock trading model. In this paper, we present a new method called real time updating genetic network programming (RTU-GNP) for adapting to the change of stock prices. There are two important points in this paper: First, the RTU-GNP method makes a stock trading decision considering both the recommendable information of technical indices and the change of stock prices according to the real time updating. Second, we combine RTU-GNP with a reinforcement learning algorithm to creat the programs efficiently. The experimental results on the Japanese stock market show that the trading model with the proposed RTU-GNP method outperforms other models without time updating method. It yielded significantly higher profits than the traditional trading model without time uptating. We also compare the experimental results using the proposed method with Buy&Hold method to confirm its effectiveness, and it is clarified that the proposed trading model can obtain much higher profits than Buy&Hold method.