A Study on Stock Markets Based on Genetic Network Programming with Learning Method
Yan Chen, Mao Dao-wei · 2008
In this paper, the Genetic Network Programming (GNP) for creating trading rules on stocks is described. GNP is an evolutionary computation, which represents its solutions using graph structures and has some useful features inherently. In this paper, GNP is applied to creating a stock trading model. There are three important points: The first important point is to combine GNP with Sarsa Learning which is one of the reinforcement learning algorithms. The second important point is that GNP uses candlestick chart and selects appropriate technical indices to judge the buying and selling timing of stocks. The third important point is that sub-nodes are used in each node to determine appropriate actions and to select appropriate stock price information depending on the situation. In the simulations, the trading model is trained and tested using the stock prices of 16 brands listed in the first section of Tokyo stock market in Japan. From the simulation results, it is clarified that the trading rules of the proposed method obtain much higher profits than Buy&Hold method and its effectiveness has been confirmed.