Generating trading rules on the stock markets with Robust Genetic Network Programming using variance of fitness values

In this paper, Robust Genetic Network Programming (R-GNP) for generating trading rules on stocks is described. R-GNP is a new evolutionary computation, which represents its solutions using graph structures. It has been clarified that R-GNP works well especially in dynamic environments. In the proposed hybrid stock trading model, R-GNP is applied to generating stock trading rules using variance of fitness values. The unique point is that the generalization ability of R-GNP is improved by using the robust fitness function, which consists of the fitness function by original data and fitness functions by a good number of correlated data. Generally speaking, the hybrid intelligent system consists of three steps, the priority selection by portfolio β, the optimization by Genetic Relation Algorithm (GRA) and stock trading by R-GNP. In the simulations, the trading model is trained using the stock prices of 10 brands in Tokyo Stock Exchange, and then the generalization ability is tested. From the simulation results, it is clarified that the trading rules created by the proposed R-GNP model obtain much higher profits than the traditional methods and its effectiveness has been confirmed.