Trading rules on stock markets using Genetic Network Programming-Sarsa Learning with plural subroutines
Yunqing Gu, Shingo Mabu, Yang Yang, Jianhua Li, Kotaro Hirasawa · Society of Instrument and Control Engineers of Japan · 2011
In this paper, Genetic Network Programming-Sarsa Learning (GNP-Sarsa) used for creating trading rules on stock markets is enhanced by adding plural subroutines. Subroutine node — a new kind of node which works like ADF (Automatically Defined Function) in Genetic Programming (GP) has been proved to have positive effects on the stock-trading model using GNP-Sarsa. In the proposed method, not only one kind of subroutine but plural subroutines with different structures are used to improve the performance of GNP-Sarsa with subroutines. Each subroutine node could indicate its own input and output node of the subroutine, which could be also evolved. In the simulations, totally 16 brands of stock from 2001 to 2004 are used to investigate the improvement of GNP-Sarsa with plural subroutines. The simulation results show that the proposed approach can obtain more flexible GNP structure and get higher profits in stock markets.