Trading rules on stock markets using Genetic Network Programming-Sarsa Learning with plural subroutines

Created by W.Langdon from gp-bibliography.bib Revision:1.8110

@InProceedings{Gu:2011:SICE,

• author = "Yunqing Gu and Shingo Mabu and Yang Yang and Jianhua Li and Kotaro Hirasawa",
• title = "Trading rules on stock markets using Genetic Network Programming-Sarsa Learning with plural subroutines",
• booktitle = "Proceedings of SICE Annual Conference (SICE 2011)",
• year = "2011",
• month = "13-18 " # sep,
• pages = "143--148",
• address = "Waseda University, Tokyo, Japan",
• keywords = "genetic algorithms, genetic programming, GNP structure, automatically defined function, genetic network programming-Sarsa learning, plural subroutines, stock markets, subroutine node, trading rules, stock markets",
• URL = "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6060592",
• isbn13 = "978-1-4577-0714-8",
• size = "6 pages",
• abstract = "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.",
• notes = "Also known as \cite{6060592}",

}

Genetic Programming entries for Yunqing Gu Shingo Mabu Yang Yang Jianhua Li Kotaro Hirasawa

Citations