Genetic Network Programming-Sarsa with Subroutines for Trading Rules on Stock Markets

Yang Yang; Shingo Mabu; Jianhua Li; Kotaro Hirasawa

doi:10.20965/jaciii.2011.p0488

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JACIII Vol.15 No.5 pp. 488-494

doi: 10.20965/jaciii.2011.p0488

(2011)

Paper:

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Genetic Network Programming-Sarsa with Subroutines for Trading Rules on Stock Markets

Yang Yang, Shingo Mabu, Jianhua Li, and Kotaro Hirasawa

The Graduate School of Information, Production and Systems, Waseda University, 2-7 Hibikino, Wakamatsu-ku, Kitakyushu, Fukuoka 808-0135, Japan

Received:

November 30, 2010

Accepted:

February 16, 2011

Published:

July 20, 2011

Keywords:

genetic network programming, subroutine, evolutionary algorithms, stock trading model, technical index

Abstract

The purpose of this paper is to propose a new approach to generate effective subroutines, which are automatically discovered by the GNP-Sarsa programs combining evolution and reinforcement learning. We name it asGNP-Sarsa with subroutines (GNP_sb-Sarsa) and apply it to the trading rules on stock markets. In the proposal method, GNP_sb-Sarsa offers an alternative population, where individuals are represented by subroutines. Each main program of GNP_sb-Sarsa can refer to an individual in the subroutine population, after adding a new kind of node, namely subroutine node, in the graph network structure of GNP-Sarsa. GNP_sb-Sarsa containing a main program and a subroutine evolves by natural selection and genetic operations, where the gene of GNP_sb-Sarsa is the pair of the main GNP and its subroutine. That is, the genetic operations on GNP_sb-Sarsa are constrained by the gene structure on which they can operate. In the simulations, the stock prices of different brands from 2001 to 2004 are used to test the effectiveness of the GNP_sb-Sarsa. The results show that the proposed approach can provide reasonable opportunities for evolving complex solutions.

Cite this article as:

Yang Yang, Shingo Mabu, Jianhua Li, and Kotaro Hirasawa, “Genetic Network Programming-Sarsa with Subroutines for Trading Rules on Stock Markets,” J. Adv. Comput. Intell. Intell. Inform., Vol.15, No.5, pp. 488-494, 2011.

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References

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[2] S.Mabu, Y. Izumi, K. Hirasawa, and T. Furuzuki, “Trading Rules on Stock Markets Using Genetic Network Programming with Candle Chart,” Trans. of the Society of Instrument and Control Engineers, Vol.43, No.4, pp. 317-322, 2007.
[3] Y. Chen, S. Mabu, K. Shimada, and K. Hirasawa, “Trading Rules on Stock Markets Using Genetic Network Programming with Sarsa Learning,” J. of Advanced Computational Intelligence and Intelligent Informatics, Vol.12, No.4, pp. 383-392, 2008.
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[5] S. Mabu, K. Hirasawa, and J. Hu, “A Graph-Based Evolutionary Algorithm: Genetic Network Programming (GNP) and Its Extension Using Reinforcement Learning,” Evolutionary Computation, Vol.15, No.3, pp. 369-398, 2007.
[6] J. H. Holland, “Adaptation in Natural and Artificial Systems,” University of Michigan Press, 1975.
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[10] R. S. Sutton and A. G. Barto, “Reinforcement Learning: An Introduction,” The MIT Press, 1998.

[1] [1] S. Mabu, K. Hirasawa, and T. Furuzuki, “Trading Rules on Stock Markets Using Genetic Network Programming with Reinforcement Learning and Importance Index,” Trans. of the Society of Instrument and Control Engineers, Vol.42, No.5, pp. 559-566, 2006.

[2] [2] S.Mabu, Y. Izumi, K. Hirasawa, and T. Furuzuki, “Trading Rules on Stock Markets Using Genetic Network Programming with Candle Chart,” Trans. of the Society of Instrument and Control Engineers, Vol.43, No.4, pp. 317-322, 2007.

[3] [3] Y. Chen, S. Mabu, K. Shimada, and K. Hirasawa, “Trading Rules on Stock Markets Using Genetic Network Programming with Sarsa Learning,” J. of Advanced Computational Intelligence and Intelligent Informatics, Vol.12, No.4, pp. 383-392, 2008.

[4] [4] T. Eguchi, K. Hirasawa, J. Hu, and N. Ota, “A Study of Evolutionary Multiagent Models Based on Symbiosis,” IEEE Trans. Syst. Man Cybern. B, Cybern., Vol.36, No.1, pp. 179-193, 2006.

[5] [5] S. Mabu, K. Hirasawa, and J. Hu, “A Graph-Based Evolutionary Algorithm: Genetic Network Programming (GNP) and Its Extension Using Reinforcement Learning,” Evolutionary Computation, Vol.15, No.3, pp. 369-398, 2007.

[6] [6] J. H. Holland, “Adaptation in Natural and Artificial Systems,” University of Michigan Press, 1975.

[7] [7] D. E. Goldberg, “Genetic Algorithms in Search, Optimization, and Machine Learning,” Addison-Wesley, 1989.

[8] [8] J. R. Koza, “Genetic Programming: On the Programming of Computers by Means of Natural Selection,” The MIT Press, 1992.

[9] [9] J. R. Koza, “Genetic Programming II: Automatic Discovery of Reusable Programs,” The MIT Press, 1994.

[10] [10] R. S. Sutton and A. G. Barto, “Reinforcement Learning: An Introduction,” The MIT Press, 1998.