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JACIII Vol.15 No.5 pp. 515-524
doi: 10.20965/jaciii.2011.p0515
(2011)

Paper:

Multi-Order Rules Extraction by Genetic Network Programming with Rule Accumulation and its Application to Stock Trading Problems

Yafei Xing, Singo Mabu, Lian Yuzhu, and Kotaro Hirasawa

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

Received:
November 23, 2010
Accepted:
April 14, 2011
Published:
July 20, 2011
Keywords:
genetic network programming, reinforcement learning, rule accumulation, stock trading, multiorder rules
Abstract

As the effectiveness of the trading rules for stock trading problems has been verified, a method of extracting multi-order rules by Genetic Network Programming (GNP) is proposed using the rule accumulation for improving the efficiency of the trading rules in this paper. GNP is one of the evolutionary computations having a directed graph structure. Because of this special structure, the rule accumulation from GNP individuals is more effective for trading the stock than other methods. In this paper, there are two main points: rule extraction and trading action determination. Rule extraction is carried out in the training period, where the rules including the 1st order rules and multi-order rules, are extracted from the best individual and accumulated into the rule pools generation by generation. In the testing period, the trading action is determined by the matching degree of the stock price information with the rules, and the profits of the trading are evaluated. In the simulations, the stock prices of 16 brands in 2004, 2005 and 2006 are used for the training and those in 2007 for the testing. The simulation results show that the multi-order rules perform better than the 1st order rules. So, it is proved that themulti-order rules extracted by GNP is more effective than the 1st order rules for stock trading.

Cite this article as:
Yafei Xing, Singo Mabu, Lian Yuzhu, and Kotaro Hirasawa, “Multi-Order Rules Extraction by Genetic Network Programming with Rule Accumulation and its Application to Stock Trading Problems,” J. Adv. Comput. Intell. Intell. Inform., Vol.15, No.5, pp. 515-524, 2011.
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References
  1. [1] J. H. Holland, “Adaptation in Natural and Artificial Systems,” University of Michigan Press, Ann Arbor, 1975.
  2. [2] D. E. Goldberg, “Genetic Algorithm in search, optimization and machine learning,” Addison-Wesley, 1989.
  3. [3] J. R. Koza, “Genetic Programming on the Programming of Computers by Means of Natural Selection,” MIT Press, Cambridge, MA, 1992.
  4. [4] J. R. Koza, “Genetic Programming II, Automatic Discovery of Reusable Programs,” Cambridge, Mass., MIT Press, 1994.
  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] T. Eguchi, K. Hirasawa, J. Hu, and N. Ota, “Study of evolutionary multiagent models based on symbiosis,” IEEE Trans. Syst., Man and Cybern. B, Vol.36, No.1, pp. 179-193, 2006.
  7. [7] R. S. Sutton and A. G. Barto, “Reinforcement Learning-An Introduction,” Cambridge, Massachusetts, London, England, MIT Press 1998.
  8. [8] S. Mabu, H. Hatakeyama, M. T. Thu, K. Hirasawa, and J. Hu, “Genetic Network Programming with Reinforcement Learning and Its Application to Making Mobile Robot Behavior,” IEEJ Trans, EIS, Vol.126, No.8, pp. 1009-1015, 2006.
  9. [9] 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.
  10. [10] S. Mabu, Y. Lian, Y. Chen, and K. Hirasawa, “Generating Stock Trading Signals Based on Matching Degree with Extracted Rules by Genetic Network Programming,” SICE Annual Conf. 2010, The Grand Hotel, Taipei, Taiwan, pp. 1164-1169, 2010.
  11. [11] K. H. Lee and G. S. Jo, “Expert System for Predicting Stock Market Timing using a Candlestick Chart,” Expert System with Applications, Vol.16, pp. 357-364, 1999.
  12. [12] Y. Izumi, T. Yamaguchi, S. Mabu, K. Hirasawa, and J. Hu, “Trading Rules on the stock Market using Genetic Network Programming with Candlestick Chart,” 2006 IEEE Congress on Evolutionary Computation, pp. 8531-8536, Sheraton Vancouver Wall Centre Hotel, Vancouver, BC, Canada, July 16-21, 2006.
  13. [13] S. Mabu, Y. Izumi, K. Hirasawa, and T. Furuzuki, “Trading Rules on Stock Markets Using Genetic Network Programming with Candle Chart,” T. SICE, Vol.43, pp. 317-322, 2007 (in Japanese).
  14. [14] Y. Izumi, K. Hirasawa, and T. Furuzuki, “Trading Rules on the Stock Markets Using Genetic Network Programming with Importance Index,” T. SICE, Vol.42, No.5, pp. 559-566, 2006 (in Japanese).
  15. [15] Y. Yang, J. Li, S. Mabu, and K. Hirasawa, “GNP-Sarsa with Subroutines for Trading Rules on Stock Markets,” 2010 IEEE Int. Conf. on Systems, Man, and Cybernetics, pp. 1161-1165, 2010.

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