Paper:

# A Cooperative Coevolutionary Stock Trading Model Using Genetic Network Programming-Sarsa

## Yang Yang, Zhaoping He, Shingo Mabu, and Kotaro Hirasawa

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

This paper presents a cooperative coevolutionary approach for stock trading model using Genetic Network Programming-Sarsa called CCGNP-Sarsa. Although theoretically, a single algorithm with sufficient size could solve any problem, in practice the stock market problem is too large and too complex to construct the appropriate algorithm to solve it. For such problems, cooperative coevolution which simultaneously evolves several species with the sum of their fitness values has been proposed as a successful alternative and was applied to make the stock trading models an integrated one. Such an approach allows different species of the GNP-Sarsa model to evolve in a parallel and cooperative manner, which makes the generated model more robust, generalized and efficient for generating stock trading strategies. CCGNP-Sarsa places as few restrictions as possible to the structure, allowing the model to obtain a wide variety of architecture during the evolution and to be easily used to solve complicated problems. To confirm the effectiveness of the proposed method, the simulations are carried out and compared with other methods like GNP-Sarsa with subroutines, GNP-Sarsa and Buy&Hold method. The results shows that the stock trading models using CCGNP-Sarsa outperforms all the other methods.

*J. Adv. Comput. Intell. Intell. Inform.*, Vol.16, No.5, pp. 581-590, 2012.

- [1] G. E. P. Box, G. M. Jenkins, and G. C. Reinsel, “Time Series Analysis: Forecasting and Control,” John Wiley, 2008.
- [2] N. Sarantis, “Nonlinearities, cyclical behaviour and predictability in stock markets: international evidence,” Int. J. of Forecasting, Vol.17, No.3, pp. 459-482, 2001.
- [3] Y. Fang and D. Xu, “The predictability of asset returns: an approach combining technical analysis and time series forecasts,” Int. J. of Forecasting, Vol.19, No.3, pp. 369-385, 2003.
- [4] T. Kohonen, “An introduction to neural computing,” Neural Networks, Vol.1, No.1, pp. 3-16, 1988.
- [5] D. E. Goldberg, “Genetic algorithms in search, optimization, and machine learning,” Addison-Wesley, 1989.
- [6] J. R. Koza, “On the programming of computers by means of natural selection,” The MIT Press, 1992.
- [7] D. Enke and S. Thawornwong, “The use of data mining and neural networks for forecasting stock market returns,” Expert Systems with Applications, Vol.29, No.4, pp. 927-940, 2005.
- [8] M. Lam, “Neural network techniques for financial performance prediction: integrating fundamental and technical analysis,” Decision Support Systems, Vol.37, No.4, pp. 567-581, 2004.
- [9] T. Chavarnakul and D. Enke, “Intelligent technical analysis based equivolume charting for stock trading using neural networks,” Expert Systems with Applications, Vol.34, No.2, pp. 1004-1017, 2008.
- [10] R. J. Bauer, “Genetic algorithms and investment strategies,” Wiley, New York, 1994.
- [11] F. Allen and R. Karjalainen, “Using genetic algorithms to find technical trading rules,” J. of Financial Economics, Vol.51, pp. 245-271, 1999.
- [12] M. A. H. Dempster and C. M. Jones, “A real-time adaptive trading system using genetic programming,” Quantitative Finance, Vol.1, pp. 397-413, 2001.
- [13] J. Y. Potvin, P. Soriano, and M. Vallee, “Generating trading rules on the stock markets with genetic programming,” Computers & Operations Research, Vol.31, No.7, pp. 1033-1047, 2004.
- [14] T. Yu, S. H. Chen, and T. W. Kuo, “Discovering financial technical trading rules using genetic programming with lambda abstraction,” Genetic programming theory and practice II, Springer, Chapter 2, pp. 11-30, 2005.
- [15] X. Yao and Y. Liu, “Making use of population information in evolutionary artificial neural networks,” Systems, Man, and Cybernetics, Part B, Vol.28, No.3, pp. 417-425, 1998.
- [16] 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.
- [17] 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.
- [18] 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.
- [19] R. S. Sutton and A. G. Barto, “Reinforcement Learning: An Introduction,” MIT Press, Cambridge, MA, 1998.
- [20] 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.
- [21] B. Li, S. Mabu, and K. Hirasawa, “Genetic Network Programming with Subroutines for Automatic Program Generation,” IEEJ Trans. on Electrical and Electronic Engineering, Vol.7, No.2, pp. 197-207, 2012.
- [22] Y. Yang, S. Mabu, J. H. Li, and K. Hirasawa, “Genetic Network Programming-Sarsa with Subroutines for Trading Rules on Stock Markets,” J. of Advanced Computational Intelligence and Intelligent Informatics, Vol.15, No.7, pp. 488-494, 2011.
- [23] Y. Yang, Y. Gu, S. Mabu, and K. Hirasawa, “Genetic Network Programming- Sarsa with Multiple Subroutines for Trading Rules on Stock Markets,” IEEJ Trans. on Electronics, Information and Systems, Vol.132, No.3, pp. 439-447, 2012.
- [24] Mi. Potter and K. D. Jong, “A cooperative coevolutionary approach to function optimization,” in Proc. of Parallel Problem Solving From Nature III, pp. 249-257, 1994.
- [25] X. Li and X. Yao, “Cooperatively Coevolving Particle Swarms for Large Scale Optimization,” IEEE Trans. on Evolutionary Computation, Vol.16, No.2, pp. 210-224, 2012.

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