Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 30132
A Hybrid Expert System for Generating Stock Trading Signals

Authors: Hosein Hamisheh Bahar, Mohammad Hossein Fazel Zarandi, Akbar Esfahanipour

Abstract:

In this paper, a hybrid expert system is developed by using fuzzy genetic network programming with reinforcement learning (GNP-RL). In this system, the frame-based structure of the system uses the trading rules extracted by GNP. These rules are extracted by using technical indices of the stock prices in the training time period. For developing this system, we applied fuzzy node transition and decision making in both processing and judgment nodes of GNP-RL. Consequently, using these method not only did increase the accuracy of node transition and decision making in GNP's nodes, but also extended the GNP's binary signals to ternary trading signals. In the other words, in our proposed Fuzzy GNP-RL model, a No Trade signal is added to conventional Buy or Sell signals. Finally, the obtained rules are used in a frame-based system implemented in Kappa-PC software. This developed trading system has been used to generate trading signals for ten companies listed in Tehran Stock Exchange (TSE). The simulation results in the testing time period shows that the developed system has more favorable performance in comparison with the Buy and Hold strategy.

Keywords: Fuzzy genetic network programming, hybrid expert system, technical trading signal, Tehran stock exchange.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1125353

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1405

References:


[1] Seng-cho, T.C., et al., A stock selection DSS combining AI and technical analysis. Annals of Operations Research, 1997. 75: p. 335-353.
[2] Bauer, R.J., Genetic algorithms and investment strategies. Vol. 19. 1994: John Wiley & Sons.
[3] Koza, J.R., Genetic programming: on the programming of computers by means of natural selection. Vol. 1. 1992: MIT press.
[4] Holland, J., Adaption in natural and artiļ¬cial systems. Ann Arbor MI: The University of Michigan Press, 1975.
[5] Mousavi, S., A. Esfahanipour, and M.H.F. Zarandi, A novel approach to dynamic portfolio trading system using multitree genetic programming. Knowledge-Based Systems, 2014. 66: p. 68-81.
[6] Esfahanipour, A. and S. Mousavi, A genetic programming model to generate risk-adjusted technical trading rules in stock markets. Expert Systems with Applications, 2011. 38(7): p. 8438-8445.
[7] Hirasawa, K., et al. Comparison between genetic network programming (GNP) and genetic programming (GP). in Evolutionary Computation, 2001. Proceedings of the 2001 Congress on. 2001. IEEE.
[8] Izumi, Y., et al. Trading rules on the stock markets using genetic network programming with candlestick chart. in Evolutionary Computation, 2006. CEC 2006. IEEE Congress on. 2006. IEEE.
[9] Mabu, S., K. Hirasawa, and J. Hu, A graph-based evolutionary algorithm: genetic network programming (GNP) and its extension using reinforcement learning. Evolutionary Computation, 2007. 15(3): p. 369-398.
[10] Mabu, S., et al. Stock trading rules using genetic network programming with actor-critic. in Evolutionary Computation, 2007. CEC 2007. IEEE Congress on. 2007. IEEE.
[11] Chen, Y., et al., A portfolio optimization model using Genetic Network Programming with control nodes. Expert Systems with Applications, 2009. 36(7): p. 10735-10745.
[12] Chen, Y., S. Mabu, and K. Hirasawa, A model of portfolio optimization using time adapting genetic network programming. Computers & operations research, 2010. 37(10): p. 1697-1707.
[13] Chen, Y., et al. Constructing portfolio investment strategy based on time adapting genetic network programming. in Evolutionary Computation, 2009. CEC'09. IEEE Congress on. 2009. IEEE.
[14] Sendari, S., S. Mabu, and K. Hirasawa. Fuzzy genetic Network Programming with Reinforcement Learning for mobile robot navigation. in Systems, Man, and Cybernetics (SMC), 2011 IEEE International Conference on. 2011. IEEE.
[15] Mabu, S., et al., Enhanced decision making mechanism of rule-based genetic network programming for creating stock trading signals. Expert Systems with Applications, 2013. 40(16): p. 6311-6320.
[16] Chen, Y. and X. Wang, A hybrid stock trading system using genetic network programming and mean conditional value-at-risk. European Journal of Operational Research, 2015. 240(3): p. 861-871.
[17] Chen, Y., et al. Trading rules on stock markets using genetic network programming with sarsa learning. in Proceedings of the 9th annual conference on Genetic and evolutionary computation. 2007. ACM.
[18] Chen, Y., et al., A genetic network programming with learning approach for enhanced stock trading model. Expert Systems with Applications, 2009. 36(10): p. 12537-12546.
[19] Mabu, S., et al. Generating stock trading signals based on matching degree with extracted rules by genetic network programming. in SICE Annual Conference 2010, Proceedings of. 2010. IEEE.