Parallel Evolutionary Algorithms for Stock Market Trading Rule Selection on Many-Core Graphics Processors

  • Piotr Lipinski
Part of the Studies in Computational Intelligence book series (SCI, volume 380)


This chapter concerns stock market decision support systems that build trading expertise on the basis of a set of specific trading rules, analysing financial time series of recent stock price quotations, and focusses on the process of rule selection. It proposes an improvement of two popular evolutionary algorithms for rule selection by reinforcing them with two local search operators. The algorithms are also adapted for parallel processing on many-core graphics processors. Using many-core graphics processors enables not only a reduction in the computing time, but also an exhaustive local search, which significantly improves solution quality, without increasing computing time. Experiments carried out on data from the Paris Stock Exchange confirmed that the approach proposed outperforms the classic approach, in terms of the financial relevance of the investment strategies discovered as well as in terms of the computing time.


Local Search Sharpe Ratio Trading Rule Iterative Local Search Trading Signal 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Baluja, S.: Population-Based Incremental Learning: A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning. Research Report CMU-CS-94-163 Carnegie Mellon University (1994)Google Scholar
  2. 2.
    Brabazon, A., O’Neill, M.: Biologically Inspired Algorithms for Financial Modelling. Springer, Heidelberg (2006)zbMATHGoogle Scholar
  3. 3.
    Brandstetter, A., Artusi, A.: Radial Basis Function Networks GPU-Based Implementation. IEEE Transactions on Neural Networks 19, 2150–2161 (2008)CrossRefGoogle Scholar
  4. 4.
    Chiosa, I., Kol, A.: GPU-Based Multilevel Clustering. IEEE Transactions on Visualization and Computer Graphics 17, 132–145 (2011)CrossRefGoogle Scholar
  5. 5.
    Dang, J., Brabazon, A., O’Neill, M., Edelman, D.: Estimation of an EGARCH Volatility Option Pricing Model using a Bacteria Foraging Optimisation Algorithm. In: Natural Computing in Computational Finance, vol. 100, pp. 109–127. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  6. 6.
    Dempsey, I., O’Neill, M., Brabazon, A.: Adaptive Trading with Grammatical Evolution. In: Proceedings of the 2006 Congress on Evolutionary Computation (CEC 2006), pp. 2587–2592. IEEE Press, Los Alamitos (2006)Google Scholar
  7. 7.
    Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison Wesley, Reading (1989)zbMATHGoogle Scholar
  8. 8.
    Korczak, J., Lipinski, P.: Evolutionary Building of Stock Trading Experts in a Real-Time System. In: Proceedings of the 2004 Congress on Evolutionary Computation (CEC 2004), pp. 940–947. IEEE Press, Los Alamitos (2004)CrossRefGoogle Scholar
  9. 9.
    Larranaga, P., Lozano, J.: Estimation of Distribution Algorithms, A New Tool for Evolutionary Computation. Kluwer Academic Publishers, Dordrecht (2002)zbMATHGoogle Scholar
  10. 10.
    Lipinski, P.: Evolutionary Decision Support System for Stock Market Trading. In: Dochev, D., Pistore, M., Traverso, P. (eds.) AIMSA 2008. LNCS (LNAI), vol. 5253, pp. 405–409. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  11. 11.
    Lipinski, P.: Evolutionary Strategies for Building Risk-Optimal Portfolios. In: Natural Computing in Computational Finance. SCI, vol. 100, pp. 53–65. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  12. 12.
    Loraschi, A., Tettamanzi, A.: An Evolutionary Algorithm for Portfolio Selection within a Downside Risk Framework. In: Dunis, C.L. (ed.) Forecasting Financial Markets, pp. 275–286. Wiley, Chichester (1996)Google Scholar
  13. 13.
    Murphy, J.: Technical Analysis of the Financial Markets NUIF (1998)Google Scholar
  14. 14.
    Saks, P., Maringer, D.: Evolutionary Money Management. In: Giacobini, M., Brabazon, A., Cagnoni, S., Di Caro, G.A., Ekárt, A., Esparcia-Alcázar, A.I., Farooq, M., Fink, A., Machado, P. (eds.) EvoWorkshops 2009. LNCS, vol. 5484, pp. 162–171. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  15. 15.
    Sharpe, W.: Capital Asset Prices: A Theory of Market Equilibrium under Conditions of Risk. Journal of Finance 19, 425–442 (1964)CrossRefGoogle Scholar
  16. 16.
    Tsang, E., Li, J., Markose, S., Er, H., Salhi, A., Iori, G.: EDDIE In Financial Decision Making. Journal of Management and Economics 4(4) (2000)Google Scholar
  17. 17.
    Wahib, M., Munawar, A., Munetomo, M., Akama, K.: A Bayesian Optimization Algorithm for De Novo ligand design based docking running over GPU. In: Proceedings of the IEEE Congress on Evolutionary Computation (CEC 2010), pp. 1–8. IEEE Press, Los Alamitos (2010)Google Scholar

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© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Piotr Lipinski
    • 1
  1. 1.Institute of Computer ScienceUniversity of WroclawWroclawPoland

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