Evolving Trading Rule-Based Policies

  • Robert Gregory Bradley
  • Anthony Brabazon
  • Michael O’Neill
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6025)

Abstract

Trading-rule representation is an important factor to consider when designing a quantitative trading system. This study implements a trading strategy as a rule-based policy. The result is an intuitive human-readable format which allows for seamless integration of domain knowledge. The components of a policy are specified and represented as a set of rewrite rules in a context-free grammar. These rewrite rules define how the components can be legally assembled. Thus, strategies derived from the grammar are well-formed, domain-specific, solutions. A grammar-based Evolutionary Algorithm, Grammatical Evolution (GE), is then employed to automatically evolve intra-day trading strategies for the U.S. Stock Market. The GE methodology managed to discover profitable rules with realistic transaction costs included. The paper concludes with a number of suggestions for future work.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Brabazon, A., O’Neill, M.: Evolving technical trading rules for spot foreign-exchange markets using grammatical evolution. Computational Management Science 1(3), 311–327 (2004)MATHCrossRefGoogle Scholar
  2. 2.
    Brabazon, A., O’Neill, M.: Intra-day trading using grammatical evolution. In: Brabazon, A., O’Neill, M. (eds.) Biologically Inspired Algorithms for Financial Modelling, pp. 203–210. Springer, Berlin (2006)Google Scholar
  3. 3.
    Dempsey, I., O’Neill, M., Brabazon, A.: Grammatical constant creation. In: Deb, K., et al. (eds.) GECCO 2004. LNCS, vol. 3103, pp. 447–458. Springer, Heidelberg (2004)Google Scholar
  4. 4.
    Dempsey, I., O’Neill, M., Brabazon, A.: Adaptive trading with grammatical evolution. In: Proceedings of the 2006 IEEE Congress on Evolutionary Computation, pp. 9137–9142. IEEE Press, Los Alamitos (2006), http://ncra.ucd.ie/papers/cec2006_adaptiveTrading.ps (Vancouver July 6-21, 2006)
  5. 5.
    Goldberg, D.E.: The Design of Innovation: Lessons from and for Competent Genetic Algorithms. Kluwer Academic Publishers, Dordrecht (2002)MATHGoogle Scholar
  6. 6.
    Istvan, S., András, L.: Learning to play using low-complexity rule-based policies: Illustrations through ms. pac-man. J. Artif. Intell. Res. (JAIR) 30, 659–684 (2007), http://www.jair.org/media/2368/live-2368-3623-jair.pdf Google Scholar
  7. 7.
    Lo, A.W.: The adaptive markets hypothesis: Market efficiency from an evolutionary perspective. Journal of Portfolio Management (2004) (forthcoming)Google Scholar
  8. 8.
    O’Neill, M., Ryan, C.: Grammatical Evolution. Kluwer, Dordrecht (2003)MATHGoogle Scholar
  9. 9.
    Saks, P., Maringer, D.: Evolutionary Money Management. In: Giacobini, M., et al. (eds.) EvoWorkshops 2009. LNCS, vol. 5484, pp. 162–171. Springer, Heidelberg (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Robert Gregory Bradley
    • 1
    • 2
  • Anthony Brabazon
    • 1
    • 2
  • Michael O’Neill
    • 1
    • 3
  1. 1.Natural Computing Research and Applications GroupUniversity College DublinIreland
  2. 2.School of BusinessUniversity College DublinIreland
  3. 3.School of Computer Science and InformaticsUniversity College DublinIreland

Personalised recommendations