Evolving Dynamic Trade Execution Strategies Using Grammatical Evolution

  • Wei Cui
  • Anthony Brabazon
  • Michael O’Neill
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6025)


Although there is a plentiful literature on the use of evolutionary methodologies for the trading of financial assets, little attention has been paid to potential use of these methods for efficient trade execution. Trade execution is concerned with the actual mechanics of buying or selling the desired amount of a financial instrument of interest. Grammatical Evolution (GE) is an evolutionary automatic programming methodology which can be used to evolve rule sets. In this paper we use a GE algorithm to discover dynamic, efficient, trade execution strategies which adapt to changing market conditions. The strategies are tested in an artificial limit order market. GE was found to be able to evolve quality trade execution strategies which are highly competitive with two benchmark trade execution strategies.


Limit Order Order Book Grammatical Evolution Limit Order Book Limit Order Market 
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.
    Almgren, R.: Execution costs. In: Encyclopedia of Quantitative Finance, pp. 346–357. Wiley, Chichester (2008)Google Scholar
  2. 2.
    Brabazon, A., O’Neill, M.: Biologically Inspired Algorithms for Financial Modeling. Springer, Heidelberg (2006)Google Scholar
  3. 3.
    Cao, C., Hansch, O., Wang, X.: Order placement strategies in a pure limit order book market. The Journal of Financial Research 26, 113–140 (2008)Google Scholar
  4. 4.
    Chakraborti, A., Toke, I., Patriarca, M., Abergel, F.: Econophysics: Empirical facts and agent-based models (2009),
  5. 5.
    Daniel, G.: Asynchronous simulations of a limit order book. PhD thesis, University of Manchester, UK (2006)Google Scholar
  6. 6.
    Duong, H., Kalev, P., Krishnamurti, C.: Order aggressiveness of institutional and individual investors. Pacific-Basin Finance Journal 1, 1–14 (2009)Google Scholar
  7. 7.
    Farmer, D., Gillemot, L., Iori, G., Krishnamurthy, S., Smith, E., Daniels, M.: A random order placement model of price formation in the continuous double auction. In: Blume, L., Durlauf, S. (eds.) The economy as an evolving complex system III. Oxford University Press, Oxford (2005)Google Scholar
  8. 8.
    Farmer, J.D., Patelli, P., Zovko, I.: The predictive power of zero intelligence models in financial markets. Proceedings of the National Academy of Sciences of the United States of America 102, 2254–2259 (2005)CrossRefGoogle Scholar
  9. 9.
    Gode, D., Sunder, S.: Allocative efficiency of markets with zero-intelligence traders. Journal of political economy 101, 119–137 (1993)CrossRefGoogle Scholar
  10. 10.
    Hall, A., Hautsch, N.: Order aggressiveness and order book dynamics. Empirical Economics 30, 973–1005 (2006)CrossRefGoogle Scholar
  11. 11.
    Kissell, R., Glantz, M.: Optimal Trading Strategies, Amacom, USA (2003)Google Scholar
  12. 12.
    LeBaron, B.: Agent-based computational finance. In: Tesfatsion, L., Judd, K. (eds.) Handbook of Computational Economics. Agent-based Computational Economics, vol. 2, pp. 134–151. Elsevier, Amsterdam (2005)Google Scholar
  13. 13.
    Lim, M., Coggins, R.: Optimal trade execution: an evolutionary approach. Proceedings of IEEE Congress on Evolutionary Computation 2, 1045–1052 (2005)CrossRefGoogle Scholar
  14. 14.
    Lo, I., Sapp, S.: Order aggressiveness and quantity: How are they determined in a limit order market (2007),
  15. 15.
    Mike, S., Farmer, J.: An empirical behavioral model of liquidity and volatility. Journal of Economic Dynamics and Control 32, 200–234 (2008)CrossRefGoogle Scholar
  16. 16.
    O’Neill, M., Ryan, C.: Grammatical Evolution. Kluwer Academic Publishers, USA (2003)zbMATHGoogle Scholar
  17. 17.
    Pascual, R., Verdas, D.: What pieces of limit order book information matter in explaining order choice by patient and impatient traders. Quantitative Finance 9, 527–545 (2009)CrossRefMathSciNetGoogle Scholar
  18. 18.
    Ranaldo, A.: Order aggressiveness in limit order book markets. Journal of Financial Markets 7, 53–74 (2004)CrossRefGoogle Scholar
  19. 19.
    Samanidou, E., Zschischang, E., Stauffer, D., Lux, T.: Agent-based models of financial markets. Reports on Progress in Physics 70, 409–450 (2007)CrossRefGoogle Scholar
  20. 20.
    Tesfatsion, L.: Agent-based computational economics: A constructive approach to economic theory. In: Tesfatsion, L., Judd, K. (eds.) Handbook of computational economics: agent-based computaional economics, pp. 52–74. Elsevier, Amsterdam (2006)Google Scholar
  21. 21.
    Toth, B., Kertesz, J., Farmer, J.: Studies of the limit order book around large price changes. European Physical Journal B 71, 499–510 (2009)CrossRefGoogle Scholar
  22. 22.
    Verhoeven, P., Ching, S., Ng, H.: Determinants of the decision to submit market or limit orders on the ASX. Pacific-Basin Finance Journal 12, 1–18 (2004)CrossRefGoogle Scholar
  23. 23.
    Xu, Y.: Order aggressiveness on the ASX market. International Journal of Economics and Finance 1, 51–75 (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Wei Cui
    • 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

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