Using LCS to Exploit Order Book Data in Artificial Markets

  • Philippe Mathieu
  • Yann SecqEmail author
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8670)


In the study of financial phenomena, multi-agent market order-driven simulators are tools that can effectively test different economic assumptions. Many studies have focused on the analysis of adaptive learning agents carrying on prices. But the prices are a consequence of the matching orders. Reasoning about orders should help to anticipate future prices.

While it is easy to populate these virtual worlds with agents analyzing “simple” prices shapes (rising or falling, moving averages, ...), it is nevertheless necessary to study the phenomena of rationality and influence between agents, which requires the use of adaptive agents that can learn from their environment. Several authors have obviously already used adaptive techniques but mainly by taking into account prices historical. But prices are only consequences of orders, thus reasoning about orders should provide a step ahead in the deductive process.

In this article, we show how to leverage information from the order books such as the best limits, the bid-ask spread or waiting cash to adapt more effectively to market offerings. Like B. Arthur, we use learning classifier systems and show how to adapt them to a multi-agent system.


Agent based computational economics Artificial stock market Market microstructure Learning classifier systems Multi-agent simulation 


  1. 1.
    Arthur, W.B., Holland, J., LeBaron, B., Palmer, R., Tayler, P.: The Economy as an Evolving Complex System II, pp. 15–44. Addison-Wesley, Reading (1997)Google Scholar
  2. 2.
    Barbosa, R.P., Belo, O.: An agent task force for stock trading. In: Demazeau, Y., Pěchoucěk, M., Corchado, J.M., Pérez, J.B. (eds.) Adv. on Prac. Appl. of Agents and Mult. Sys., AISC, vol. 88, pp. 287–297. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  3. 3.
    Beaufils, B., Mathieu, P.: Cheating is not playing: methodological issues of computational game theory. In: ECAI’06 (2006)Google Scholar
  4. 4.
    Belter, K.: Supply and information content of order book depth: the case of displayed and hidden depth (2007)Google Scholar
  5. 5.
    Boland, E., Klingebiel, K., Stodgell, T.: The xcs classifier system in a financial market (2005)Google Scholar
  6. 6.
    Booker, L.B., Golbergand, D.E., Holland, J.I.: Classifier systems and genetic algorithms. Artif. Intell. 40, 235–282 (1989)CrossRefGoogle Scholar
  7. 7.
    Brandouy, O., Mathieu, P.: Efficient monitoring of financial orders with agent-based technologies. In: Demazeau, Y., Pěchoucěk, M., Corchado, J.M., Pérez, J.B. (eds.) Adv. on Prac. Appl. of Agents and Mult. Sys., AISC, vol. 88, pp. 277–286. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  8. 8.
    Mathieu, P., Brandouy, O.: A generic architecture for realistic simulations of complex financial dynamics. In: Demazeau, Y., Dignum, F., Corchado, J.M., Pérez, J.B. (eds.) Advances in PAAMS. AISC, vol. 70, pp. 185–197. Springer, Heidelberg (2010) CrossRefGoogle Scholar
  9. 9.
    Brenner, T.: Chapter 18 Agent Learning Representation: Advice on Modelling Economic Learning. Handbook of Computational Economics, vol. 2, pp. 895–947. Elsevier, Amsterdam (2006)Google Scholar
  10. 10.
    Cao, C., Hansch, O., Wang, X.: The informational content of an open limit order book. In: EFA 2004 Maastricht MeetingsGoogle Scholar
  11. 11.
    Cao, L., Tay, F.: Application of support vector machines in financial time series forecasting. Omega: Int. J. Manage. Sci. 29, 309–317 (2001)CrossRefGoogle Scholar
  12. 12.
    Cornuéjols, A., Miclet, L., Kodratoff, Y.: Apprentissage Artificiel Concepts et Algorithmes. Eyrolles, Paris (2002)Google Scholar
  13. 13.
    Gode, D.K., Sunder, S.: Allocative efficiency of markets with zero-intelligence traders: market as a partial substitute for individual rationality. J. Polit. Econ. 101, 119–137 (1993)CrossRefGoogle Scholar
  14. 14.
    Kozhan, R., Salmon, M.: The information content of a limit order book: the case of an fx market (2010)Google Scholar
  15. 15.
    LeBaron, B.: Building the santa fe artificial stock market. Brandeis University (2002)Google Scholar
  16. 16.
    Lotka, A.J.: Elements of Physical Biology. Williams and Wilkins, Baltimore (1925)zbMATHGoogle Scholar
  17. 17.
    Mathieu, P., Secq, Y.: Environment updating and agent scheduling policies in agent-based simulators. In: ICAART’2012 (2012)Google Scholar
  18. 18.
    Volterra, V.: Variations and fluctuations of the number of individuals in animal species living together. In: Chapman, R.N. (ed.) Animal Ecology. McGraw-Hill, New York (1926)Google Scholar
  19. 19.
    Wilson, S.: Classifier fitness based on accuracy. Evol. Comput. 3, 149–175 (1995)CrossRefGoogle Scholar
  20. 20.
    Gershoff, M., Schulenburg, S.: Collective behavior based hierarchical XCS. In: Proceedings of the 9th Annual Conference Companion on Genetic and Evolutionary Computation, GECCO ’07, London, UK, pp. 2695–2700. ACM, New York (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.Laboratoire d’Informatique Fondamentale de Lille (UMR CNRS 8022)Université Lille 1Villeneuve-d’AscqFrance

Personalised recommendations