On the Utility of Trading Criteria Based Retraining in Forex Markets

  • Alexander Loginov
  • Malcolm I. Heywood
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7835)

Abstract

This research investigates the ability of genetic programming (GP) to build profitable trading strategies for the Foreign Exchange Market (FX) of three major currency pairs (EURUSD, USDCHF and EURCHF) using one hour prices from 2008 to 2011. We recognize that such environments are likely to be non-stationary. Thus, we do not require a single training partition to capture all likely future behaviours. We address this by detecting poor trading behaviours and use this to trigger retraining. In addition the task of evolving good technical indicators (TI) and the rules for deploying trading actions is explicitly separated. Thus, separate GP populations are used to coevolve TI and trading behaviours under a mutualistic symbiotic association. The results of 100 simulations demonstrate that an adaptive retraining algorithm significantly outperforms a single-strategy approach (population evolved once) and generates profitable solutions with a high probability.

Keywords

Coevolution non-stationary FX Forex Currency 

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References

  1. 1.
    Morozov, I.V., Fatkhullin, R.R.: Forex: from simple to complex. Teletrade Ltd. (2004)Google Scholar
  2. 2.
    Settlement, B.F.I.: Triennial central bank survey of foreign exchange and otc derivatives market activity - preliminary global results (April 2010), http://www.bis.org/press/p100901.htm
  3. 3.
    Passamonte, A.: Six facts that give forex traders an edge. Forex Journal (2011), http://www.forexjournal.com/fx-education/forex-trading/12125-six-facts-that-give-forex-traders-an-edge.html
  4. 4.
    Dempsey, I., O’Neill, M., Brabazon, A.: Foundations in Grammatical Evolution for Dynamic Environments. SCI, vol. 194. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  5. 5.
    Tuite, C., Agapitos, A., O’Neill, M., Brabazon, A.: A Preliminary Investigation of Overfitting in Evolutionary Driven Model Induction: Implications for Financial Modelling. In: Di Chio, C., Brabazon, A., Di Caro, G.A., Drechsler, R., Farooq, M., Grahl, J., Greenfield, G., Prins, C., Romero, J., Squillero, G., Tarantino, E., Tettamanzi, A.G.B., Urquhart, N., Uyar, A.Ş. (eds.) EvoApplications 2011, Part II. LNCS, vol. 6625, pp. 120–130. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  6. 6.
    Mayo, M.: Evolutionary Data Selection for Enhancing Models of Intraday Forex Time Series. In: Di Chio, C., Agapitos, A., Cagnoni, S., Cotta, C., de Vega, F.F., Di Caro, G.A., Drechsler, R., Ekárt, A., Esparcia-Alcázar, A.I., Farooq, M., Langdon, W.B., Merelo-Guervós, J.J., Preuss, M., Richter, H., Silva, S., Simões, A., Squillero, G., Tarantino, E., Tettamanzi, A.G.B., Togelius, J., Urquhart, N., Uyar, A.Ş., Yannakakis, G.N. (eds.) EvoApplications 2012. LNCS, vol. 7248, pp. 184–193. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  7. 7.
    Atwater, A., Heywood, M.I., Zincir-Heywood, A.N.: GP under streaming data constraints: A case for Pareto archiving? In: ACM Genetic and Evolutionary Computation Conference, pp. 703–710 (2012)Google Scholar
  8. 8.
    Lichodzijewski, P., Heywood, M.I.: Symbiosis, complexification and simplicity under GP. In: ACM Genetic and Evolutionary Computation Conference, pp. 853–860 (2010)Google Scholar
  9. 9.
    Doucette, J.A., McIntyre, A.R., Lichodzijewski, P., Heywood, M.I.: Symbiotic coevolutionary genetic programming. Genetic Programming and Evolvable Machines 13(1), 71–101 (2012)CrossRefGoogle Scholar
  10. 10.
    Contreras, I., Hidalgo, J.I., Núñez-Letamendia, L.: A GA Combining Technical and Fundamental Analysis for Trading the Stock Market. In: Di Chio, C., Agapitos, A., Cagnoni, S., Cotta, C., de Vega, F.F., Di Caro, G.A., Drechsler, R., Ekárt, A., Esparcia-Alcázar, A.I., Farooq, M., Langdon, W.B., Merelo-Guervós, J.J., Preuss, M., Richter, H., Silva, S., Simões, A., Squillero, G., Tarantino, E., Tettamanzi, A.G.B., Togelius, J., Urquhart, N., Uyar, A.Ş., Yannakakis, G.N. (eds.) EvoApplications 2012. LNCS, vol. 7248, pp. 174–183. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  11. 11.
    ICM Trade Capital Markets Ltd.: Guide to online forex trading 19 pagesGoogle Scholar
  12. 12.
    Wagner, N., Michalewicz, Z., Khouja, M., McGregor, R.R.: Time series forecasting for dynamic environments: The DyFor genetic program model. IEEE Transactions on Evolutionary Computation 11(4), 433–452 (2007)CrossRefGoogle Scholar
  13. 13.
    Brameier, M., Banzhaf, W.: Linear Genetic Programming. Springer (2007)Google Scholar
  14. 14.
    MetaQuotes Software Corp., http://www.fxpro.com/trading/cfd/mt4/forex (accessed September 2012)
  15. 15.
    Investopedia, http://www.investopedia.com/terms/n/noise.asp#axzz27d0d2rid (accessed September 2012)
  16. 16.
  17. 17.
    RBC Global Asset Management: Investment portfolio tools (January 2013), https://services.rbcgam.com/portfolio-tools/public/investment-performance/

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Alexander Loginov
    • 1
  • Malcolm I. Heywood
    • 1
  1. 1.Faculty of Computer ScienceDalhousie UniversityHalifaxCanada

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