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Evolving technical trading rules for spot foreign-exchange markets using grammatical evolution

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  • Anthony Brabazon
  • Michael O’Neill

Abstract

Grammatical Evolution (GE) is a novel, data-driven, model-induction tool, inspired by the biological gene-to-protein mapping process. This study provides an introduction to GE, and applies the methodology in an attempt to uncover useful technical trading rules which can be used to trade foreign exchange markets. In this study, each of the evolved rules (programs) represents a market trading system. The form of these programs is not specified ex-ante, but emerges by means of an evolutionary process. Daily US-DM, US-Stg and US-Yen exchange rates for the period 1992 to 1997 are used to train and test the model. The findings suggest that the developed rules earn positive returns in hold-out sample test periods, after allowing for trading and slippage costs. This suggests potential for future research to determine whether further refinement of the methodology adopted in this study could improve the returns earned by the developed rules. It is also noted that this novel methodology has general utility for rule-induction, and data mining applications. Copyright Springer-Verlag Berlin/Heidelberg 2004

Suggested Citation

  • Anthony Brabazon & Michael O’Neill, 2004. "Evolving technical trading rules for spot foreign-exchange markets using grammatical evolution," Computational Management Science, Springer, vol. 1(3), pages 311-327, October.
    • Handle: RePEc:spr:comgts:v:1:y:2004:i:3:p:311-327
    DOI: 10.1007/s10287-004-0018-5
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    Citations

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    Cited by:

    1. Marco Corazza & Claudio Pizzi & Andrea Marchioni, 2024. "A financial trading system with optimized indicator setting, trading rule definition, and signal aggregation through Particle Swarm Optimization," Computational Management Science, Springer, vol. 21(1), pages 1-29, June.
    2. Alexandros Agapitos & Anthony Brabazon & Michael O’Neill, 2017. "Regularised gradient boosting for financial time-series modelling," Computational Management Science, Springer, vol. 14(3), pages 367-391, July.
    3. Ivan Contreras & Remei Calm & Miguel A. Sainz & Pau Herrero & Josep Vehi, 2021. "Combining Grammatical Evolution with Modal Interval Analysis: An Application to Solve Problems with Uncertainty," Mathematics, MDPI, vol. 9(6), pages 1-20, March.
    4. Luís Lobato Macedo & Pedro Godinho & Maria João Alves, 2020. "A Comparative Study of Technical Trading Strategies Using a Genetic Algorithm," Computational Economics, Springer;Society for Computational Economics, vol. 55(1), pages 349-381, January.
    5. Manahov, Viktor & Hudson, Robert & Gebka, Bartosz, 2014. "Does high frequency trading affect technical analysis and market efficiency? And if so, how?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 28(C), pages 131-157.
    6. George Albanis & Roy Batchelor, 2007. "Combining heterogeneous classifiers for stock selection," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 15(1‐2), pages 1-21, January.
    7. Christian Oesch & Dietmar Maringer, 2017. "Low-latency liquidity inefficiency strategies," Quantitative Finance, Taylor & Francis Journals, vol. 17(5), pages 717-727, May.
    8. Pedro Godinho, 2012. "Can abnormal returns be earned on bandwidth-bounded currencies? Evidence from a genetic algorithm," Economic Issues Journal Articles, Economic Issues, vol. 17(1), pages 1-26, March.
    9. Gianni Filograsso & Giacomo Tollo, 2023. "Adaptive evolutionary algorithms for portfolio selection problems," Computational Management Science, Springer, vol. 20(1), pages 1-38, December.

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