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Evolving Trading Rule-Based Policies

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Applications of Evolutionary Computation (EvoApplications 2010)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6025))

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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.

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Author information

Authors and Affiliations

  1. Natural Computing Research and Applications Group, University College Dublin, Ireland

    Robert Gregory Bradley, Anthony Brabazon & Michael O’Neill

  2. School of Business, University College Dublin, Ireland

    Robert Gregory Bradley & Anthony Brabazon

  3. School of Computer Science and Informatics, University College Dublin, Ireland

    Michael O’Neill

Authors
  1. Robert Gregory Bradley
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  2. Anthony Brabazon
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  3. Michael O’Neill
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Editor information

Editors and Affiliations

  1. Department of Mathematics and Statistics, University of Strathclyde, 16 Richmond Street, G1 1XQ, Glasgow, UK

    Cecilia Di Chio

  2. School of Business, University College Dublin, Belfield, Dublin 4, UK

    Anthony Brabazon

  3. ’Dalla Molle’ Institute for Artificial Intelligence (IDSIA), Galleria 2, 6928, Manno-Lugano, Switzerland

    Gianni A. Di Caro

  4. Wilhelm Schickard-Institut für Informatik, Universität Tübingen, Sand 1, 72076, Tübingen, Germany

    Marc Ebner

  5. National University of Computer and Emerging Sciences (NUCES), A.K. Brohi Road, H-11/4, Islamabad, Pakistan

    Muddassar Farooq

  6. Fakultät für WiSo, HelmutSchmidt-Universität, Holstenhofweg 85, 22043, Hamburg, Germany

    Andreas Fink

  7. Department of Information Systems, Johannes Gutenberg-Universität, 55099, Mainz, Germany

    Jörn Grahl

  8. Mathematics and Computer Science Depatment, University of Richmond, 23173, Richmond, VA, USA

    Gary Greenfield

  9. Department of Informatics Engineering, Polo II - Pinhal de Marrocos, University of Coimbra, 3030 - 290, Coimbra, Portugal

    Penousal Machado

  10. School of Computer Science and Informatics, University College Dublin, Belfield, Dublin 4, UK

    Michael O’Neill

  11. Istituto di Calcolo e Reti ad Alte Prestazioni (ICAR), Consiglio Nazionale delle Ricerche (CNR), Via P. Castellino 111, 80131, Naples, Italy

    Ernesto Tarantino

  12. School of Computing, Edinburgh Napier University, 10 Clinton Road, EH10 5DT, Edinburgh, UK

    Neil Urquhart

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Bradley, R.G., Brabazon, A., O’Neill, M. (2010). Evolving Trading Rule-Based Policies. In: Di Chio, C., et al. Applications of Evolutionary Computation. EvoApplications 2010. Lecture Notes in Computer Science, vol 6025. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12242-2_26

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  • DOI: https://doi.org/10.1007/978-3-642-12242-2_26

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-12241-5

  • Online ISBN: 978-3-642-12242-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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