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Identifying Trade Entry and Exit Timing Using Mathematical Technical Indicators in XCS

  • Conference paper
Learning Classifier Systems (IWLCS 2009, IWLCS 2008)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6471))

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Abstract

This paper extends current LCS research into financial time series forecasting by analysing the performance of agents utilising mathematical technical indicators for both environment classification and in selecting actions to be executed. It compares these agents with traditional models which only use such indicators to classify the environment and exit at the close of the next day. It is proposed that XCS agents utilising mathematical technical indicators for exit conditions will not only outperform similar agents which close the trade at the end of the next day, but also result in fewer trades and consequently lower commissions paid. The results show that in four of five assets, agents using indicator exit conditions outperformed those exiting at the close of the next day, before commissions were factored in. After commissions are factored in, the performance gap between the two agent classes further widens.

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Authors and Affiliations

  1. Department of Computer Science, University of the West of England, Bristol, BS16 1QY, UK

    Richard Preen

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  1. Richard Preen
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Editor information

Editors and Affiliations

  1. School of Computer Science, ASAP research group, University of Nottingham, Jubilee Campus, Nottingham, NG8 1BB, and Multidisciplinary Centre for Integrative Biology, School of Biosciences, LE12 5RD, Sutton Bonington, UK

    Jaume Bacardit

  2. School of Engineering and Computer Science, Victoria University of Wellington, PO Box 600, 6140, Wellington, New Zealand

    Will Browne

  3. Department of Brain and Cognitive Sciences, University of Rochester, Meliora Hall, 14627, Rochester, NY, USA

    Jan Drugowitsch

  4. Enginyeria i Arquitectura La Salle, Universitat Ramon Llull, Quatre Camins, 2, 08022, Barcelona, Spain

    Ester Bernadó-Mansilla

  5. Department of Psychology III, University of Würzburg, COBOSLAB – Cognitive Bodyspaces: Learning and Behavior,, Röntgenring 11, 97070, Würzburg, Germany

    Martin V. Butz

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Preen, R. (2010). Identifying Trade Entry and Exit Timing Using Mathematical Technical Indicators in XCS. In: Bacardit, J., Browne, W., Drugowitsch, J., Bernadó-Mansilla, E., Butz, M.V. (eds) Learning Classifier Systems. IWLCS IWLCS 2009 2008. Lecture Notes in Computer Science(), vol 6471. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17508-4_11

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  • DOI: https://doi.org/10.1007/978-3-642-17508-4_11

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-17507-7

  • Online ISBN: 978-3-642-17508-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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