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Fuzzy-Evolutionary Modeling for Single-Position Day Trading

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Book cover Natural Computing in Computational Finance

Part of the book series: Studies in Computational Intelligence ((SCI,volume 100))

Summary

This chapter illustrates a data-mining approach to single-position day trading which uses an evolutionary algorithm to construct a fuzzy predictive model of a financial instrument. The model is expressed as a set of fuzzy IF-THEN rules. The model takes as inputs the open, high, low, and close prices, as well as the values of a number of popular technical indicators on day t and produces a go short, do nothing, go long trading signal for day t+1 based on a dataset of past observations of which actions would have been most profitable. The approach has been applied to trading several financial instruments (large-cap stocks and indices): the experimental results are presented and discussed. A method to enhance the performance of trading rules based on the approach by using ensembles of fuzzy models is finally illustrated. The results clearly indicate that, despite its simplicity, the approach may yield significant returns, outperforming a buy-and-hold strategy.

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Author information

Authors and Affiliations

  1. Dipartimento di Tecnologie dell’Informazione, Università Degli Studi di Milano, Milan, Italy

    Célia da Costa Pereira & Andrea G. B. Tettamanzi

Authors
  1. Célia da Costa Pereira
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  2. Andrea G. B. Tettamanzi
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Editor information

Editors and Affiliations

  1. School of Business Quinn School, University College Dublin, Belfield, Dublin 4, Ireland

    Anthony Brabazon (Head of Research) (Head of Research)

  2. Natural Computing Research and Applications School of Computer Science and Informatics, University College Dublin, Belfield, Dublin 4, Ireland

    Michael O’Neill (Director) (Director)

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© 2008 Springer-Verlag Berlin Heidelberg

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da Costa Pereira, C., Tettamanzi, A.G.B. (2008). Fuzzy-Evolutionary Modeling for Single-Position Day Trading. In: Brabazon, A., O’Neill, M. (eds) Natural Computing in Computational Finance. Studies in Computational Intelligence, vol 100. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77477-8_8

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  • DOI: https://doi.org/10.1007/978-3-540-77477-8_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-77476-1

  • Online ISBN: 978-3-540-77477-8

  • eBook Packages: EngineeringEngineering (R0)

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