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Australasian Conference on Artificial Life and Computational Intelligence

ACALCI 2016: Artificial Life and Computational Intelligence pp 360–373Cite as

Genetic Algorithm Based Trading System Design

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9592))

Abstract

We investigate the design of trading systems using a genetic algorithm (GA). Technical indicators are used to define entry and exit rules. The choice of indicators and their associated parameters are optimized by the GA which operates on integer values only. Holding time and profit target exit rules are also evaluated. It is found that a fitness function based on winning probability coupled with a profit target and one based on the Sharpe ratio are useful in maximizing percentage of winning trades as well as overall profit. Strategies are developed which are highly competitive to buy and hold.

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Author information

Authors and Affiliations

  1. Department of Electrical & Computer Engineering, Portland State University, Portland, OR, 97201, USA

    Richard Tymerski, Ethan Ott & Garrison Greenwood

Authors
  1. Richard Tymerski
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  2. Ethan Ott
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  3. Garrison Greenwood
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Corresponding author

Correspondence to Richard Tymerski .

Editor information

Editors and Affiliations

  1. Canberra, Aust Capital Terr, Australia

    Tapabrata Ray

  2. University of New South Wales, Canberra, Aust Capital Terr, Australia

    Ruhul Sarker

  3. RMIT University, Melbourne, Victoria, Australia

    Xiaodong Li

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© 2016 Springer International Publishing Switzerland

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Tymerski, R., Ott, E., Greenwood, G. (2016). Genetic Algorithm Based Trading System Design. In: Ray, T., Sarker, R., Li, X. (eds) Artificial Life and Computational Intelligence. ACALCI 2016. Lecture Notes in Computer Science(), vol 9592. Springer, Cham. https://doi.org/10.1007/978-3-319-28270-1_30

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  • DOI: https://doi.org/10.1007/978-3-319-28270-1_30

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-28269-5

  • Online ISBN: 978-3-319-28270-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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