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Australasian Conference on Artificial Life and Computational Intelligence

ACALCI 2017: Artificial Life and Computational Intelligence pp 25–38Cite as

Equity Option Strategy Discovery and Optimization Using a Memetic Algorithm

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10142))

Abstract

Options in finance are becoming an increasingly popular investment instrument. Good returns, however, do depend on finding the right strategy for trading and risk management. In this paper we describe a memetic algorithm designed to discover and optimize multi-leg option strategies for the S&P500 index. Strategies comprising from one up to six option legs are examined. The fitness function is specifically designed to maximize profitability while seeking a certain trade success percentage and equity drawdown limit. Using historical option data from 2005 to 2016, our memetic algorithm discovered a four-leg option strategy that offers optimum performance.

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Author information

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Portland State University, Portland, Oregon, 97201, USA

    Richard Tymerski, Garrison Greenwood & Devin Sills

Authors
  1. Richard Tymerski
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  2. Garrison Greenwood
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  3. Devin Sills
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Corresponding author

Correspondence to Richard Tymerski .

Editor information

Editors and Affiliations

  1. The University of Adelaide, Adelaide, South Australia, Australia

    Markus Wagner

  2. RMIT University, Melbourne, Victoria, Australia

    Xiaodong Li

  3. Swinburne University, Melbourne, Victoria, Australia

    Tim Hendtlass

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Tymerski, R., Greenwood, G., Sills, D. (2017). Equity Option Strategy Discovery and Optimization Using a Memetic Algorithm. In: Wagner, M., Li, X., Hendtlass, T. (eds) Artificial Life and Computational Intelligence. ACALCI 2017. Lecture Notes in Computer Science(), vol 10142. Springer, Cham. https://doi.org/10.1007/978-3-319-51691-2_3

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  • DOI: https://doi.org/10.1007/978-3-319-51691-2_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-51690-5

  • Online ISBN: 978-3-319-51691-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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