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On Evolving Multi-agent FX Traders

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Applications of Evolutionary Computation (EvoApplications 2014)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8602))

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Abstract

Current frameworks for identifying trading agents using machine learning are able to simultaneously address the characterization of both technical indicator and decision tree. Moreover, multi-agent frameworks have also been proposed with the goal of improving the reliability and trust in the agent policy identified. Such advances need weighing against the computational overhead of assuming such flexibility. In this work a framework for evolutionary multi-agent trading is introduced and systematically benchmarked for FX currency trading; including the impact of FX trading spread. It is demonstrated that simplifications can be made to the ‘base’ trading agent that do not impact on the quality of solutions, but provide considerable computational speedups. The resulting evolutionary multi-agent architecture is demonstrated to provide significant benefits to the profitability and improve the reliability with which profitable policies are returned.

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Authors and Affiliations

  1. Faculty of Computer Science, Dalhousie University, Halifax, NS, Canada

    Alexander Loginov & Malcolm I. Heywood

Authors
  1. Alexander Loginov
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  2. Malcolm I. Heywood
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Corresponding author

Correspondence to Malcolm I. Heywood .

Editor information

Editors and Affiliations

  1. Depto. Estadística e Investigación, Universidad Politécnica de Valencia, Valencia, Spain

    Anna I. Esparcia-Alcázar

  2. University of Granada, Granada, Spain

    Antonio M. Mora

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Loginov, A., Heywood, M.I. (2014). On Evolving Multi-agent FX Traders. In: Esparcia-Alcázar, A., Mora, A. (eds) Applications of Evolutionary Computation. EvoApplications 2014. Lecture Notes in Computer Science(), vol 8602. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45523-4_17

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  • DOI: https://doi.org/10.1007/978-3-662-45523-4_17

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-45522-7

  • Online ISBN: 978-3-662-45523-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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