Computational learning techniques for intraday FX trading using popular technical indicators

  • Dempster M
  • Payne T
  • Romahi Y
 et al. 
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Abstract

We consider strategies which use a collection of popular technical indicators as input and seek a profitable trading rule defined in terms of them. We consider two popular computational learning approaches, reinforcement learning and genetic programming, and compare them to a pair of simpler methods: the exact solution of an appropriate Markov decision problem, and a simple heuristic. We find that although all methods are able to generate significant in-sample and out-of-sample profits when transaction costs are zero, the genetic algorithm approach is superior for non-zero transaction costs, although none of the methods produce significant profits at realistic transaction costs. We also find that there is a substantial danger of overfitting if in-sample learning is not constrained.

Author-supplied keywords

  • Computational learning
  • Foreign exchange (FX)
  • Genetic algorithms (GA)
  • Linear programming
  • Markov chains
  • Reinforcement learning
  • Technical trading
  • Trading systems

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Find this document

  • ISSN: 10459227
  • SCOPUS: 2-s2.0-0035391018
  • PII: S1045922701050184
  • PUI: 32732817
  • PMID: 18249910
  • SGR: 0035391018
  • DOI: 10.1109/72.935088

Authors

  • M. A.H. Dempster

  • T. W. Payne

  • Y. Romahi

  • G. W.P. Thompson

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