Computational learning techniques for intraday FX trading using popular technical indicators

Created by W.Langdon from gp-bibliography.bib Revision:1.4504

  author =       "M. A. H. Dempster and Tom W. Payne and 
                 Yazann Romahi and G. W. P. Thompson",
  title =        "Computational learning techniques for intraday {FX}
                 trading using popular technical indicators",
  journal =      "IEEE Transactions on Neural Networks",
  year =         "2001",
  volume =       "12",
  number =       "4",
  pages =        "744--754",
  month =        jul,
  keywords =     "genetic algorithms, genetic programming, Markov
                 processes, foreign exchange trading, genetic
                 algorithms, learning (artificial intelligence), Markov
                 decision, computational learning, foreign exchange
                 trading, heuristic, reinforcement learning, technical
                 trading, transaction costs",
  ISSN =         "1045-9227",
  URL =          "",
  DOI =          "doi:10.1109/72.935088",
  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",
  notes =        "CODEN: ITNNEP. INSPEC Accession Number:6997298
                 Location: technical report WP30/2000


Genetic Programming entries for Michael Dempster Tom W Payne Yazann Romahi Giles W P Thompson