Evolving Technical Trading Rules for Spot Foreign-Exchange Markets Using Grammatical Evolution

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@Article{BrabazonONeill:2004:CMSETTRfSFEMuGE,
  author =       "Anthony Brabazon and Michael O'Neill",
  title =        "Evolving Technical Trading Rules for Spot
                 Foreign-Exchange Markets Using Grammatical Evolution",
  journal =      "Computational Management Science",
  year =         "2004",
  volume =       "1",
  number =       "3-4",
  pages =        "311--327",
  month =        oct,
  keywords =     "genetic algorithms, genetic programming, Grammatical
                 evolution, Foreign exchange prediction, Technical
                 trading rules",
  publisher =    "Springer-Verlag",
  ISSN =         "1619-697X",
  DOI =          "doi:10.1007/s10287-004-0018-5",
  abstract =     "Grammatical Evolution (GE) is a novel, data-driven,
                 model-induction tool, inspired by the biological
                 gene-to-protein mapping process. This study provides an
                 introduction to GE, and applies the methodology in an
                 attempt to uncover useful technical trading rules which
                 can be used to trade foreign exchange markets. In this
                 study, each of the evolved rules (programs) represents
                 a market trading system. The form of these programs is
                 not specified ex-ante, but emerges by means of an
                 evolutionary process. Daily US-DM, US-Stg and US-Yen
                 exchange rates for the period 1992 to 1997 are used to
                 train and test the model. The findings suggest that the
                 developed rules earn positive returns in hold-out
                 sample test periods, after allowing for trading and
                 slippage costs. This suggests potential for future
                 research to determine whether further refinement of the
                 methodology adopted in this study could improve the
                 returns earned by the developed rules. It is also noted
                 that this novel methodology has general utility for
                 rule-induction, and data mining applications.",
}

Genetic Programming entries for Anthony Brabazon Michael O'Neill

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