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",
• URL = "https://rdcu.be/dO4Fe",
• DOI = "doi:10.1007/s10287-004-0018-5",
• size = "17 pages",
• 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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