Evolving Trading Rule-Based Policies

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@InProceedings{bradley:2010:evofin,

• author = "Robert Gregory Bradley and Anthony Brabazon and Michael O'Neill",
• title = "Evolving Trading Rule-Based Policies",
• booktitle = "EvoFIN",
• year = "2010",
• editor = "Cecilia {Di Chio} and Anthony Brabazon and Gianni A. {Di Caro} and Marc Ebner and Muddassar Farooq and Andreas Fink and Jorn Grahl and Gary Greenfield and Penousal Machado and Michael O'Neill and Ernesto Tarantino and Neil Urquhart",
• volume = "6025",
• series = "LNCS",
• pages = "251--260",
• address = "Istanbul",
• month = "7-9 " # apr,
• organisation = "EvoStar",
• publisher = "Springer",
• keywords = "genetic algorithms, genetic programming, grammatical evolution",
• isbn13 = "978-3-642-12241-5",
• DOI = "doi:10.1007/978-3-642-12242-2_26",
• abstract = "Trading-rule representation is an important factor to consider when designing a quantitative trading system. This study implements a trading strategy as a rule-based policy. The result is an intuitive human-readable format which allows for seamless integration of domain knowledge. The components of a policy are specified and represented as a set of rewrite rules in a context-free grammar. These rewrite rules define how the components can be legally assembled. Thus, strategies derived from the grammar are well-formed, domain-specific, solutions. A grammar-based Evolutionary Algorithm, Grammatical Evolution (GE), is then employed to automatically evolve intra-day trading strategies for the U.S. Stock Market. The GE methodology managed to discover profitable rules with realistic transaction costs included. The paper concludes with a number of suggestions for future work.",
• notes = "EvoFIN'2010 held in conjunction with EuroGP'2010 EvoCOP2010 EvoBIO2010",

}

Genetic Programming entries for Robert Gregory Bradley Anthony Brabazon Michael O'Neill

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