Evolving Dynamic Trade Execution Strategies Using Grammatical Evolution

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{cui:2010:evofin,

• author = "Wei Cui and Anthony Brabazon and Michael O'Neill",
• title = "Evolving Dynamic Trade Execution Strategies Using Grammatical Evolution",
• 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 = "192--201",
• 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_20",
• abstract = "Although there is a plentiful literature on the use of evolutionary methodologies for the trading of financial assets, little attention has been paid to potential use of these methods for efficient trade execution. Trade execution is concerned with the actual mechanics of buying or selling the desired amount of a financial instrument of interest. Grammatical Evolution (GE) is an evolutionary automatic programming methodology which can be used to evolve rule sets. In this paper we use a GE algorithm to discover dynamic, efficient, trade execution strategies which adapt to changing market conditions. The strategies are tested in an artificial limit order market. GE was found to be able to evolve quality trade execution strategies which are highly competitive with two benchmark trade execution strategies.",
• notes = "EvoFIN'2010 held in conjunction with EuroGP'2010 EvoCOP2010 EvoBIO2010",

}

Genetic Programming entries for Wei Cui Anthony Brabazon Michael O'Neill

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