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dc.contributor.authorMayo, Michael
dc.date.accessioned2012-11-14T22:29:09Z
dc.date.available2012-11-14T22:29:09Z
dc.date.issued2012
dc.identifier.citationMayo, M. (2012, December). Cartesian genetic programming for trading: a preliminary Investigation. Paper presented at the Tenth Australasian Data Mining Conference: AusDM 2012, Sydney, Australia.en_NZ
dc.identifier.urihttp://hdl.handle.net/10289/6838
dc.description.abstractIn this paper, a preliminary investigation of Cartesian Genetic Programming (CGP) for algorithmic intraday trading is conducted. CGP is a recent new variant of genetic programming that differs from traditional approaches in a number of ways, including being able to evolve programs with limited size and with multiple outputs. CGP is used to evolve a predictor for intraday price movements, and trading strategies using the evolved predictors are evaluated along three dimensions (return, maximum drawdown and recovery factor) and against four different financial datasets (the Euro/US dollar exchange rate and the Dow Jones Industrial Average during periods from 2006 and 2010). We show that CGP is capable in many instances of evolving programs that, when used as trading strategies, lead to modest positive returns.en_NZ
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.publisherAustralian Computer Society, Inc.en_NZ
dc.rightsCopyright © 2012, Australian Computer Society, Inc. This paper appeared at the 10th Australasian Data Mining Conference (AusDM 2012), Sydney, Australia, December 2012. Conferences in Research and Practice in Information Technology (CRPIT), Vol. 134, Yanchang Zhao, Jiuyong Li, Paul Kennedy, and Peter Christen, Ed. Reproduction for academic, not-for-profit purposes permitted provided this text is included.en_NZ
dc.subjectCartesian Genetic Programmingen_NZ
dc.subjectAlgorithmic Tradingen_NZ
dc.subjectRule Learningen_NZ
dc.titleCartesian genetic programming for trading: a preliminary investigationen_NZ
dc.typeConference Contributionen_NZ


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