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Cartesian genetic programming for trading: a preliminary investigation

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Show simple item record Mayo, Michael 2012-11-14T22:29:09Z 2012-11-14T22:29:09Z 2012
dc.identifier.citation Mayo, 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.description.abstract In 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.mimetype application/pdf
dc.language.iso en
dc.publisher Australian Computer Society, Inc. en_NZ
dc.rights Copyright © 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.subject Cartesian Genetic Programming en_NZ
dc.subject Algorithmic Trading en_NZ
dc.subject Rule Learning en_NZ
dc.title Cartesian genetic programming for trading: a preliminary investigation en_NZ
dc.type Conference Contribution en_NZ

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