Evolutionary Learning of Technical Trading Rules without Data-mining Bias

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@InProceedings{agapitos_etal:ppsn2010,

• author = "Alexandros Agapitos and Michael O'Neill and Anthony Brabazon",
• title = "Evolutionary Learning of Technical Trading Rules without Data-mining Bias",
• booktitle = "PPSN 2010 11th International Conference on Parallel Problem Solving From Nature",
• pages = "294--303",
• year = "2010",
• volume = "6238",
• editor = "Robert Schaefer and Carlos Cotta and Joanna Kolodziej and Guenter Rudolph",
• publisher = "Springer",
• series = "Lecture Notes in Computer Science",
• isbn13 = "978-3-642-15843-8",
• address = "Krakow, Poland",
• month = "11-15 " # sep,
• keywords = "genetic algorithms, genetic programming",
• DOI = "doi:10.1007/978-3-642-15844-5_30",
• abstract = "In this paper we investigate the profitability of evolved technical trading rules when controlling for data-mining bias. For the first time in the evolutionary computation literature, a comprehensive test for a rule's statistical significance using Hansen's Superior Predictive Ability is explicitly taken into account in the fitness function, and multi-objective evolutionary optimisation is employed to drive the search towards individual rules with better generalisation abilities. Empirical results on a spot foreign-exchange market index suggest that increased out-of-sample performance can be obtained after accounting for data-mining bias effects in a multi-objective fitness function, as compared to a single-criterion fitness measure that considers solely the average return.",

}

Genetic Programming entries for Alexandros Agapitos Michael O'Neill Anthony Brabazon

Citations