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Computational intelligence algorithms for risk-adjusted trading strategies.

By Nicos Pavlidis, Efthymios Pavlidis, Michael Epitropakis, Vasilis Plagianakos and Michael Vrahatis

Abstract

This paper investigates the performance of trading strategies identified through Computational Intelligence techniques. We focus on trading rules derived by Genetic Programming, as well as, Generalized Moving Average rules optimized through Differential Evolution. The performance of these rules is investigated using recently proposed risk–adjusted evaluation measures and statistical testing is carried out through simulation. Overall, the moving average rules proved to be more robust, but Genetic Programming seems more promising in terms of generating higher profits and detecting novel patterns in the data

Publisher: IEEE
Year: 2008
OAI identifier: oai:eprints.lancs.ac.uk:39677
Provided by: Lancaster E-Prints

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