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.
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