Computational intelligence algorithms for risk-adjusted trading strategies.
Pavlidis, Nicos and Pavlidis, Efthymios and Epitropakis, Michael and Plagianakos, Vasilis and Vrahatis, Michael
(2008)
Computational intelligence algorithms for risk-adjusted trading strategies.
In:
IEEE Congress on Evolutionary Computation CEC 2007.
IEEE, Singapore, pp. 540-547.
ISBN 978-1-4244-1339-3
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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.
Item Type:
Contribution in Book/Report/Proceedings
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Uncontrolled Keywords:
/dk/atira/pure/researchoutput/libraryofcongress/hb
Subjects:
?? ECONOMICSHB ECONOMIC THEORY ??
Deposited On:
03 Feb 2011 09:46
Last Modified:
20 Sep 2023 02:11