Computational Intelligence Algorithms For Risk-Adjusted Trading Strategies

TitleComputational Intelligence Algorithms For Risk-Adjusted Trading Strategies
Publication TypeConference Paper
Year of Publication2007
AuthorsPavlidis, NG, Pavlidis, EG, Epitropakis, MG, Plagianakos, VP, Vrahatis, MN
Conference NameIEEE Congress on Evolutionary Computation, 2007. CEC 2007
Date PublishedSeptember
Conference LocationSingapore
Keywordscomputational intelligence algorithm, differential evolution, financial market, foreign exchange market, foreign exchange trading, generalized moving average rule, genetic algorithms, genetic programming, optimization, pattern detection, risk analysis, risk-adjusted trading strategy, statistical testing

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.

PDF icon PavlidisPEPV-final.pdf228.21 KB

Scholarly Lite is a free theme, contributed to the Drupal Community by More than Themes.