Skip to main content
Article thumbnail
Location of Repository

Computational intelligence algorithms for risk-adjusted trading strategies.

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


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:
Provided by: Lancaster E-Prints

Suggested articles


  1. (2005). Clustering in evolutionary algorithms to efficiently compute simultaneously local and global minima,”
  2. (2001). Currency traders and exchange rate dynamics: a survey of the us market,”
  3. (1999). Data-snooping, technical trading rule performance, and the bootstrap,”
  4. (1997). Differential evolution – a simple and efficient adaptive scheme for global optimization over continuous spaces,”
  5. (2001). Effective retrun, risk aversion and drawdowns,”
  6. (2006). Exchange rate puzzles. a tale of switching attractors,”
  7. (1998). Genetic programming: An Introduction: on the automatic evolution of computer programs and its applications.
  8. (1992). Genetic Programming: On the Programming of Computers by Means of Natural Selection.
  9. (2004). Have trading rule profits in the currency market declined over time?”
  10. (1997). Is technical analysis in the foreign exchange market profitable? a genetic programming approach,”
  11. (1990). Meteor showers or heat waves? heteroskedastic intra-daily volatility in the foreign exchange market,”
  12. (1966). Mutual fund performance,”
  13. (1992). Numerical Recipes in Fortran 77. doi
  14. (1998). On the search properties of different crossover operators in genetic programming,” in Genetic Programming
  15. (2005). Optimization of technical rules by genetic algorithms: evidence from the madrid stock market,”
  16. (2004). Parallel differential evolution,”
  17. (1992). Quasi–maximum likelihood estimation and inference in dynamic models with time varying covariances,” doi
  18. (1999). Technical trading rule profitability and foreign exchange intervention,”
  19. (1982). The jackknife, the bootstrap and other resampling. doi
  20. (1963). The variation of certain speculative prices,” doi

To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.