Computational intelligence algorithms for risk-adjusted trading strategies
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 i...
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| creator | Pavlidis, N.G. Pavlidis, E.G. Epitropakis, M.G. Plagianakos, V.P. Vrahatis, M.N. |
| description | 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. |
| doi_str_mv | 10.1109/CEC.2007.4424517 |
| format | Conference Proceeding |
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| ispartof | 2007 IEEE Congress on Evolutionary Computation, 2007, p.540-547 |
| issn | 1089-778X 1941-0026 |
| language | eng |
| recordid | cdi_ieee_primary_4424517 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Computational intelligence Computational modeling Genetic mutations Genetic programming Pattern analysis Robustness Signal analysis Signal generators Signal processing Statistical analysis |
| title | Computational intelligence algorithms for risk-adjusted trading strategies |
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