Investment using evolutionary learning methods and technical rules |
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Authors: | Massimiliano Kaucic |
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Affiliation: | Dipartimento di Economia e Tecnica Aziendale, University of Trieste, Piazzale Europa 1, Trieste 34127, Italy; Capp & CAPP S.r.l., via Giudecca 15, Mirano (VE) 30035, Italy |
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Abstract: | In this paper, I propose a genetic learning approach to generate technical trading systems for stock timing. The most informative technical indicators are selected from a set of almost 5000 signals by a multi-objective genetic algorithm with variable string length. Successively, these signals are combined into a unique trading signal by a learning method. I test the expert weighting solution obtained by the plurality voting committee, the Bayesian model averaging and Boosting procedures with data from the S&P 500 Composite Index, in three market phases, up-trend, down-trend and sideways-movements, covering the period 2000–2006. Computational results indicate that the near-optimal set of rules varies among market phases but presents stable results and is able to reduce or eliminate losses in down-trend periods. |
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Keywords: | Genetic algorithms Evolutionary learning Technical analysis Expert trading system |
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