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Erschienen in:
2008 | OriginalPaper | Buchkapitel
Fuzzy-Evolutionary Modeling for Single-Position Day Trading
verfasst von : Célia da Costa Pereira, Andrea G. B. Tettamanzi
Erschienen in: Natural Computing in Computational Finance
Verlag: Springer Berlin Heidelberg
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This chapter illustrates a data-mining approach to single-position day trading which uses an evolutionary algorithm to construct a fuzzy predictive model of a financial instrument. The model is expressed as a set of fuzzy IF-THEN rules. The model takes as inputs the open, high, low, and close prices, as well as the values of a number of popular technical indicators on day
t
and produces a
go short, do nothing, go long
trading signal for day
t
+1 based on a dataset of past observations of which actions would have been most profitable. The approach has been applied to trading several financial instruments (large-cap stocks and indices): the experimental results are presented and discussed. A method to enhance the performance of trading rules based on the approach by using ensembles of fuzzy models is finally illustrated. The results clearly indicate that, despite its simplicity, the approach may yield significant returns, outperforming a buy-and-hold strategy.