Fuzzy-Evolutionary Modeling for Single-Position Day Trading

  • Célia da Costa Pereira
  • Andrea G. B. Tettamanzi
Part of the Studies in Computational Intelligence book series (SCI, volume 100)

Summary

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.

Keywords

Membership Function Fuzzy Rule Sharpe Ratio Trading Rule Closing Price 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Célia da Costa Pereira
    • 1
  • Andrea G. B. Tettamanzi
    • 1
  1. 1.Dipartimento di Tecnologie dell’InformazioneUniversità Degli Studi di MilanoMilanItaly

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