Interday and Intraday Stock Trading Using Probabilistic Adaptive Mapping Developmental Genetic Programming and Linear Genetic Programming

  • Garnett Wilson
  • Wolfgang Banzhaf
Part of the Studies in Computational Intelligence book series (SCI, volume 293)

Summary

A developmental co-evolutionary genetic programming approach (PAM DGP) is compared to a standard linear genetic programming (LGP) implementation for trading of stocks in the technology sector. Both interday and intraday data for these stocks were analyzed, where both implementations were found to be impressively robust to market fluctuations while reacting efficiently to opportunities for profit. PAM DGP proved slightly more reactive to market changes compared to LGP for intraday data, where the converse held true for interday data. Both implementations had very impressive accuracy in choosing both profitable buy trades and sells that prevented losses for both interday and intraday stock data. These successful trades occurred in the context of moderately active trading for interday prices and lower levels of trading for intraday prices.

Keywords

Genetic Programming Trading Rule Technical Indicator Grammatical Evolution Linear Genetic Programming 
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 2010

Authors and Affiliations

  • Garnett Wilson
    • 1
  • Wolfgang Banzhaf
    • 1
  1. 1.Memorial University of NewfoundlandSt. John’sCanada

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