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Algorithmic Trading with Developmental and Linear Genetic Programming

  • Garnett Wilson
  • Wolfgang Banzhaf
Chapter
Part of the Genetic and Evolutionary Computation book series (GEVO)

Abstract

A developmental co-evolutionary genetic programming approach (PAM DGP) and a standard linear genetic programming (LGP) stock trading systemare applied to a number of stocks across market sectors. Both GP techniques were found to be robust to market fluctuations and reactive to opportunities associated with stock price rise and fall, with PAMDGP generating notably greater profit in some stock trend scenarios. Both algorithms were very accurate at buying to achieve profit and selling to protect assets, while exhibiting bothmoderate trading activity and the ability to maximize or minimize investment as appropriate. The content of the trading rules produced by both algorithms are also examined in relation to stock price trend scenarios.

Keywords

Developmental Genetic Programming Linear Genetic Programming Computational Finance 

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References

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Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

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

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