Evolutionary Induction of Trading Models

  • Siddhartha Bhattacharyya
  • Kumar Mehta
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 100)


Financial markets data present a challenging opportunity for the learning of complex patterns not readily discernable. This paper investigates the use of genetic algorithms for the mining of financial time-series for patterns aimed at the provision of trading decision models. A simple yet flexible representation for trading rules is proposed, and issues pertaining to fitness evaluation examined. Two key issues in fitness evaluation, the design of a suitable fitness function reflecting desired trading characteristics and choice of appropriate training duration, are discussed and empirically examined. Two basic measures are also proposed for characterizing rules obtained with alternate fitness criteria.


Fitness Function Excess Return Fitness Evaluation Foreign Exchange Market Trading Rule 
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 2002

Authors and Affiliations

  • Siddhartha Bhattacharyya
    • 1
  • Kumar Mehta
    • 1
  1. 1.Information and Decision Sciences, College of Business AdministrationUniversity of Illinois at ChicagoUSA

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