Pretests for Genetic-Programming Evolved Trading Programs: “zero-intelligence” Strategies and Lottery Trading

  • Shu-Heng Chen
  • Nicolas Navet
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4234)


Over the last decade, numerous papers have investigated the use of GP for creating financial trading strategies. Typically in the literature results are inconclusive but the investigators always suggest the possibility of further improvements, leaving the conclusion regarding the effectiveness of GP undecided. In this paper, we discuss a series of pretests, based on several variants of random search, aiming at giving more clear-cut answers on whether a GP scheme, or any other machine-learning technique, can be effective with the training data at hand. The analysis is illustrated with GP-evolved strategies for three stock exchanges exhibiting different trends.


Genetic Programming Trading Strategy Random Search Stock Index Training Interval 
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 2006

Authors and Affiliations

  • Shu-Heng Chen
    • 1
  • Nicolas Navet
    • 1
    • 2
  1. 1.AI-ECON Research Center, Department of EconomicsNational Chengchi UniversityTaipeiTaiwan
  2. 2.LORIA-INRIAVandoeuvreFrance

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