Artificial Adaptive Market Traders Based in Genetic Algorithms for a Stock Market Simulator

  • Pedro Isasi
  • Manuel Velasco
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 105)


Some aspects of classical stock market economic theories are unrealistic and do not fulfill the expectations of market traders. Other paradigms can help to overcome the shortcomings of these theories. In this paper, a paradigm based on learning conception is presented. A simulator has been developed to research the stock market. Some aspects like dynamic prices, investment strategies, market statistics have been considered. Also, an artificial adapted market trader based on genetic algorithms has been implemented. Some experimental work has been done to observe the behavior of these automatic market traders in different situations.


Genetic Algorithm Stock Market Genetic Operator State String Mating Pool 
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

  • Pedro Isasi
    • 1
  • Manuel Velasco
    • 1
  1. 1.Computer Science DepartmentUniversidad Carlos III de MadridLeganésSpain

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