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Deriving Trading Rules Using Gene Expression Programming

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  • Adrian VISOIU

Abstract

This paper presents how buy and sell trading rules are generated using gene expression programming with special setup. Market concepts are presented and market analysis is discussed with emphasis on technical analysis and quantitative methods. The use of genetic algorithms in deriving trading rules is presented. Gene expression programming is applied in a form where multiple types of operators and operands are used. This gives birth to multiple gene contexts and references between genes in order to keep the linear structure of the gene expression programming chromosome. The setup of multiple gene contexts is presented. The case study shows how to use the proposed gene setup to derive trading rules encoded by Boolean expressions, using a dataset with the reference exchange rates between the Euro and the Romanian leu. The conclusions highlight the positive results obtained in deriving useful trading rules.

Suggested Citation

  • Adrian VISOIU, 2011. "Deriving Trading Rules Using Gene Expression Programming," Informatica Economica, Academy of Economic Studies - Bucharest, Romania, vol. 15(1), pages 22-30.
    • Handle: RePEc:aes:infoec:v:15:y:2011:i:1:p:22-30
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    Cited by:

    1. Mostafa, Mohamed M. & El-Masry, Ahmed A., 2016. "Oil price forecasting using gene expression programming and artificial neural networks," Economic Modelling, Elsevier, vol. 54(C), pages 40-53.

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