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Using Genetic Programming with Lambda Abstraction to Find Technical Trading Rules

Author

Listed:
  • Tina Yu
  • Shu-Heng Chen

Abstract

Using GP with lambda abstraction module mechanism to generate technical trading rules based on S&P 500 index, we find strong evidence of excess returns over buy-and-hold after transaction cost on the testing period from 1989 to 2002. The rules can be interpreted easily; each uses a combination of one to four widely used technical indicators to make trading decisions. The consensus among GP rules is high, with most of the time 80% of the evolved rules give the same decision. The GP rules give high transaction frequency. Regardless of market climate, they are able to identify opportunities to make profitable trades and out-perform buy-and-hold

Suggested Citation

  • Tina Yu & Shu-Heng Chen, 2004. "Using Genetic Programming with Lambda Abstraction to Find Technical Trading Rules," Computing in Economics and Finance 2004 200, Society for Computational Economics.
    • Handle: RePEc:sce:scecf4:200
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    Citations

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    Cited by:

    1. Thomas S. Coe & Kittipong Laosethakul, 2021. "Applying Technical Trading Rules to Beat Long-Term Investing: Evidence from Asian Markets," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 28(4), pages 587-611, December.

    More about this item

    Keywords

    modular GP; lambda abstraction; strongly typed GP; technical trading rules;
    All these keywords.

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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