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Using Genetic Programming with Lambda Abstraction to Find Technical Trading Rules

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  • 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

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Bibliographic Info

Paper provided by Society for Computational Economics in its series Computing in Economics and Finance 2004 with number 200.

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Date of creation: 11 Aug 2004
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Handle: RePEc:sce:scecf4:200

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Keywords: modular GP; lambda abstraction; strongly typed GP; technical trading rules;

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