A new modular genetic programming for finding attractive technical patterns in stock markets

  • Authors:
  • Seung-Kyu Lee;Byung-Ro Moon

  • Affiliations:
  • Seoul National University, Seoul, South Korea;Seoul National University, Seoul, South Korea

  • Venue:
  • Proceedings of the 12th annual conference on Genetic and evolutionary computation
  • Year:
  • 2010

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Abstract

We propose a new modular genetic programming for finding attractive and statistically sound technical patterns forstock trading. We restrict the problem space to combinations of modules for more effective space search. We carefully prepared the set of modules based on existing studies of technical indicators and our own experience. Our modular genetic programming successfully found unknown attractive technical patterns for the Korean stock market. A trading simulation with the generated patterns by a commercial tool showed significantly higher accumulative returns than the KOSPI index.