Evolving Trading Rule-Based Policies

  • Robert Gregory Bradley
  • Anthony Brabazon
  • Michael O’Neill
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6025)

Abstract

Trading-rule representation is an important factor to consider when designing a quantitative trading system. This study implements a trading strategy as a rule-based policy. The result is an intuitive human-readable format which allows for seamless integration of domain knowledge. The components of a policy are specified and represented as a set of rewrite rules in a context-free grammar. These rewrite rules define how the components can be legally assembled. Thus, strategies derived from the grammar are well-formed, domain-specific, solutions. A grammar-based Evolutionary Algorithm, Grammatical Evolution (GE), is then employed to automatically evolve intra-day trading strategies for the U.S. Stock Market. The GE methodology managed to discover profitable rules with realistic transaction costs included. The paper concludes with a number of suggestions for future work.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Robert Gregory Bradley
    • 1
    • 2
  • Anthony Brabazon
    • 1
    • 2
  • Michael O’Neill
    • 1
    • 3
  1. 1.Natural Computing Research and Applications GroupUniversity College DublinIreland
  2. 2.School of BusinessUniversity College DublinIreland
  3. 3.School of Computer Science and InformaticsUniversity College DublinIreland

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