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.

Keywords

Trading Policy Trading Strategy Trading Rule Technical Indicator Grammatical Evolution 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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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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