An automated FX trading system using adaptive reinforcement learning

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "M. A. H. Dempster and V. Leemans",
  title =        "An automated {FX} trading system using adaptive
                 reinforcement learning",
  journal =      "Expert Systems with Applications",
  year =         "2006",
  volume =       "30",
  number =       "3",
  pages =        "543--552",
  month =        apr,
  note =         "Special Issue on Financial Engineering",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "",
  DOI =          "doi:10.1016/j.eswa.2005.10.012",
  abstract =     "This paper introduces adaptive reinforcement learning
                 (ARL) as the basis for a fully automated trading system
                 application. The system is designed to trade foreign
                 exchange (FX) markets and relies on a layered structure
                 consisting of a machine learning algorithm, a risk
                 management overlay and a dynamic utility optimisation
                 layer. An existing machine-learning method called
                 recurrent reinforcement learning (RRL) was chosen as
                 the underlying algorithm for ARL. One of the strengths
                 of our approach is that the dynamic optimization layer
                 makes a fixed choice of model tuning parameters
                 unnecessary. It also allows for a risk-return trade-off
                 to be made by the user within the system. The trading
                 system is able to make consistent gains out-of-sample
                 while avoiding large draw-downs.",
  notes =        "Centre for Financial Research, Judge Business School,
                 University of Cambridge & Cambridge Systems Associates
                 Limited, Cambridge, UK Also technical report


Genetic Programming entries for Michael Dempster Vasco Leemans