Intraday FX Trading: An Evolutionary Reinforcement Learning Approach

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{DBLP:conf/ideal/DempsterR02,
  author =       "M. A. H. Dempster and Y. S. Romahi",
  title =        "Intraday FX Trading: An Evolutionary Reinforcement
                 Learning Approach",
  booktitle =    "Proceedings of Third International Conference on
                 Intelligent Data Engineering and Automated Learning -
                 IDEAL 2002",
  year =         "2002",
  editor =       "Hujun Yin and Nigel M. Allinson and 
                 Richard T. Freeman and John A. Keane and Simon J. Hubbard",
  publisher =    "Springer",
  series =       "Lecture Notes in Computer Science",
  volume =       "2412",
  pages =        "347--358",
  address =      "Manchester",
  month =        "12-14 " # aug,
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-44025-9",
  bibsource =    "DBLP, http://dblp.uni-trier.de",
  URL =          "http://mahd-pc.jbs.cam.ac.uk/archive/PAPERS/2002/WP3-2002.pdf",
  DOI =          "doi:10.1007/3-540-45675-9_52",
  abstract =     "We have previously described trading systems based on
                 unsupervised learning approaches such as reinforcement
                 learning and genetic algorithms which take as input a
                 collection of commonly used technical indicators and
                 generate profitable trading decisions from them. This
                 article demonstrates the advantages of applying
                 evolutionary algorithms to the reinforcement learning
                 problem using a hybrid credit assignment approach. In
                 earlier work, the temporal difference reinforcement
                 learning approach suffered from problems with
                 overfitting the in-sample data. This motivated the
                 present approach.

                 Technical analysis has been shown previously to have
                 predictive value regarding future movements of foreign
                 exchange prices and this article presents methods for
                 automated high-frequency FX trading based on
                 evolutionary reinforcement learning about signals from
                 a variety of technical indicators. These methods are
                 applied to GBPUSD, USDCHF and USDJPY exchange rates at
                 various frequencies. Statistically significant profits
                 are made consistently at transaction costs of up to 4bp
                 for the hybrid system while the standard RL is only
                 able to trade profitably up to about 1bp slippage per
                 trade.",
  notes =        "Location: technical report WP03/2002

                 ",
}

Genetic Programming entries for Michael Dempster Yazann Romahi

Citations