A trading method in FX using evolutionary algorithms: extensions based on reverse trend and settlement timing

Created by W.Langdon from gp-bibliography.bib Revision:1.2031

@InProceedings{Tserenchimed:2011:GECCOcomp,
  author =       "Badarch Tserenchimed and Shu Liu and Hitoshi Iba",
  title =        "A trading method in {FX} using evolutionary
                 algorithms: extensions based on reverse trend and
                 settlement timing",
  booktitle =    "GECCO '11: Proceedings of the 13th annual conference
                 companion on Genetic and evolutionary computation",
  year =         "2011",
  editor =       "Natalio Krasnogor and Pier Luca Lanzi and 
                 Andries Engelbrecht and David Pelta and Carlos Gershenson and 
                 Giovanni Squillero and Alex Freitas and 
                 Marylyn Ritchie and Mike Preuss and Christian Gagne and 
                 Yew Soon Ong and Guenther Raidl and Marcus Gallager and 
                 Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and 
                 Nikolaus Hansen and Silja Meyer-Nieberg and 
                 Jim Smith and Gus Eiben and Ester Bernado-Mansilla and 
                 Will Browne and Lee Spector and Tina Yu and Jeff Clune and 
                 Greg Hornby and Man-Leung Wong and Pierre Collet and 
                 Steve Gustafson and Jean-Paul Watson and 
                 Moshe Sipper and Simon Poulding and Gabriela Ochoa and 
                 Marc Schoenauer and Carsten Witt and Anne Auger",
  isbn13 =       "978-1-4503-0690-4",
  keywords =     "genetic algorithms, genetic programming: Poster",
  pages =        "139--140",
  month =        "12-16 " # jul,
  organisation = "SIGEVO",
  address =      "Dublin, Ireland",
  doi =          "doi:10.1145/2001858.2001937",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "In foreign exchange (FX) markets, the key issues to
                 achieve profitable trading rules are the combination of
                 the indicators, selection of their parameters, and
                 decision of the trade timing for orders and
                 settlements. In this paper, we present a trading system
                 using a combination of genetic algorithm (GA) and
                 genetic programming (GP). Unlike related researches on
                 this problem, our work focuses on two aspects. First, a
                 calculation of appropriate settlement timing is
                 proposed, to make more profits and less losses. Second,
                 reverse trend data are generated using in-sample data,
                 to overcome the over fitting problem and suppress the
                 risk of loss. To examine the effectiveness of the
                 method, we employed simulations using real-world
                 trading intraday data. It is verified the enhanced
                 capability of our method to make consistent gain
                 out-of-sample and avoid large draw-downs.",
  notes =        "Also known as \cite{2001937} Distributed on CD-ROM at
                 GECCO-2011.

                 ACM Order Number 910112.",
}

Genetic Programming entries for Badarch Tserenchimed Shu Liu Hitoshi Iba