On the Utility of Trading Criteria Based Retraining in Forex Markets

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

  author =       "Alexander Loginov and Malcolm I. Heywood",
  title =        "On the Utility of Trading Criteria Based Retraining in
                 Forex Markets",
  booktitle =    "Applications of Evolutionary Computing,
                 EvoApplications 2013: EvoCOMNET, EvoCOMPLEX, EvoENERGY,
                 EvoFIN, EvoGAMES, EvoIASP, EvoINDUSTRY, EvoNUM, EvoPAR,
                 EvoRISK, EvoROBOT, EvoSTOC",
  year =         "2013",
  month =        "3-5 " # apr,
  editor =       "Anna I. Esparcia-Alcazar and Antonio Della Cioppa and 
                 Ivanoe {De Falco} and Ernesto Tarantino and 
                 Carlos Cotta and Robert Schaefer and Konrad Diwold and 
                 Kyrre Glette and Andrea Tettamanzi and 
                 Alexandros Agapitos and Paolo Burrelli and J. J. Merelo and 
                 Stefano Cagnoni and Mengjie Zhang and Neil Urquhart and Kevin Sim and 
                 Aniko Ekart and Francisco {Fernandez de Vega} and 
                 Sara Silva and Evert Haasdijk and Gusz Eiben and 
                 Anabela Simoes and Philipp Rohlfshagen",
  series =       "LNCS",
  volume =       "7835",
  publisher =    "Springer Verlag",
  address =      "Vienna",
  publisher_address = "Berlin",
  pages =        "192--202",
  organisation = "EvoStar",
  keywords =     "genetic algorithms, genetic programming, Coevolution,
                 non-stationary, FX, Forex, Currency",
  isbn13 =       "978-3-642-37191-2",
  DOI =          "doi:10.1007/978-3-642-37192-9_20",
  size =         "11 pages",
  abstract =     "This research investigates the ability of genetic
                 programming (GP) to build profitable trading strategies
                 for the Foreign Exchange Market (FX) of three major
                 currency pairs (EURUSD, USDCHF and EURCHF) using one
                 hour prices from 2008 to 2011. We recognise that such
                 environments are likely to be non-stationary. Thus, we
                 do not require a single training partition to capture
                 all likely future behaviours. We address this by
                 detecting poor trading behaviours and use this to
                 trigger retraining. In addition the task of evolving
                 good technical indicators (TI) and the rules for
                 deploying trading actions is explicitly separated.
                 Thus, separate GP populations are used to coevolve TI
                 and trading behaviours under a mutualistic symbiotic
                 association. The results of 100 simulations demonstrate
                 that an adaptive retraining algorithm significantly
                 outperforms a single-strategy approach (population
                 evolved once) and generates profitable solutions with a
                 high probability.",
  notes =        "http://www.kevinsim.co.uk/evostar2013/cfpEvoApplications.html
                 EvoApplications2013 held in conjunction with
                 EuroGP2013, EvoCOP2013, EvoBio'2013 and EvoMusArt2013",

Genetic Programming entries for Alexander Loginov Malcolm Heywood