Stock trading using linear genetic programming with multiple time frames

Created by W.Langdon from gp-bibliography.bib Revision:1.4534

  author =       "Garnett Wilson and Derek Leblanc and 
                 Wolfgang Banzhaf",
  title =        "Stock trading using linear genetic programming with
                 multiple time frames",
  booktitle =    "GECCO '11: Proceedings of the 13th annual conference
                 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 =       "978-1-4503-0557-0",
  pages =        "1667--1674",
  keywords =     "genetic algorithms, genetic programming, Real world
  month =        "12-16 " # jul,
  organisation = "SIGEVO",
  address =      "Dublin, Ireland",
  DOI =          "doi:10.1145/2001576.2001801",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "A number of researchers have attempted to take
                 successful GP trading systems and make them even better
                 through the use of filters. We investigate the use of a
                 linear genetic programming (LGP) system that combines
                 GP signals provided over multiple intraday time frames
                 to produce one trading action. Four combinations of
                 time frames stretching further into the past are
                 examined. Two different decision mechanisms for
                 evaluating the overall signal given the GP signals over
                 all time frames are also examined, one based on
                 majority vote and another based on temporal proximity
                 to the buying decision. Results indicated that majority
                 vote outperformed emphasis on proximity of time frames
                 to the current trading decision. Analyses also
                 indicated that longer time frame combinations were more
                 conservative and outperformed shorter combinations for
                 both overall upward and downward price trends.",
  notes =        "Also known as \cite{2001801} GECCO-2011 A joint
                 meeting of the twentieth international conference on
                 genetic algorithms (ICGA-2011) and the sixteenth annual
                 genetic programming conference (GP-2011)",

Genetic Programming entries for Garnett Carl Wilson Derek Leblanc Wolfgang Banzhaf