Designing safe, profitable automated stock trading agents using evolutionary algorithms

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{1144285,
  author =       "Harish Subramanian and Subramanian Ramamoorthy and 
                 Peter Stone and Benjamin J. Kuipers",
  title =        "Designing safe, profitable automated stock trading
                 agents using evolutionary algorithms",
  booktitle =    "{GECCO 2006:} Proceedings of the 8th annual conference
                 on Genetic and evolutionary computation",
  year =         "2006",
  editor =       "Maarten Keijzer and Mike Cattolico and Dirk Arnold and 
                 Vladan Babovic and Christian Blum and Peter Bosman and 
                 Martin V. Butz and Carlos {Coello Coello} and 
                 Dipankar Dasgupta and Sevan G. Ficici and James Foster and 
                 Arturo Hernandez-Aguirre and Greg Hornby and 
                 Hod Lipson and Phil McMinn and Jason Moore and Guenther Raidl and 
                 Franz Rothlauf and Conor Ryan and Dirk Thierens",
  volume =       "2",
  ISBN =         "1-59593-186-4",
  pages =        "1777--1784",
  address =      "Seattle, Washington, USA",
  URL =          "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2006/docs/p1777.pdf",
  DOI =          "doi:10.1145/1143997.1144285",
  publisher =    "ACM Press",
  publisher_address = "New York, NY, 10286-1405, USA",
  month =        "8-12 " # jul,
  organisation = "ACM SIGEVO (formerly ISGEC)",
  keywords =     "genetic algorithms, genetic programming, Real-World
                 Applications, finance, fitness evaluation",
  size =         "8 pages",
  abstract =     "Trading rules are widely used by practitioners as an
                 effective means to mechanize aspects of their reasoning
                 about stock price trends. However, due to the
                 simplicity of these rules, each rule is susceptible to
                 poor behaviour in specific types of adverse market
                 conditions. Naive combinations of such rules are not
                 very effective in mitigating the weaknesses of
                 component rules. We demonstrate that sophisticated
                 approaches to combining these trading rules enable us
                 to overcome these problems and gainfully use them in
                 autonomous agents. We achieve this combination through
                 the use of genetic algorithms and genetic programs.
                 Further, we show that it is possible to use qualitative
                 characterizations of stochastic dynamics to improve the
                 performance of these agents by delineating safe, or
                 feasible, regions. We present the results of
                 experiments conducted within the Penn-Lehman Automated
                 Trading project. In this way we are able to demonstrate
                 that autonomous agents can achieve consistent
                 profitability in a variety of market conditions, in
                 ways that are human competitive.",
  notes =        "GECCO-2006 A joint meeting of the fifteenth
                 international conference on genetic algorithms
                 (ICGA-2006) and the eleventh annual genetic programming
                 conference (GP-2006).

                 ACM Order Number 910060",
}

Genetic Programming entries for Harish K Subramanian Subramanian Ramamoorthy Peter Stone Benjamin J Kuipers

Citations