Co-evolving online high-frequency trading strategies using grammatical evolution

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

@InProceedings{conf/cifer/GabrielssonJK14,
  author =       "Patrick Gabrielsson and Ulf Johansson and 
                 Rikard Konig",
  title =        "Co-evolving online high-frequency trading strategies
                 using grammatical evolution",
  booktitle =    "IEEE Conference on Computational Intelligence for
                 Financial Engineering Economics (CIFEr 2104)",
  year =         "2014",
  pages =        "473--480",
  address =      "London",
  month =        "27-28 " # mar,
  publisher =    "IEEE",
  keywords =     "genetic algorithms, genetic programming, grammatical
                 evolution",
  bibdate =      "2014-11-06",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/db/conf/cifer/cifer2014.html#GabrielssonJK14",
  URL =          "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6901616",
  DOI =          "doi:10.1109/CIFEr.2014.6924111",
  abstract =     "Numerous sophisticated algorithms exist for
                 discovering reoccurring patterns in financial time
                 series. However, the most accurate techniques available
                 produce opaque models, from which it is impossible to
                 discern the rationale behind trading decisions. It is
                 therefore desirable to sacrifice some degree of
                 accuracy for transparency. One fairly recent
                 evolutionary computational technology that creates
                 transparent models, using a user-specified grammar, is
                 grammatical evolution (GE). In this paper, we explore
                 the possibility of evolving transparent entry- and exit
                 trading strategies for the E-mini S&P 500 index futures
                 market in a high-frequency trading environment using
                 grammatical evolution. We compare the performance of
                 models incorporating risk into their calculations with
                 models that do not. Our empirical results suggest that
                 profitable, risk-averse, transparent trading strategies
                 for the E-mini S&P 500 can be obtained using
                 grammatical evolution together with technical
                 indicators.",
  notes =        "Also known as \cite{6924111}",
}

Genetic Programming entries for Patrick Gabrielsson Ulf Johansson Rikard Konig

Citations