Evolving Efficient Limit Order Strategy using Grammatical Evolution

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@InProceedings{cui_etal:cec2010,
  author =       "Wei Cui and Anthony Brabazon and Michael O'Neill",
  title =        "Evolving Efficient Limit Order Strategy using
                 Grammatical Evolution",
  booktitle =    "2010 IEEE World Congress on Computational
                 Intelligence",
  pages =        "2408--2413",
  year =         "2010",
  address =      "Barcelona, Spain",
  month =        "18-23 " # jul,
  organization = "IEEE Computational Intelligence Society",
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, grammatical
                 evolution",
  isbn13 =       "978-1-4244-6910-9",
  DOI =          "doi:10.1109/CEC.2010.5586040",
  abstract =     "Trade execution is concerned with the actual mechanics
                 of buying or selling the desired amount of a financial
                 instrument of interest. A practical problem in trade
                 execution is how to trade a large order as efficiently
                 as possible. A trade execution strategy is designed for
                 this task to minimise total trade cost. Grammatical
                 Evolution (GE) is an evolutionary automatic programming
                 methodology which can be used to evolve rule sets. It
                 has been proved successfully to be able to evolve
                 quality trade execution strategies in our previous
                 work. In this paper, the previous work is extended by
                 adopting two different limit order lifetimes and three
                 benchmark limit order strategies. GE is used to evolve
                 efficient limit order strategies which can determine
                 the aggressiveness levels of limit orders. We found
                 that GE evolved limit order strategies were highly
                 competitive against three benchmark strategies and the
                 limit order strategies with long-term lifetime
                 performed better than those with short-term lifetime.",
  notes =        "WCCI 2010. Also known as \cite{5586040}",
}

Genetic Programming entries for Wei Cui Anthony Brabazon Michael O'Neill

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