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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