Evolving efficient limit order strategy using grammatical evolution

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Title: Evolving efficient limit order strategy using grammatical evolution
Authors: Cui, Wei
Brabazon, Anthony
O'Neill, Michael
Permanent link: http://hdl.handle.net/10197/2140
Date: 2010
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 minimize 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.
Funding Details: Science Foundation Ireland
Type of material: Conference Publication
Publisher: IEEE Press
Copyright (published version): 2010 IEEE
Keywords: Trade executionFinanceRisk managementGrammatical evolutionLimit order
Subject LCSH: Financial instruments
International finance
Financial risk
DOI: 10.1109/CEC.2010.5586040
Language: en
Status of Item: Peer reviewed
Is part of: Evolutionary Computation (CEC), 2010 IEEE Congress on (proceedings)
Conference Details: IEEE World Congress on Computational Intelligence, July 18-23 2010, Barcelona
Appears in Collections:FMC² Research Collection
Computer Science Research Collection
Business Research Collection
CASL Research Collection

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