Evolving dynamic trade execution strategies using grammatical evolution

  • Authors:

  • Wei Cui;Anthony Brabazon;Michael O'Neill

  • Affiliations:

  • Natural Computing Research and Applications Group, University College Dublin, Ireland;Natural Computing Research and Applications Group, University College Dublin, Ireland;Natural Computing Research and Applications Group, University College Dublin, Ireland

  • Venue:
  • EvoCOMNET'10 Proceedings of the 2010 international conference on Applications of Evolutionary Computation - Volume Part II
  • Year:
  • 2010

Quantified Score

Hi-index 0.00

Visualization

Abstract

Although there is a plentiful literature on the use of evolutionary methodologies for the trading of financial assets, little attention has been paid to potential use of these methods for efficient trade execution. Trade execution is concerned with the actual mechanics of buying or selling the desired amount of a financial instrument of interest. Grammatical Evolution (GE) is an evolutionary automatic programming methodology which can be used to evolve rule sets. In this paper we use a GE algorithm to discover dynamic, efficient, trade execution strategies which adapt to changing market conditions. The strategies are tested in an artificial limit order market. GE was found to be able to evolve quality trade execution strategies which are highly competitive with two benchmark trade execution strategies.