Options
Evolving dynamic trade execution strategies using grammatical evolution
Author(s)
Date Issued
2010
Date Available
2010-07-14T14:03:23Z
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.
Sponsorship
Science Foundation Ireland
Type of Material
Conference Publication
Publisher
Springer
Copyright (Published Version)
2010 Springer Verlag
Subject – LCSH
Evolutionary computation
International finance
Financial risk
Financial instruments
Web versions
Language
English
Status of Item
Peer reviewed
Part of
Di Chio, C. ... et al. (eds.). Applications of Evolutionary Computation : EvoApplications 2010: EvoCOMNET, EvoENVIRONMENT, EvoFIN, EvoMUSART, and EvoTRANSLOG Istanbul, Turkey, April 7-9, 2010 Proceedings, Part II
Conference Details
Paper presented at EvoFin 2010, 4th European Event on Evolutionary and Natural Computation in Finance and Economics, as part of EvoStar 2010, 7-9 April 2010, Istanbul
ISBN
978-3-642-12241-5
This item is made available under a Creative Commons License
File(s)
Name
Evolving Dynamic Trade Execution Strategies Using Grammatical Evolution.pdf
Size
194.24 KB
Format
Owning collection
Scopus© citations
6
Acquisition Date
Apr 17, 2024
Apr 17, 2024
Views
2252
Last Month
1
1
Acquisition Date
Apr 17, 2024
Apr 17, 2024
Downloads
2511
Last Week
1
1
Last Month
2
2
Acquisition Date
Apr 17, 2024
Apr 17, 2024