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Evolving trading rule-based policies
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File | Description | Size | Format | |
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Bradley2010.pdf | 397.58 KB |
Date Issued
April 2010
Date Available
21T14:59:07Z January 2011
Abstract
Trading-rule representation is an important factor to consider when designing a quantitative trading system. This study implements a trading strategy as a rule-based policy. The result is an intuitive human-readable format which allows for seamless integration of domain knowledge. The components of a policy are specified and represented as a set of rewrite rules in a context-free grammar. These rewrite rules define how the components can be legally assembled. Thus, strategies derived from the grammar are well-formed, domain-specific, solutions. A grammar-based Evolutionary Algorithm, Grammatical Evolution (GE), is then employed to automatically evolve intra-day trading strategies for the U.S. Stock Market. The GE methodology managed to discover profitable rules with realistic transaction costs included. The paper concludes with a number of suggestions for future work.
Sponsorship
Science Foundation Ireland
Type of Material
Conference Publication
Publisher
Springer
Journal
Proceedings of EvoFIn 2010, Applications of Evolutionary Computation, Lecture Notes in Computer Science
Copyright (Published Version)
2010 Verlag Berlin Heidelberg
Subject – LCSH
Evolutionary computation
Stocks
Web versions
Language
English
Status of Item
Peer reviewed
Part of
Di Chio, C. ...et al (ed.s). Applications of Evolutionary Computation EvoApplications 2010: EvoCOMNET, EvoENVIRONMENT, EvoFIN, EvoMUSART, and EvoTRANSLOG, Istanbul, Turkey, April 7-9, 2010, Proceedings, Part II
Description
EvoFIN 4th European Event on Evolutionary and Natural Computation in Finance and Economics, at EvoStar 2010, Istanbul, 7-9 April 2010
ISBN
978-3-642-12241-5
This item is made available under a Creative Commons License
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