Evolving Rule-Based Trading Systems
Created by W.Langdon from
gp-bibliography.bib Revision:1.8010
- @TechReport{oai:CiteSeerPSU:503310,
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title = "Evolving Rule-Based Trading Systems",
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author = "Christian Setzkorn and Laura Dipietro and
Robin Purshouse",
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institution = "Department of Computer Science, University of
Liverpool",
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year = "2002",
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number = "ULCS-02-005",
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address = "UK",
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keywords = "genetic algorithms, genetic programming",
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citeseer-isreferencedby = "oai:CiteSeerPSU:78975",
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citeseer-references = "oai:CiteSeerPSU:212034;
oai:CiteSeerPSU:470345",
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annote = "The Pennsylvania State University CiteSeer Archives",
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language = "en",
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oai = "oai:CiteSeerPSU:503310",
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rights = "unrestricted",
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URL = "http://www.csc.liv.ac.uk/research/techreports/tr2002/tr02005abs.html",
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URL = "http://www.csc.liv.ac.uk/research/techreports/tr2002/ulcs-02-005.ps",
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URL = "http://citeseer.ist.psu.edu/503310.html",
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abstract = "In this study, a market trading rulebase is optimised
using genetic programming (GP). The rulebase is
comprised of simple relationships between technical
indicators, and generates signals to buy, sell short,
and remain inactive. The methodology is applied to
prediction of the Standard & Poor's composite index
(02-Jan-1990 to 18-Oct-2001). Two potential market
systems are inferred: a simple system using few rules
and nodes, and a more complex system. Results are
compared with a benchmark buy-and-hold strategy.
Neither trading system was found capable of
consistently outperforming this benchmark. More
complicated rulebases, in addition to being difficult
to understand, are susceptible to overfitting. Simpler
rulebases are more robust to changing market
conditions, but cannot take advantage of
high-profit-making opportunities. By increasing the
richness of the available rulebase building-blocks and
the variety of training data, it is anticipated that
subsequent systems will surpass the benchmark
strategy.",
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size = "10 pages",
- }
Genetic Programming entries for
Christian Setzkorn
Laura Dipietro
Robin C Purshouse
Citations