Comprehensibility, Overfitting and Co-Evolution in Genetic Programming for Technical Trading Rules
Created by W.Langdon from
gp-bibliography.bib Revision:1.8028
- @MastersThesis{seshadri:mastersthesis,
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author = "Mukund Seshadri",
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title = "Comprehensibility, Overfitting and Co-Evolution in
Genetic Programming for Technical Trading Rules",
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school = "Worcester Polytechnic Institute",
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year = "2003",
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type = "Master's Thesis",
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address = "6 Castle Road, Northboro, MA 10532, USA",
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month = may,
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email = "mukund@cs.wpi.edu",
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keywords = "genetic algorithms, genetic programming, technical
analysis, coevolution, S&P500, market timing",
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URL = "http://www.wpi.edu/Pubs/ETD/Available/etd-0430103-121518/unrestricted/Seshadri.pdf",
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URL = "http://www.wpi.edu/Pubs/ETD/Available/etd-0430103-121518/",
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abstract = "This thesis presents Genetic Programming methodologies
to find successful and understandable technical trading
rules for financial markets. The methods when applied
to the S&P500 consistently beat the buy-and-hold
strategy over a 12-year period, even when considering
transaction costs. Some of the methods described
discover rules that beat the S&P500 with 99%
significance. The work describes the use of a
complexity-penalising factor to avoid overfitting and
improve comprehensibility of the rules produced by GPs.
The effect of this factor on the returns for this
domain area is studied and the results indicated that
it increased the predictive ability of the rules. A
restricted set of operators and domain knowledge were
used to improve comprehensibility. In particular,
arithmetic operators were eliminated and a number of
technical indicators in addition to the widely used
moving averages, such as trend lines and local maxima
and minima were added. A new evaluation function that
tests for consistency of returns in addition to total
returns is introduced. Different cooperative
coevolutionary genetic programming strategies for
improving returns are studied and the results analysed.
We find that paired collaborator coevolution has the
best results.",
-
size = "87 pages",
- }
Genetic Programming entries for
Mukund Seshadri
Citations