Discovering Financial Technical Trading Rules Using Genetic Programming with Lambda Abstraction
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @InCollection{yu:2004:GPTP,
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author = "Tina Yu and Shu-Heng Chen and Tzu-Wen Kuo",
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title = "Discovering Financial Technical Trading Rules Using
Genetic Programming with Lambda Abstraction",
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booktitle = "Genetic Programming Theory and Practice {II}",
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year = "2004",
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editor = "Una-May O'Reilly and Tina Yu and Rick L. Riolo and
Bill Worzel",
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chapter = "2",
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pages = "11--30",
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address = "Ann Arbor",
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month = "13-15 " # may,
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publisher = "Springer",
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keywords = "genetic algorithms, genetic programming, modular
genetic programming, lambda abstraction modules,
higher-order functions, financial trading rules,
buy-and-hold, S&P 500 index, automatically defined
functions, ADF, PolyGP system, stock market, technical
analysis, constrained syntactic structure, strongly
typed genetic programming, STGP, financial time series,
lambda abstraction GP",
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ISBN = "0-387-23253-2",
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URL = "http://www.cs.mun.ca/~tinayu/Publications_files/gptp2004.pdf",
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DOI = "doi:10.1007/0-387-23254-0_2",
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size = "20 pages",
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abstract = "We applied genetic programming with a lambda
abstraction module mechanism to learn technical trading
rules based on S&P 500 index from 1982 to 2002. The
results show strong evidence of excess returns over
buy-and-hold after transaction cost. The discovered
trading rules can be interpreted easily; each rule uses
a combination of one to four widely used technical
indicators to make trading decisions. The consensus
among these trading rules is high. For the majority of
the testing period, 80percent of the trading rules give
the same decision. These rules also give high
transaction frequency. Regardless of the stock market
climate, they are able to identify opportunities to
make profitable trades and out-perform buy-and-hold.",
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notes = "part of \cite{oreilly:2004:GPTP2}
",
- }
Genetic Programming entries for
Tina Yu
Shu-Heng Chen
Tzu-Wen Kuo
Citations