Using Genetic Programming with Lambda Abstraction to Find Technical Trading Rules

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InProceedings{RePEc:sce:scecf4:200,

• author = "Tina Yu and Shu-Heng Chen",
• title = "Using Genetic Programming with Lambda Abstraction to Find Technical Trading Rules",
• booktitle = "Computing in Economics and Finance",
• year = "2004",
• address = "University of Amsterdam",
• month = "8-10 " # jul,
• organisation = "Society for Computational Economics",
• keywords = "genetic algorithms, genetic programming",
• URL = "https://ideas.repec.org/p/sce/scecf4/200.html",
• abstract = "Using GP with lambda abstraction module mechanism to generate technical trading rules based on S&P 500 index, we find strong evidence of excess returns over buy-and-hold after transaction cost on the testing period from 1989 to 2002. The rules can be interpreted easily; each uses a combination of one to four widely used technical indicators to make trading decisions. The consensus among GP rules is high, with most of the time 80% of the evolved rules give the same decision. The GP rules give high transaction frequency. Regardless of market climate, they are able to identify opportunities to make profitable trades and out-perform buy-and-hold",
• notes = "22 aug 2004 http://ideas.repec.org/p/sce/scecf4/200.html CEF 2004 number 200",

}

Genetic Programming entries for Tina Yu Shu-Heng Chen

Citations