Using Genetic Programming with Lambda Abstraction to Find Technical Trading Rules

Tina Yu and Shu-Heng Chen

No 200, Computing in Economics and Finance 2004 from Society for Computational Economics

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

Keywords: modular GP; lambda abstraction; strongly typed GP; technical trading rules (search for similar items in EconPapers)
JEL-codes: C53 C61 (search for similar items in EconPapers)
Date: 2004-08-11
References: Add references at CitEc
Citations: View citations in EconPapers (1)

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:sce:scecf4:200

Access Statistics for this paper

More papers in Computing in Economics and Finance 2004 from Society for Computational Economics Contact information at EDIRC.
Bibliographic data for series maintained by Christopher F. Baum ().