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Pretests for Genetic-Programming Evolved Trading Programs: “zero-intelligence” Strategies and Lottery Trading

Identifieur interne : 005409 ( Main/Exploration ); précédent : 005408; suivant : 005410

Pretests for Genetic-Programming Evolved Trading Programs: “zero-intelligence” Strategies and Lottery Trading

Auteurs : Shu-Heng Chen [Taïwan] ; Nicolas Navet [Taïwan, France]

Source :

RBID : ISTEX:BE8606AD2845681E7E572975C406AFFF6F118F4F

Descripteurs français

English descriptors

Abstract

Abstract: Over the last decade, numerous papers have investigated the use of GP for creating financial trading strategies. Typically in the literature results are inconclusive but the investigators always suggest the possibility of further improvements, leaving the conclusion regarding the effectiveness of GP undecided. In this paper, we discuss a series of pretests, based on several variants of random search, aiming at giving more clear-cut answers on whether a GP scheme, or any other machine-learning technique, can be effective with the training data at hand. The analysis is illustrated with GP-evolved strategies for three stock exchanges exhibiting different trends.

Url:
DOI: 10.1007/11893295_50


Affiliations:


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