Pretests for genetic-programming evolved trading programs: “zero-intelligence” strategies and lottery trading

Shu-Heng Chen 1 Nicolas Navet 2
2 TRIO - Real time and interoperability
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
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 clearcut 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.
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Communication dans un congrès
The 13th International Conference on Neural Information Processing - ICONIP2006, Oct 2006, Hong-Kong, Hong Kong SAR China. Springer Berlin / Heidelberg, 4234/2006 (4234/2006), pp.450-460, 2006, Lecture Notes in Computer Science. 〈10.1007/11893295_50〉
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Shu-Heng Chen, Nicolas Navet. Pretests for genetic-programming evolved trading programs: “zero-intelligence” strategies and lottery trading. The 13th International Conference on Neural Information Processing - ICONIP2006, Oct 2006, Hong-Kong, Hong Kong SAR China. Springer Berlin / Heidelberg, 4234/2006 (4234/2006), pp.450-460, 2006, Lecture Notes in Computer Science. 〈10.1007/11893295_50〉. 〈inria-00105926〉

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