Genetic Programming for Financial Trading : a Tutorial

Nicolas Navet 1
1 TRIO - Real time and interoperability
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
Abstract : Genetic Programming (GP) is an appealing machine-learning technique for tackling financial engineering problems: it belongs to the family of evolutionary algorithms that have proven to be often remarkably successful at handling complex optimization problems, and possesses the unique feature to produce solutions under a symbolic form that can be understood and analyzed by humans. Over the last decade, numerous studies have investigated the use of GP for creating financial trading strategies. We will first provide a comprehensive review of existing work and identify stylized facts about trading returns and trading behaviour that can be drawn from the literature. We will then discuss practical implementation issues. Finally, we will present a series of pretests, similar in the spirit to pretests in econometrics, aimed at giving clear-cut answers on whether GP can be effective with the time series at hand. We conclude by highlighting directions for future work.
Type de document :
Communication dans un congrès
5th International Conference on Computational Intelligence in Economics and Finance - CIEF 2006, Oct 2006, Kaohsiung, Taiwan. 2006
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https://hal.inria.fr/inria-00113706
Contributeur : Nicolas Navet <>
Soumis le : mardi 28 août 2007 - 12:49:55
Dernière modification le : jeudi 11 janvier 2018 - 06:20:05

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  • HAL Id : inria-00113706, version 1

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Nicolas Navet. Genetic Programming for Financial Trading : a Tutorial. 5th International Conference on Computational Intelligence in Economics and Finance - CIEF 2006, Oct 2006, Kaohsiung, Taiwan. 2006. 〈inria-00113706〉

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