Stateful program representations for evolving technical trading rules

Authors:
Alexandros Agapitos;Michael O'Neill;Anthony Brabazon
Affiliations:
University College Dublin, Dublin, Ireland;University College Dublin, Dublin, Ireland;University College Dublin, Dublin, Ireland
Venue:
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Year:
2011

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Abstract

A family of stateful program representations in grammar-based Genetic Programming are being compared against their stateless counterpart in the problem of binary classification of sequences of daily prices of a financial asset. Empirical results suggest that stateful classifiers learn as fast as stateless ones but generalise better to unseen data, rendering this form of program representation strongly appealing to the automatic programming of technical trading rules.