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IEEE Trans Neural Netw. 2001;12(4):744-54. doi: 10.1109/72.935088.

Computational learning techniques for intraday FX trading using popular technical indicators.

Author information

1
Centre for Financial Research, Judge institute of Management, University of Cambridge, Cambridge, UK.

Abstract

We consider strategies which use a collection of popular technical indicators as input and seek a profitable trading rule defined in terms of them. We consider two popular computational learning approaches, reinforcement learning and genetic programming, and compare them to a pair of simpler methods: the exact solution of an appropriate Markov decision problem, and a simple heuristic. We find that although all methods are able to generate significant in-sample and out-of-sample profits when transaction costs are zero, the genetic algorithm approach is superior for non-zero transaction costs, although none of the methods produce significant profits at realistic transaction costs. We also find that there is a substantial danger of overfitting if in-sample learning is not constrained.

PMID:
18249910
DOI:
10.1109/72.935088

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