Computational learning techniques for intraday FX trading using popular technical indicators

Abstract

There is reliable evidence that technical analysis, as used by traders in the foreign exchange (FX) markets, has predictive value regarding future movements of foreign exchange prices. Although the use of artificial intelligence (AI)-based trading algorithms has been an active research area over the last decade, there have been relatively few applications to intraday foreign exchange-the trading frequency at which technical analysis is most commonly used. Previous academic studies have concentrated on testing popular trading rules in isolation or have used a genetic algorithm approach to construct new rules in an attempt to make positive out-of-sample profits after transaction costs. In this paper 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 (GP), 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 nonzero 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-simple learning is not constrained.