  We propose a Genetic Programming architecture for the generation of foreign
exchange trading strategies. The system's principal features are the evolution
of free-form strategies which do not rely on any prior models and the
utilization of price series from multiple instruments as input data. This
latter feature constitutes an innovation with respect to previous works
documented in literature. In this article we utilize Open, High, Low, Close bar
data at a 5 minutes frequency for the AUD.USD, EUR.USD, GBP.USD and USD.JPY
currency pairs. We will test the implementation analyzing the in-sample and
out-of-sample performance of strategies for trading the USD.JPY obtained across
multiple algorithm runs. We will also evaluate the differences between
strategies selected according to two different criteria: one relies on the
fitness obtained on the training set only, the second one makes use of an
additional validation dataset. Strategy activity and trade accuracy are
remarkably stable between in and out of sample results. From a profitability
aspect, the two criteria both result in strategies successful on out-of-sample
data but exhibiting different characteristics. The overall best performing
out-of-sample strategy achieves a yearly return of 19%.
