FX trading via Recurrent Reinforcement Learning

This study investigates high frequency currency trading with neural networks trained via Recurrent Reinforcement Learning (RRL). We compare the performance of single layer networks with networks having a hidden layer, and examine the impact of the fixed system parameters on performance. In general, we conclude that the trading systems may be effective, but the performance varies widely for different currency markets and this variability cannot be explained by simple statistics of the markets. Also we find that the single layer network outperforms the two layer network in this application.