Evolutionary reinforcement learning in FX order book and order flow analysis

As macroeconomic fundamentals based modelling of FX time series have been shown not to fit the empirical evidence at horizons of less than one year, interest has moved towards microstructure-based approaches. Order flow data has recently been receiving an increasing amount of attention in equity market analyses and thus increasingly in foreign exchange as well. In this paper, order flow data is coupled with order book derived indicators and we explore whether pattern recognition techniques derived from computational learning can be applied to successfully infer trading strategies on the underlying timeseries. Due to the limited amount of data available the results are preliminary. However, the approach demonstrates promise and it is shown that using order flow and order book data is usually superior to trading on technical signals alone.