Intraday high-frequency FX trading with adaptive neuro-fuzzy inference systems

This paper introduces an adaptive neuro-fuzzy inference system (ANFIS) for financial trading, which learns to predict price movements from training data consisting of intraday tick data sampled at high frequency. The empirical data used in our investigation are five-minute mid-price time series from FX markets. The ANFIS optimisation involves back-testing as well as varying the number of epochs, and is combined with a new method of capturing volatility using an event-driven approach that takes into consideration directional changes within pre-specified thresholds. The results show that the proposed model outperforms standard strategies such as buy-and-hold or linear forecasting.