Robust trading in stochastic chaos: a hybrid fuzzy multi-expert approach

Abstract

Classical trend-following strategies often exhibit unstable performance in the EUR/USD foreign exchange market due to filter lag, noise-induced false signals, and abrupt regime transitions associated with chaotic price dynamics. This study proposes a hybrid fuzzy multi-expert framework that integrates bidirectional exponential filtering for low-latency trend extraction with a Mamdani-type fuzzy inference system that jointly evaluates quantitative trend indicators and qualitative information derived from economic news and textual forecasts. The proposed approach is evaluated on minute-level EUR/USD data over 10-, 25-, and 100-day test windows. Empirical results show that the fuzzy consensus mechanism improves robustness under non-stationary conditions. In a representative 25-day evaluation period, net profit increases from 182 to 560 pips, the win rate improves from 55% to 67%, and the maximum drawdown decreases from 118 to 38 pips. These findings suggest that incorporating linguistic uncertainty and sentiment-like contextual cues into technical trading signals can mitigate spurious trade activation and stabilize decision-making in stochastic and chaotic market environments, offering a practical route toward more resilient algorithmic trading systems.