Conditional Effectiveness of Volatility-Adaptive Exit Rules in Algorithmic Trading Systems: Evidence from the USD/JPY Market

This study examines Average True Range (ATR)-based take-profit (TP) and stop-loss (SL) rules as components of adaptive risk-management design within robust algorithmic trading systems in the USD/JPY foreign exchange market. Rather than simply evaluating whether volatility-adaptive exit rules improve trading performance, the study investigates the conditions under which they contribute to trading outcomes. To this end, the study adopts a systematic framework combining a broad Moving Average Convergence Divergence (MACD) parameter space with ATR-based TP/SL multiplier settings and distinguishes heterogeneous outcome patterns before and after ATR implementation. The results show that the effectiveness of ATR-based exit rules is conditional. Performance improvements occur only for specific combinations of model structures, exit-rule specifications, and market conditions. A notable finding is that optimizing trading-model parameters plays a primary role in determining the effectiveness of exit rules, while ATR-based exit rules function as complementary components that reinforce well-specified trading models. Moreover, under mildly mean-reverting market conditions, profit-enhancement cases expand across a broader range of optimized MACD parameter configurations, suggesting a previously underexplored interaction between market dynamics, model structure, and volatility-adaptive exit-rule design. Overall, the findings provide new evidence that the effectiveness of adaptive risk-management mechanisms depends on the interaction between model structure, exit-rule design, and market conditions, thereby offering broader insights into adaptive trading-system design under changing market conditions. For practitioners, the results suggest that adaptive ATR-based exit rules are most effective when combined with appropriately optimized trading models.