Cost-Aware Multiwindow Ensemble Decision Support for Nonstationary Foreign Exchange Markets

Financial markets are strongly nonstationary, making short-horizon foreign exchange decision support sensitive to estimation windows, feature design, and transaction costs. This paper proposes a transparent multiwindow ensemble framework in which interpretable multiregression experts use rolling-statistic features computed over different lookback windows. Time-scale diversity is combined with a supervisory layer that converts recent cost-aware utility scores into expert weights and selects execution thresholds on a meta window. The utility criterion is based on net pips after transaction costs and includes penalties for drawdown and turnover, while the execution rule incorporates volatility gating and minimum holding constraints. The framework is evaluated on synchronized one-minute quotes for 16 major currency pairs using a nonoverlapping walk-forward protocol. The benchmark set includes lag-based machine-learning models, LSTM, Transformer, Echo State Network, Bayesian model averaging, dynamic model averaging, stacking, and alternative multiexpert aggregation rules. In the main fixed-horizon experiment with τ=15 minutes, the proposed utility-weighted ensemble achieves the highest mean Sharpe ratio of 1.085 and a mean net profit of 312.3 pips. The results support utility-calibrated time-scale diversification as an auditable approach to FX decision support under nonstationarity.