Information-Driven Resampling and Market Regime Detection: A Futures Trading Framework Based on GMM and Multi-Model Ensemble Learning

Abstract

Fixed-interval candlestick sampling cannot adequately represent the non-uniform arrival of information in futures markets. This study develops an information-driven market-regime detection and trading framework. One-minute data are resampled along a hybrid information axis constructed from standardized trading-volume intensity, realized volatility, price momentum, and a high-low spread proxy. A GMM-HMM identifies latent regimes, after which XGBoost and CatBoost are trained on 400-dimensional lagged-price features; trades are executed only when both models agree. Using rebar futures data from 2010 to 2025 and reserving 2023-2025 for out-of-sample testing, the complete strategy achieves an annualized return of 11.33%, a maximum drawdown of -7.80%, and a Sharpe ratio of 0.88, outperforming conventional sampling, single-model, and no-regime-filtering alternatives in ablation tests. The results show that combining information-driven resampling with regime-aware model fusion improves trade selectivity and risk-adjusted performance.