A Drift-Aware Continuous Hybrid Learning Framework for Adaptive and Robust Stock Trading
This paper introduces DACH-DDPG, a drift-aware continuous hybrid learning framework that combines SVM and XGBoost for adaptive signal generation with a DDPG agent for sequential trading decisions, effectively addressing market non-stationarity through performance-triggered model adaptation to achieve superior risk-adjusted returns.
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