This　paper　presents　an　uncertainty-aware　deep　reinforcement　learning　(DRL)　framework　for　algorithmic　trading　agents,　aimed　at　improving　both　profitability　and　risk　management　in　volatile　market　environments.　We　integrate　two　uncertainty　estimation　methods-Monte　Carlo　Dropout　for　epistemic　uncertainty　and　Reconstruction　Uncertainty　Estimate　for　distributional　uncertainty-into　the　decision-making　process　of　a　DRL　trading　agent.　The　proposed　approach　was　evaluated　on　the　U.S.　stock　market,　using　historical　data　from　the　S&P　500　index,　segmented　into　multiple　sub-periods　based　on　recession　phases.　Empirical　results　demonstrate　that　uncertainty-aware　agents　consistently　outperform　traditional　models　and　risk-unaware　agents　in　terms　of　cumulative　returns　and　risk　mitigation,　as　evidenced　by　lower　maximum　drawdowns.　The　Aggregated　Agent,　which　combines　both　uncertainty　sources,　achieved　the　best　overall　performance,　highlighting　the　importance　of　incorporating　both　risk-awareness　and　adaptive　decision-making　in　automated　trading　systems.　This　work　highlights　the　broader　significance　of　integrating　uncertainty　estimation　in　DRL,　paving　the　way　for　more　robust　and　resilient　decision-making　across　a　variety　of　complex,　dynamic　environments.　©　2024　IEEE.