Virtual Power Plant, Deep Learning, Multi-market Trading, Path Planning, Risk Management Abstract: In virtual power plant (VPP) multi-energy trading and scheduling, electricity price volatility, renewable energy uncertainty, and complex trading paths limit conventional single-point forecasting/rule-based methods in balancing profit maximization and risk mitigation. This study proposes a DFS-CLM quantitative trading framework integrating depthfirst search (DFS). It uses deep neural networks to improve load and price forecast accuracy via time-series modeling, supplemented by multistage path planning and risk constraints for dynamic balance between profitability and drawdown control. Multi-dimensional optimization (incorporating ROI and MDD) enhances prediction accuracy, financial performance, and operational resilience. Validations on GEFCom2014 and PJM datasets show DFS-CLM outperforms traditional baselines in RMSE/MAE (predictive accuracy) and profitability/risk regulation. Ablation analyses confirm the value of its forecasting, path planning, and risk modules. This method provides an efficient, robust solution for VPP multidimensional trading, supporting intelligent, sustainable electricity markets. https://mc.manuscriptcentral.com/jcmse Journal of Computational Methods in Science and Engineering For Peer Review Design of a Multi-Quantitative Trading Model for Virtual Power Plants Based on a Depth-First Search Algorithm Abstract: In virtual power plant (VPP) multi-energy trading and scheduling, electricity price volatility, renewable energy uncertainty, and complex trading paths limit conventional single-point forecasting or rule-based methods in balancing profit maximization and risk mitigation. This study proposes a DFS-CLM (Depth-First Search– Convolutional LSTM) quantitative trading framework that innovatively couples quantilebased CNN–LSTM forecasting with DFS-driven path optimization under ROI–MDD tradeoff constraints. The CNN–LSTM component captures probabilistic load and price distributions to enhance uncertainty-aware forecasting, while the DFS-based heuristic pruning strategy dynamically explores multi-stage trading paths to optimize risk-adjusted returns. By integrating forecasting, optimization, and drawdown control into a unified framework, DFS-CLM achieves a dynamic balance between profitability and resilience. Validations on GEFCom2014 and PJM datasets show that DFS-CLM outperforms traditional baselines in RMSE/MAE (predictive accuracy) and profitability/risk regulation. Ablation results confirm that its quantile forecasting, heuristic pruning, and ROI–MDD integration jointly contribute to performance gains. The proposed method provides an efficient, interpretable, and robust solution for VPP multi-dimensional trading, supporting intelligent and sustainable electricity markets.In virtual power plant (VPP) multi-energy trading and scheduling, electricity price volatility, renewable energy uncertainty, and complex trading paths limit conventional single-point forecasting/rulebased methods in balancing profit maximization and risk mitigation. This study proposes a DFS-CLM quantitative trading framework integrating depth-first search (DFS). It uses deep neural networks to improve load and price forecast accuracy via time-series modeling, supplemented by multi-stage path planning and risk constraints for dynamic balance between profitability and drawdown control. Multi-dimensional optimization (incorporating ROI and MDD) enhances prediction accuracy, financial performance, and operational resilience. Validations on GEFCom2014 and PJM datasets show DFS-CLM outperforms traditional baselines in RMSE/MAE (predictive accuracy) and profitability/risk regulation. Ablation analyses confirm the value of its forecasting, path planning, and risk modules. This method provides an efficient, robust solution for VPP multi-dimensional trading, supporting intelligent, sustainable electricity markets. Keywords: Virtual Power Plant; Deep Learning; Multi-market Trading; Path Planning; Risk Management 1.