PPO-HRAP: Proximal Policy Optimization with a Hybrid Regime-Aware Policy for Risk-Controlled Trading

cs.AI, q-fin.TR

Submitted: 2026-10-01

Updated: 2026-10-01

Comments: 8 pages, 6 figures, 8 tables. Code: https://github.com/chikien07012006/PPO_Regime-Aware-Trading

Code: https://github.com/chikien07012006/PPO_Regime-Aware-Trading

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Sources

• MTS: A Deep Reinforcement Learning Portfolio Management Framework with Time-Awareness and Short-Selling
• A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem
• Adversarial Deep Reinforcement Learning in Portfolio Management
• Continuous control with deep reinforcement learning
• Proximal Policy Optimization Algorithms

Related papers

• MAVEN-T: Reinforced Heterogeneous Distillation for Real-Time Multi-Agent Trajectory Prediction
• Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models
• Agents' Last Exam
• The Clinician's Veto: Navigating Trust, Liability, and Uncertainty in Autonomous AI Prescribing
• MindHelper: Closed-Loop Embodied Mental-State Reasoning for Precision Intervention
• Incumbent Advantage: Brand Bias and Cognitive Manipulation Dynamics in LLM Recommendation Systems