A new reinforcement learning policy, PPO-HRAP, is introduced to improve trading strategies by balancing risk and reward more effectively.
combines Proximal Policy Optimization with an interpretable regime prior
Abstract: Reinforcement learning for trading often struggles to balance upside participation with drawdown control. Profit-only policies can collapse toward passive long exposure on upward-drifting assets, while aggressively risk-penalized rewards can become too defensive during volatile periods. This paper proposes PPO-HRAP, a hybrid regime-aware policy that combines Proximal Policy Optimization…
