Paper: arXiv 2610.01325

Authors: Duong Hien Chi Kien, Thanh Trung Huynh

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 with an interpretable regime prior. The agent observes both market features and portfolio-state variables, receives a reward combining portfolio log return, VIX-conditioned drawdown-increase penalty, target-exposure deviation, and turnover cost, and executes a blended action between the PPO actor output and a regime-derived target exposure. On the held-out 2020-2022 SPY test window, PPO-HRAP achieves 27.62% total return, 8.48% annualized return, 0.6447 Sharpe ratio, 0.8588 Sortino ratio, and 0.4592 Calmar ratio, while reducing maximum drawdown from 34.10% for Buy and Hold to 18.47%. Across five SPY seeds, PPO-HRAP remains stable with mean total return $0.2725 \pm 0.0109$ and mean Sharpe ratio $0.6219 \pm 0.0565$. Single-run cross-asset tests on QQQ and DIA further show that the proposed method ranks first on total return and Sharpe ratio for all three reported assets. These results suggest that blending learned actions with a volatility-aware regime prior is a practical way to improve risk-adjusted trading behavior, although the current policy still incurs high turnover and cross-asset robustness beyond SPY remains limited to single-run evidence.

Complexity vs Empirical Score

  • Math Complexity: 6.0/10
  • Empirical Rigor: 7.0/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper presents a novel hybrid RL approach with a clear methodology and strong empirical results on a relevant dataset. While the mathematical derivations are present, the focus is more on the practical application and empirical validation, which is well-executed.

Research Flowchart

  flowchart TD
    A[Research Goal: Improve Risk-Controlled Trading via RL] --> B{Key Methodology: PPO-HRAP};
    B --> C[Data/Inputs: Market Features, Portfolio State, VIX, Price Data (SPY, QQQ, DIA)];
    C --> D{Computational Process: PPO Algorithm, Hybrid Policy Blending, Reward Function Optimization};
    D --> E[Outcomes: High Risk-Adjusted Returns (Sharpe 0.64), Reduced Max Drawdown (18.47% vs 34.10% BH), Cross-Asset Performance];
    E --> F[Limitations: High Turnover, Limited Cross-Asset Robustness (Single-Run Evidence)];

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0 of 43 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.