Sentiment-Augmented Deep Reinforcement Learning for Active Trading: An Alpha-Reward Approach for CLaC@FinMMEval 2026
Score
77提供了情感增强强化学习在金融交易中的完整比较，泛化缺口分析具有新颖性。
方法设计系统，实验充分，结果清晰，写作规范。
对量化交易从业者部署强化学习系统有直接参考价值，尤其是泛化问题警示。
发表于2026年，涉及最新大语言模型和强化学习技术，契合当前市场。
来自arXiv预印本，虽未经同行评审，但属于CLEF竞赛方案，方法透明。
Base 85 × T2 Source weight ×0.9 = 77
Curation line 70Selected
Why selected
The methodology is rigorous (multiple algorithm comparison, hyperparameter optimization, alpha reward), results are significant, and the generalization gap insight is practically important—high overall quality.
AI commentary
「This paper is valuable for both quantitative trading practitioners and financial AI researchers. It not only demonstrates how to integrate LLM-based sentiment scores with deep RL on real market data, but also honestly reveals the generalization gap when transferring strategies from bull to bear markets, offering direct insights for risk management and backtesting frameworks in production. I recommend paying attention to the alpha reward design and the performance differences between DDPG and DQL across different assets.」
Quick overview
This paper presents the system for CLEF 2026 FinMMEval Lab Task 3, using daily news sentiment scores from LLaMA 3.2 1B combined with technical indicators and calendar encodings to build deep reinforcement learning trading agents for Bitcoin and Tesla. Four algorithms—PG, PPO, DQL, and DDPG—are compared, with a novel alpha reward based on excess market return to encourage outperforming buy-and-hold. DDPG achieves the strongest test-set performance; DQL is selected a priori for the live endpoint due to the highest validation Sharpe ratio. For TSLA, DDPG and DQL yield cumulative returns of 54.96% and 52.62%, respectively, versus 16.45% for buy-and-hold. For BTC, DDPG returns +1.58% while buy-and-hold suffers -34.27%. The results also highlight a significant validation-to-test generalization gap when transferring bull-market policies to a bear regime.
- Daily news sentiment scores from LLaMA 3.2 1B are integrated into the deep RL state space.
- An alpha reward function based on excess market return is introduced to incentivize outperforming the buy-and-hold baseline.
- DDPG and DQL significantly beat buy-and-hold on both Tesla and Bitcoin tests, with positive returns even in bearish Bitcoin markets.
- A substantial generalization gap between validation and test periods is identified, warning against transferring bull-market policies to bear regimes.
Full text
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