用大模型分析情绪，再用强化学习融合信号，提升量化交易表现
Integrating Large Language Models and Reinforcement Learning for Sentiment-Driven Quantitative Trading
- 用FinGPT提取情绪信号，结合技术指标
- 强化学习比传统规则融合更有效，提升收益
- 适合想用AI优化交易策略的研究者和从业者
本研究构建了一个基于情绪的量化交易系统，利用大语言模型FinGPT进行情感分析，并探索一种基于强化学习算法Twin Delayed Deep Deterministic Policy Gradient（TD3）的新信号融合方法。对比了传统规则驱动方法与强化学习框架在整合情绪与技术信号时的表现。结果表明，由FinGPT生成的情绪信号在与传统技术指标结合后具有实际价值，且强化学习在动态交易环境中能有效融合异构信号，展现出良好前景。
原文摘要 · Abstract (English)
This research develops a sentiment-driven quantitative trading system that leverages a large language model, FinGPT, for sentiment analysis, and explores a novel method for signal integration using a reinforcement learning algorithm, Twin Delayed Deep Deterministic Policy Gradient (TD3). We compare the performance of strategies that integrate sentiment and technical signals using both a conventional rule-based approach and a reinforcement learning framework. The results suggest that sentiment signals generated by FinGPT offer value when combined with traditional technical indicators, and that reinforcement learning algorithm presents a promising approach for effectively integrating heterogeneous signals in dynamic trading environments.
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