Integrating Large Language Models and Reinforcement Learning for Sentiment-Driven Quantitative Trading

Wo Long; Wenxin Zeng; Xiaoyu Zhang; Ziyao Zhou

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arXiv · 2510.10526

Integrating Large Language Models and Reinforcement Learning for Sentiment-Driven Quantitative Trading

Wo Long·Wenxin Zeng·Xiaoyu Zhang·Ziyao Zhou

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Abstract

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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Wo Long, Wenxin Zeng, Xiaoyu Zhang, Ziyao Zhou. 2025-10-12. Integrating Large Language Models and Reinforcement Learning for Sentiment-Driven Quantitative Trading. https://arxiv.org/abs/2510.10526

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