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

Wo Long, Wenxin Zeng, Xiaoyu Zhang and Ziyao Zhou

Papers from arXiv.org

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.

Date: 2025-10
New Economics Papers: this item is included in nep-cmp
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