{"ID":2856730,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.10526","arxiv_id":"2510.10526","title":"Integrating Large Language Models and Reinforcement Learning for Sentiment-Driven Quantitative Trading","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.","short_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 s...","url_abs":"https://arxiv.org/abs/2510.10526","url_pdf":"https://arxiv.org/pdf/2510.10526v1","authors":"[\"Wo Long\",\"Wenxin Zeng\",\"Xiaoyu Zhang\",\"Ziyao Zhou\"]","published":"2025-10-12T09:49:39Z","proceeding":"q-fin.CP","tasks":"[\"q-fin.CP\",\"cs.LG\"]","methods":"[\"Reinforcement Learning\",\"Language Model\"]","has_code":false}