Integrating Large Language Models and Reinforcement Learning for Sentiment-Driven Quantitative Trading
This work addresses the challenge of effectively combining heterogeneous signals in quantitative trading, though it appears incremental as it builds on existing methods like FinGPT and TD3.
The research tackled the problem of integrating sentiment analysis from a large language model (FinGPT) with technical indicators for quantitative trading, finding that combining these signals using reinforcement learning (TD3) offers value and is promising for dynamic trading environments.
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.