Can LLMs trade better with fact-subjectivity reasoning?
Enhancing LLM Trading Performance with Fact-Subjectivity Aware Reasoning
This research investigates the effectiveness of stronger vs. weaker LLMs in cryptocurrency trading, finding that stronger LLMs don't automatically guarantee better returns. They introduce FS-ReasoningAgent, a multi-agent framework that separates factual and subjective information from news, utilizing separate reasoning agents for each. This allows the system to weigh facts and sentiment differently based on market conditions (bull vs. bear), leading to more adaptable and profitable trading decisions compared to simpler models or those relying solely on advanced reasoning. FS-ReasoningAgent also highlights the importance of a "reflection agent" to analyze past trades and adjust future strategies, showcasing the value of self-reflection in LLM-based agents for improved performance.