The exceptional potential of large language models (LLMs) in handling text information has garnered significant attention in the field of financial trading. However, current trading agents primarily focus on single-step trading tasks and lack awareness of continuous position management. Therefore, we propose a position-aware trading task designed to simulate a more realistic market. To address this task, we develop a trading agent system, FinPos, optimized for position management. FinPos is able to interpret various types of market information from a professional perspective, providing a reliable basis for positioning decisions. To mitigate the substantial market risks arising from position fluctuations, FinPos employs dual decision agents. Furthermore, the continuous nature of position management necessitates our adoption of multi-timescale rewards, which in turn empowers FinPos to effectively balance short-term fluctuations against long-term trends. Extensive experiments demonstrate that FinPos surpasses state-of-the-art trading agents in the position-aware trading task, which closely mirrors real market conditions. More importantly, our findings reveal that LLM-centered agent systems exhibit a vast, largely unexplored potential in long-term market decision-making.

翻译：大语言模型（LLMs）在处理文本信息方面展现出的卓越潜力，已在金融交易领域引起广泛关注。然而，当前的交易代理主要聚焦于单步交易任务，缺乏对连续持仓管理的感知能力。为此，我们提出了一项旨在模拟更真实市场的持仓感知交易任务。为应对此任务，我们开发了一个针对持仓管理优化的交易代理系统——FinPos。FinPos能够从专业视角解读各类市场信息，为持仓决策提供可靠依据。为缓解由持仓波动引发的重大市场风险，FinPos采用了双决策代理机制。此外，持仓管理的连续性要求我们采用多时间尺度奖励，这进而使FinPos能够有效平衡短期波动与长期趋势。大量实验表明，在高度模拟真实市场环境的持仓感知交易任务中，FinPos超越了当前最先进的交易代理。更重要的是，我们的研究发现，以LLM为核心的代理系统在长期市场决策中展现出巨大且尚未被充分挖掘的潜力。