The exceptional potential of large language models (LLMs) in handling text information has garnered significant attention in the field of financial trading. However, most existing trading agents operate under intraday, independent unit-based trading tasks, where decisions are made as isolated directional actions, and thus 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 propose FinPos, a position-aware trading agent system designed to explicitly model and manage continuous positions. FinPos enhances position awareness through three key mechanisms: (1) professional-level interpretation of heterogeneous market information; (2) a dual-agent decision structure that separates directional reasoning from risk-aware position adjustment; and (3) multi-timescale reward signals, allowing the agent to internalize position awareness through experiential feedback rather than static instructions alone. 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：大型语言模型（LLM）在处理文本信息方面的卓越潜力已在金融交易领域引起广泛关注。然而，现有大多数交易代理均基于日内、独立的单元交易任务运行，其决策作为孤立的定向动作执行，因而缺乏对连续持仓管理的感知能力。为此，我们提出一种旨在模拟更真实市场的持仓感知交易任务。针对该任务，我们提出FinPos——一个显式建模并管理连续持仓的持仓感知交易代理系统。FinPos通过三项关键机制增强持仓感知能力：（1）对异构市场信息进行专业级解读；（2）采用双代理决策架构，将方向性推理与风险感知的仓位调整相分离；（3）引入多时间尺度奖励信号，使代理能够通过经验反馈而非仅依赖静态指令内化持仓感知。大量实验表明，在高度贴近真实市场环境的持仓感知交易任务中，FinPos超越了现有最先进的交易代理。更重要的是，我们的研究揭示了以LLM为核心的代理系统在长期市场决策中存在着广阔且尚未被充分挖掘的潜力。