While Large Language Model (LLM) agents show promise in automated trading, they still face critical limitations. Prominent multi-agent frameworks often suffer from inefficiency, produce inconsistent signals, and lack the end-to-end optimization required to learn a coherent strategy from market feedback. To address this, we introduce AlphaQuanter, a single-agent framework that uses reinforcement learning (RL) to learn a dynamic policy over a transparent, tool-augmented decision workflow, which empowers a single agent to autonomously orchestrate tools and proactively acquire information on demand, establishing a transparent and auditable reasoning process. Extensive experiments demonstrate that AlphaQuanter achieves state-of-the-art performance on key financial metrics. Moreover, its interpretable reasoning reveals sophisticated strategies, offering novel and valuable insights for human traders. Our code for data acquisition and agent training is publicly available at: https://github.com/AlphaQuanter/AlphaQuanter

翻译：尽管大型语言模型（LLM）代理在自动化交易中展现出潜力，但仍面临关键局限。当前主流的多代理框架常存在效率低下、信号不一致的问题，且缺乏从市场反馈中学习连贯策略所需的端到端优化能力。为解决这些问题，我们提出了AlphaQuanter——一种单代理强化学习框架。该框架通过强化学习（RL）在透明、工具增强的决策工作流上学习动态策略，使单个代理能够自主编排工具、按需主动获取信息，从而建立透明且可审计的推理过程。大量实验表明，AlphaQuanter在关键金融指标上达到了最先进的性能水平。此外，其可解释的推理过程揭示了复杂的交易策略，为人类交易者提供了新颖且有价值的洞见。我们的数据采集与代理训练代码已公开于：https://github.com/AlphaQuanter/AlphaQuanter