标题： AlphaQuanter：一种用于股票交易的端到端工具编排代理强化学习框架 显示英文标题

标题： AlphaQuanter: An End-to-End Tool-Orchestrated Agentic Reinforcement Learning Framework for Stock Trading

摘要： 虽然大型语言模型（LLM）代理在自动化交易中展现出潜力，但它们仍然面临关键的限制。著名的多代理框架通常存在效率低下、产生不一致的信号以及缺乏从市场反馈中学习连贯策略所需的端到端优化问题。为了解决这个问题，我们引入了AlphaQuanter，这是一个单代理框架，它使用强化学习（RL）在一个透明的、工具增强的决策工作流上学习动态策略，这使得单个代理能够自主协调工具并主动按需获取信息，建立一个透明且可审计的推理过程。大量实验表明，AlphaQuanter在关键财务指标上达到了最先进水平。此外，其可解释的推理揭示了复杂的策略，为人类交易者提供了新颖且有价值的见解。我们用于数据获取和代理训练的代码可在以下位置公开获取：https://github.com/AlphaQuanter/AlphaQuanter 显示英文摘要

摘要： 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