将交易系统拆解为多个智能体，实现普通人也能操作的自动化交易。
Orchestration Framework for Financial Agents: From Algorithmic Trading to Agentic Trading
- 把传统交易系统分解为规划、执行、风控等10个专业智能体协同工作
- 股票任务年化收益20.42%，超越S&P 500的15.97%；比特币任务收益8.39%，高于价格涨幅3.80%
- 适合想入门量化交易但缺乏经验的个人投资者
金融市场因时间动态性与低信噪比，成为人工智能代理的关键应用场域。构建高效算法交易系统通常需专业团队多年开发测试。本文提出一个金融智能体编排框架，旨在让普通公众也能掌握金融智能。将传统算法交易系统的各组件映射为多个智能体，包括规划者、协调者、α智能体、风险智能体、组合管理智能体、回测智能体、执行智能体、审计智能体和记忆智能体。我们展示了两个内部交易案例：在股票交易任务（2024年4月至12月，小时级数据）中，本方法实现20.42%收益率、2.63夏普比率、最大回撤-3.59%，优于同期S&P 500的15.97%；在比特币交易任务（2025年7月27日至8月13日，分钟级数据）中，实现8.39%收益率、0.38夏普比率、最大回撤-2.80%，而比特币价格仅上涨3.80%。代码已开源至GitHub。
原文摘要 · Abstract (English)
The financial market is a mission-critical playground for AI agents due to its temporal dynamics and low signal-to-noise ratio. Building an effective algorithmic trading system may require a professional team to develop and test over the years. In this paper, we propose an orchestration framework for financial agents, which aims to democratize financial intelligence to the general public. We map each component of the traditional algorithmic trading system to agents, including planner, orchestrator, alpha agents, risk agents, portfolio agents, backtest agents, execution agents, audit agents, and memory agent. We present two in-house trading examples. For the stock trading task (hourly data from 04/2024 to 12/2024), our approach achieved a return of $20.42\%$, a Sharpe ratio of 2.63, and a maximum drawdown of $-3.59\%$, while the S&P 500 index yielded a return of $15.97\%$. For the BTC trading task (minute data from 27/07/2025 to 13/08/2025), our approach achieved a return of $8.39\%$, a Sharpe ratio of $0.38$, and a maximum drawdown of $-2.80\%$, whereas the BTC price increased by $3.80\%$. Our code is available on \href{https://github.com/Open-Finance-Lab/AgenticTrading}{GitHub}.
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