项目简介
AI 驱动的对冲基金概念验证项目，模拟多位知名投资者策略的智能体进行交易决策。提供命令行和 Web 界面用于回测与多智能体策略探索，仅供教育用途。
README 预览
# AI Hedge Fund\n\nThis is a proof of concept for an AI-powered hedge fund. The goal of this project is to explore the use of AI to make trading decisions. This project is for **educational** purposes only and is not intended for real trading or investment.\n\n> **🚧 The project is evolving.** We're rebuilding it into a persistent, always-on AI hedge fund — a *fund* as a first-class entity you can backtest, paper-trade, and (opt-in) run live, with the investor agents reimagined as pluggable, backtestable "alpha models." Read the **[Vision →](VISION.md)** and the **[Roadmap →](ROADMAP.md)**.\n\nThis system employs several agents working together:\n\n1. Aswath Damodaran Agent - The Dean of Valuation, focuses on story, numbers, and disciplined valuation\n2. Ben Graham Agent - The godfather of value investing, only buys hidden gems with a margin of safety\n3. Bill Ackman Agent - An activist investor, takes bold positions and pushes for change\n4. Cathie Wood Agent - The queen of growth investing, believes in the power of innovation and disruption\n5. Charlie Munger Agent - Warren Buffett's partner, only buys wonderful businesses at fair prices\n6. Michael Burry Agent - The Big Short contrarian who hunts for deep value\n7. Mohnish Pabrai Agent - The Dhandho investor, who looks for doubles at low risk\n8. Nassim Taleb Agent - The Black Swan risk analyst, focuses on tail risk, antifragility, and asymmetric payoffs\n9. Peter Lynch Agent - Practical investor who seeks "ten-baggers" in everyday businesses\n10. Phil Fisher Agent - Meticulous growth investor who uses deep "scuttlebutt" research \n11. Rakesh Jhunjhunwala Agent - The Big Bull of India\n12. Stanley Druckenmiller Agent - Macro legend who hunts for asymmetric opportunities with growth potential\n13. Warren Buffett Agent - The oracle of Omaha, seeks wonderful companies at a fair price\n14. Valuation Agent - Calculates the intrinsic value of a stock and generates trading signals\n15. Sentiment Agent - Analyzes marke
常见问题 (2)
故障排除如何防止 LLM 训练数据记忆对回测结果造成偏差？
故障排除
如何防止 LLM 训练数据记忆对回测结果造成偏差？使用 PR #721 中引入的可选盲测模式。运行 CLI 时添加 --blind 参数，或为交互式/TUI 工作流设置 HEDGE_FUND_BLIND=1。在盲测模式下，FundamentalsSnapshot.render() 会移除股票代码和行业信息，并将 report_period/filing_date 标签替换为 t-0、t-1 和 t-2 等相对标签。默认提示词和现有缓存结果保持不变。这可以减少前视回忆，但无法完全消除：板块信息仍会保留，且独特的财务数据可能会让人识别出大型公司。为了获得最干净的验证结果，请评估模型训练截止时间之后的回测窗口；截止时间之前的结果可能包含被记忆的未来结果。
故障排除如何修复 GitHub 仓库在 README 中声明为 MIT 却显示无许可证的问题？
故障排除
如何修复 GitHub 仓库在 README 中声明为 MIT 却显示无许可证的问题？在仓库根目录添加一个名为 LICENSE 的标准 MIT 许可证文件。文件必须包含带有实际版权持有者和年份的 MIT 许可证文本,例如:
MIT License
Copyright (c) 2025 virattt
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.提交并推送该文件。依赖目录中已有的许可证文件(例如 React Flow 的 app/frontend/LICENSE)仅适用于该捆绑依赖,并不为项目自身的代码授予许可。