Quant Research Platform MCP Server
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Quant Research Platform MCP ServerAnalyze the performance of the size factor"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Quant Research Platform
一个 LLM 原生的量化研究平台,提供因子管理、策略回测、研报分析等功能,并通过 MCP 协议让 AI 助手直接访问量化知识库。
Features
因子知识库 - 因子管理、评分、分析
策略回测 - 策略管理、参数搜索
研报知识库 - PDF 上传、RAG 对话
经验知识库 - 结构化研究经验、语义检索、知识提炼
数据服务 - K线数据、因子计算
MCP 协议 - LLM 直接访问量化知识库
Related MCP server: OpenFinClaw CLI
Quick Start
1. 安装依赖
按顺序安装以下依赖:
序号 | 依赖 | macOS / Linux | Windows |
1 | Docker | ||
2 | Node.js 20+ | 下载 或 | |
3 | pnpm |
|
|
4 | uv |
|
|
Windows 提示: 安装完成后需要重启终端使环境变量生效
2. 配置环境变量
# 复制环境变量模板
cp .env.example .env编辑 .env 文件,填入以下必要配置:
配置项 | 说明 | 示例 |
| 预处理K线数据路径 |
|
| 市值数据路径 (可选) |
|
| LLM API 地址 |
|
| LLM API 密钥 |
|
3. 启动项目
macOS / Linux:
# 克隆项目
git clone https://github.com/your-username/quant-research-platform.git
cd quant-research-platform
# 启动(自动安装依赖、启动数据库、启动服务)
make start local
# 查看服务状态
make status
# 查看日志
make logs local
# 停止服务
make stop localWindows (PowerShell):
# 一键启动(自动安装依赖、启动所有服务、打开浏览器)
uv run python scripts/dev.py start
# 停止所有服务
uv run python scripts/dev.py stop
# 查看服务状态
uv run python scripts/dev.py status
# 查看日志
uv run python scripts/dev.py logs # 全部服务
uv run python scripts/dev.py logs api # 仅 API
uv run python scripts/dev.py logs -n 100 mcp-factor # 最近 100 行
scripts/dev.py是跨平台任务运行器,也可在 macOS/Linux 上使用,等价于make start local。 该脚本会自动启动 PostgreSQL、Redis、API、7 个 MCP 服务和前端,无需手动开多个终端。
4. 访问服务
启动后访问:
前端界面: http://127.0.0.1:5173
API 文档: http://127.0.0.1:8000/docs
MCP Integration
通过 MCP 协议,LLM 可以直接访问平台的所有功能。
Claude 配置
在 .mcp.json 中添加:
{
"mcpServers": {
"factor-hub": {
"url": "http://localhost:6789/mcp"
},
"data-hub": {
"url": "http://localhost:6790/mcp"
},
"strategy-hub": {
"url": "http://localhost:6791/mcp"
},
"note-hub": {
"url": "http://localhost:6792/mcp"
},
"research-hub": {
"url": "http://localhost:6793/mcp"
},
"experience-hub": {
"url": "http://localhost:6794/mcp"
}
}
}Development
目录结构
quant-research-platform/
├── backend/
│ ├── app/routes/ # HTTP 路由层
│ ├── app/schemas/ # Pydantic 模型
│ └── domains/ # 业务域
│ ├── mcp_core/ # MCP 基础设施
│ ├── factor_hub/ # 因子知识库
│ ├── strategy_hub/ # 策略回测
│ ├── research_hub/ # 研报 RAG
│ ├── experience_hub/ # 经验知识库
│ ├── data_hub/ # 数据服务
│ └── note_hub/ # 笔记管理
├── frontend/
│ └── src/
│ ├── features/ # 功能模块
│ ├── components/ # 通用组件
│ └── lib/ # 工具库
├── docker/ # 容器配置
├── docs/ # 文档
└── factors/ # 因子定义库常用命令
# 开发
make start local # 本地开发模式
make start dev # Docker 开发模式
make logs-api # API 实时日志
make logs-frontend # 前端实时日志
# 生产
make start # 生产模式
make healthcheck prod # 健康检查运行模式
模式 | 命令 | 说明 |
local |
| 本地运行 Python/Node,仅 Docker 运行数据库 |
dev |
| 全容器化,支持热重载 |
prod |
| 全容器化,优化构建 |
Tech Stack
Backend
FastAPI, SQLAlchemy 2.0, PostgreSQL 16 + pgvector
LangChain, Redis, aiohttp
structlog, ruff, pytest
Frontend
React 18, TypeScript, Vite 6
TanStack Query, Zustand, shadcn/ui
AG Grid, ECharts, Tailwind CSS
Infrastructure
Docker, Docker Compose, Caddy, Supervisor
License
MIT
重要更新
因子和截面因子文件已迁移至 private/ 目录。
# 旧位置(已废弃)
factors/
sections/
# 新位置
private/
factors/ # 因子代码 (.py)
sections/ # 截面因子 (.py)
metadata/ # 因子元数据 (YAML)private/ 目录已被 gitignore,需通过独立的私有仓库管理。详见 private/.gitkeep。
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