by guangxiangdebizi · MCP Server · ★ 238
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TradingAgents-MCPmode 基于MCP工具的多智能体交易分析系统 🌟 项目概述 TradingAgents-MCPmode 是一个创新的多智能体交易分析系统，集成了 Model Context Protocol (MCP) 工具，实现了智能化的股票分析和交易决策流程。系统通过15个专业化智能体的协作，提供全面的市场分析、投资建议和风险管理。 🎯 核心特性 🤖 多智能体协作: 15个专业化智能体分工合作 ⚡ 并行处理: 分析师团队采用并行架构，显著提升分析效率 🔧 MCP工具集成: 支持外部数据源和实时信息获取 📊 全面分析: 公司概述、市场、情绪、新闻、基本面、股东结构、产品业务七维度分析 💭 智能辩论: 看涨/看跌研究员辩论机制，可配置辩论轮次 ⚠️ 风险管理: 三层风险分析和管理决策，支持动态风险辩论 🎛️ 智能体控制: 前端可动态启用/禁用特定智能体，灵活定制工作流 🌀 辩论轮次配置: 前端实时设置投资和风险辩论轮次，精确控制分析深度 🔧 灵活配置: 通过环境变量控制智能体MCP权限 🌍 多市场支持: 美股(US)、A股(CN)、港股(HK) 🗣️ 自然语言: 支持自然语言查询，无需指定市场和日期 📈 实时决策: 基于最新数据的交易建议 🌐 Web前端: 完整的Streamlit Web界面，支持实时分析和历史管理 🏗️ 系统架构 智能体组织结构 ┌─────────────────────────────────────────────────────────────┐ │ TradingAgents-MCPmode │ ├─────────────────────────────────────────────────────────────┤ │ 📊 分析师团队 (Analysts) - 并行执行 │ │ ├── CompanyOverviewAnalyst (公司概述分析师) │ │ ├── MarketAnalyst (市场分析师) ┐ │ │ ├── SentimentAnalyst (情绪分析师) │ 并行处理 │ │ ├── NewsAnalyst (新闻分析师) │ 6个分析师 │ │ ├── FundamentalsAnalyst(基本面分析师) │ 同时执行 │ │ ├── ShareholderAnalyst (股东分析师) │ │ │ └── ProductAnalyst (产品分析师) ┘ │ ├─────────────────────────────────────────────────────────────┤ │ 🔬 研究员团队 (Researchers) │ │ ├── BullR
| Stars | 238 |
| Forks | 57 |
| Language | Python |
| Category | MCP Server |
| Quality Score | 41.2/100 |
| Open Issues | 5 |
| Last Updated | 2025-11-22 |
| Created | 2025-07-28 |
| Platforms | mcp, python |
| Est. Tokens | ~1531k |
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TradingAgents-MCPmode is TradingAgents-MCPmode 是一个创新的多智能体交易分析系统，集成了 Model Context Protocol (MCP) 工具，实现了智能化的股票分析和交易决策流程。系统通过多个专业化智能体的协作，提供全面的市场分析、投资建议和风险管理。. It is categorized as a MCP Server with 238 GitHub stars.
TradingAgents-MCPmode is primarily written in Python. It covers topics such as agent, finance, fintech.
You can find installation instructions and usage details in the TradingAgents-MCPmode GitHub repository at github.com/guangxiangdebizi/TradingAgents-MCPmode. The project has 238 stars and 57 forks, indicating an active community.
The top alternatives to TradingAgents-MCPmode on Agent Skills Hub include open-trading-api, maverick-mcp, mcp_massive. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.