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官方说明7 个来源MCP 服务8 天前
netanelavr/trading-mcp

netanelavr/trading-mcp

Local stock research: screening,
在 GitHub 查看 ↗直链 · 无中转页
配置模板(请按你的客户端调整)
claude mcp add trading-mcp -- npx -y trading-mcp
这是根据仓库名生成的可粘贴骨架,命令行参数与包名请以该项目 README 为准。
★ 数取值
75 ★ · netanelavr/trading-mcp · 取自该仓库的 GitHub 页面 · (2026-10-16 20:00:20 核对)
来源明细
收录自 punkpeye/awesome-mcp-servers 榜单(我们从中收集的条目之一) · 条目类型:MCP 服务
收录与活跃
收录于 2026-10-09 · 最近见到 2026-10-16 · 共见到 32 次 · 被 7 个来源列出
评分构成
综合分 703 · 75 ★ · 7 个来源 · mcp
常见问题(答案都来自我们的记录)
怎么安装?
我们【没有】从该仓库取到安装命令(我们的 install 覆盖率只有约 4%)。请看它的 README 或 releases 页。
它适合谁 / 用来做什么?
我们把它归在这些标签下: MCP 服务。标签来自条目自身,不是我们强加的分类。
谁维护它、还在更新吗?
仓库 netanelavr/trading-mcp;我们收录于 2026-10-09;最近见到 2026-10-16。这里没有"官方认证"或"质量评级"——我们只记录来源与时间。
第三方质量标记

Trading MCP Server

以下摘录自该仓库的 README(片段) · 在 GitHub 读完整 README →

A comprehensive Model Context Protocol (MCP) server for stock analysis and trading insights. This server provides advanced stock screening, fundamental analysis, insider trading data, social media sentiment, and news analysis capabilities.
🎬 Demo
https://github.com/user-attachments/assets/71995014-7cbb-48fa-80fb-f1f6d22fc91c
*See the Trading MCP Server in action - from stock screening to comprehensive analysis*
📋 MCP Configuration
Add this to your MCP configuration file (typically ~/.cursor/mcp.json or your IDE's MCP settings):
🚀 Features
- **📊 Stock Screening**: Technical pattern recognition and advanced multi-criteria filtering
- **📈 Fundamental Analysis**: Comprehensive metrics, valuation comparison, and AI health scoring
- **🏢 Insider Trading**: Real-time insider activity tracking and sentiment analysis
- **📊 Options Analysis**: Put/call ratio data and options market sentiment analysis
- **💭 Social Media Research**: Reddit integration with AI-powered sentiment analysis
- **📰 News Analysis**: AI-curated news with market impact assessment
- **🎯 Comprehensive Analysis**: All-in-one stock analysis combining multiple data sources
📚 Available Tools
**Note:**
- OpenAI API key is required for news analysis and social sentiment analysis
- Reddit credentials are required for social media research features
Stock Screening Tools
screen_stocks_advanced_filters
Advanced stock screening using Finviz filters with support for technical patterns, fundamental criteria, and multi-parameter filtering. Returns stocks matching specific criteria with key metrics.
**Parameters:**
- filters (object): Finviz format filters. Use "f" for basic filters, "o" for ordering. Example: {"f": "cap_large,fa_pe_profitable,ta_pattern_channeldown", "o": "marketcap"}
- limit (number, default: 50): Maximum results to return
Fundamental Analysis Tools
get_fundamental_stock_metrics
Retrieves comprehensive financial metrics including P/E ratios, PEG, ROE, debt ratios, growth rates, and profitability margins.
**Parameters:**
- ticker (string, required): Stock ticker symbol
- metrics (array, optional): Specific metrics to retrieve (returns all if not specified)
compare_stock_valuations
Compares valuation metrics across multiple stocks to identify relative value opportunities. Perfect for peer analysis and sector comparisons.
**Parameters:**
- tickers (array, required): Stock ticker symbols to compare
- metrics (array, default: ['pe', 'forwardPE', 'peg', 'priceToBook']): Valuation metrics to compare
calculate_financial_health_score
Calculates a comprehensive financial health score (0-100) based on profitability, liquidity, leverage, efficiency, and growth metrics with customizable weightings.
**Parameters:**
- ticker (string, required): Stock ticker symbol
- weights (object, optional): Custom weights for health factors
- profitability (default: 0.3), liquidity (default: 0.2), leverage (default: 0.2), efficiency (default: 0.15), growth (default: 0.15)
Insider Trading Tools
analyze_insider_activity
Monitors insider transactions and analyzes sentiment patterns. Returns transaction history with sentiment analysis and confidence scores.
**Parameters:**
- ticker (string, required): Stock ticker symbol
- limit (number, default: 10): Maximum transactions to return
- transaction_types (array, optional): Filter by transaction types
- analysis_period (number, default: 90): Analysis period in days
- min_transaction_value (number, default: 10000): Minimum transaction value threshold
来源
awesome · https://github.com/netanelavr/trading-mcp
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