Stonks

Stonks

Automates stock analysis and news updates in Google Sheets using AI-powered insights, enabling users to manage stock data and generate comprehensive analysis through natural language.

Category
访问服务器

README

Stock News Automation Service

An intelligent MCP (Model Context Protocol) server that automates stock analysis and news updates in Google Sheets using AI-powered insights.

🚀 Features

Core Capabilities

  • Google Sheets Integration: Seamlessly connects to your Google Sheets for real-time stock data management
  • AI-Powered Analysis: Uses Google Gemini to generate comprehensive stock analysis
  • Multi-Type Analysis: Supports 5 different analysis types for comprehensive coverage
  • Institutional Data Visualization: Creates charts for institutional ownership data
  • Automated News Updates: Fetches and summarizes latest stock news

Analysis Types Available

  1. 📰 News Analysis + Market Context (Column D)

    • Company-specific news and earnings
    • Broader market trends and economic factors
    • Geopolitical impact and industry disruption
  2. 🤖 AI-Powered Insights (Column E)

    • Predictive modeling and pattern recognition
    • Scenario analysis with probability assessments
    • Market timing indicators
  3. 📊 Investor Insights (Column F)

    • Technical and fundamental analysis
    • Risk assessment and competitive analysis
    • Sector dynamics and catalyst calendar
  4. 💰 Quick Financials Overview (Column G)

    • Key financial metrics and ratios
    • Growth rates and profitability analysis
    • Liquidity and valuation metrics
  5. 🏢 Institutional Information (Column H)

    • Institutional ownership data
    • Hedge fund and mutual fund activity
    • Insider trading and analyst coverage

🛠️ Setup Instructions

Prerequisites

  • Python 3.8+
  • Google Cloud Console account
  • Google Sheets API access

1. Google Sheets API Setup

Follow the official Google guide to set up API access: Google Sheets API Quickstart

2. Installation

# Clone the repository
git clone <your-repo-url>
cd stocks

# Create virtual environment
python -m venv stocks
source stocks/bin/activate  # On Windows: stocks\Scripts\activate

# Install dependencies
pip install -r requirements.txt

3. Configuration

  1. Download credentials.json from Google Cloud Console
  2. Place it in the project root directory
  3. Create .env file with:
GOOGLE_API_KEY=your_gemini_api_key
SPREADSHEET_ID=your_google_sheets_id
RANGE_NAME=Sheet1!A6:C9

4. Usage

Option A: Standalone Mode

# Start the MCP Server
python server.py

# Run the Client
python client.py server.py

Option B: MCP Client Integration

Add to your MCP client configuration:

{
  "mcpServers": {
    "stonks-server": {
      "command": "path to your python bin",
      "args": ["path to server.py"]
    }
  }
}

Note: Update the paths to match your actual installation directory.

🎯 How It Works

  1. Initialize: Authenticate with Google Sheets API
  2. Get Stocks: Retrieve stock data from your spreadsheet
  3. Analyze: Generate AI-powered analysis for each stock
  4. Update: Automatically populate analysis in designated columns
  5. Visualize: Create institutional ownership charts

📋 Available MCP Tools

  • initialize(): Set up Google Sheets authentication
  • get_stocks(): Fetch stock data from spreadsheet
  • update(): Add analysis to specific cells with multiple analysis types
  • search_news(): Generate news summaries using Gemini AI
  • plot_institutional_chart(): Create ownership visualization charts

These tools are available through any MCP-compatible client (Claude Desktop, etc.)

🔧 Technical Architecture

  • MCP Server: FastMCP framework for tool orchestration
  • AI Integration: Google Gemini for intelligent analysis
  • Data Visualization: Matplotlib for institutional charts
  • Authentication: OAuth2 for secure Google Sheets access
  • Client Interface: LangGraph with React agent for natural language interaction

📊 Sample Workflow

User: "Update all stocks with latest news and analysis"
↓
Agent: 
1. Initializes Google Sheets connection
2. Retrieves stock symbols from spreadsheet
3. Generates news analysis for each stock
4. Updates respective columns with AI insights
5. Creates institutional ownership charts

🚨 Important Notes

  • Analysis is based on last 10 days of data
  • Includes references to SeekingAlpha, TradingTerminal, and TradingView
  • Not financial advice - for informational purposes only
  • Requires valid Google API credentials

📈 Future Enhancements

  • Real-time data integration
  • Advanced charting capabilities
  • Portfolio performance tracking
  • Custom analysis templates
  • Multi-exchange support

Setup Guide: Google Sheets API Quickstart

推荐服务器

Baidu Map

Baidu Map

百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。

官方
精选
JavaScript
Playwright MCP Server

Playwright MCP Server

一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。

官方
精选
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。

官方
精选
本地
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。

官方
精选
本地
TypeScript
VeyraX

VeyraX

一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。

官方
精选
本地
graphlit-mcp-server

graphlit-mcp-server

模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。

官方
精选
TypeScript
Kagi MCP Server

Kagi MCP Server

一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。

官方
精选
Python
e2b-mcp-server

e2b-mcp-server

使用 MCP 通过 e2b 运行代码。

官方
精选
Neon MCP Server

Neon MCP Server

用于与 Neon 管理 API 和数据库交互的 MCP 服务器

官方
精选
Exa MCP Server

Exa MCP Server

模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。

官方
精选