MCP Server for Stock Market Analysis
Description
[](https://mseep.ai/app/hasan-syed25-mcp-server-alphavantage) # MCP Server for Stock Market Analysis [](https://mseep.ai/app/hasan-syed25-mcp-server-alphavantage) # MCP Server for Stock Market Analysis [](https://mseep.ai/app/7f9ee518-9288-478e-986c-6b5866890473) ##…
Details
- Author
- Hasan-Syed25
- GitHub stars
- 7
- Downloads
- 54
- Categories
- Finance, Other
Jump to
- Fetches real-time intraday stock data
- Calculates short-term and long-term moving averages
- Detects Golden Cross and Death Cross trends
- Computes Relative Strength Index (RSI)
- Determines overbought/oversold conditions
- Exposes MCP tools and resources for AI integration
Setting up with Highlight
This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:
- Download and install Highlight from highlightai.com/download
- Navigate to the plugins tab and select "Add Custom Plugin"
-
Configure the plugin with the settings below
Plugin Name
MCP Server for Stock Market AnalysisCommand (node, npx, python, etc.)Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
- Enable "Start Automatically" if you want the plugin to start when Highlight launches
From the repository
Install dependencies (mcp[cli] httpx), clone the repository, and set your AlphaVantage API key in a .env file. Start the server with python server.py or use mcp install server.py for installation, or mcp dev server.py for development mode.
Claude Desktop / Cursor
Paste into your MCP client config file to install this server.
{
"mcpServers": {
"mcp server for stock market analysis": {
"MCP-Server-AlphaVantage": {
"command": "python",
"args": [
"server.py"
]
}
}
}
}McpServers
{
"MCP-Server-AlphaVantage": {
"command": "python",
"args": [
"server.py"
]
}
}MCP Server for Stock Market Analysis
Overview
This project implements a Model Context Protocol (MCP) server designed for stock market analysis. It provides real-time stock price data, calculates key technical indicators like moving averages and RSI, and enables trend detection using AlphaVantage API. The server can be integrated with LLMs like Claude for enhanced financial insights.Why I Built It
I developed this MCP server to simplify stock market analysis by automating data fetching and technical indicator calculations. The goal is to help traders and investors make data-driven decisions by leveraging AI-assisted insights. By using MCP, this server can seamlessly connect with LLMs, allowing them to retrieve financial data and perform analysis in real time.Features
- Fetch real-time stock data using AlphaVantage API - Calculate moving averages (short-term & long-term) to analyze trends - Detect trend crossovers (Golden Cross & Death Cross) - Compute Relative Strength Index (RSI) to determine overbought/oversold conditions - Expose MCP tools and resources for seamless AI integration - Future extensibility (e.g., backtesting strategies, trading platform integration)Prerequisites
Before getting started, ensure you have the following: - Python 3.10+ installed - MCP SDK 1.2.0+ - AlphaVantage API key (Sign up at AlphaVantage) - Basic understanding of Python and stock market indicatorsInstallation
To set up the project, follow these steps:1. Install Dependencies
Usinguv (recommended):
``sh
uv add mcp[cli] httpx
`
Using pip:
`sh
pip install mcp httpx
`
2. Get an AlphaVantage API Key
- Register at AlphaVantage
- Note down your API key for later use
3. Clone the Repository
`sh
git clone https://github.com/your-username/mcp-stock-analysis.git
cd mcp-stock-analysis
`
4. Set Up Environment Variables
Create a .env file and add your AlphaVantage API key:
`
ALPHA_VANTAGE_API_KEY=your_api_key_here
`
Running the MCP Server
To start the server, run:
`sh
python server.py
`
Or using MCP:
`sh
mcp install server.py
`
For development mode:
`sh
mcp dev server.py
`
How It Works
Fetching Intraday Stock Data
The server fetches real-time stock price data from AlphaVantage:
`python
@mcp.tool()
def fetch_intraday_data(symbol: str, interval: str = "5min") -> dict:
"""Fetches intraday stock price data."""
url = f"https://www.alphavantage.co/query?function=TIME_SERIES_INTRADAY&symbol={symbol}&interval={interval}&apikey={API_KEY}"
response = httpx.get(url)
return response.json()
`
Moving Averages (Short-Term vs Long-Term)
Moving averages help smooth out price data to identify trends:
`python
@mcp.tool()
def calculate_moving_averages(prices: list[float], short_window: int = 50, long_window: int = 200) -> dict:
"""Calculates short-term and long-term moving averages."""
short_ma = sum(prices[-short_window:]) / short_window
long_ma = sum(prices[-long_window:]) / long_window
return {"short_ma": short_ma, "long_ma": long_ma}
`
Detecting Trend Crossovers (Golden Cross & Death Cross)
- Golden Cross: Short-term MA crosses above long-term MA → Bullish signal
- Death Cross: Short-term MA crosses below long-term MA → Bearish signal
`python
@mcp.tool()
def detect_trend_crossover(short_ma: float, long_ma: float) -> str:
"""Detects if a Golden Cross or Death Cross has occurred."""
if short_ma > long_ma:
return "Golden Cross - Bullish Trend"
elif short_ma < long_ma:
return "Death Cross - Bearish Trend"
return "No significant crossover"
`
Relative Strength Index (RSI)
RSI measures momentum and helps determine if a stock is overbought/oversold:
`python
@mcp.tool()
def calculate_rsi(prices: list[float], period: int = 14) -> float:
"""Calculates RSI indicator."""
gains = [max(0, prices[i] - prices[i-1]) for i in range(1, len(prices))]
losses = [max(0, prices[i-1] - prices[i]) for i in range(1, len(prices))]
avg_gain = sum(gains[-period:]) / period
avg_loss = sum(losses[-period:]) / period
rs = avg_gain / avg_loss if avg_loss != 0 else float('inf')
return 100 - (100 / (1 + rs))
`
Overbought/Oversold Conditions
- RSI > 70 → Overbought (potential sell signal)
- RSI < 30 → Oversold (potential buy signal)
`python
@mcp.tool()
def determine_rsi_condition(rsi: float) -> str:
"""Determines whether the RSI indicates overbought or oversold conditions."""
if rsi > 70:
return "Overbought - Consider Selling"
elif rsi < 30:
return "Oversold - Consider Buying"
return "Neutral"
``
Future Enhancements
This MCP server can be extended with additional features: - More technical indicators: MACD, Bollinger Bands, etc. - Backtesting: Simulate past trades based on strategy rules - Trading platform integration: Connect with brokers for live tradingContributions
Feel free to contribute by submitting pull requests or reporting issues. Let’s make stock market analysis more accessible and AI-driven!Connect With Me
If you have any questions or suggestions, feel free to reach out! 🔗 LinkedIn: Syed Hasan 🤗 Hugging Face: Syed-Hasan-8503Sign in to leave a review
Use Google, GitHub, or an email account so ratings stay tied to real people.
No reviews posted yet.
