Stock Market Analysis MCP Server

A comprehensive Model Context Protocol (MCP) server for advanced stock market analysis, trend identification, portfolio management, dividend tracking, sector analysis, risk metrics, and market sentiment tracking.

Version: 0.4.0
Tools Available: 48+

📑 Table of Contents

Features
Installation
Usage Examples
Stake Trading Integration
Technical Indicators
Architecture
Data Storage & Privacy
Best Practices
Dependencies

🚀 Features

📈 Price & Data Tools (`price_data.py`)

get_latest_price(ticker) - Get the most recent market price for any stock
get_historical_data(ticker, period) - Retrieve historical OHLC data (Open, High, Low, Close, Volume)
get_stock_info(ticker) - Comprehensive stock information including fundamentals, P/E ratio, market cap, dividend yield, etc.

💼 Portfolio Management (`portfolio.py`)

add_holding(ticker, shares, purchase_price, purchase_date?) - Add stocks to your portfolio with automatic cost basis tracking
remove_holding(ticker, shares, sale_price, sale_date?) - Sell stocks with automatic profit/loss calculation
view_portfolio() - See all your holdings with real-time valuations and P&L
view_transactions(limit?) - Review your transaction history

🔍 Technical Analysis (`analysis.py`)

analyze_buy_opportunity(ticker) - Simple SMA crossover strategy (20/50 day)
calculate_rsi(ticker, period?, timeframe?) - Relative Strength Index to identify overbought/oversold conditions
calculate_macd(ticker, timeframe?) - Moving Average Convergence Divergence for trend momentum
analyze_trends(ticker, timeframe?) - Multi-indicator comprehensive trend analysis
compare_stocks(tickers[]) - Side-by-side comparison of multiple stocks

🔔 Alert System (`alerts.py`)

set_price_alert(ticker, target_price, alert_type, alert_name?) - Set price alerts (above/below thresholds)
set_rsi_alert(ticker, rsi_threshold, alert_type, alert_name?) - Set RSI alerts for overbought/oversold conditions
check_alerts() - Check all active alerts and see which ones have been triggered
list_alerts() - View all configured alerts
clear_triggered_alerts() - Remove triggered alerts
delete_all_alerts() - Delete all alerts

💰 Dividend Tracking (`dividends.py`)

get_dividend_history(ticker, period?) - View dividend payment history with trends
get_dividend_yield(ticker) - Get current dividend yield and related metrics
calculate_portfolio_dividend_income() - Calculate expected annual dividend income from your portfolio
find_high_dividend_stocks(min_yield?, sector?) - Discover high-yield dividend stocks

🏢 Sector Analysis (`sector.py`)

analyze_sector(sector_name) - Comprehensive analysis of a specific market sector
compare_sectors() - Compare performance across all major market sectors
get_sector_leaders(sector_name, metric?) - Get top performing stocks in a sector
analyze_portfolio_sector_allocation() - Analyze your portfolio's sector diversification

⚠️ Risk Metrics (`risk.py`)

calculate_sharpe_ratio(ticker, risk_free_rate?, period?) - Measure risk-adjusted returns
calculate_beta(ticker, benchmark?, period?) - Calculate stock volatility vs market
calculate_portfolio_risk() - Comprehensive portfolio risk analysis with recommendations
calculate_var(ticker, confidence_level?, period?, position_size?) - Value at Risk calculation
calculate_drawdown(ticker, period?) - Maximum drawdown and peak-to-trough analysis

📊 Market Sentiment Tracking (`sentiment.py`) ✨ NEW

get_market_sentiment() - Overall market sentiment score (-100 to +100) with actionable recommendations
get_detailed_sentiment_signals() - Detailed breakdown of all 9 market indicators
get_vix_analysis() - VIX volatility analysis (fear/greed gauge)
get_market_breadth() - Sector participation and market health analysis
get_sector_rotation_signal() - Defensive vs growth sector rotation patterns
get_ai_sector_signal() - AI/Tech sector leadership and strength analysis
analyze_leverage_indicators() - Market leverage and deleveraging signals
track_sentiment_history(days?) - Historical sentiment trends and momentum

Tracked Indicators: VIX, SPY/QQQ trends, Put/Call ratio, Sector rotation, Market breadth, Volume patterns, AI/Tech leadership, Leverage stress

🤖 Stake Trading (`stake.py`)

configure_stake_connection(...) - Supply Stake API endpoint, account id, and session tokens
stake_execute_graphql(query, variables?) - Send custom GraphQL operations to Stake
stake_place_order(symbol, side, quantity, order_type?, ...) - Submit market/limit/stop orders
stake_cancel_order(order_id) - Cancel an existing order
stake_list_orders(status_filter?) - Review open or historical orders

📦 Installation

Prerequisites

Before you begin, ensure you have:

Python 3.8 or higher installed on your system
Cursor IDE (or another MCP-compatible client)
Git (optional, for cloning the repository)
Internet connection for fetching stock market data

Step 1: Get the Code

Option A: Clone from GitHub

git clone https://github.com/CalvinLiuu/stock_mcp_server.git
cd stock_mcp_server

Option B: Download ZIP

Download the repository as a ZIP file
Extract it to your desired location
Open a terminal and navigate to the extracted folder

Step 2: Set Up Virtual Environment

Create and activate a virtual environment (recommended):

On macOS/Linux:

python3 -m venv .venv
source .venv/bin/activate

On Windows:

python -m venv .venv
.venv\Scripts\activate

Step 3: Install Dependencies

With your virtual environment activated, install the required packages:

pip install -r requirements.txt

This will install:

yfinance - For real-time stock market data
pandas - For data analysis
numpy - For numerical computations
fastmcp - For MCP server functionality

Step 4: Configure MCP in Cursor

To use this server with Cursor IDE, you need to add it to your MCP configuration file.

Locate Your MCP Configuration

The MCP configuration file is typically located at:

macOS/Linux: ~/.cursor/mcp.json
Windows: %USERPROFILE%\.cursor\mcp.json

Add the Server Configuration

Open the mcp.json file and add the following configuration (adjust the path to match your installation):

Option A: Using Python Module (Recommended)

{
  "mcpServers": {
    "stock-analyzer": {
      "command": "/absolute/path/to/stock_mcp_server/.venv/bin/python",
      "args": [
        "-m",
        "mcp.server.fastmcp",
        "run",
        "/absolute/path/to/stock_mcp_server/stock.server.py"
      ],
      "env": {}
    }
  }
}

Option B: Using fastmcp CLI

{
  "mcpServers": {
    "stock-analyzer": {
      "command": "/absolute/path/to/stock_mcp_server/.venv/bin/fastmcp",
      "args": [
        "dev",
        "/absolute/path/to/stock_mcp_server/stock.server.py"
      ],
      "env": {}
    }
  }
}

Important:

Replace /absolute/path/to/stock_mcp_server/ with the actual full path to where you installed the server
On Windows, use backslashes and the appropriate paths (e.g., C:\\Users\\YourName\\...)
Make sure to use the Python interpreter from your virtual environment (.venv/bin/python)

Example for macOS:

{
  "mcpServers": {
    "stock-analyzer": {
      "command": "/Users/johndoe/Documents/stock_mcp_server/.venv/bin/python",
      "args": [
        "-m",
        "mcp.server.fastmcp",
        "run",
        "/Users/johndoe/Documents/stock_mcp_server/stock.server.py"
      ],
      "env": {}
    }
  }
}

Example for Windows:

{
  "mcpServers": {
    "stock-analyzer": {
      "command": "C:\\Users\\JohnDoe\\Documents\\stock_mcp_server\\.venv\\Scripts\\python.exe",
      "args": [
        "-m",
        "mcp.server.fastmcp",
        "run",
        "C:\\Users\\JohnDoe\\Documents\\stock_mcp_server\\stock.server.py"
      ],
      "env": {}
    }
  }
}

Step 5: Restart Cursor

After updating your mcp.json configuration:

Save the file
Completely restart Cursor (close and reopen)
The MCP server should automatically start when Cursor launches

Step 6: Verify Installation

You can verify the installation by checking the MCP logs in Cursor or by trying to use one of the tools:

In Cursor's AI chat, try:

Can you get the latest price for AAPL using the stock analyzer?

You should see the server responding with real-time stock data.

Or check available tools:

What tools are available in the stock-analyzer MCP server?

🧪 Testing the Server Manually (Optional)

To test the server outside of Cursor:

# Activate your virtual environment first
source .venv/bin/activate  # macOS/Linux
# or
.venv\Scripts\activate  # Windows

# Run the server in development mode
fastmcp dev stock.server.py

You should see output like:

Registering price data tools...
Registering portfolio management tools...
Registering technical analysis tools...
Registering alert system tools...
Registering dividend tracking tools...
Registering sector analysis tools...
Registering risk analysis tools...
✅ All tools registered successfully!
📊 Stock Market Analyzer v0.3.0 is ready!

🔧 Troubleshooting

Server not starting?

Verify the paths in your mcp.json are absolute paths (not relative)
Ensure you're using the Python from your virtual environment (.venv/bin/python)
Check that all dependencies are installed: pip list should show yfinance, pandas, numpy, fastmcp

"Module not found" errors?

Make sure your virtual environment is activated
Reinstall dependencies: pip install -r requirements.txt

Can't fetch stock data?

Check your internet connection
Some stocks may have delayed data or require different ticker symbols
Try a common stock like "AAPL" or "MSFT" first

MCP server not appearing in Cursor?

Ensure the mcp.json file is valid JSON (no trailing commas, proper syntax)
Check Cursor's MCP logs for error messages
Try restarting Cursor completely

Permission errors on macOS/Linux?

Make sure the Python executable is executable: chmod +x .venv/bin/python

📱 Quick Start After Installation

Once installed and configured, you can immediately start using the tools through Cursor's AI chat:

# Check a stock price
"Get the latest price for Tesla"

# Add to your portfolio
"Add 10 shares of AAPL at $150 to my portfolio"

# View your portfolio
"Show me my current portfolio"

# Set an alert
"Alert me when TSLA goes below $250"

# Analyze a stock
"Analyze trends for NVDA"

🎯 Usage Examples

Portfolio Management

# Add stocks to your portfolio
add_holding("AAPL", 10, 150.00)
add_holding("MSFT", 5, 380.00)
add_holding("NVDA", 8, 450.00)

# View your portfolio with current valuations
view_portfolio()

# Sell some shares
remove_holding("AAPL", 5, 175.00)

# View transaction history
view_transactions(limit=20)

Alert System

# Set a price alert
set_price_alert("TSLA", 250.00, "below")

# Set an RSI alert for oversold conditions
set_rsi_alert("NVDA", 30, "below")

# Check if any alerts triggered
check_alerts()

# View all configured alerts
list_alerts()

Dividend Analysis

# View dividend history
get_dividend_history("JNJ", period="5y")

# Check dividend yield
get_dividend_yield("KO")

# Calculate portfolio dividend income
calculate_portfolio_dividend_income()

# Find high-yield stocks in utilities sector
find_high_dividend_stocks(min_yield=4.0, sector="Utilities")

Sector Analysis

# Analyze technology sector
analyze_sector("Technology")

# Compare all sectors
compare_sectors()

# Get sector leaders by performance
get_sector_leaders("Healthcare", metric="return")

# Check portfolio sector diversification
analyze_portfolio_sector_allocation()

Risk Analysis

# Calculate Sharpe ratio
calculate_sharpe_ratio("AAPL", risk_free_rate=0.04, period="1y")

# Calculate beta vs S&P 500
calculate_beta("TSLA", benchmark="SPY")

# Comprehensive portfolio risk analysis
calculate_portfolio_risk()

# Calculate Value at Risk
calculate_var("NVDA", confidence_level=0.95, position_size=10000)

# Analyze maximum drawdown
calculate_drawdown("MSFT", period="5y")

Technical Analysis

# Get detailed stock information
get_stock_info("GOOGL")

# Check RSI for overbought/oversold signals
calculate_rsi("TSLA", period=14, timeframe="3mo")

# Analyze MACD for momentum
calculate_macd("MSFT", timeframe="6mo")

# Comprehensive trend analysis
analyze_trends("NVDA", timeframe="1y")

# Compare multiple stocks
compare_stocks(["AAPL", "MSFT", "GOOGL"])

🤖 Stake Trading Integration

The MCP server now includes helper tools for forwarding trades to Stake Australia. Because Stake does not publish an official API, these helpers reuse the same GraphQL calls that the Stake web client performs. You must provide valid session tokens obtained from a manual login.

1. Collect the required session details

Log into trade.stake.com using your normal browser.
Open the network inspector and copy the latest Authorization: Bearer ... header from any GraphQL request (this becomes your access_token).
Locate your accountId value inside the GraphQL request payloads.
Optional: copy any extra headers Stake expects (for example x-client or x-stake-platform).

⚠️ Tokens eventually expire. If you provide an epoch timestamp via the token_expiry parameter the tools will remind you to refresh the session.

2. Configure the Stake connection

configure_stake_connection(
    api_url="https://global-prd-api.stake.com",
    graphql_path="/graphql",
    account_id="<your-account-id>",
    access_token="eyJhbGciOi...",
    extra_headers={"x-client": "web"},
    token_expiry=1719878400
)

Alternatively, export environment variables before launching the MCP server:

export STAKE_API_URL="https://global-prd-api.stake.com"
export STAKE_GRAPHQL_PATH="/graphql"
export STAKE_ACCOUNT_ID="<your-account-id>"
export STAKE_ACCESS_TOKEN="eyJhbGciOi..."
export STAKE_EXTRA_HEADERS='{"x-client": "web"}'

3. Execute trades or manage orders

# Submit a market buy
stake_place_order("AAPL", "BUY", quantity=2)

# Submit a limit sell outside regular hours
stake_place_order(
    "TSLA", "SELL", quantity=1,
    order_type="LIMIT", limit_price=310.0,
    outside_regular_hours=True
)

# Inspect and cancel orders
stake_list_orders("OPEN")
stake_cancel_order("order-id-from-list")

For advanced workflows you can run custom GraphQL operations:

stake_execute_graphql(
    """
    query CashBalance($accountId: ID!) {
      accountBalances(accountId: $accountId) {
        cashAvailableForWithdrawal
        cashAvailableToTrade
      }
    }
    """,
    {"accountId": "<your-account-id>"}
)

ℹ️ Stake may change their internal schema without notice. The stake_execute_graphql tool lets you adapt by supplying updated queries or mutations without editing the MCP server.

📊 Technical Indicators Explained

RSI (Relative Strength Index)

Above 70: Overbought - potential sell signal
Below 30: Oversold - potential buy opportunity
30-70: Neutral range

MACD (Moving Average Convergence Divergence)

Bullish Crossover: MACD line crosses above signal line
Bearish Crossover: MACD line crosses below signal line
Used to identify momentum and trend changes

SMA Crossover Strategy

Buy Signal: Short-term SMA (20-day) crosses above long-term SMA (50-day)
Sell Signal: Short-term SMA crosses below long-term SMA

Sharpe Ratio

> 2.0: Excellent risk-adjusted returns
1.0-2.0: Good
0-1.0: Fair
< 0: Poor (returns below risk-free rate)

Beta

β > 1: More volatile than market
β = 1: Moves with market
0 < β < 1: Less volatile than market
β < 0: Moves opposite to market

🏗️ Architecture

stock_mcp_server/
├── stock.server.py         # Main server entry point
├── run_mcp_server.py       # Helper script to run server
├── utils.py                # Shared utilities (load/save data)
├── price_data.py           # Price and stock information
├── portfolio.py            # Portfolio management
├── analysis.py             # Technical analysis tools
├── alerts.py               # Alert system
├── dividends.py            # Dividend tracking
├── sector.py               # Sector analysis
├── risk.py                 # Risk metrics
├── sentiment.py            # Market sentiment tracking ✨ NEW
├── stake.py                # Stake Australia trading helpers
├── requirements.txt        # Python dependencies
├── README.md               # Main documentation
├── data/                   # Data files (auto-generated)
│   ├── portfolio.json      # Your portfolio holdings
│   ├── alerts.json         # Your price/RSI alerts
│   └── sentiment_history.json  # Sentiment tracking history
├── docs/                   # Documentation files
│   ├── TOOLS_REFERENCE.md  # Complete tools reference
│   ├── IMPLEMENTATION_SUMMARY.md  # Implementation details
│   ├── ROADMAP.md          # Future features roadmap
│   ├── SENTIMENT_TRACKER_SUMMARY.md  # Sentiment tracker docs
│   └── CURRENT_MARKET_ANALYSIS.md   # Market analysis example
└── venv/                   # Python virtual environment

Modular Design

Each module is self-contained and can be updated independently:

utils.py: Shared functions for data persistence
price_data.py: Basic stock data retrieval
portfolio.py: Holdings and transaction tracking
analysis.py: Technical indicators and trend analysis
alerts.py: Price and RSI alert system
dividends.py: Dividend history and income tracking
sector.py: Sector-wide analysis and comparison
risk.py: Risk metrics and portfolio risk management
sentiment.py: Market sentiment aggregation and tracking ✨ NEW
stake.py: Unofficial Stake Australia order routing helpers

📁 Data Storage & Privacy

🔒 Local Storage Only

Your data stays completely private and local:

✅ All portfolio data is stored on your computer only
✅ No cloud storage or external servers
✅ No authentication or account required
✅ Full control over your data files
✅ Can backup/edit JSON files directly

What gets stored locally:

Your stock holdings and transactions → data/portfolio.json
Your price and RSI alerts → data/alerts.json
Your sentiment tracking history → data/sentiment_history.json

What goes to the internet:

Only market data requests (stock prices, fundamentals, etc.) via Yahoo Finance API
Your personal portfolio data is NEVER transmitted anywhere

Portfolio Data (`data/portfolio.json`)

Location: data/ folder in the project directory

Automatically created and saved with:

Current holdings with average cost basis
Complete transaction history (buy/sell)
Profit/loss calculations
Last update dates

Example structure:

{
  "holdings": {
    "AAPL": {
      "shares": 10,
      "avg_price": 150.00,
      "last_updated": "2025-01-15"
    }
  },
  "transactions": [
    {
      "type": "BUY",
      "ticker": "AAPL",
      "shares": 10,
      "price": 150.00,
      "date": "2025-01-15",
      "total": 1500.00
    }
  ]
}

Alert Data (`data/alerts.json`)

Location: data/ folder in the project directory

Automatically created and saved with:

Active price alerts (trigger above/below thresholds)
Active RSI alerts (overbought/oversold conditions)
Alert status and trigger history

Example structure:

{
  "price_alerts": [
    {
      "id": "alert_123",
      "ticker": "TSLA",
      "target_price": 250.00,
      "alert_type": "below",
      "status": "active"
    }
  ],
  "rsi_alerts": []
}

Sentiment History Data (`data/sentiment_history.json`) ✨ NEW

Location: data/ folder in the project directory

Automatically created when you run sentiment analysis:

Daily sentiment scores (-100 to +100)
Historical classifications (bearish, neutral, bullish)
Keeps last 90 days of data
Enables trend analysis

Example structure:

{
  "daily_scores": [
    {
      "date": "2025-10-23",
      "score": 2.4,
      "classification": "🟡 NEUTRAL"
    }
  ]
}

Backup Your Data

Since all data is stored locally in JSON files, you can easily:

Backup: Copy the entire data/ folder to another location
Restore: Replace the data/ folder with your backup
Edit: Manually edit the JSON files if needed (be careful with formatting)
Version Control: Add to Git (but remember to add to .gitignore if sharing publicly)

🆕 What's New in v0.4.0

New Features ✨

📊 Market Sentiment Tracker - Aggregate 9 market indicators for overall sentiment (-100 to +100)
- VIX analysis, sector rotation, market breadth, AI/Tech signals
- Leverage indicators, Put/Call proxy, volume patterns
- Historical tracking and trend analysis
- Actionable recommendations based on market conditions

Project Organization

New Folders: data/ for all JSON files, docs/ for documentation
Improved Structure: Cleaner project layout with separated concerns
Updated Docs: Comprehensive documentation in docs/ folder

🆕 What's New in v0.3.0

New Features

🔔 Alert System - Set price and RSI alerts, check status automatically
💰 Dividend Tracking - Track dividend history, yields, and portfolio income
🏢 Sector Analysis - Analyze entire sectors, compare performance, check diversification
⚠️ Risk Metrics - Sharpe ratio, beta, VaR, drawdown, comprehensive portfolio risk

Enhanced Features

Modular architecture for better maintainability
Separated concerns into dedicated modules
Improved error handling across all tools
Better data persistence with separate files for alerts

Previous Features (v0.2.0)

Portfolio management with P&L tracking
Technical indicators (RSI, MACD, SMA)
Comprehensive trend analysis
Stock comparison tools

Original Features (v0.1.0)

Basic price data retrieval
Historical data access
Simple SMA crossover analysis

🔧 Configuration

Risk-Free Rate

Default: 4% (0.04) - Can be adjusted in Sharpe ratio calculations

Available Sectors

Technology
Healthcare
Financial Services
Energy
Consumer Cyclical
Consumer Defensive
Utilities
Industrials
Real Estate
Materials
Communication Services

Alert Types

Price Alerts: above or below target price
RSI Alerts: above or below RSI threshold

📚 Dependencies

yfinance (>=0.2.40): Real-time and historical market data
pandas (>=2.0.0): Data manipulation and analysis
numpy (>=1.24.0): Numerical computations for risk metrics
fastmcp (>=0.1.0): MCP server framework

🎓 Best Practices

Portfolio Management

Diversify across 10-20 different stocks
Keep largest position below 20% of portfolio
Regular rebalancing (quarterly or annually)
Track cost basis for tax purposes

Risk Management

Target Sharpe ratio > 1.0 for good risk-adjusted returns
Keep portfolio beta between 0.8-1.2 for moderate risk
Monitor maximum drawdown - be prepared for historical volatility
Use VaR to understand daily loss potential

Sector Allocation

Diversify across at least 5 different sectors
Avoid concentration > 35% in any single sector
Consider sector rotation based on economic cycles
Balance growth and defensive sectors

Dividend Investing

Look for payout ratios < 80% for sustainability
Prefer dividend growth over just high yield
Reinvest dividends for compound growth
Track ex-dividend dates for planning

⚠️ Disclaimer

This tool is for educational and informational purposes only. It is NOT financial advice.

Market data may be delayed
Past performance doesn't guarantee future results
Always do your own research before investing
Consider consulting a financial advisor
Be aware of tax implications

🚀 Future Enhancements

Potential additions:

Options analysis and Greeks calculation
Fibonacci retracement levels
Support/resistance identification
News sentiment analysis
Backtesting capabilities
Tax lot tracking for capital gains
Portfolio rebalancing suggestions
Correlation analysis between holdings
Monte Carlo simulations
Real-time streaming quotes

📄 License

MIT License - Feel free to use and modify as needed.

🤝 Contributing

Contributions are welcome! Please ensure:

Code follows the modular architecture
New features are in appropriate modules
Documentation is updated
Error handling is comprehensive

📞 Support

For issues or questions:

Check the documentation above
Review the module-specific code
Ensure all dependencies are installed
Verify API data is accessible

Version: 0.3.0
Last Updated: 2025
Author: Stock Market Analysis MCP Server

Stock Market Analysis MCP Server

Stock Market Analysis MCP Server

📑 Table of Contents

🚀 Features

📈 Price & Data Tools (price_data.py)

💼 Portfolio Management (portfolio.py)

🔍 Technical Analysis (analysis.py)

🔔 Alert System (alerts.py)

💰 Dividend Tracking (dividends.py)

🏢 Sector Analysis (sector.py)

⚠️ Risk Metrics (risk.py)

📊 Market Sentiment Tracking (sentiment.py) ✨ NEW

🤖 Stake Trading (stake.py)

📦 Installation

Prerequisites

Step 1: Get the Code

Step 2: Set Up Virtual Environment

Step 3: Install Dependencies

Step 4: Configure MCP in Cursor

Locate Your MCP Configuration

Add the Server Configuration

Step 5: Restart Cursor

Step 6: Verify Installation

🧪 Testing the Server Manually (Optional)

🔧 Troubleshooting

📱 Quick Start After Installation

🎯 Usage Examples

Portfolio Management

Alert System

Dividend Analysis

Sector Analysis

Risk Analysis

Technical Analysis

🤖 Stake Trading Integration

1. Collect the required session details

2. Configure the Stake connection

3. Execute trades or manage orders

📊 Technical Indicators Explained

RSI (Relative Strength Index)

MACD (Moving Average Convergence Divergence)

SMA Crossover Strategy

Sharpe Ratio

Beta

🏗️ Architecture

Modular Design

📁 Data Storage & Privacy

🔒 Local Storage Only

Portfolio Data (data/portfolio.json)

Alert Data (data/alerts.json)

Sentiment History Data (data/sentiment_history.json) ✨ NEW

Backup Your Data

🆕 What's New in v0.4.0

New Features ✨

Project Organization

🆕 What's New in v0.3.0

New Features

Enhanced Features

Previous Features (v0.2.0)

Original Features (v0.1.0)

🔧 Configuration

Risk-Free Rate

Available Sectors

Alert Types

📚 Dependencies

🎓 Best Practices

Portfolio Management

Risk Management

Sector Allocation

Dividend Investing

⚠️ Disclaimer

🚀 Future Enhancements

📄 License

🤝 Contributing

📞 Support

Stock Market Analysis MCP Server

📑 Table of Contents

🚀 Features

📈 Price & Data Tools (price_data.py)

💼 Portfolio Management (portfolio.py)

🔍 Technical Analysis (analysis.py)

📈 Price & Data Tools (`price_data.py`)

💼 Portfolio Management (`portfolio.py`)

🔍 Technical Analysis (`analysis.py`)

🔔 Alert System (`alerts.py`)

💰 Dividend Tracking (`dividends.py`)

🏢 Sector Analysis (`sector.py`)

⚠️ Risk Metrics (`risk.py`)

📊 Market Sentiment Tracking (`sentiment.py`) ✨ NEW

🤖 Stake Trading (`stake.py`)

Portfolio Data (`data/portfolio.json`)

Alert Data (`data/alerts.json`)

Sentiment History Data (`data/sentiment_history.json`) ✨ NEW

📈 Price & Data Tools (`price_data.py`)

💼 Portfolio Management (`portfolio.py`)

🔍 Technical Analysis (`analysis.py`)

🔔 Alert System (`alerts.py`)

💰 Dividend Tracking (`dividends.py`)

🏢 Sector Analysis (`sector.py`)

⚠️ Risk Metrics (`risk.py`)

📊 Market Sentiment Tracking (`sentiment.py`) ✨ NEW

🤖 Stake Trading (`stake.py`)

Portfolio Data (`data/portfolio.json`)

Alert Data (`data/alerts.json`)

Sentiment History Data (`data/sentiment_history.json`) ✨ NEW