🎬 Demo

CryptoSignal MCP in action - Real-time crypto direction predictions with confidence scores

✨ Features

🚀 Installation

Prerequisites

• Python 3.11+
• Required packages (automatically installed)

Quick Start

# Clone the repository
git clone https://github.com/khalilbalaree/CryptoSignal-MCP.git
cd CryptoSignal-MCP

# Install dependencies
pip install -r requirements.txt

# Run the server
python crypto_predictor_server.py

🔧 MCP Integration

With Claude Desktop

Add this server to your Claude Desktop configuration:

macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
Windows: %APPDATA%/Claude/claude_desktop_config.json

{
  "mcpServers": {
    "cryptosignal-mcp": {
      "command": "python",
      "args": ["/path/to/CryptoSignal-MCP/crypto_predictor_server.py"],
      "env": {}
    }
  }
}

🛠️ API Tools

🎯 predict_crypto_direction

Advanced ML prediction using ensemble models to predict price direction

predict_crypto_direction(
    symbol="BTCUSDT",           # Trading pair
    interval="1h",              # Time interval (default: 1h)
    training_periods=1000       # Training data size (default: 1000)
)

Supported Intervals: 1m 3m 5m 15m 30m 1h 2h 4h 6h 8h 12h 1d 3d 1w 1M

Returns: Prediction direction, confidence scores, model performance, market context, feature importance, risk assessment

📈 analyze_crypto_indicators

Fast technical analysis without ML training - immediate market insights

analyze_crypto_indicators(
    symbol="ETHUSDT",           # Trading pair
    interval="1h",              # Time interval (default: 1h)
    limit=100,                  # Data points (default: 100)
    short_period=5,             # Short-term period (default: 5)
    medium_period=10,           # Medium-term period (default: 10)
    long_period=20              # Long-term period (default: 20)
)

Returns: Moving averages, trends, momentum analysis, volatility metrics, support/resistance levels, trend signals

🔍 get_crypto_news_search

Generate optimized search queries for Claude Code's WebSearch tool

get_crypto_news_search(
    symbol="bitcoin"            # Crypto symbol (default: bitcoin)
)

Returns: Structured search data including optimized queries, reliable domains, and analysis prompts for use with Claude Code's WebSearch tool

📊 monitor_polymarket_trader

Analyze successful crypto traders' positions and patterns on Polymarket

monitor_polymarket_trader(
    trader_address="0x1234567890abcdef1234567890abcdef12345678",  # Ethereum wallet address
    limit=100                                                    # Activities to fetch (default: 100)
)

Returns: Complete trading activity history including positions, bet sizes, outcomes, timing, and P&L performance across crypto prediction markets

💡 Usage Examples

# Get ML prediction for Bitcoin (1-hour timeframe)
predict_crypto_direction("BTCUSDT", "1h", 1000)

# Quick technical analysis for Ethereum (4-hour timeframe)
analyze_crypto_indicators("ETHUSDT", "4h", 200)

# Get search query for Bitcoin news analysis
get_crypto_news_search("bitcoin")

# Monitor successful crypto trader's activities
monitor_polymarket_trader("0x1234567890abcdef1234567890abcdef12345678", 100)

# Short-term scalping prediction (15-minute intervals)
predict_crypto_direction("BTCUSDT", "15m", 500)

# Long-term investment analysis (daily timeframe)
analyze_crypto_indicators("ETHUSDT", "1d", 365, 10, 20, 50)

# Custom altcoin analysis
analyze_crypto_indicators("ADAUSDT", "2h", 100, 3, 7, 14)

# Multi-timeframe analysis
for timeframe in ["1h", "4h", "1d"]:
    analyze_crypto_indicators("BTCUSDT", timeframe)

# Copy trading successful traders
successful_traders = [
    "0x1234567890abcdef1234567890abcdef12345678",
    "0xabcdef1234567890abcdef1234567890abcdef12"
]
for trader in successful_traders:
    monitor_polymarket_trader(trader, 100)

📊 Technical Indicators

Our ML models leverage 30+ advanced technical indicators across multiple categories:

🎯 Model Architecture

graph TD
    A[Historical Data] --> B[Feature Engineering]
    B --> C[30+ Technical Indicators]
    C --> D[Data Preprocessing]
    D --> E[Ensemble Models]
    E --> F[Random Forest]
    E --> G[Gradient Boosting]
    E --> H[Extra Trees]
    F --> I[Voting Classifier]
    G --> I
    H --> I
    I --> J[Prediction + Confidence]

⚠️ Risk Disclaimer

🚨 IMPORTANT: This tool is designed for educational and research purposes only.

Cryptocurrency trading involves significant financial risk. Past performance does not guarantee future results. Always:

• Conduct your own research and analysis
• Implement proper risk management strategies
• Never invest more than you can afford to lose
• Consider seeking advice from qualified financial professionals

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.