A
security-
licenseA
qualityCoinmarket API integration to fetch cryptocurrency listings and quotes
Last updated -
2
41
MIT License
Provides access to Binance API for real-time cryptocurrency market data, price feeds, and historical trading data to power machine learning predictions and technical analysis
Enables monitoring and analysis of successful cryptocurrency traders' activities, positions, and trading patterns on Polymarket prediction markets for behavioral trading insights
๐ CryptoSignal MCP
AI-Powered Cryptocurrency Direction Prediction & Market Signal Analysis
Powered by Machine Learning Ensemble Models with 30+ Technical Indicators
Features โข Installation โข API Tools โข Examples โข Indicators
๐ฌ Demo
โจ Features
Feature | Description |
๐ง Advanced ML Predictions | Ensemble models (Random Forest + Gradient Boosting) with 30+ technical indicators |
๐ Comprehensive Technical Analysis | RSI, MACD, Bollinger Bands, Stochastic, Williams %R, ATR, and more |
โฐ Multiple Timeframes | Support for 1m, 3m, 5m, 15m, 30m, 1h, 2h, 4h, 6h, 8h, 12h, 1d, 3d, 1w, 1M |
๐ Real-time Data | Live market data from Binance API with intelligent rate limiting and caching |
๐ฏ Smart Filtering | Automatically filters incomplete trading periods for accurate analysis |
๐ WebSearch Integration | Optimized search queries for Claude Code's WebSearch tool with sentiment analysis prompts |
๐ Polymarket Trader Analysis | Monitor successful crypto traders' activities, positions, and trading patterns for behavioral insights |
๐ 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
๐ง 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": {}
}
}
}
๐ ๏ธ API Tools
๐ฏ 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
๐ 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
๐ 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
๐ 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
๐ก 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)
๐ Technical Indicators
Our ML models leverage 30+ advanced technical indicators across multiple categories:
Category | Indicators |
๐ Price & Momentum | Price change, acceleration, velocity Momentum (3, 5, 10, 20 periods) Rate of change, Sharpe ratio |
๐ Moving Averages | Simple MA (5, 10, 20, 50) Exponential MA (5, 12, 26, 50) MA ratios and crossover signals |
๐ฏ Oscillators | RSI (7, 14 periods) Stochastic Oscillator (K%, D%) Williams %R |
๐ Volume Analysis | Volume ratios and rate of change On-Balance Volume (OBV) Volume spikes and trends |
๐ Volatility & Bands | Bollinger Bands (width, position) Average True Range (ATR) Volatility regimes |
๐๏ธ Market Structure | Support/resistance levels Fractal patterns (local max/min) Trend strength and regime detection |
๐ฏ 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]
โ ๏ธ Risk Disclaimer
๐จ 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
๐ License
This project is licensed under the MIT License - see the LICENSE file for details.
Built with โค๏ธ for the crypto community
โญ Star this repo โข ๐ Report Issues โข ๐ก Request Features
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AI-Powered Cryptocurrency Direction Prediction & Market Signal Analysis
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