Hyperliquid Vault Analyzer

An advanced ML-powered analysis tool for Hyperliquid vaults that provides portfolio optimization, risk analysis, and performance predictions.

Features

ML-Based Portfolio Optimization

• Intelligent weight allocation across vaults
• Risk-adjusted return optimization
• Dynamic rebalancing recommendations

Risk Analysis & Metrics

• Volatility assessment
• Drawdown calculations
• Sharpe ratio computation
• Risk level classification

Performance Prediction

• Machine learning-based return forecasting
• Confidence interval estimation
• Feature importance analysis

APR Calculations

• 30-day and all-time APR tracking
• Weighted portfolio APR
• ROI analysis

Comprehensive Reporting

• Excel report generation
• Multi-sheet detailed analysis
• Portfolio summary statistics

Installation

1. Clone the repository:

git clone https://github.com/StreetJammer/hyperliquid-vault-analyzer.git
cd hyperliquid-vault-analyzer

2. Install dependencies:

pip install -r requirements.txt

Configuration

1. Copy the example configuration file:

cp config.example.json config.json

2. Edit config.json with your credentials:

{
    "account_address": "your_wallet_address",
    "secret_key": "your_api_key"
}

Generate API Key from here - https://app.hyperliquid.xyz/API

Usage

Basic Usage

from analyzer.vault_analyzer import EnhancedVaultAnalyzer

# Initialize analyzer
analyzer = EnhancedVaultAnalyzer()

# Analyze vaults for a user
results = analyzer.analyze_vault(user_address="your_address")

# Print analysis results
if results['status'] == 'success':
    data = results['data']
    print("\nTop Performing Vaults:")
    for vault in data['ranked_vaults']:
        print(f"\n{vault['name']}")
        print(f"Predicted Monthly Return: {vault['predicted_return']:.2f}%")
        print(f"Risk Level: {vault['risk_level']}")
        print(f"Recommended Allocation: {vault['recommended_allocation']:.1f}%")

ML Optimization

from analyzer.ml_optimizer import EnhancedMLPortfolioOptimizer

# Initialize optimizer
optimizer = EnhancedMLPortfolioOptimizer()

# Fetch and analyze historical data
hist_data = optimizer.fetch_historical_data(vault_address)
if hist_data is not None:
    prediction, importances = optimizer.predict_expected_returns(hist_data)

Performance Prediction

from analyzer.predictor import predict_profit

# Predict future profit
future_profit = predict_profit(
    initial_equity=1000,
    apr=20,
    months=3,
    compounding=True
)

Security Considerations

1. API Keys: Store your API keys and wallet addresses securely. Never commit them to version control.

2. Configuration: Use environment variables or secure configuration management for sensitive data.

3. Private Keys: Never share or expose your private keys. The analyzer only requires read access to vault data.

Contributing

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

License

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

Disclaimer

This tool is for informational purposes only. Always conduct your own research and due diligence before making investment decisions. Past performance does not guarantee future results.