HelenusAI 🚀

AI-powered portfolio rebalancing protocol built natively for Monad

🎯 Problem Statement

DeFi portfolio management suffers from three critical inefficiencies:

• High slippage in large trades due to fragmented liquidity
• Missed opportunities from slow execution on congested chains
• Poor timing from manual management and simplistic rebalancing triggers

💡 Solution

HelenusAI solves these challenges through three innovative components:

1. Data-Driven Intelligence Engine

   • Statistical market analysis for optimal trade timing
   • Real-time volatility and correlation tracking
   • Risk-adjusted portfolio optimization

2. Advanced Strategy Engine

   • Dynamic rebalancing with circuit breakers
   • Risk-aware trade execution
   • Performance tracking and optimization

🧠 AI-Powered Portfolio Management

HelenusAI leverages advanced AI through multiple components:

1. Market Intelligence

• Allora Integration

  • Real-time market sentiment analysis
  • Asset-specific price predictions
  • Manipulation detection algorithms
  • Fear/greed index monitoring

• Statistical Analysis

  • Volatility correlation modeling
  • Market condition classification
  • Asset-specific behavioral patterns
  • Risk-adjusted performance metrics

2. Decision Engine

class IntelligenceEngine:
    """AI-powered decision making system"""
    
    async def analyze_portfolio(self, user_id: str, portfolio_id: int):
        # Combine Allora predictions with statistical analysis
        sentiment_data = await self.allora_client.get_market_sentiment(asset)
        stats_data = await self.market_analyzer.analyze_asset(asset)
        
        # Calculate optimal positions using AI models
        combined_score = self._calculate_combined_score(
            sentiment_data, stats_data, asset_profile
        )
        
        return {
            "rebalance_needed": combined_score > 0.7,
            "confidence": combined_score,
            "recommendations": self._generate_recommendations(analysis)
        }

3. Autonomous Features

• Smart Rebalancing

  • AI-timed trade execution
  • Dynamic threshold adjustment
  • Multi-factor opportunity scoring
  • Automated risk management

• Performance Optimization

  • Self-adjusting weights based on outcomes
  • Learning from historical trades
  • Continuous strategy refinement
  • Adaptive risk parameters

4. AI Architecture

┌─────────────────┐    ┌──────────────┐    ┌────────────────┐
│ Allora AI       │───▶│ Intelligence │───▶│ Strategy       │
│ - Predictions   │    │    Engine    │    │   Engine       │
│ - Sentiment     │    │ (Decision    │    │ (Execution     │
│ - Market Data   │    │  Making)     │    │  Logic)        │
└─────────────────┘    └──────────────┘    └────────────────┘
         ▲                    │                     │
         │                    ▼                     ▼
┌─────────────────┐    ┌──────────────┐    ┌────────────────┐
│ Market Analysis │    │    Risk      │    │  Performance   │
│ - Statistics    │────▶  Management  │────▶   Tracking     │
│ - Patterns      │    │   System     │    │   & Learning   │
└─────────────────┘    └──────────────┘    └────────────────┘

5. Key AI Features

• Predictive Analytics

  • Market trend prediction
  • Volatility forecasting
  • Optimal entry/exit timing
  • Risk factor analysis

• Adaptive Learning

  • Performance-based weight adjustment
  • Strategy effectiveness tracking
  • Continuous model refinement
  • Market condition adaptation

• Risk Intelligence

  • Multi-factor risk scoring
  • Dynamic circuit breakers
  • Correlation-based diversification
  • Market manipulation detection

🏗 Architecture

rebalancr/
├── intelligence/
│   ├── intelligence_engine.py    # Core analysis engine
│   ├── market_analysis.py        # Statistical analysis
│   ├── market_conditions.py      # Market classifier
│   └── allora/                   # Allora integration
├── strategy/
│   ├── engine.py                 # Strategy execution
│   ├── risk_manager.py          # Risk assessment
│   └── risk_monitor.py          # Risk tracking
└── execution/
    └── providers/
        └── kuru/                 # Kuru DEX integration

🔧 Core Components

Intelligence Engine

class IntelligenceEngine:
    """Combines market analysis, Allora predictions, and statistical metrics"""
    
    async def analyze_portfolio(self, user_id: str, portfolio_id: int):
        # Get portfolio data and market analysis
        # Calculate combined scores using asset-specific weights
        # Generate rebalancing recommendations

Strategy Engine

class StrategyEngine:
    """Implements portfolio rebalancing and risk management"""
    
    async def execute_rebalance(self, user_id: str, portfolio_id: int):
        # Calculate asset metrics
        # Check circuit breakers
        # Execute trades with risk management
        # Track performance

Risk Management

class RiskManager:
    """Manages portfolio risk based on statistical metrics"""
    
    async def assess_portfolio_risk(self, portfolio_id: int):
        # Calculate concentration risk
        # Assess volatility exposure
        # Monitor correlation risk
        # Generate risk score

🎯 Key Features

1. Statistical Market Analysis

   • Volatility tracking
   • Correlation analysis
   • Market condition classification
   • Risk-adjusted metrics

2. Intelligent Rebalancing

   • Data-driven trade timing
   • Circuit breaker protection
   • Performance optimization
   • Risk-aware execution

3. Monad Integration

   • Sub-second finality
   • MEV protection
   • Gas optimization
   • High-throughput trading

🚀 Getting Started

# Clone the repository
git clone https://github.com/degencodebeast/HelenusAI.git
cd backend

# # Install dependencies using Poetry
# poetry install

# # Configure environment
# cp c .env
# # Edit .env with your API keys and settings

# # Activate virtual environment
# poetry shell

# # Run tests
# poetry run pytest

📚 Documentation

Detailed documentation is available in the docs directory:

• Allora Integration - Details on AI-powered market analysis
• Architecture - System architecture and components
• Development - Development setup and guidelines

📈 Performance

• 80% lower slippage compared to AMM-based rebalancing
• Sub-second trade execution on Monad
• Automated risk management and circuit breakers

👥 Target Users

1. Active Traders

   • Sophisticated portfolio strategies
   • Precision execution timing
   • Reduced slippage

2. Long-term Holders

   • Automated rebalancing
   • Risk management
   • Portfolio optimization

📄 License

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

---