AI-Powered Rebalancing

Yield Delta's AI rebalancing system uses machine learning to automatically optimize liquidity positions, maximizing yields while minimizing risk on the SEI Network.

Overview

Our AI engine continuously analyzes market conditions, price movements, and liquidity dynamics to determine optimal rebalancing strategies. Unlike static positions, AI rebalancing adapts to changing market conditions in real-time.

Key Benefits

• Automated Optimization: No manual intervention required
• Real-time Analysis: Market conditions analyzed every block (~400ms)
• Risk Management: Built-in risk controls and position limits
• Gas Efficiency: Optimized for SEI's low-cost, high-speed network
• Performance Tracking: Transparent metrics and historical analysis

How AI Rebalancing Works

1. Data Collection

The AI system continuously collects market data:

interface MarketData {
  prices: {
    current: number;
    high24h: number;
    low24h: number;
    change24h: number;
  };
  volume: {
    current: number;
    average24h: number;
    trend: 'increasing' | 'decreasing' | 'stable';
  };
  liquidity: {
    tvl: number;
    utilization: number;
    depth: LiquidityDepth;
  };
  volatility: {
    realized: number;
    implied: number;
    garch: number;  // GARCH model prediction
  };
}

2. Model Prediction

Multiple ML models work together to generate predictions:

class RebalancingPredictor:
    def __init__(self):
        self.price_model = load_model('price_prediction.onnx')
        self.volatility_model = load_model('volatility_prediction.onnx')
        self.yield_model = load_model('yield_optimization.onnx')
        
    def predict_optimal_range(self, market_data: MarketData) -> OptimalRange:
        # Price prediction (next 24 hours)
        price_forecast = self.price_model.predict([
            market_data.prices.current,
            market_data.volume.current,
            market_data.volatility.realized
        ])
        
        # Volatility prediction
        volatility_forecast = self.volatility_model.predict([
            market_data.volatility.realized,
            market_data.volume.trend,
            market_data.prices.change24h
        ])
        
        # Optimal range calculation
        optimal_range = self.yield_model.predict([
            price_forecast,
            volatility_forecast,
            market_data.liquidity.utilization
        ])
        
        return OptimalRange(
            lower_tick=int(optimal_range[0]),
            upper_tick=int(optimal_range[1]),
            confidence=optimal_range[2],
            expected_apy=optimal_range[3]
        )

3. Decision Making

The AI evaluates whether rebalancing is profitable:

interface RebalancingDecision {
  shouldRebalance: boolean;
  confidence: number;
  expectedImprovement: number;
  riskLevel: 'low' | 'medium' | 'high';
  
  proposal: {
    newRange: {
      lowerTick: number;
      upperTick: number;
    };
    estimatedCost: {
      gasUsed: number;
      gasCostSEI: number;
      slippage: number;
    };
    expectedBenefit: {
      additionalAPY: number;
      timeHorizon: number; // Hours to break even
    };
  };
}

4. Execution Strategy

When rebalancing is determined to be profitable:

function executeAIRebalancing(
    RebalanceParams calldata params,
    bytes calldata signature
) external nonReentrant {
    // Verify AI signature
    require(verifyAISignature(params, signature), "Invalid AI signature");
    
    // Check time constraints
    require(
        block.timestamp >= lastRebalance + rebalanceInterval,
        "Too soon to rebalance"
    );
    
    // Risk checks
    require(params.maxSlippage <= maxAllowedSlippage, "Slippage too high");
    require(params.gasLimit <= maxGasLimit, "Gas limit exceeded");
    
    // Execute rebalancing
    _executeRebalance(params);
    
    // Update state
    lastRebalance = block.timestamp;
    emit AIRebalanceExecuted(params.newLowerTick, params.newUpperTick);
}

Rebalancing Strategies

Concentrated Liquidity Optimization

Objective: Maximize fee collection by maintaining optimal tick ranges

• Calculate current position efficiency
• Model different range scenarios
• Score each scenario based on expected returns
• Select optimal scenario with best risk-adjusted returns

Dynamic Range Adjustment

The AI continuously adjusts ranges based on market conditions:

• Volatility Adjustment: Wider ranges during high volatility
• Volume Adjustment: Tighter ranges during high volume
• Trend Adjustment: Bias ranges in direction of strong trends
• Liquidity Adjustment: Optimize for current liquidity depth

Risk Management

Automated Risk Controls

interface RiskControls {
  maxPositionSize: number;     // Maximum position as % of total
  maxDailyRebalances: number;  // Limit rebalancing frequency
  minProfitThreshold: number;  // Minimum expected improvement
  maxSlippage: number;         // Maximum acceptable slippage
  
  stopLoss: {
    enabled: boolean;
    threshold: number;         // % loss to trigger stop
    cooldown: number;          // Time before re-entering
  };
  
  volatilityLimits: {
    maxVolatility: number;     // Pause if volatility exceeds
    lookbackPeriod: number;    // Hours to calculate volatility
  };
}

Circuit Breakers

Automatic halts during extreme market conditions:

• Extreme price movements (>10% in 1 hour)
• Low liquidity conditions
• High gas price environment
• Network congestion or instability

Performance Metrics

Real-time Tracking

interface RebalancingMetrics {
  execution: {
    totalRebalances: number;
    successRate: number;
    averageGasCost: number;
    averageSlippage: number;
  };
  
  performance: {
    additionalYield: number;    // Extra yield from rebalancing
    sharpeImprovement: number;  // Risk-adjusted improvement
    maxDrawdownReduction: number;
  };
  
  accuracy: {
    predictionAccuracy: number; // % of predictions within tolerance
    rangeUtilization: number;   // % of time price stays in range
    falseSenalRate: number;     // % of unnecessary rebalances
  };
}

Backtesting Results

Historical performance analysis shows consistent improvements:

Configuration

AI Settings

Users can customize AI behavior:

interface AIConfiguration {
  riskTolerance: 'conservative' | 'moderate' | 'aggressive';
  rebalanceFrequency: 'low' | 'medium' | 'high';
  gasOptimization: boolean;
  
  thresholds: {
    minImprovement: number;    // Minimum % improvement required
    maxSlippage: number;       // Maximum acceptable slippage
    maxGasCost: number;        // Maximum gas cost in SEI
  };
  
  restrictions: {
    maxDailyRebalances: number;
    pauseDuringVolatility: boolean;
    respectManualOverrides: boolean;
  };
}

Strategy Selection

Different AI strategies for different market conditions:

• Trend Following: Optimizes for trending markets
• Mean Reversion: Optimizes for ranging markets
• Volatility Targeting: Maintains constant volatility exposure
• Adaptive: Automatically switches between strategies

AI-powered rebalancing transforms static positions into dynamic, intelligent yield generators.