DeFi Strategy Management System

Overview

This project implements an intelligent DeFi strategy management system that automates investment decisions across multiple protocols on StarkNet. The system combines AI-driven strategy evaluation with smart contract-based execution to provide optimized DeFi investments based on user risk preferences.

Architecture

Technical Stack

• Backend Services

  • Protocol Data Provider: Fetches real-time data from DeFi protocols (zkLend, Ekubo)
  • Investment Strategy Provider: Analyzes market conditions and generates recommendations
  • Portfolio Manager: Tracks and optimizes portfolio performance
  • Market Data Provider: Provides market sentiment and token metrics

• Smart Contracts

  • PoolManager: Manages user deposits and risk-segregated pools
  • StrategyManager: Executes investment strategies across protocols
  • Protocol-specific Adapters: Handle protocol-specific interactions

Smart Contract Architecture

1. PoolManager Contract

• Purpose: Manages user deposits and risk-segregated pools
• Features:
  • Multiple instances for different risk levels (Low, Medium, High)
  • Secure token deposit and withdrawal functionality
  • Risk-isolated pools to protect user assets
  • Balance tracking and accounting
• Key Functions:

  - deposit(token, amount)
  - withdraw(token, amount)
  - getRiskPool(riskLevel)
  - getPoolBalance(token)

2. StrategyManager Contract

• Purpose: Executes investment strategies based on AI recommendations
• Features:
  • Acts as middleware between Eliza AI and DeFi protocols
  • Protocol-specific strategy implementations
  • Safe transaction handling and calldata refactoring
  • Risk management and exposure limits
• Key Functions:

  - executeStrategy(strategy, amount)
  - rebalancePosition(protocol, token)
  - withdrawFromStrategy(strategy, amount)

Implementation Details

1. Protocol Integration

• zkLend Integration:

  • Lending protocol integration with supply/borrow capabilities
  • APY calculation and risk assessment
  • Utilization rate monitoring

  // Example zkLend data structure
  {
    protocol: "zkLend",
    token: "ETH",
    apy: supplyAPY,
    totalBorrow: totalBorrowUSD,
    totalSupply: totalSupplyUSD
  }

• Ekubo Integration:

  • AMM protocol integration with liquidity provision
  • Pair trading and fee collection
  • Pool metrics monitoring

  // Example Ekubo pool structure
  {
    protocol: "Ekubo",
    token0: "ETH",
    token1: "USDC",
    apy: apr,
    fee: poolFee,
    tickSpacing: spacing
  }

2. Risk Management

• Three-tiered risk strategy:

  • Conservative (Low Risk): 80% lending, 20% AMM
  • Moderate (Medium Risk): 65% lending, 35% AMM
  • Aggressive (High Risk): 45% lending, 55% AMM

• Risk Metrics:

  {
    maxDrawdown: number,
    volatility: number,
    correlationLimit: number,
    rebalanceThreshold: number
  }

3. Investment Strategy

• Dynamic allocation based on:

  • Market sentiment
  • Protocol performance
  • Risk-adjusted returns
  • Pool liquidity and volume

• Strategy Evaluation:

  function calculateRiskAdjustedReturn(pool, config) {
    // Risk-adjusted return calculation
    const sharpeRatio = (apy - riskFreeRate) / volatility;
    return sharpeRatio * (1 - drawdownPenalty);
  }

Eliza AI Implementation

1. DeFi Evaluator System

interface DeFiRecommendation {
    protocol: string;
    token: string;
    amount: number;
    expectedReturn: number;
    riskScore: number;
    confidence: number;
    poolData?: {
        token0Address: string;
        token1Address: string;
        fee: number;
        tickSpacing: number;
    };
}

class AdvancedMemoryManager {
    // Manages historical performance and user context
    async storeRecommendation(recommendation: DeFiRecommendation, userId: string, context: any);
    async getUserContext(userId: string): Promise<UserContext | null>;
    async getHistoricalPerformance(protocol: string, token: string): Promise<HistoricalPerformance | null>;
}

// Evaluation metrics calculation
function calculateRiskAdjustedReturn(pool: any, config: RiskStrategy): number {
    const apy = pool.apy || pool.apr || 0;
    const volatility = volume / tvl;
    const sharpeRatio = (apy - 0.02) / (volatility || 0.1);
    const estimatedDrawdown = Math.min(volatility, 0.3);
    return Math.max(0, sharpeRatio * (1 - drawdownPenalty));
}

2. Investment Strategy Provider

// Risk level configurations
const STRATEGY_CONFIGS: Record<RiskLevel, RiskStrategy> = {
    [RiskLevel.LOW]: {
        maxDrawdown: 0.05,
        protocols: {
            zkLend: { maxAllocation: 80, minAllocation: 60 },
            ekubo: { maxAllocation: 20, minAllocation: 10 }
        }
    },
    [RiskLevel.MEDIUM]: {
        maxDrawdown: 0.15,
        protocols: {
            zkLend: { maxAllocation: 65, minAllocation: 45 },
            ekubo: { maxAllocation: 35, minAllocation: 25 }
        }
    },
    [RiskLevel.HIGH]: {
        maxDrawdown: 0.30,
        protocols: {
            zkLend: { maxAllocation: 45, minAllocation: 25 },
            ekubo: { maxAllocation: 55, minAllocation: 35 }
        }
    }
};

3. Strategy Generation Process

1. Character-Based Risk Assessment:

   // Risk level determination based on character
   if (characterName.includes('conservative')) {
       userRiskLevel = RiskLevel.LOW;
   } else if (characterName.includes('moderate')) {
       userRiskLevel = RiskLevel.MEDIUM;
   } else if (characterName.includes('aggressive')) {
       userRiskLevel = RiskLevel.HIGH;
   }

2. Pool Selection and Evaluation:

   // Pool filtering criteria
   const selectedPools = matchingPools.filter(pool => {
       if (pool.protocol === 'Ekubo') {
           return pool.riskAdjustedReturn > 0.1 && 
                  pool.marketFit > 0.3 && 
                  pool.tvl > 1000;
       } else {
           const utilizationRate = pool.totalBorrow / pool.totalSupply;
           return utilizationRate > 0.3 && 
                  utilizationRate < 0.8 && 
                  pool.volatility < config.crossProtocolMetrics.maxVolatility;
       }
   });

3. Protocol-Specific Handling:

   // Ekubo (AMM) specific recommendation structure
   {
       protocol: "Ekubo",
       token: `${pool.token0}/${pool.token1}`,
       amount: amount,
       expectedReturn: pool.apy,
       poolData: {
           token0Address: pool.token0Address,
           token1Address: pool.token1Address,
           fee: pool.fee,
           tickSpacing: pool.tickSpacing
       }
   }
   
   // zkLend (Lending) specific recommendation structure
   {
       protocol: "zkLend",
       token: pool.token0,
       amount: amount,
       expectedReturn: pool.apy
   }

4. Performance Metrics

interface HistoricalPerformance {
    successRate: number;
    averageReturn: number;
    totalRecommendations: number;
    volatility?: number;
    sharpeRatio?: number;
    maxDrawdown?: number;
}

// Market fit calculation
function calculateMarketFit(pool: any, sentiment: any): number {
    const sentimentAlignment = sentiment.overall > 0 ? 
        pool.apy : (1 / pool.volatility);
    const volumeScore = Math.min(pool.volume24h / 1e6, 1);
    const tvlScore = Math.min(pool.tvl / 1e7, 1);
    return (sentimentAlignment * 0.4) + 
           (volumeScore * 0.3) + 
           (tvlScore * 0.3);
}

5. Memory Management and Historical Data

class RecommendationEngine {
    private memoryManager: AdvancedMemoryManager;

    async generateRecommendations(
        message: Memory,
        userContext: UserContext,
        marketContext: MarketContext
    ): Promise<DeFiRecommendation[]> {
        const baseRecommendations = await this.getBaseRecommendations(message);
        const enhancedRecommendations = await this.enhanceRecommendations(
            baseRecommendations,
            userContext,
            marketContext
        );
        return this.prioritizeRecommendations(
            enhancedRecommendations, 
            userContext
        );
    }
}

System Flow

1. User Interaction:

   • User deposits assets into PoolManager
   • Selects risk preference (Conservative/Moderate/Aggressive)

2. Strategy Generation:

   • System fetches protocol data
   • Analyzes market conditions
   • Generates risk-adjusted recommendations

3. Execution:

   • StrategyManager receives recommendations
   • Validates and refactors transactions
   • Executes positions across protocols

4. Monitoring:

   • Continuous performance tracking
   • Risk metric monitoring
   • Rebalancing when thresholds are breached

Technical Considerations

Security

• Smart contract security measures:
  • Risk isolation between pools
  • Transaction validation
  • Emergency withdrawal mechanisms
  • Access control and permissions

Performance

• Optimized data fetching and processing
• Efficient smart contract interactions
• Gas optimization for StarkNet

Scalability

• Modular architecture for adding new protocols
• Extensible strategy framework
• Upgradeable smart contracts

Development and Deployment

Prerequisites

• StarkNet development environment
• Node.js and TypeScript
• Cairo smart contract knowledge

Setup

# Install dependencies
npm install

# Configure environment
cp .env.example .env
# Add your StarkNet RPC URL and credentials

# Run tests
npm test

# Deploy contracts
npm run deploy

Configuration

• Set protocol addresses in src/config
• Configure risk parameters in src/providers/utils.ts
• Adjust strategy thresholds in src/providers/investmentStrategy.ts

Future Enhancements

1. Additional protocol integrations
2. Advanced risk management features
3. Multi-chain support
4. Enhanced analytics and reporting
5. Automated rebalancing strategies

Contributing

Contributions are welcome! Please read our contributing guidelines and submit pull requests for any enhancements.

License

MIT License - see LICENSE file for details