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Trust Engine System

Overview​

The Trust Engine is a sophisticated system for tracking, evaluating, and managing trust scores in decentralized recommendation networks. It provides a comprehensive framework for monitoring recommender performance, token metrics, and trading outcomes.

Core Components​

1. Recommender Management​

interface Recommender {
    id: string;          // Unique identifier
    address: string;     // Blockchain address
    solanaPubkey?: string;
    telegramId?: string;
    discordId?: string;
    twitterId?: string;
    ip?: string;
}

The system tracks recommenders across multiple platforms and identifiers, enabling:

  • Cross-platform identity verification
  • Multi-channel recommendation tracking
  • Unified reputation management

2. Trust Metrics​

interface RecommenderMetrics {
    recommenderId: string;
    trustScore: number;          // Overall trust rating
    totalRecommendations: number;
    successfulRecs: number;
    avgTokenPerformance: number;
    riskScore: number;
    consistencyScore: number;
    virtualConfidence: number;
    lastUpdated: Date;
}

Key metrics tracked:

  • Trust Score: Overall reliability rating
  • Success Rate: Ratio of successful recommendations
  • Risk Assessment: Evaluation of risk-taking behavior
  • Consistency: Pattern analysis of recommendations

3. Token Performance Tracking​

interface TokenPerformance {
    tokenAddress: string;
    priceChange24h: number;
    volumeChange24h: number;
    trade_24h_change: number;
    liquidity: number;
    liquidityChange24h: number;
    holderChange24h: number;
    rugPull: boolean;
    isScam: boolean;
    marketCapChange24h: number;
    sustainedGrowth: boolean;
    rapidDump: boolean;
    suspiciousVolume: boolean;
    lastUpdated: Date;
}

Usage Guide​

1. Initializing Trust Tracking​

const trustDB = new TrustScoreDatabase(sqliteDb);

// Add a new recommender
const recommender = {
    id: "uuid",
    address: "0x...",
    telegramId: "@username"
};
trustDB.addRecommender(recommender);

// Initialize metrics
trustDB.initializeRecommenderMetrics(recommender.id);

2. Tracking Recommendations​

// Record a new token recommendation
const recommendation = {
    id: "uuid",
    recommenderId: recommender.id,
    tokenAddress: "0x...",
    timestamp: new Date(),
    initialMarketCap: 1000000,
    initialLiquidity: 500000,
    initialPrice: 0.001
};
trustDB.addTokenRecommendation(recommendation);

3. Performance Monitoring​

// Update token performance metrics
const performance = {
    tokenAddress: "0x...",
    priceChange24h: 15.5,
    volumeChange24h: 25.0,
    liquidity: 1000000,
    holderChange24h: 5.2,
    rugPull: false,
    isScam: false,
    // ... other metrics
};
trustDB.upsertTokenPerformance(performance);

4. Trade Tracking​

// Record a trade based on recommendation
const trade = {
    token_address: "0x...",
    recommender_id: "uuid",
    buy_price: 0.001,
    buy_timeStamp: new Date().toISOString(),
    buy_amount: 1000,
    buy_sol: 1.5,
    buy_value_usd: 1500,
    buy_market_cap: 1000000,
    buy_liquidity: 500000
};
trustDB.addTradePerformance(trade, false);

Trust Score Calculation​

The system calculates trust scores based on multiple factors:

  1. Performance Metrics

    • Success rate of recommendations
    • Average token performance
    • Risk-adjusted returns

  2. Risk Factors

    const riskFactors = {
        rugPull: -1.0,      // Maximum penalty
        scam: -0.8,         // Severe penalty
        rapidDump: -0.4,    // Moderate penalty
        suspicious: -0.2    // Minor penalty
    };

  3. Historical Analysis

    • Performance consistency
    • Long-term success rate
    • Risk pattern analysis

Best Practices​

1. Regular Updates​

// Update metrics regularly
function updateRecommenderMetrics(recommenderId: string) {
    const metrics = calculateUpdatedMetrics(recommenderId);
    trustDB.updateRecommenderMetrics(metrics);
    trustDB.logRecommenderMetricsHistory(recommenderId);
}

2. Risk Management​

  1. Monitor suspicious patterns:

    const riskFlags = {
        rapidPriceChange: price24h > 100,
        lowLiquidity: liquidity < minLiquidityThreshold,
        suspiciousVolume: volume24h > marketCap
    };

  2. Implement automatic warnings:

    if (metrics.riskScore > riskThreshold) {
        triggerRiskAlert(recommenderId);
    }

3. Performance Tracking​

// Track historical performance
const history = trustDB.getRecommenderMetricsHistory(recommenderId);
const performanceTrend = analyzePerformanceTrend(history);

Advanced Features​

1. Simulation Support​

// Test strategies without affecting real metrics
trustDB.addTradePerformance(trade, true); // Simulation mode

2. Cross-Platform Verification​

const verifyIdentity = async (recommender: Recommender) => {
    const telegramVerified = await verifyTelegram(recommender.telegramId);
    const walletVerified = await verifyWallet(recommender.address);
    return telegramVerified && walletVerified;
};

3. Historical Analysis​

const analyzeRecommenderHistory = (recommenderId: string) => {
    const recommendations = trustDB.getRecommendationsByRecommender(recommenderId);
    const metrics = trustDB.getRecommenderMetrics(recommenderId);
    const history = trustDB.getRecommenderMetricsHistory(recommenderId);
    
    return {
        successRate: metrics.successfulRecs / metrics.totalRecommendations,
        averagePerformance: metrics.avgTokenPerformance,
        riskProfile: calculateRiskProfile(history),
        consistencyScore: metrics.consistencyScore
    };
};

Security Considerations​

  1. Data Integrity

    • Use foreign key constraints
    • Implement transaction management
    • Regular backup of metrics history

  2. Fraud Prevention

    // Implement rate limiting
    const checkRateLimit = (recommenderId: string) => {
        const recentRecs = getRecentRecommendations(recommenderId, timeWindow);
        return recentRecs.length < maxRecommendations;
    };

  3. Identity Verification

    • Cross-reference multiple identifiers
    • Implement progressive trust building
    • Regular verification checks

Future Enhancements​

  1. Machine Learning Integration

    • Pattern recognition for fraud detection
    • Automated risk assessment
    • Predictive analytics for recommendation quality

  2. Decentralized Validation

    • Peer verification system
    • Consensus-based trust scoring
    • Distributed reputation management

  3. Enhanced Metrics

    • Market sentiment analysis
    • Social signal integration
    • Network effect measurement

Additional Resources​

Remember to regularly monitor and adjust trust parameters based on market conditions and system performance.