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Autonomous Trading System

Overview​

Eliza's autonomous trading system provides a sophisticated framework for monitoring market conditions, analyzing tokens, and executing trades on Solana-based decentralized exchanges. The system combines real-time market data, technical analysis, and risk management to make informed trading decisions.

Core Components​

1. Token Analysis Engine​

The system tracks multiple market indicators:

interface TokenPerformance {
    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;
}

2. Order Book Management​

interface Order {
    userId: string;
    ticker: string;
    contractAddress: string;
    timestamp: string;
    buyAmount: number;
    price: number;
}

3. Market Data Integration​

The system integrates with multiple data sources:

  • BirdEye API for real-time market data
  • DexScreener for liquidity analysis
  • Helius for on-chain data

Trading Features​

1. Real-Time Market Analysis​

const PROVIDER_CONFIG = {
    BIRDEYE_API: "https://public-api.birdeye.so",
    MAX_RETRIES: 3,
    RETRY_DELAY: 2000,
    TOKEN_SECURITY_ENDPOINT: "/defi/token_security?address=",
    TOKEN_TRADE_DATA_ENDPOINT: "/defi/v3/token/trade-data/single?address="
};

Key metrics monitored:

  • Price movements
  • Volume changes
  • Liquidity levels
  • Holder distribution
  • Trading patterns

2. Risk Assessment System​

The system evaluates multiple risk factors:

async analyzeRisks(token: string) {
    const risks = {
        liquidityRisk: await checkLiquidity(),
        holderConcentration: await analyzeHolderDistribution(),
        priceVolatility: await calculateVolatility(),
        marketManipulation: await detectManipulation()
    };
    return risks;
}

3. Trading Strategies​

Market Analysis​

async getProcessedTokenData(): Promise<ProcessedTokenData> {
    const security = await this.fetchTokenSecurity();
    const tradeData = await this.fetchTokenTradeData();
    const dexData = await this.fetchDexScreenerData();
    const holderDistributionTrend = await this.analyzeHolderDistribution(tradeData);
    // ... additional analysis
}

Trade Execution​

interface TradePerformance {
    token_address: string;
    buy_price: number;
    sell_price: number;
    buy_timeStamp: string;
    sell_timeStamp: string;
    profit_percent: number;
    market_cap_change: number;
    liquidity_change: number;
}

Configuration Options​

1. Trading Parameters​

const tradingConfig = {
    minLiquidity: 50000,  // Minimum liquidity in USD
    maxSlippage: 0.02,    // Maximum allowed slippage
    positionSize: 0.01,   // Position size as percentage of portfolio
    stopLoss: 0.05,       // Stop loss percentage
    takeProfit: 0.15      // Take profit percentage
};

2. Risk Management Settings​

const riskSettings = {
    maxDrawdown: 0.20,           // Maximum portfolio drawdown
    maxPositionSize: 0.10,       // Maximum single position size
    minLiquidityRatio: 50,       // Minimum liquidity to market cap ratio
    maxHolderConcentration: 0.20 // Maximum single holder concentration
};

Implementation Guide​

1. Setting Up Market Monitoring​

async monitorMarket(token: string) {
    const provider = new TokenProvider(token);
    const marketData = await provider.getProcessedTokenData();
    
    return {
        price: marketData.tradeData.price,
        volume: marketData.tradeData.volume_24h,
        liquidity: marketData.tradeData.liquidity,
        holderMetrics: marketData.security
    };
}

2. Implementing Trading Logic​

async evaluateTradeOpportunity(token: string) {
    const analysis = await this.getProcessedTokenData();
    
    const signals = {
        priceSignal: analysis.tradeData.price_change_24h > 0,
        volumeSignal: analysis.tradeData.volume_24h_change_percent > 20,
        liquiditySignal: analysis.tradeData.liquidity > MIN_LIQUIDITY,
        holderSignal: analysis.holderDistributionTrend === "increasing"
    };
    
    return signals.priceSignal && signals.volumeSignal && 
           signals.liquiditySignal && signals.holderSignal;
}

3. Risk Management Implementation​

async checkTradeRisks(token: string): Promise<boolean> {
    const security = await this.fetchTokenSecurity();
    const tradeData = await this.fetchTokenTradeData();
    
    return {
        isRugPull: security.ownerPercentage > 50,
        isPumpAndDump: tradeData.price_change_24h > 100,
        isLowLiquidity: tradeData.liquidity < MIN_LIQUIDITY,
        isSuspiciousVolume: tradeData.suspiciousVolume
    };
}

Performance Monitoring​

1. Trade Tracking​

async trackTradePerformance(trade: TradePerformance): Promise<void> {
    const performance = {
        entryPrice: trade.buy_price,
        exitPrice: trade.sell_price,
        profitLoss: trade.profit_percent,
        holdingPeriod: calculateHoldingPeriod(
            trade.buy_timeStamp, 
            trade.sell_timeStamp
        ),
        marketImpact: trade.market_cap_change
    };
    
    await this.logTradePerformance(performance);
}

2. Portfolio Analytics​

async analyzePortfolioPerformance(userId: string) {
    const trades = await this.getTradeHistory(userId);
    return {
        totalTrades: trades.length,
        winRate: calculateWinRate(trades),
        averageReturn: calculateAverageReturn(trades),
        maxDrawdown: calculateMaxDrawdown(trades),
        sharpeRatio: calculateSharpeRatio(trades)
    };
}

Best Practices​

  1. Risk Management

    • Always implement stop-loss orders
    • Diversify trading positions
    • Monitor liquidity levels continuously
    • Set maximum position sizes

  2. Trade Execution

    • Use slippage protection
    • Implement rate limiting
    • Monitor gas costs
    • Verify transaction success

  3. Market Analysis

    • Cross-reference multiple data sources
    • Implement data validation
    • Monitor market manipulation indicators
    • Track historical patterns

  4. System Maintenance

    • Regular performance reviews
    • Strategy backtesting
    • Risk parameter adjustments
    • System health monitoring

Security Considerations​

  1. Transaction Security

    • Implement transaction signing
    • Verify contract addresses
    • Monitor for malicious tokens
    • Implement rate limiting

  2. Data Validation

    • Verify data sources
    • Implement error handling
    • Monitor for anomalies
    • Cross-validate market data

Additional Resources​

Remember to thoroughly test all trading strategies in a sandbox environment before deploying to production.