trading_bot_v3/test-automation-insights.js

const { PrismaClient } = require('@prisma/client');
const prisma = new PrismaClient();

// Test the automation insights functionality
async function testAutomationInsights() {
  try {
    console.log('🧠 Testing Automation Insights for Manual Analysis Enhancement...\n');
    
    const targetSymbol = 'SOLUSD';
    
    // Get recent automation sessions for context
    const sessions = await prisma.automationSession.findMany({
      where: {
        userId: 'default-user',
        symbol: targetSymbol,
        lastAnalysisData: { not: null }
      },
      orderBy: { createdAt: 'desc' },
      take: 3
    });

    // Get top performing trades for pattern recognition
    const successfulTrades = await prisma.trade.findMany({
      where: {
        userId: 'default-user',
        symbol: targetSymbol,
        status: 'COMPLETED',
        profit: { gt: 0 }
      },
      orderBy: { profit: 'desc' },
      take: 5
    });

    // Get recent market context
    const allTrades = await prisma.trade.findMany({
      where: {
        userId: 'default-user',
        symbol: targetSymbol,
        status: 'COMPLETED'
      },
      orderBy: { createdAt: 'desc' },
      take: 10
    });

    const recentPnL = allTrades.reduce((sum, t) => sum + (t.profit || 0), 0);
    const winningTrades = allTrades.filter(t => (t.profit || 0) > 0);
    const winRate = allTrades.length > 0 ? (winningTrades.length / allTrades.length * 100) : 0;

    const automationContext = {
      multiTimeframeSignals: sessions.map(s => ({
        timeframe: s.timeframe,
        decision: s.lastAnalysisData?.decision,
        confidence: s.lastAnalysisData?.confidence,
        sentiment: s.lastAnalysisData?.sentiment,
        winRate: s.winRate,
        totalPnL: s.totalPnL,
        totalTrades: s.totalTrades
      })),
      topPatterns: successfulTrades.map(t => ({
        side: t.side,
        profit: t.profit,
        confidence: t.confidence,
        entryPrice: t.price,
        exitPrice: t.exitPrice,
        profitPercent: t.exitPrice ? ((t.exitPrice - t.price) / t.price * 100).toFixed(2) : null
      })),
      marketContext: {
        recentPnL,
        winRate: winRate.toFixed(1),
        totalTrades: allTrades.length,
        avgProfit: allTrades.length > 0 ? (recentPnL / allTrades.length).toFixed(2) : 0,
        trend: sessions.length > 0 ? sessions[0].lastAnalysisData?.sentiment : 'NEUTRAL'
      }
    };

    // Generate enhanced recommendation
    function generateEnhancedRecommendation(automationContext) {
      if (!automationContext) return null;

      const { multiTimeframeSignals, topPatterns, marketContext } = automationContext;
      
      // Multi-timeframe consensus
      const signals = multiTimeframeSignals.filter(s => s.decision);
      const bullishSignals = signals.filter(s => s.decision === 'BUY').length;
      const bearishSignals = signals.filter(s => s.decision === 'SELL').length;
      
      // Pattern strength
      const avgWinRate = signals.length > 0 ? 
        signals.reduce((sum, s) => sum + (s.winRate || 0), 0) / signals.length : 0;
      
      // Profitability insights
      const avgProfit = topPatterns.length > 0 ?
        topPatterns.reduce((sum, p) => sum + Number(p.profitPercent || 0), 0) / topPatterns.length : 0;

      let recommendation = '🤖 AUTOMATION-ENHANCED: ';
      
      if (bullishSignals > bearishSignals) {
        recommendation += `BULLISH CONSENSUS (${bullishSignals}/${signals.length} timeframes)`;
        if (avgWinRate > 60) recommendation += ` ✅ Strong pattern (${avgWinRate.toFixed(1)}% win rate)`;
        if (avgProfit > 3) recommendation += ` 💰 High profit potential (~${avgProfit.toFixed(1)}%)`;
      } else if (bearishSignals > bullishSignals) {
        recommendation += `BEARISH CONSENSUS (${bearishSignals}/${signals.length} timeframes)`;
      } else {
        recommendation += 'NEUTRAL - Mixed signals across timeframes';
      }

      return recommendation;
    }

    const automationInsights = {
      multiTimeframeConsensus: automationContext.multiTimeframeSignals.length > 0 ? 
        automationContext.multiTimeframeSignals[0].decision : null,
      avgConfidence: automationContext.multiTimeframeSignals.length > 0 ?
        (automationContext.multiTimeframeSignals.reduce((sum, s) => sum + (s.confidence || 0), 0) / automationContext.multiTimeframeSignals.length).toFixed(1) : null,
      marketTrend: automationContext.marketContext.trend,
      winRate: automationContext.marketContext.winRate + '%',
      profitablePattern: automationContext.topPatterns.length > 0 ? 
        `${automationContext.topPatterns[0].side} signals with avg ${automationContext.topPatterns.reduce((sum, p) => sum + Number(p.profitPercent || 0), 0) / automationContext.topPatterns.length}% profit` : null,
      recommendation: generateEnhancedRecommendation(automationContext)
    };

    console.log('🎯 ENHANCED MANUAL ANALYSIS INSIGHTS:');
    console.log('=====================================');
    console.log('Multi-timeframe Consensus:', automationInsights.multiTimeframeConsensus);
    console.log('Average Confidence:', automationInsights.avgConfidence + '%');
    console.log('Market Trend:', automationInsights.marketTrend);
    console.log('Win Rate:', automationInsights.winRate);
    console.log('Profitable Pattern:', automationInsights.profitablePattern);
    console.log('Recommendation:', automationInsights.recommendation);

    console.log('\n📊 RAW DATA:');
    console.log('============');
    console.log('Timeframe Signals:', automationContext.multiTimeframeSignals);
    console.log('Top Patterns:', automationContext.topPatterns.slice(0, 3));
    console.log('Market Context:', automationContext.marketContext);

    await prisma.$disconnect();
    console.log('\n✅ Test completed successfully!');
  } catch (error) {
    console.error('❌ Test failed:', error);
    await prisma.$disconnect();
  }
}

testAutomationInsights();