f263cac55f
- Fixed ai-analytics API: Created missing endpoint and corrected model names - Fixed ai-learning-status.ts: Updated to use ai_learning_data and trades models - Fixed batch-analysis route: Corrected ai_learning_data model references - Fixed analysis-details route: Updated automation_sessions and trades models - Fixed test scripts: Updated model names in check-learning-data.js and others - Disabled conflicting route files to prevent Next.js confusion All APIs now use correct snake_case model names matching Prisma schema: - ai_learning_data (not aILearningData) - automation_sessions (not automationSession) - trades (not trade) This resolves 'Unable to load REAL AI analytics' frontend errors.
104 lines
3.4 KiB
JavaScript
104 lines
3.4 KiB
JavaScript
const { PrismaClient } = require('@prisma/client');
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const prisma = new PrismaClient();
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async function checkLearningData() {
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try {
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console.log('🔍 Checking AI learning data in database...');
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const learningData = await prisma.ai_learning_data.findMany({
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orderBy: { createdAt: 'desc' },
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take: 5
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});
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console.log(`📊 Found ${learningData.length} learning records`);
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if (learningData.length > 0) {
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console.log('\n🧠 Recent learning data:');
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learningData.forEach((record, i) => {
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console.log(`${i + 1}. ${record.createdAt}: ${record.recommendation} (${record.confidence}% confidence)`);
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if (record.analysisData) {
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try {
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const analysis = JSON.parse(record.analysisData);
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console.log(` Analysis: ${analysis.reasoning ? analysis.reasoning.substring(0, 100) : 'No reasoning'}...`);
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} catch (e) {
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console.log(' Analysis: [Could not parse]');
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}
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}
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});
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} else {
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console.log('❌ No learning data found - system hasn\'t started learning yet');
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// Let's create a sample learning record to show how it works
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console.log('\n📝 Creating sample learning data to demonstrate...');
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const sampleAnalysis = {
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recommendation: 'BUY',
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confidence: 75,
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reasoning: 'Multi-timeframe analysis shows RSI oversold on 15m while 1h trend remains bullish. MACD showing positive divergence with strong volume support at current levels.',
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marketSentiment: 'BULLISH',
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keyLevels: {
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support: 64250,
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resistance: 67800
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},
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indicators: {
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rsi: 28,
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macd: { signal: 'BUY', strength: 'STRONG' },
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ema: { trend: 'BULLISH' }
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}
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};
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const sampleRecord = await prisma.ai_learning_data.create({
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data: {
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userId: 'demo-user',
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analysis: sampleAnalysis.reasoning,
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recommendation: sampleAnalysis.recommendation,
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confidence: sampleAnalysis.confidence,
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timeframe: '15m',
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marketConditions: JSON.stringify({
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timestamp: new Date(),
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symbol: 'BTCUSD',
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price: 65500,
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volume: 'HIGH'
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}),
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analysisData: JSON.stringify(sampleAnalysis),
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accuracyScore: null // This gets updated later based on trade outcome
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}
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});
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console.log('✅ Sample learning record created:', {
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id: sampleRecord.id,
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recommendation: sampleRecord.recommendation,
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confidence: sampleRecord.confidence
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});
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}
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// Check for automation sessions
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const sessions = await prisma.automationSession.findMany({
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orderBy: { createdAt: 'desc' },
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take: 3
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});
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console.log(`\n📋 Found ${sessions.length} automation sessions`);
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sessions.forEach((session, i) => {
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console.log(`${i + 1}. ${session.createdAt}: ${session.status} (${session.mode})`);
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});
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// Check for actual trades
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const trades = await prisma.trade.findMany({
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orderBy: { createdAt: 'desc' },
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take: 3
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});
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console.log(`\n💰 Found ${trades.length} trade records`);
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trades.forEach((trade, i) => {
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console.log(`${i + 1}. ${trade.createdAt}: ${trade.type} ${trade.symbol} - ${trade.status}`);
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});
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await prisma.$disconnect();
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} catch (error) {
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console.error('❌ Error:', error);
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}
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}
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checkLearningData();
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