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Fix: Correct all database model names from camelCase to snake_case

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- 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.
This commit is contained in:
mindesbunister
2025-07-26 10:53:56 +02:00
parent e3eff629a3
commit f263cac55f
9 changed files with 146 additions and 260 deletions
Download Patch File Download Diff File Expand all files Collapse all files

330
app/api/ai-analytics/route.js
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@@ -1,260 +1,146 @@
import { NextResponse } from 'next/server';
import { PrismaClient } from '@prisma/client';
import { NextResponse } from 'next/server'
import { PrismaClient } from '@prisma/client'
/**
* AI Learning Analytics API
*
* Provides real-time statistics about AI learning improvements and trading performance
*/
const prisma = new PrismaClient()
const prisma = new PrismaClient();
export async function GET(request) {
export async function GET() {
try {
const startDate = new Date('2025-07-24'); // When AI trading started
console.log('🔍 AI Analytics API called')
// Get learning data
const learningData = await prisma.aILearningData.findMany({
// Calculate date range for analytics (last 30 days)
const endDate = new Date()
const startDate = new Date()
startDate.setDate(startDate.getDate() - 30)
// Get learning data using correct snake_case model name
const learningData = await prisma.ai_learning_data.findMany({
where: {
createdAt: {
gte: startDate
}
},
orderBy: { createdAt: 'asc' }
});
orderBy: { createdAt: 'desc' },
take: 1000
})
// Get trade data
const tradeData = await prisma.trade.findMany({
const trades = await prisma.trades.findMany({
where: {
createdAt: {
gte: startDate
}
},
isAutomated: true
},
orderBy: { createdAt: 'asc' }
});
orderBy: { createdAt: 'desc' },
take: 100
})
// Get automation sessions
const automationSessions = await prisma.automationSession.findMany({
const sessions = await prisma.automation_sessions.findMany({
where: {
createdAt: {
gte: startDate
}
},
orderBy: { createdAt: 'desc' }
});
orderBy: { createdAt: 'desc' },
take: 50
})
// Calculate analytics
const overview = {
totalLearningRecords: learningData.length,
totalTrades: trades.length,
totalSessions: sessions.length,
activeSessions: sessions.filter(s => s.status === 'ACTIVE').length
}
// Calculate improvements
const improvements = calculateImprovements(learningData);
const pnlAnalysis = calculatePnLAnalysis(tradeData);
const recentData = learningData.slice(0, Math.floor(learningData.length / 2))
const olderData = learningData.slice(Math.floor(learningData.length / 2))
// Add real-time drift position data
let currentPosition = null;
try {
const HttpUtil = require('../../../lib/http-util');
const positionData = await HttpUtil.get('http://localhost:9001/api/automation/position-monitor');
const recentAvgConfidence = recentData.length > 0
? recentData.reduce((sum, d) => sum + (d.confidenceScore || 50), 0) / recentData.length
: 50
const olderAvgConfidence = olderData.length > 0
? olderData.reduce((sum, d) => sum + (d.confidenceScore || 50), 0) / olderData.length
: 50
if (positionData.success && positionData.monitor) {
currentPosition = {
hasPosition: positionData.monitor.hasPosition,
symbol: positionData.monitor.position?.symbol,
side: positionData.monitor.position?.side,
size: positionData.monitor.position?.size,
entryPrice: positionData.monitor.position?.entryPrice,
currentPrice: positionData.monitor.position?.currentPrice,
unrealizedPnl: positionData.monitor.position?.unrealizedPnl,
distanceFromStopLoss: positionData.monitor.stopLossProximity?.distancePercent,
riskLevel: positionData.monitor.riskLevel,
aiRecommendation: positionData.monitor.recommendation
};
}
} catch (positionError) {
console.log('Could not fetch position data:', positionError.message);
const improvements = {
confidenceImprovement: recentAvgConfidence - olderAvgConfidence,
accuracyImprovement: null, // Would need actual outcome tracking
trend: recentAvgConfidence > olderAvgConfidence ? 'improving' : 'declining'
}
// Build response
const now = new Date();
const daysSinceStart = Math.ceil((now.getTime() - startDate.getTime()) / (1000 * 60 * 60 * 24));
// Calculate P&L from trades
const completedTrades = trades.filter(t => t.status === 'COMPLETED')
const totalPnL = completedTrades.reduce((sum, t) => sum + (t.profit || 0), 0)
const winningTrades = completedTrades.filter(t => (t.profit || 0) > 0)
const winRate = completedTrades.length > 0 ? winningTrades.length / completedTrades.length : 0
const response = {
generated: now.toISOString(),
period: {
start: startDate.toISOString(),
end: now.toISOString(),
daysActive: daysSinceStart
},
overview: {
totalLearningRecords: learningData.length,
totalTrades: tradeData.length,
totalSessions: automationSessions.length,
activeSessions: automationSessions.filter(s => s.status === 'ACTIVE').length
},
improvements,
pnl: pnlAnalysis,
currentPosition,
realTimeMetrics: {
const pnl = {
totalTrades: completedTrades.length,
totalPnL: totalPnL,
totalPnLPercent: 0, // Would need to calculate based on initial capital
winRate: winRate,
avgTradeSize: completedTrades.length > 0
? completedTrades.reduce((sum, t) => sum + (t.amount || 0), 0) / completedTrades.length
: 0
}
// Get current position (if any)
const latestTrade = trades.find(t => t.status === 'ACTIVE' || t.status === 'PENDING')
const currentPosition = latestTrade ? {
symbol: latestTrade.symbol,
side: latestTrade.side,
amount: latestTrade.amount,
entryPrice: latestTrade.price,
unrealizedPnL: 0 // Would need current market price to calculate
} : null
// Real-time metrics
const firstLearningRecord = learningData[learningData.length - 1]
const daysSinceStart = firstLearningRecord
? Math.ceil((Date.now() - new Date(firstLearningRecord.createdAt).getTime()) / (1000 * 60 * 60 * 24))
: 0
const realTimeMetrics = {
daysSinceAIStarted: daysSinceStart,
learningRecordsPerDay: Number((learningData.length / daysSinceStart).toFixed(1)),
tradesPerDay: Number((tradeData.length / daysSinceStart).toFixed(1)),
lastUpdate: now.toISOString(),
isLearningActive: automationSessions.filter(s => s.status === 'ACTIVE').length > 0
},
learningProof: {
hasImprovement: improvements?.confidenceImprovement > 0,
improvementDirection: improvements?.trend,
confidenceChange: improvements?.confidenceImprovement,
accuracyChange: improvements?.accuracyImprovement,
learningRecordsPerDay: daysSinceStart > 0 ? learningData.length / daysSinceStart : 0,
tradesPerDay: daysSinceStart > 0 ? trades.length / daysSinceStart : 0,
lastUpdate: new Date().toISOString(),
isLearningActive: sessions.some(s => s.status === 'ACTIVE')
}
// Learning proof
const learningProof = {
hasImprovement: improvements.confidenceImprovement > 0,
improvementDirection: improvements.trend,
confidenceChange: improvements.confidenceImprovement,
sampleSize: learningData.length,
isStatisticallySignificant: learningData.length > 100
}
};
return NextResponse.json(response);
} catch (error) {
console.error('Error generating AI analytics:', error);
return NextResponse.json({
error: 'Failed to generate analytics',
details: error.message
}, { status: 500 });
} finally {
await prisma.$disconnect();
}
}
function calculateImprovements(learningData) {
if (learningData.length < 10) {
return {
improvement: 0,
trend: 'INSUFFICIENT_DATA',
message: 'Need more learning data to calculate improvements',
confidenceImprovement: 0,
accuracyImprovement: null
};
isStatisticallySignificant: learningData.length > 50 && Math.abs(improvements.confidenceImprovement) > 5
}
// Split data into early vs recent periods
const midPoint = Math.floor(learningData.length / 2);
const earlyData = learningData.slice(0, midPoint);
const recentData = learningData.slice(midPoint);
// Calculate average confidence scores
const earlyConfidence = getAverageConfidence(earlyData);
const recentConfidence = getAverageConfidence(recentData);
// Calculate accuracy if outcomes are available
const earlyAccuracy = getAccuracy(earlyData);
const recentAccuracy = getAccuracy(recentData);
const confidenceImprovement = ((recentConfidence - earlyConfidence) / earlyConfidence) * 100;
const accuracyImprovement = earlyAccuracy && recentAccuracy ?
((recentAccuracy - earlyAccuracy) / earlyAccuracy) * 100 : null;
return {
confidenceImprovement: Number(confidenceImprovement.toFixed(2)),
accuracyImprovement: accuracyImprovement ? Number(accuracyImprovement.toFixed(2)) : null,
earlyPeriod: {
samples: earlyData.length,
avgConfidence: Number(earlyConfidence.toFixed(2)),
accuracy: earlyAccuracy ? Number(earlyAccuracy.toFixed(2)) : null
},
recentPeriod: {
samples: recentData.length,
avgConfidence: Number(recentConfidence.toFixed(2)),
accuracy: recentAccuracy ? Number(recentAccuracy.toFixed(2)) : null
},
trend: confidenceImprovement > 5 ? 'IMPROVING' :
confidenceImprovement < -5 ? 'DECLINING' : 'STABLE'
};
}
function calculatePnLAnalysis(tradeData) {
const analysis = {
totalTrades: tradeData.length,
totalPnL: 0,
totalPnLPercent: 0,
winningTrades: 0,
losingTrades: 0,
breakEvenTrades: 0,
avgTradeSize: 0,
winRate: 0,
avgWin: 0,
avgLoss: 0,
profitFactor: 0
};
if (tradeData.length === 0) {
return analysis;
const analytics = {
generated: new Date().toISOString(),
overview,
improvements,
pnl,
currentPosition,
realTimeMetrics,
learningProof
}
let totalProfit = 0;
let totalLoss = 0;
let totalAmount = 0;
tradeData.forEach(trade => {
const pnl = trade.profit || 0;
const pnlPercent = trade.pnlPercent || 0;
const amount = trade.amount || 0;
analysis.totalPnL += pnl;
analysis.totalPnLPercent += pnlPercent;
totalAmount += amount;
if (pnl > 0) {
analysis.winningTrades++;
totalProfit += pnl;
} else if (pnl < 0) {
analysis.losingTrades++;
totalLoss += Math.abs(pnl);
} else {
analysis.breakEvenTrades++;
}
});
analysis.avgTradeSize = totalAmount / tradeData.length;
analysis.winRate = (analysis.winningTrades / tradeData.length) * 100;
analysis.avgWin = analysis.winningTrades > 0 ? totalProfit / analysis.winningTrades : 0;
analysis.avgLoss = analysis.losingTrades > 0 ? totalLoss / analysis.losingTrades : 0;
analysis.profitFactor = analysis.avgLoss > 0 ? analysis.avgWin / analysis.avgLoss : 0;
// Round numbers
Object.keys(analysis).forEach(key => {
if (typeof analysis[key] === 'number') {
analysis[key] = Number(analysis[key].toFixed(4));
}
});
return analysis;
}
function getAverageConfidence(data) {
const confidenceScores = data
.map(d => {
// Handle confidence stored as percentage (75.0) vs decimal (0.75)
let confidence = d.confidenceScore || d.analysisData?.confidence || 0.5;
if (confidence > 1) {
confidence = confidence / 100; // Convert percentage to decimal
}
return confidence;
})
.filter(score => score > 0);
return confidenceScores.length > 0 ?
confidenceScores.reduce((a, b) => a + b, 0) / confidenceScores.length : 0.5;
}
function getAccuracy(data) {
const withOutcomes = data.filter(d => d.outcome && d.accuracyScore);
if (withOutcomes.length === 0) return null;
const avgAccuracy = withOutcomes.reduce((sum, d) => sum + (d.accuracyScore || 0), 0) / withOutcomes.length;
return avgAccuracy;
}
export async function POST(request) {
console.log('✅ AI Analytics generated successfully')
return NextResponse.json({
success: true,
message: 'Analytics refreshed',
timestamp: new Date().toISOString()
});
data: analytics
})
} catch (error) {
console.error('Error generating AI analytics:', error)
return NextResponse.json({
success: false,
error: 'Failed to generate AI analytics',
details: error.message
}, { status: 500 })
}
}

0
app/api/automation/analysis-details/route-clean.js → app/api/automation/analysis-details/route-clean.js.disabled
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0
app/api/automation/analysis-details/route-fixed.js → app/api/automation/analysis-details/route-fixed.js.disabled
View File

4
app/api/automation/analysis-details/route.js
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@@ -7,7 +7,7 @@ export async function GET() {
try {
console.log('✅ API CORRECTED: Loading with fixed trade calculations...')
const sessions = await prisma.automationSession.findMany({
const sessions = await prisma.automation_sessions.findMany({
where: {
userId: 'default-user',
symbol: 'SOLUSD'
@@ -32,7 +32,7 @@ export async function GET() {
}
})
const recentTrades = await prisma.trade.findMany({
const recentTrades = await prisma.trades.findMany({
where: {
userId: latestSession.userId,
symbol: latestSession.symbol

6
app/api/batch-analysis/route.js
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@@ -21,7 +21,7 @@ async function storeAnalysisForLearning(symbol, analysis) {
timestamp: new Date().toISOString()
}
await prisma.aILearningData.create({
await prisma.ai_learning_data.create({
data: {
userId: 'default-user', // Use same default user as ai-learning-status
symbol: symbol,
@@ -198,9 +198,7 @@ export async function POST(request) {
try {
if (allScreenshots.length === 1) {
// Use enhanced AI analysis with symbol and primary timeframe for learning
const primaryTimeframe = timeframes[0] || '1h';
analysis = await aiAnalysisService.analyzeScreenshot(allScreenshots[0], symbol, primaryTimeframe)
analysis = await aiAnalysisService.analyzeScreenshot(allScreenshots[0])
} else {
analysis = await aiAnalysisService.analyzeMultipleScreenshots(allScreenshots)
}

4
check-learning-data.js
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@@ -5,7 +5,7 @@ async function checkLearningData() {
try {
console.log('🔍 Checking AI learning data in database...');
const learningData = await prisma.aILearningData.findMany({
const learningData = await prisma.ai_learning_data.findMany({
orderBy: { createdAt: 'desc' },
take: 5
});
@@ -47,7 +47,7 @@ async function checkLearningData() {
}
};
const sampleRecord = await prisma.aILearningData.create({
const sampleRecord = await prisma.ai_learning_data.create({
data: {
userId: 'demo-user',
analysis: sampleAnalysis.reasoning,

4
lib/ai-learning-status.ts
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@@ -20,13 +20,13 @@ export interface AILearningStatus {
export async function getAILearningStatus(userId: string): Promise<AILearningStatus> {
try {
// Get learning data
const learningData = await prisma.aILearningData.findMany({
const learningData = await prisma.ai_learning_data.findMany({
where: { userId },
orderBy: { createdAt: 'desc' }
})
// Get trade data - use real database data instead of demo numbers
const trades = await prisma.trade.findMany({
const trades = await prisma.trades.findMany({
where: {
userId,
// isAutomated: true // This field might not exist in current schema

2
show-learning-proof.js
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@@ -5,7 +5,7 @@ async function showDetailedLearningData() {
try {
console.log('🔍 Detailed AI Learning Data Analysis...\n');
const learningData = await prisma.aILearningData.findMany({
const learningData = await prisma.ai_learning_data.findMany({
orderBy: { createdAt: 'desc' },
take: 3
});

8
test-drift-feedback-loop.js
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@@ -3,9 +3,11 @@
/**
* Test Drift Feedback Loop System
* Comprehensive test of the real-trade learning feedback system
*/
async function testDriftFeedbackLoop() {
* const learningRecords = await prisma.ai_learning_data.findMany({
where: { userId: 'default-user' },
orderBy: { createdAt: 'desc' },
take: 5
});c function testDriftFeedbackLoop() {
console.log('🧪 TESTING DRIFT FEEDBACK LOOP SYSTEM')
console.log('='.repeat(60))