🔧 CRITICAL FIX: Price Data Sync & Position Monitor Enhancement
Fixed major price data sync issues: - Removed hardcoded price (77.63) from position monitor - Added real-time oracle data instead of stale TWAP pricing - Implemented cache-busting headers for fresh data - Updated fallback prices to current market levels - Real-time P&L tracking with trend indicators (📈📉➡️) - Enhanced stop loss proximity alerts with color-coded risk levels - Analysis progress indicators during automation cycles - Performance metrics (runtime, cycles, trades, errors) - Fresh data validation and improved error handling - Price accuracy: 77.63 → 84.47 (matches Drift UI) - P&L accuracy: -.91 → -.59 (correct calculation) - Risk assessment: CRITICAL → MEDIUM (proper evaluation) - Stop loss distance: 0.91% → 4.8% (safe distance) - CLI monitor script with 8-second updates - Web dashboard component (PositionMonitor.tsx) - Real-time automation status tracking - Database and error monitoring improvements This fixes the automation showing false emergency alerts when position was actually performing normally.
This commit is contained in:
mindesbunister
361
ai-learning-analytics.js
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361
ai-learning-analytics.js
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#!/usr/bin/env node
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/**
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* AI Learning Analytics System
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*
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* Analyzes AI trading performance improvements and generates proof of learning effectiveness
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*/
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const { PrismaClient } = require('@prisma/client');
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const prisma = new PrismaClient();
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class AILearningAnalytics {
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constructor() {
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this.startDate = new Date('2025-07-24'); // When AI trading started
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}
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async generateLearningReport() {
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console.log('🧠 AI LEARNING EFFECTIVENESS REPORT');
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console.log('=' .repeat(60));
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console.log('');
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try {
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// Get all learning data since AI started
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const learningData = await this.getLearningData();
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const tradeData = await this.getTradeData();
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const automationSessions = await this.getAutomationSessions();
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// Calculate improvement metrics
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const improvements = await this.calculateImprovements(learningData);
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const pnlAnalysis = await this.calculateTotalPnL(tradeData);
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const accuracyTrends = await this.calculateAccuracyTrends(learningData);
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const confidenceEvolution = await this.calculateConfidenceEvolution(learningData);
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// Generate report
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this.displayOverallStats(learningData, tradeData, automationSessions);
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this.displayLearningImprovements(improvements);
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this.displayPnLAnalysis(pnlAnalysis);
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this.displayAccuracyTrends(accuracyTrends);
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this.displayConfidenceEvolution(confidenceEvolution);
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// Generate JSON for frontend
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const reportData = {
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generated: new Date().toISOString(),
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period: {
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start: this.startDate.toISOString(),
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end: new Date().toISOString(),
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daysActive: Math.ceil((Date.now() - this.startDate.getTime()) / (1000 * 60 * 60 * 24))
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},
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overview: {
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totalLearningRecords: learningData.length,
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totalTrades: tradeData.length,
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totalSessions: automationSessions.length,
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activeSessions: automationSessions.filter(s => s.status === 'ACTIVE').length
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},
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improvements,
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pnl: pnlAnalysis,
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accuracy: accuracyTrends,
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confidence: confidenceEvolution
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};
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// Save report for API
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await this.saveReport(reportData);
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console.log('\n📊 Report saved and ready for dashboard display!');
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return reportData;
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} catch (error) {
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console.error('❌ Error generating learning report:', error.message);
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throw error;
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}
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}
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async getLearningData() {
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return await prisma.aILearningData.findMany({
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where: {
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createdAt: {
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gte: this.startDate
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}
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},
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orderBy: { createdAt: 'asc' }
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});
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}
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async getTradeData() {
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return await prisma.trade.findMany({
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where: {
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createdAt: {
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gte: this.startDate
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},
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isAutomated: true // Only AI trades
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},
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orderBy: { createdAt: 'asc' }
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});
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}
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async getAutomationSessions() {
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return await prisma.automationSession.findMany({
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where: {
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createdAt: {
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gte: this.startDate
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}
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},
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orderBy: { createdAt: 'desc' }
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});
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}
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async calculateImprovements(learningData) {
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if (learningData.length < 10) {
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return {
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improvement: 0,
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trend: 'INSUFFICIENT_DATA',
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message: 'Need more learning data to calculate improvements'
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};
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}
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// Split data into early vs recent periods
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const midPoint = Math.floor(learningData.length / 2);
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const earlyData = learningData.slice(0, midPoint);
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const recentData = learningData.slice(midPoint);
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// Calculate average confidence scores
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const earlyConfidence = this.getAverageConfidence(earlyData);
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const recentConfidence = this.getAverageConfidence(recentData);
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// Calculate accuracy if outcomes are available
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const earlyAccuracy = this.getAccuracy(earlyData);
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const recentAccuracy = this.getAccuracy(recentData);
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const confidenceImprovement = ((recentConfidence - earlyConfidence) / earlyConfidence) * 100;
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const accuracyImprovement = earlyAccuracy && recentAccuracy ?
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((recentAccuracy - earlyAccuracy) / earlyAccuracy) * 100 : null;
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return {
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confidenceImprovement: Number(confidenceImprovement.toFixed(2)),
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accuracyImprovement: accuracyImprovement ? Number(accuracyImprovement.toFixed(2)) : null,
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earlyPeriod: {
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samples: earlyData.length,
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avgConfidence: Number(earlyConfidence.toFixed(2)),
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accuracy: earlyAccuracy ? Number(earlyAccuracy.toFixed(2)) : null
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},
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recentPeriod: {
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samples: recentData.length,
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avgConfidence: Number(recentConfidence.toFixed(2)),
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accuracy: recentAccuracy ? Number(recentAccuracy.toFixed(2)) : null
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},
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trend: confidenceImprovement > 5 ? 'IMPROVING' :
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confidenceImprovement < -5 ? 'DECLINING' : 'STABLE'
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};
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}
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async calculateTotalPnL(tradeData) {
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const analysis = {
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totalTrades: tradeData.length,
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totalPnL: 0,
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totalPnLPercent: 0,
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winningTrades: 0,
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losingTrades: 0,
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breakEvenTrades: 0,
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avgTradeSize: 0,
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bestTrade: null,
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worstTrade: null,
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winRate: 0,
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avgWin: 0,
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avgLoss: 0,
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profitFactor: 0
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};
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if (tradeData.length === 0) {
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return analysis;
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}
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let totalProfit = 0;
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let totalLoss = 0;
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let totalAmount = 0;
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tradeData.forEach(trade => {
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const pnl = trade.profit || 0;
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const pnlPercent = trade.pnlPercent || 0;
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const amount = trade.amount || 0;
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analysis.totalPnL += pnl;
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analysis.totalPnLPercent += pnlPercent;
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totalAmount += amount;
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if (pnl > 0) {
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analysis.winningTrades++;
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totalProfit += pnl;
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} else if (pnl < 0) {
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analysis.losingTrades++;
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totalLoss += Math.abs(pnl);
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} else {
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analysis.breakEvenTrades++;
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}
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// Track best/worst trades
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if (!analysis.bestTrade || pnl > analysis.bestTrade.profit) {
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analysis.bestTrade = trade;
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}
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if (!analysis.worstTrade || pnl < analysis.worstTrade.profit) {
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analysis.worstTrade = trade;
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}
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});
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analysis.avgTradeSize = totalAmount / tradeData.length;
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analysis.winRate = (analysis.winningTrades / tradeData.length) * 100;
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analysis.avgWin = analysis.winningTrades > 0 ? totalProfit / analysis.winningTrades : 0;
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analysis.avgLoss = analysis.losingTrades > 0 ? totalLoss / analysis.losingTrades : 0;
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analysis.profitFactor = analysis.avgLoss > 0 ? analysis.avgWin / analysis.avgLoss : 0;
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// Round numbers
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Object.keys(analysis).forEach(key => {
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if (typeof analysis[key] === 'number') {
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analysis[key] = Number(analysis[key].toFixed(4));
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}
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});
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return analysis;
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}
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async calculateAccuracyTrends(learningData) {
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const trends = [];
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const chunkSize = Math.max(5, Math.floor(learningData.length / 10)); // At least 5 samples per chunk
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for (let i = 0; i < learningData.length; i += chunkSize) {
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const chunk = learningData.slice(i, i + chunkSize);
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const accuracy = this.getAccuracy(chunk);
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const confidence = this.getAverageConfidence(chunk);
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trends.push({
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period: i / chunkSize + 1,
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samples: chunk.length,
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accuracy: accuracy ? Number(accuracy.toFixed(2)) : null,
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confidence: Number(confidence.toFixed(2)),
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timestamp: chunk[chunk.length - 1]?.createdAt
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});
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}
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return trends;
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}
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async calculateConfidenceEvolution(learningData) {
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return learningData.map((record, index) => ({
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index: index + 1,
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timestamp: record.createdAt,
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confidence: record.confidenceScore || 0,
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accuracy: record.accuracyScore || null,
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symbol: record.symbol,
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outcome: record.outcome
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}));
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}
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getAverageConfidence(data) {
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const confidenceScores = data
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.map(d => d.confidenceScore || d.analysisData?.confidence || 0.5)
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.filter(score => score > 0);
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return confidenceScores.length > 0 ?
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confidenceScores.reduce((a, b) => a + b, 0) / confidenceScores.length : 0.5;
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}
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getAccuracy(data) {
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const withOutcomes = data.filter(d => d.outcome && d.accuracyScore);
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if (withOutcomes.length === 0) return null;
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const avgAccuracy = withOutcomes.reduce((sum, d) => sum + (d.accuracyScore || 0), 0) / withOutcomes.length;
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return avgAccuracy;
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}
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displayOverallStats(learningData, tradeData, automationSessions) {
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console.log('📈 OVERALL AI TRADING STATISTICS');
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console.log(` Period: ${this.startDate.toDateString()} - ${new Date().toDateString()}`);
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console.log(` Learning Records: ${learningData.length}`);
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console.log(` AI Trades Executed: ${tradeData.length}`);
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console.log(` Automation Sessions: ${automationSessions.length}`);
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console.log(` Active Sessions: ${automationSessions.filter(s => s.status === 'ACTIVE').length}`);
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console.log('');
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}
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displayLearningImprovements(improvements) {
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console.log('🧠 AI LEARNING IMPROVEMENTS');
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if (improvements.trend === 'INSUFFICIENT_DATA') {
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console.log(` ⚠️ ${improvements.message}`);
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} else {
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console.log(` 📊 Confidence Improvement: ${improvements.confidenceImprovement > 0 ? '+' : ''}${improvements.confidenceImprovement}%`);
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if (improvements.accuracyImprovement !== null) {
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console.log(` 🎯 Accuracy Improvement: ${improvements.accuracyImprovement > 0 ? '+' : ''}${improvements.accuracyImprovement}%`);
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}
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console.log(` 📈 Trend: ${improvements.trend}`);
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console.log(` Early Period: ${improvements.earlyPeriod.avgConfidence}% confidence (${improvements.earlyPeriod.samples} samples)`);
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console.log(` Recent Period: ${improvements.recentPeriod.avgConfidence}% confidence (${improvements.recentPeriod.samples} samples)`);
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}
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console.log('');
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}
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displayPnLAnalysis(pnl) {
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console.log('💰 TOTAL PnL ANALYSIS');
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console.log(` Total Trades: ${pnl.totalTrades}`);
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console.log(` Total PnL: $${pnl.totalPnL.toFixed(4)}`);
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console.log(` Total PnL %: ${pnl.totalPnLPercent.toFixed(2)}%`);
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console.log(` Win Rate: ${pnl.winRate.toFixed(1)}%`);
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console.log(` Winning Trades: ${pnl.winningTrades}`);
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console.log(` Losing Trades: ${pnl.losingTrades}`);
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console.log(` Break Even: ${pnl.breakEvenTrades}`);
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if (pnl.totalTrades > 0) {
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console.log(` Average Trade Size: $${pnl.avgTradeSize.toFixed(2)}`);
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console.log(` Average Win: $${pnl.avgWin.toFixed(4)}`);
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console.log(` Average Loss: $${pnl.avgLoss.toFixed(4)}`);
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console.log(` Profit Factor: ${pnl.profitFactor.toFixed(2)}`);
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}
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console.log('');
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}
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displayAccuracyTrends(trends) {
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console.log('📊 ACCURACY TRENDS OVER TIME');
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trends.forEach(trend => {
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console.log(` Period ${trend.period}: ${trend.confidence}% confidence, ${trend.accuracy ? trend.accuracy + '% accuracy' : 'no accuracy data'} (${trend.samples} samples)`);
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});
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console.log('');
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}
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displayConfidenceEvolution(evolution) {
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console.log('📈 RECENT CONFIDENCE EVOLUTION');
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const recentData = evolution.slice(-10); // Last 10 records
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recentData.forEach(record => {
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const date = new Date(record.timestamp).toLocaleDateString();
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console.log(` ${date}: ${(record.confidence * 100).toFixed(1)}% confidence (${record.symbol})`);
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});
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console.log('');
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}
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async saveReport(reportData) {
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const fs = require('fs');
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const reportPath = './public/ai-learning-report.json';
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// Ensure public directory exists
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if (!fs.existsSync('./public')) {
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fs.mkdirSync('./public', { recursive: true });
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}
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fs.writeFileSync(reportPath, JSON.stringify(reportData, null, 2));
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console.log(`📁 Report saved to: ${reportPath}`);
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}
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}
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// Run the analytics
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async function main() {
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const analytics = new AILearningAnalytics();
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try {
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await analytics.generateLearningReport();
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} catch (error) {
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console.error('Failed to generate report:', error);
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} finally {
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await prisma.$disconnect();
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}
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}
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if (require.main === module) {
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main();
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}
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module.exports = AILearningAnalytics;
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