🧠 LEARNING-ENHANCED AI: Historical Performance Integration
Core Implementation: - Enhanced AI Analysis Service: Uses historical learning data in OpenAI prompts - Learning Context Retrieval: Queries database for symbol/timeframe specific performance - Pattern Matching: Adjusts confidence based on successful vs failed historical setups - Database Integration: Automatic storage of analysis for continuous learning - Smart Confidence Calibration: AI knows when it's accurate vs uncertain - lib/ai-analysis.ts: Complete learning integration with getLearningContext() - lib/db.ts: Optimized Prisma client for database operations - Enhanced AnalysisResult: Added learningApplication field for pattern insights - Symbol/Timeframe Optimization: AI learns specific market behavior patterns - Automatic Learning Storage: Every analysis builds future intelligence 1. AI retrieves last 30 analyses for specific symbol/timeframe 2. Calculates historical accuracy and identifies successful patterns 3. Compares current setup to historical successes/failures 4. Adjusts confidence and reasoning based on learned patterns 5. Stores new analysis for continuous improvement efits: - AI references: 'This matches my 85% success pattern from...' - Pattern avoidance: 'Reducing confidence due to similarity to failed trade...' - Smart calibration: 'Historical data shows 90% accuracy with this confluence...' - Self-improving: Gets better with every analysis for YOUR trading style 695 existing learning records ready to enhance decisions Automation service updated to pass symbol/timeframe to AI Complete learning workflow: Analyze → Store → Learn → Improve Symbol-specific optimization (SOL vs ETH vs BTC patterns) Timeframe-specific learning (1h vs 4h vs 1D strategies) Your AI now learns from its own trading history! 🧠✨
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mindesbunister
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demo-learning-enhanced-ai.js
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266
demo-learning-enhanced-ai.js
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#!/usr/bin/env node
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/**
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* Learning-Enhanced AI Analysis Demonstration
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*
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* Shows how the AI uses historical performance data to make better decisions
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*/
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// Note: Using require directly for demo - in production this would be properly imported
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const { PrismaClient } = require('@prisma/client');
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async function demonstrateLearningEnhancedAI() {
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console.log('🧠 LEARNING-ENHANCED AI ANALYSIS DEMONSTRATION');
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console.log('='.repeat(80));
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console.log(`
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🔬 How Your AI Now Uses Learning Data:
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BEFORE (Standard AI):
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❌ No memory of past performance
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❌ Fixed confidence levels
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❌ No pattern recognition from history
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❌ Repeats same mistakes
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AFTER (Learning-Enhanced AI):
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✅ Remembers successful vs failed predictions
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✅ Adjusts confidence based on historical accuracy
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✅ Recognizes patterns that worked before
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✅ Avoids setups that previously failed
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✅ Gets smarter with every analysis
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`);
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console.log('\n🎬 SIMULATED LEARNING WORKFLOW:\n');
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// Simulate the learning process
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const scenarios = [
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{
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phase: 'INITIAL ANALYSIS (No Learning Data)',
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symbol: 'SOL-PERP',
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timeframe: '1h',
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description: 'First time analyzing this symbol/timeframe',
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expectedBehavior: 'Standard technical analysis, no historical context'
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},
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{
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phase: 'LEARNING PHASE (Building Data)',
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symbol: 'SOL-PERP',
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timeframe: '1h',
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description: 'After 10 analyses with outcomes recorded',
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expectedBehavior: 'AI starts recognizing patterns, adjusting confidence'
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},
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{
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phase: 'EXPERT PHASE (Rich Learning Data)',
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symbol: 'SOL-PERP',
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timeframe: '1h',
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description: 'After 50+ analyses with clear success/failure patterns',
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expectedBehavior: 'AI confidently avoids bad setups, favors proven patterns'
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}
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];
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for (const scenario of scenarios) {
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console.log(`📊 ${scenario.phase}:`);
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console.log(` Symbol: ${scenario.symbol}`);
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console.log(` Timeframe: ${scenario.timeframe}`);
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console.log(` Context: ${scenario.description}`);
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console.log(` Expected: ${scenario.expectedBehavior}`);
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console.log('');
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}
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console.log('🔍 LEARNING CONTEXT EXAMPLES:\n');
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// Show what learning context looks like
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const learningExamples = [
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{
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scenario: 'High Success Rate (85%)',
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context: `**AI LEARNING CONTEXT for SOL-PERP 1h:**
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- Historical Accuracy: 85% (17/20 successful predictions)
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- Optimal Confidence Range: 78% (successful predictions average)
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**SUCCESSFUL PATTERNS:**
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1. ✅ Confidence: 82% | Sentiment: BULLISH | Accuracy: 92%
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Setup: RSI oversold + MACD bullish crossover + EMA stack bullish
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2. ✅ Confidence: 75% | Sentiment: BEARISH | Accuracy: 88%
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Setup: RSI overbought + Volume divergence + Support break
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**LEARNED OPTIMIZATION:**
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- Favor bullish setups with 75%+ confidence
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- Avoid bearish calls below 70% confidence
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- RSI + MACD confluence has 90% success rate`,
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impact: 'AI will be MORE confident in similar bullish setups, LESS confident in weak bearish signals'
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},
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{
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scenario: 'Mixed Performance (60%)',
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context: `**AI LEARNING CONTEXT for ETH-PERP 4h:**
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- Historical Accuracy: 60% (12/20 successful predictions)
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- Warning: Below optimal performance threshold
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**FAILURE PATTERNS:**
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1. ❌ Confidence: 85% | Sentiment: BULLISH | Accuracy: 25%
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Failed Setup: High confidence bull call during range-bound market
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2. ❌ Confidence: 90% | Sentiment: BEARISH | Accuracy: 15%
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Failed Setup: Aggressive short during strong uptrend
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**LEARNED RULES:**
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- Reduce confidence by 15% in range-bound markets
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- Avoid high-confidence calls during unclear trends`,
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impact: 'AI will be MORE cautious, LOWER confidence in uncertain conditions'
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}
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];
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learningExamples.forEach((example, index) => {
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console.log(`Example ${index + 1}: ${example.scenario}`);
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console.log('');
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console.log(example.context);
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console.log('');
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console.log(`🎯 Impact: ${example.impact}`);
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console.log('');
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console.log('-'.repeat(60));
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console.log('');
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});
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console.log('🤖 AI DECISION ENHANCEMENT PROCESS:\n');
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console.log(`
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STEP 1: 📸 Screenshot Analysis
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Standard technical analysis of chart indicators
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STEP 2: 🔍 Learning Context Retrieval
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Query database for historical performance on this symbol/timeframe
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- Get last 30 analyses with known outcomes
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- Calculate success rate and confidence patterns
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- Identify successful vs failed setups
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STEP 3: 🧠 Pattern Matching
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Compare current setup to historical data:
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- Does this match a successful pattern? → INCREASE confidence
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- Does this resemble a failed setup? → DECREASE confidence
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- Is this a new scenario? → Use standard confidence
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STEP 4: ✨ Enhanced Analysis
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Generate analysis with learning-enhanced reasoning:
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- "This bullish setup matches my 92% success pattern from March 15..."
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- "Reducing confidence due to similarity to failed trade on April 2..."
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- "Historical data shows 85% accuracy with this indicator confluence..."
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STEP 5: 💾 Store for Future Learning
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Record this analysis in database:
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- Setup details, confidence, reasoning
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- Market conditions, indicators used
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- Later: Outcome will be added for continuous learning
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`);
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console.log('\n📈 CONTINUOUS IMPROVEMENT CYCLE:\n');
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console.log(`
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🔄 LEARNING LOOP:
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Analysis → Store → Outcome → Learn → Better Analysis
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1. 📊 AI analyzes chart with current knowledge
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2. 💾 Analysis stored with confidence & reasoning
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3. ⏰ Time passes, trade outcome becomes known
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4. 📝 Outcome recorded (WIN/LOSS/accuracy score)
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5. 🧠 Next analysis uses this outcome as learning data
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6. 🎯 AI gets progressively better at this symbol/timeframe
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RESULT: Self-improving AI that learns from every single prediction!
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`);
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console.log('\n🏗️ INTEGRATION WITH YOUR TRADING SYSTEM:\n');
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console.log(`
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🎯 ENHANCED AUTOMATION WORKFLOW:
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OLD: Screenshot → AI Analysis → Trade Decision
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NEW: Screenshot → AI Analysis + Learning Context → Smarter Trade Decision
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🔧 Technical Implementation:
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✅ Enhanced lib/ai-analysis.ts with learning integration
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✅ Database queries for historical performance
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✅ Pattern matching and confidence adjustment
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✅ Learning data storage for continuous improvement
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✅ Symbol/timeframe specific optimization
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🎮 How to Use:
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1. Your automation calls: aiAnalysisService.analyzeScreenshot(screenshot, 'SOL-PERP', '1h')
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2. AI automatically gets learning context for SOL-PERP 1h
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3. Analysis enhanced with historical patterns
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4. Decision confidence adjusted based on past performance
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5. New analysis stored for future learning
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🏖️ Beach Mode Benefits:
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- AI learns your trading style and market conditions
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- Avoids repeating historical mistakes
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- Favors setups that have actually worked
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- Gets better the longer it runs!
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`);
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console.log('\n🚀 NEXT STEPS TO ACTIVATE:\n');
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console.log(`
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TO ENABLE LEARNING-ENHANCED AI:
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1. ✅ DONE: Enhanced AI analysis service with learning
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2. 🔄 UPDATE: Your automation calls to pass symbol & timeframe
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3. 📊 ENJOY: AI that gets smarter with every trade!
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Example Update in Your Automation:
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// OLD
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const analysis = await aiAnalysisService.analyzeScreenshot('screenshot.png')
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// NEW
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const analysis = await aiAnalysisService.analyzeScreenshot('screenshot.png', 'SOL-PERP', '1h')
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That's it! Your AI now uses learning data automatically! 🧠✨
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`);
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try {
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console.log('\n🔍 DATABASE CHECK:\n');
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const prisma = new PrismaClient();
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// Check if we have any learning data
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const learningCount = await prisma.aILearningData.count();
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const recentAnalyses = await prisma.aILearningData.findMany({
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take: 5,
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orderBy: { createdAt: 'desc' },
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select: {
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symbol: true,
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timeframe: true,
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confidenceScore: true,
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outcome: true,
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createdAt: true
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}
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});
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console.log(`📊 Current Learning Database Status:`);
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console.log(` Total AI Learning Records: ${learningCount}`);
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if (recentAnalyses.length > 0) {
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console.log(`\n🕐 Recent Analyses:`);
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recentAnalyses.forEach((analysis, i) => {
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console.log(` ${i + 1}. ${analysis.symbol} ${analysis.timeframe} - Confidence: ${analysis.confidenceScore}% - Outcome: ${analysis.outcome || 'Pending'}`);
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});
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} else {
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console.log(` No analyses yet - Learning will begin with first enhanced analysis!`);
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}
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await prisma.$disconnect();
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} catch (dbError) {
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console.log(`⚠️ Database check failed: ${dbError.message}`);
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console.log(` Learning will work once database is accessible`);
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}
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console.log('\n✨ YOUR AI IS NOW LEARNING-ENHANCED! ✨');
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console.log('\nEvery analysis it performs will make it smarter for the next one! 🧠🚀');
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}
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// Run the demonstration
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if (require.main === module) {
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demonstrateLearningEnhancedAI().catch(console.error);
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}
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module.exports = { demonstrateLearningEnhancedAI };
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