#!/usr/bin/env node

/**
 * Learning-Enhanced AI Analysis Demonstration
 * 
 * Shows how the AI uses historical performance data to make better decisions
 */

// Note: Using require directly for demo - in production this would be properly imported
const { PrismaClient } = require('@prisma/client');

async function demonstrateLearningEnhancedAI() {
  console.log('🧠 LEARNING-ENHANCED AI ANALYSIS DEMONSTRATION');
  console.log('='.repeat(80));
  
  console.log(`
🔬 How Your AI Now Uses Learning Data:

BEFORE (Standard AI):
❌ No memory of past performance
❌ Fixed confidence levels
❌ No pattern recognition from history
❌ Repeats same mistakes

AFTER (Learning-Enhanced AI):
✅ Remembers successful vs failed predictions
✅ Adjusts confidence based on historical accuracy
✅ Recognizes patterns that worked before
✅ Avoids setups that previously failed
✅ Gets smarter with every analysis
`);

  console.log('\n🎬 SIMULATED LEARNING WORKFLOW:\n');

  // Simulate the learning process
  const scenarios = [
    {
      phase: 'INITIAL ANALYSIS (No Learning Data)',
      symbol: 'SOL-PERP',
      timeframe: '1h',
      description: 'First time analyzing this symbol/timeframe',
      expectedBehavior: 'Standard technical analysis, no historical context'
    },
    {
      phase: 'LEARNING PHASE (Building Data)',
      symbol: 'SOL-PERP', 
      timeframe: '1h',
      description: 'After 10 analyses with outcomes recorded',
      expectedBehavior: 'AI starts recognizing patterns, adjusting confidence'
    },
    {
      phase: 'EXPERT PHASE (Rich Learning Data)',
      symbol: 'SOL-PERP',
      timeframe: '1h', 
      description: 'After 50+ analyses with clear success/failure patterns',
      expectedBehavior: 'AI confidently avoids bad setups, favors proven patterns'
    }
  ];

  for (const scenario of scenarios) {
    console.log(`📊 ${scenario.phase}:`);
    console.log(`   Symbol: ${scenario.symbol}`);
    console.log(`   Timeframe: ${scenario.timeframe}`);
    console.log(`   Context: ${scenario.description}`);
    console.log(`   Expected: ${scenario.expectedBehavior}`);
    console.log('');
  }

  console.log('🔍 LEARNING CONTEXT EXAMPLES:\n');

  // Show what learning context looks like
  const learningExamples = [
    {
      scenario: 'High Success Rate (85%)',
      context: `**AI LEARNING CONTEXT for SOL-PERP 1h:**
- Historical Accuracy: 85% (17/20 successful predictions)
- Optimal Confidence Range: 78% (successful predictions average)

**SUCCESSFUL PATTERNS:**
1. ✅ Confidence: 82% | Sentiment: BULLISH | Accuracy: 92%
   Setup: RSI oversold + MACD bullish crossover + EMA stack bullish
2. ✅ Confidence: 75% | Sentiment: BEARISH | Accuracy: 88%
   Setup: RSI overbought + Volume divergence + Support break

**LEARNED OPTIMIZATION:**
- Favor bullish setups with 75%+ confidence
- Avoid bearish calls below 70% confidence
- RSI + MACD confluence has 90% success rate`,
      
      impact: 'AI will be MORE confident in similar bullish setups, LESS confident in weak bearish signals'
    },
    {
      scenario: 'Mixed Performance (60%)',
      context: `**AI LEARNING CONTEXT for ETH-PERP 4h:**
- Historical Accuracy: 60% (12/20 successful predictions)
- Warning: Below optimal performance threshold

**FAILURE PATTERNS:**
1. ❌ Confidence: 85% | Sentiment: BULLISH | Accuracy: 25%
   Failed Setup: High confidence bull call during range-bound market
2. ❌ Confidence: 90% | Sentiment: BEARISH | Accuracy: 15%
   Failed Setup: Aggressive short during strong uptrend

**LEARNED RULES:**
- Reduce confidence by 15% in range-bound markets
- Avoid high-confidence calls during unclear trends`,
      
      impact: 'AI will be MORE cautious, LOWER confidence in uncertain conditions'
    }
  ];

  learningExamples.forEach((example, index) => {
    console.log(`Example ${index + 1}: ${example.scenario}`);
    console.log('');
    console.log(example.context);
    console.log('');
    console.log(`🎯 Impact: ${example.impact}`);
    console.log('');
    console.log('-'.repeat(60));
    console.log('');
  });

  console.log('🤖 AI DECISION ENHANCEMENT PROCESS:\n');

  console.log(`
STEP 1: 📸 Screenshot Analysis
   Standard technical analysis of chart indicators

STEP 2: 🔍 Learning Context Retrieval  
   Query database for historical performance on this symbol/timeframe
   - Get last 30 analyses with known outcomes
   - Calculate success rate and confidence patterns
   - Identify successful vs failed setups

STEP 3: 🧠 Pattern Matching
   Compare current setup to historical data:
   - Does this match a successful pattern? → INCREASE confidence
   - Does this resemble a failed setup? → DECREASE confidence  
   - Is this a new scenario? → Use standard confidence

STEP 4: ✨ Enhanced Analysis
   Generate analysis with learning-enhanced reasoning:
   - "This bullish setup matches my 92% success pattern from March 15..."
   - "Reducing confidence due to similarity to failed trade on April 2..."
   - "Historical data shows 85% accuracy with this indicator confluence..."

STEP 5: 💾 Store for Future Learning
   Record this analysis in database:
   - Setup details, confidence, reasoning
   - Market conditions, indicators used
   - Later: Outcome will be added for continuous learning
`);

  console.log('\n📈 CONTINUOUS IMPROVEMENT CYCLE:\n');

  console.log(`
🔄 LEARNING LOOP:

Analysis → Store → Outcome → Learn → Better Analysis

1. 📊 AI analyzes chart with current knowledge
2. 💾 Analysis stored with confidence & reasoning  
3. ⏰ Time passes, trade outcome becomes known
4. 📝 Outcome recorded (WIN/LOSS/accuracy score)
5. 🧠 Next analysis uses this outcome as learning data
6. 🎯 AI gets progressively better at this symbol/timeframe

RESULT: Self-improving AI that learns from every single prediction!
`);

  console.log('\n🏗️ INTEGRATION WITH YOUR TRADING SYSTEM:\n');

  console.log(`
🎯 ENHANCED AUTOMATION WORKFLOW:

OLD: Screenshot → AI Analysis → Trade Decision
NEW: Screenshot → AI Analysis + Learning Context → Smarter Trade Decision

🔧 Technical Implementation:
✅ Enhanced lib/ai-analysis.ts with learning integration
✅ Database queries for historical performance
✅ Pattern matching and confidence adjustment  
✅ Learning data storage for continuous improvement
✅ Symbol/timeframe specific optimization

🎮 How to Use:
1. Your automation calls: aiAnalysisService.analyzeScreenshot(screenshot, 'SOL-PERP', '1h')
2. AI automatically gets learning context for SOL-PERP 1h
3. Analysis enhanced with historical patterns
4. Decision confidence adjusted based on past performance
5. New analysis stored for future learning

🏖️ Beach Mode Benefits:
- AI learns your trading style and market conditions
- Avoids repeating historical mistakes
- Favors setups that have actually worked
- Gets better the longer it runs!
`);

  console.log('\n🚀 NEXT STEPS TO ACTIVATE:\n');

  console.log(`
TO ENABLE LEARNING-ENHANCED AI:

1. ✅ DONE: Enhanced AI analysis service with learning
2. 🔄 UPDATE: Your automation calls to pass symbol & timeframe
3. 📊 ENJOY: AI that gets smarter with every trade!

Example Update in Your Automation:
// OLD
const analysis = await aiAnalysisService.analyzeScreenshot('screenshot.png')

// NEW  
const analysis = await aiAnalysisService.analyzeScreenshot('screenshot.png', 'SOL-PERP', '1h')

That's it! Your AI now uses learning data automatically! 🧠✨
`);

  try {
    console.log('\n🔍 DATABASE CHECK:\n');
    
    const prisma = new PrismaClient();
    
    // Check if we have any learning data
    const learningCount = await prisma.aILearningData.count();
    const recentAnalyses = await prisma.aILearningData.findMany({
      take: 5,
      orderBy: { createdAt: 'desc' },
      select: {
        symbol: true,
        timeframe: true,
        confidenceScore: true,
        outcome: true,
        createdAt: true
      }
    });

    console.log(`📊 Current Learning Database Status:`);
    console.log(`   Total AI Learning Records: ${learningCount}`);
    
    if (recentAnalyses.length > 0) {
      console.log(`\n🕐 Recent Analyses:`);
      recentAnalyses.forEach((analysis, i) => {
        console.log(`   ${i + 1}. ${analysis.symbol} ${analysis.timeframe} - Confidence: ${analysis.confidenceScore}% - Outcome: ${analysis.outcome || 'Pending'}`);
      });
    } else {
      console.log(`   No analyses yet - Learning will begin with first enhanced analysis!`);
    }

    await prisma.$disconnect();

  } catch (dbError) {
    console.log(`⚠️  Database check failed: ${dbError.message}`);
    console.log(`   Learning will work once database is accessible`);
  }

  console.log('\n✨ YOUR AI IS NOW LEARNING-ENHANCED! ✨');
  console.log('\nEvery analysis it performs will make it smarter for the next one! 🧠🚀');
}

// Run the demonstration
if (require.main === module) {
  demonstrateLearningEnhancedAI().catch(console.error);
}

module.exports = { demonstrateLearningEnhancedAI };