#!/usr/bin/env node /** * Simple AI Learning System Demo (In-Memory) * * Demonstrates the learning system without database dependencies */ async function demonstrateAILearningSimple() { console.log('🧠 AI LEARNING SYSTEM - SIMPLE DEMONSTRATION'); console.log('='.repeat(80)); console.log(` šŸŽÆ WHAT YOUR ENHANCED AUTONOMOUS SYSTEM NOW INCLUDES: šŸ“Š DECISION RECORDING: āœ… Records every AI decision made near stop loss āœ… Captures context: distance, market conditions, confidence āœ… Stores reasoning and expected outcomes šŸ” OUTCOME TRACKING: āœ… Monitors what happens after each decision āœ… Measures P&L impact and time to resolution āœ… Determines if decisions were correct or not 🧠 PATTERN ANALYSIS: āœ… Identifies successful decision patterns āœ… Finds failure patterns to avoid āœ… Optimizes distance thresholds based on results āœ… Analyzes timing patterns (time of day, market conditions) šŸš€ SMART RECOMMENDATIONS: āœ… Suggests best actions based on learned patterns āœ… Provides confidence scores from historical data āœ… Adapts to what actually works in your trading šŸ”„ CONTINUOUS IMPROVEMENT: āœ… Updates decision thresholds automatically āœ… Improves confidence calibration over time āœ… Becomes more accurate with each decision šŸ–ļø BEACH MODE EVOLUTION: Before: Basic autonomous monitoring After: Self-improving AI that learns from every decision! `); // Simulate the learning process console.log('\nšŸŽ¬ SIMULATED LEARNING EVOLUTION:\n'); const learningPhases = [ { phase: 'Week 1 - Initial Learning', decisions: 15, successRate: 45, confidence: 30, status: 'LEARNING', insight: 'Collecting initial decision data, identifying basic patterns' }, { phase: 'Week 2 - Pattern Recognition', decisions: 35, successRate: 62, confidence: 55, status: 'IMPROVING', insight: 'Found that EMERGENCY_EXIT at <1% distance works 89% of the time' }, { phase: 'Month 1 - Optimization', decisions: 68, successRate: 74, confidence: 73, status: 'OPTIMIZED', insight: 'Optimal thresholds: Emergency=0.8%, Risk=1.9%, Medium=4.2%' }, { phase: 'Month 2 - Expert Level', decisions: 124, successRate: 82, confidence: 87, status: 'EXPERT', insight: 'TIGHTEN_STOP_LOSS in afternoon trading shows 94% success rate' } ]; for (const phase of learningPhases) { console.log(`šŸ“ˆ ${phase.phase}`); console.log(` Decisions Made: ${phase.decisions}`); console.log(` Success Rate: ${phase.successRate}%`); console.log(` System Confidence: ${phase.confidence}%`); console.log(` Status: ${phase.status}`); console.log(` šŸ’” Key Insight: ${phase.insight}`); console.log(''); } console.log('šŸŽÆ EXAMPLE LEARNED DECISION PATTERNS:\n'); const examplePatterns = [ { pattern: 'EMERGENCY_EXIT at <1% distance', successRate: 89, samples: 23, insight: 'Consistently saves 3-8% more than letting stop loss hit' }, { pattern: 'TIGHTEN_STOP_LOSS during afternoon hours', successRate: 94, samples: 18, insight: 'Lower volatility makes tighter stops more effective' }, { pattern: 'HOLD decision when trend is bullish', successRate: 76, samples: 31, insight: 'Strong trends often recover from temporary dips' }, { pattern: 'PARTIAL_EXIT in high volatility', successRate: 81, samples: 15, insight: 'Reduces risk while maintaining upside potential' } ]; examplePatterns.forEach(pattern => { console.log(`āœ… ${pattern.pattern}`); console.log(` Success Rate: ${pattern.successRate}% (${pattern.samples} samples)`); console.log(` šŸ“ Insight: ${pattern.insight}`); console.log(''); }); console.log('šŸŽÆ SMART RECOMMENDATION EXAMPLE:\n'); console.log(`Situation: SOL-PERP position 2.3% from stop loss, bullish trend, afternoon`); console.log(` 🧠 AI RECOMMENDATION: Suggested Action: TIGHTEN_STOP_LOSS Confidence: 87% (based on 18 similar situations) Reasoning: Afternoon trading + bullish trend shows 94% success rate for tightening Expected Outcome: Improve risk/reward by 0.4% on average šŸ“Š Supporting Data: - 18 similar situations in learning database - 94% success rate for this pattern - Average P&L improvement: +0.4% - Time-based optimization: Afternoon = optimal `); console.log('\nšŸ—ļø SYSTEM ARCHITECTURE ENHANCEMENT:\n'); console.log(` šŸ“ NEW COMPONENTS ADDED: šŸ“„ lib/stop-loss-decision-learner.js 🧠 Core learning engine that records decisions and analyzes patterns šŸ“„ lib/enhanced-autonomous-risk-manager.js šŸ¤– Enhanced AI that uses learning data to make smarter decisions šŸ“„ database/stop-loss-learning-schema.sql šŸ—„ļø Database schema for storing decision history and patterns šŸ“„ app/api/ai/learning/route.ts 🌐 API endpoints for accessing learning insights šŸ“„ app/components/AILearningDashboard.tsx šŸŽØ Beautiful dashboard to visualize learning progress šŸ“„ demo-ai-learning.js šŸŽ¬ Demonstration script showing learning capabilities `); console.log('\nšŸš€ INTEGRATION WITH EXISTING SYSTEM:\n'); console.log(` šŸ”— ENHANCED BEACH MODE FLOW: 1. šŸ“Š Position Monitor detects proximity to stop loss 2. šŸ¤– Enhanced Risk Manager analyzes situation 3. 🧠 Learning System provides smart recommendation 4. ⚔ AI makes decision (enhanced by learned patterns) 5. šŸ“ Decision is recorded with context for learning 6. ā±ļø System monitors outcome over time 7. šŸ” Outcome is assessed and learning score calculated 8. šŸ“ˆ Patterns are updated, thresholds optimized 9. šŸŽÆ Next decision is even smarter! RESULT: Your AI doesn't just execute rules... It EVOLVES and improves with every decision! 🧬 `); console.log('\nšŸŽ›ļø NEW API ENDPOINTS:\n'); console.log(` 🌐 /api/ai/learning (GET) šŸ“Š Get comprehensive learning insights and system status 🌐 /api/ai/learning (POST) ⚔ Trigger learning actions (update thresholds, generate reports) Example Usage: curl http://localhost:9001/api/ai/learning | jq . `); console.log('\nšŸŽØ BEAUTIFUL LEARNING DASHBOARD:\n'); console.log(` šŸ’» NEW UI COMPONENTS: šŸ“Š System Overview Cards - Confidence level with trend indicators - Total decisions and success rate - System maturity assessment - Data quality metrics šŸŽÆ Current Learning Thresholds - Emergency distance (auto-optimized) - Risk levels (learned from outcomes) - Visual threshold indicators āœ… Successful Decision Patterns - Which decisions work best - Success rates and sample sizes - Optimal conditions for each pattern āŒ Areas for Improvement - Decisions that need work - Failure pattern analysis - Actionable improvement suggestions šŸ’” AI Recommendations - Smart suggestions based on learning - Priority levels and actionability - Real-time optimization tips šŸ„ System Health Indicators - Learning system status - Data quality assessment - Beach mode readiness ⚔ Action Buttons - Update thresholds from learning - Generate new reports - Assess pending decisions - Refresh learning data `); console.log('\n🌟 THE RESULT - ULTIMATE BEACH MODE:\n'); console.log(` šŸ–ļø BEFORE: Basic Autonomous Trading āœ… Makes rule-based decisions āœ… Executes stop loss management āœ… Monitors positions automatically šŸš€ AFTER: Self-Improving AI Trader āœ… Everything above PLUS: āœ… Records every decision for learning āœ… Tracks outcomes and measures success āœ… Identifies what works vs what doesn't āœ… Optimizes thresholds based on results āœ… Provides smart recommendations āœ… Adapts to market conditions over time āœ… Builds confidence through validated patterns āœ… Becomes more profitable with experience šŸŽÆ OUTCOME: Your AI trading system doesn't just work... It gets BETTER every single day! šŸ“ˆ šŸ–ļø TRUE BEACH MODE: Start automation, walk away, come back to a smarter AI that learned from every decision while you relaxed! ā˜€ļø `); console.log('\n✨ YOUR AI IS NOW READY TO LEARN AND DOMINATE! ✨\n'); } // Run the demonstration if (require.main === module) { demonstrateAILearningSimple().catch(console.error); }