🧠 Implement AI Learning System for Stop Loss Decisions
- Add stop-loss-decision-learner.js: Core learning engine
- Add enhanced-autonomous-risk-manager.js: Learning-enhanced decisions
- Add AI learning API and dashboard components
- Add database schema for decision tracking
- Integrate with existing automation system
- Demo scripts and documentation
Result: AI learns from every decision and improves over time! 🚀
This commit is contained in:
mindesbunister
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demo-ai-learning.js
Executable file
218
demo-ai-learning.js
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#!/usr/bin/env node
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/**
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* AI Learning System Demo
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*
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* Demonstrates the complete stop loss decision learning system
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*/
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const EnhancedAutonomousRiskManager = require('./lib/enhanced-autonomous-risk-manager.js');
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const StopLossDecisionLearner = require('./lib/stop-loss-decision-learner.js');
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async function demonstrateAILearning() {
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console.log('🧠 AI LEARNING SYSTEM DEMONSTRATION');
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console.log('='.repeat(80));
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console.log(`
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🎯 WHAT THIS SYSTEM DOES:
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1. 📊 Records every AI decision made near stop loss
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2. 🔍 Tracks what happens after each decision
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3. 🧠 Learns from outcomes to improve future decisions
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4. 🚀 Gets smarter with every trade and decision
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5. 🏖️ Enables true autonomous beach mode trading
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🔄 LEARNING CYCLE:
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Decision Made → Outcome Tracked → Pattern Analysis → Improved Decisions
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`);
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const riskManager = new EnhancedAutonomousRiskManager();
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const learner = new StopLossDecisionLearner();
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console.log('\n🎬 DEMO SCENARIO: Simulating Decision Learning Process\n');
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// Simulate a series of decisions and outcomes
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const demoDecisions = [
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{
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scenario: 'SOL-PERP position 1.5% from stop loss',
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decision: 'EMERGENCY_EXIT',
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distanceFromSL: 1.5,
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outcome: 'AVOIDED_MAJOR_LOSS',
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pnlImpact: 5.2
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},
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{
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scenario: 'SOL-PERP position 2.8% from stop loss',
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decision: 'TIGHTEN_STOP_LOSS',
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distanceFromSL: 2.8,
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outcome: 'IMPROVED_PROFIT',
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pnlImpact: 2.1
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},
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{
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scenario: 'SOL-PERP position 4.2% from stop loss',
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decision: 'HOLD',
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distanceFromSL: 4.2,
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outcome: 'CORRECT_HOLD',
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pnlImpact: 1.8
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},
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{
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scenario: 'BTC-PERP position 1.2% from stop loss',
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decision: 'PARTIAL_EXIT',
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distanceFromSL: 1.2,
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outcome: 'REDUCED_RISK',
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pnlImpact: 0.8
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}
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];
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console.log('📝 RECORDING DECISIONS FOR LEARNING:\n');
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const decisionIds = [];
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for (const demo of demoDecisions) {
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console.log(`🎯 Scenario: ${demo.scenario}`);
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console.log(` Decision: ${demo.decision}`);
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// Record the decision
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const decisionId = await learner.recordDecision({
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tradeId: `demo_${Date.now()}_${Math.random().toString(36).substr(2, 5)}`,
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symbol: demo.scenario.split(' ')[0],
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decision: demo.decision,
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distanceFromSL: demo.distanceFromSL,
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reasoning: `Demo decision at ${demo.distanceFromSL}% distance`,
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currentPrice: 180 + Math.random() * 10,
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confidenceScore: 0.7 + Math.random() * 0.2,
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expectedOutcome: 'BETTER_RESULT'
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});
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if (decisionId) {
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decisionIds.push({ id: decisionId, demo });
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console.log(` ✅ Recorded decision ${decisionId}`);
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}
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console.log('');
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}
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console.log('⏱️ Simulating time passage and outcome assessment...\n');
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// Wait a moment to simulate time passage
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await new Promise(resolve => setTimeout(resolve, 2000));
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console.log('🔍 ASSESSING DECISION OUTCOMES:\n');
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for (const { id, demo } of decisionIds) {
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console.log(`📊 Assessing decision ${id}:`);
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console.log(` Outcome: ${demo.outcome}`);
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console.log(` P&L Impact: +$${demo.pnlImpact}`);
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// Assess the outcome
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const assessment = await learner.assessDecisionOutcome({
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decisionId: id,
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actualOutcome: demo.outcome,
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timeToOutcome: 5 + Math.floor(Math.random() * 10), // 5-15 minutes
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pnlImpact: demo.pnlImpact,
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additionalContext: {
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scenario: demo.scenario,
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marketConditions: 'Demo simulation'
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}
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});
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if (assessment) {
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console.log(` ✅ Assessment: ${assessment.wasCorrect ? 'CORRECT' : 'INCORRECT'} (Score: ${assessment.learningScore.toFixed(2)})`);
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}
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console.log('');
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}
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console.log('🧠 ANALYZING LEARNING PATTERNS:\n');
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const patterns = await learner.analyzeDecisionPatterns();
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if (patterns) {
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console.log('📈 SUCCESSFUL DECISION PATTERNS:');
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patterns.successfulPatterns.forEach(pattern => {
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console.log(` ${pattern.decisionType}: ${pattern.successRate.toFixed(1)}% success rate (${pattern.sampleSize} samples)`);
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});
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if (patterns.failurePatterns.length > 0) {
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console.log('\n📉 AREAS FOR IMPROVEMENT:');
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patterns.failurePatterns.forEach(pattern => {
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console.log(` ${pattern.decisionType}: ${pattern.successRate.toFixed(1)}% success rate (${pattern.sampleSize} samples)`);
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});
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}
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console.log('\n🎯 DISTANCE OPTIMIZATION:');
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Object.entries(patterns.distanceOptimization).forEach(([range, data]) => {
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console.log(` ${range}: ${data.successRate.toFixed(1)}% success, optimal threshold: ${data.optimalThreshold.toFixed(2)}%`);
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});
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}
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console.log('\n🚀 GENERATING SMART RECOMMENDATION:\n');
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// Test smart recommendation system
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const recommendation = await learner.getSmartRecommendation({
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distanceFromSL: 2.5,
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symbol: 'SOL-PERP',
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marketConditions: {
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trend: 'BULLISH',
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volatility: 0.05,
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timeOfDay: new Date().getHours()
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}
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});
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console.log(`🎯 Smart Recommendation for 2.5% distance from SL:`);
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console.log(` Suggested Action: ${recommendation.suggestedAction}`);
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console.log(` Confidence: ${(recommendation.confidence * 100).toFixed(1)}%`);
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console.log(` Reasoning: ${recommendation.reasoning}`);
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console.log(` Learning-Based: ${recommendation.learningBased ? 'YES' : 'NO'}`);
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if (recommendation.supportingData) {
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console.log(` Supporting Data: ${recommendation.supportingData.historicalSamples} similar situations`);
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}
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console.log('\n📊 GENERATING COMPREHENSIVE LEARNING REPORT:\n');
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const report = await learner.generateLearningReport();
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if (report) {
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console.log('📋 LEARNING SYSTEM STATUS:');
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console.log(` Total Decisions: ${report.summary.totalDecisions}`);
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console.log(` System Confidence: ${(report.summary.systemConfidence * 100).toFixed(1)}%`);
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console.log(` Successful Patterns: ${report.summary.successfulPatterns}`);
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if (report.recommendations.length > 0) {
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console.log('\n💡 AI RECOMMENDATIONS:');
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report.recommendations.forEach(rec => {
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console.log(` ${rec.type} (${rec.priority}): ${rec.message}`);
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});
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}
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}
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console.log('\n🏖️ BEACH MODE DEMONSTRATION:\n');
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console.log(`
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🌊 ENHANCED BEACH MODE WITH AI LEARNING:
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✅ System now records every decision made near stop loss
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✅ Tracks outcomes and learns from what works/doesn't work
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✅ Adjusts decision thresholds based on historical success
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✅ Provides smart recommendations based on learned patterns
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✅ Continuously improves decision-making quality
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✅ Builds confidence through validated success patterns
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🎯 RESULT: Your AI doesn't just make autonomous decisions...
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It LEARNS from every decision to become smarter!
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🚀 NEXT STEPS:
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1. Start automation with enhanced learning system
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2. Let it run and make decisions autonomously
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3. Check learning dashboard for insights
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4. Watch confidence and success rates improve over time
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5. Enjoy the beach knowing your AI is getting smarter! 🏖️
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`);
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console.log('\n✨ DEMO COMPLETE! Your AI is ready to learn and improve! ✨\n');
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}
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// Run the demonstration
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if (require.main === module) {
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demonstrateAILearning().catch(console.error);
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}
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