416f72181e
New Features: - 📊 Detailed Market Analysis Panel (similar to pro trading interface) * Market sentiment, recommendation, resistance/support levels * Detailed trading setup with entry/exit points * Risk management with R:R ratios and confirmation triggers * Technical indicators (RSI, OBV, VWAP) analysis - 🧠 AI Learning Insights Panel * Real-time learning status and success rates * Winner/Loser trade outcome tracking * AI reflection messages explaining what was learned * Current thresholds and pattern recognition data - 🔮 AI Database Integration * Shows what AI learned from previous trades * Current confidence thresholds and risk parameters * Pattern recognition for symbol/timeframe combinations * Next trade adjustments based on learning - 🎓 Intelligent Learning from Outcomes * Automatic trade outcome analysis (winner/loser) * AI generates learning insights from each trade result * Confidence adjustment based on trade performance * Pattern reinforcement or correction based on results - Beautiful gradient panels with color-coded sections - Clear winner/loser indicators with visual feedback - Expandable detailed analysis view - Real-time learning progress tracking - Completely isolated paper trading (no real money risk) - Real market data integration for authentic learning - Safe practice environment with professional analysis tools This provides a complete AI learning trading simulation where users can: 1. Get real market analysis with detailed reasoning 2. Execute safe paper trades with zero risk 3. See immediate feedback on trade outcomes 4. Learn from AI reflections and insights 5. Understand how AI adapts and improves over time
63 lines
2.3 KiB
JavaScript
63 lines
2.3 KiB
JavaScript
#!/usr/bin/env node
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// Test script to verify cost control implementation
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const { PaperTradingUsageTracker } = require('./lib/paper-trading-config');
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async function testCostControl() {
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console.log('🧪 Testing Paper Trading Cost Control System...\n');
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const tracker = new PaperTradingUsageTracker();
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// Test 1: Check timeframe configurations
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console.log('📊 Timeframe Configurations:');
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const timeframes = [
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{ value: '5', label: '5 Minutes' },
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{ value: '30', label: '30 Minutes' },
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{ value: '60', label: '1 Hour' },
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{ value: '240', label: '4 Hours' }
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];
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timeframes.forEach(tf => {
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const config = tracker.getTimeframeConfig(tf.value);
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console.log(` ${tf.label}: ${config.maxDaily} analyses/day, Risk: ${config.riskLevel}, Cost: ${config.cost}`);
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});
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// Test 2: Usage tracking
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console.log('\n💰 Usage Tracking:');
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const stats = tracker.getTodaysUsage('60');
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console.log(` Today's usage for 1H: ${stats.dailyCount} analyses`);
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console.log(` Estimated cost: $${stats.estimatedDailyCost.toFixed(4)}`);
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console.log(` Hit daily limit: ${stats.hitDailyLimit ? 'Yes' : 'No'}`);
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// Test 3: Cooldown system
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console.log('\n⏱️ Cooldown System:');
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const canAnalyze = tracker.canAnalyze('60');
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console.log(` Can analyze 1H timeframe: ${canAnalyze ? 'Yes' : 'No'}`);
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if (canAnalyze) {
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console.log(' Recording simulated analysis...');
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tracker.recordAnalysis('60');
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const canAnalyzeAgain = tracker.canAnalyze('60');
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console.log(` Can analyze again immediately: ${canAnalyzeAgain ? 'Yes' : 'No'}`);
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const cooldownTime = tracker.getCooldownRemaining('60');
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console.log(` Cooldown remaining: ${Math.ceil(cooldownTime / 1000)} seconds`);
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}
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// Test 4: Cost estimation
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console.log('\n📈 Cost Estimation:');
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const dailyAnalyses = [5, 10, 20, 50];
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dailyAnalyses.forEach(count => {
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const cost = count * 0.006;
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const monthlyEst = cost * 30;
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console.log(` ${count} analyses/day = $${cost.toFixed(3)}/day (~$${monthlyEst.toFixed(2)}/month)`);
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});
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console.log('\n✅ Cost Control System Test Complete!');
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console.log('💡 Your selected timeframes (5m, 30m, 1h, 4h) are configured with appropriate limits.');
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console.log('🛡️ 5-minute cooldowns and daily limits will prevent excessive OpenAI costs.');
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}
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testCostControl().catch(console.error);
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