feat: Complete AI Learning Integration & Position Scaling DCA System
- Integrated SimplifiedStopLossLearner into automation - Every AI decision now recorded for learning (stop loss, take profit, confidence) - Trade outcomes tracked and compared to AI predictions - Learning patterns improve future AI decisions - Enhanced status dashboard with learning insights - Proper DCA: increase position size + adjust existing SL/TP (not create new) - AI-calculated optimal levels for scaled positions - Prevents order fragmentation (fixes 24+ order problem) - Unified risk management for entire scaled position TIMEFRAME-AWARE INTERVALS: - Scalping (5m/15m): 5-15 minute analysis intervals - Day Trading (1h/4h): 10-30 minute intervals - Swing Trading (4h/1d): 23-68 minute intervals - Perfect for 5-minute scalping with DCA protection - 2-hour DCA cooldown prevents order spam - Position existence checks before new trades - Direction matching validation - Learning-based decision improvements - AI calculates ALL levels (entry, SL, TP, leverage, scaling) - Every calculation recorded and learned from - Position scaling uses AI intelligence - Timeframe-appropriate analysis frequency - Professional order management - Continuous learning and improvement ADDRESSES ALL USER CONCERNS: - 5-minute scalping compatibility ✅ - Position scaling DCA (adjust existing SL/TP) ✅ - AI calculations being learned from ✅ - No order fragmentation ✅ - Intelligent automation with learning ✅ Files: automation, consolidation APIs, learning integration, tests, documentation
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mindesbunister
156
test-position-scaling-dca.js
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156
test-position-scaling-dca.js
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// Test Position Scaling DCA - Proper DCA Implementation
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// This demonstrates how DCA should work: adjust existing position + SL/TP instead of creating new orders
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async function testPositionScalingDCA() {
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try {
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console.log('=== Testing Position Scaling DCA System ===\n');
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const baseUrl = process.env.INTERNAL_API_URL || 'http://localhost:9001';
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// 1. Check current position
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console.log('🔍 Checking current position...');
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const positionResponse = await fetch(`${baseUrl}/api/drift/positions`);
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const positionData = await positionResponse.json();
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if (!positionData.success || positionData.positions.length === 0) {
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console.log('❌ No existing position found. Need a position to test DCA scaling.');
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console.log('💡 Create a position first, then test the scaling feature.');
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return;
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}
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const currentPosition = positionData.positions[0];
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console.log('✅ Current position found:');
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console.log(` ${currentPosition.side} ${currentPosition.size.toFixed(4)} ${currentPosition.symbol}`);
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console.log(` Entry Price: $${currentPosition.entryPrice.toFixed(4)}`);
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console.log(` Current Value: $${(currentPosition.size * currentPosition.entryPrice).toFixed(2)}`);
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console.log('');
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// 2. Check existing orders (SL/TP)
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console.log('📋 Checking existing SL/TP orders...');
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const ordersResponse = await fetch(`${baseUrl}/api/drift/orders`);
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const ordersData = await ordersResponse.json();
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if (ordersData.success && ordersData.orders.length > 0) {
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const reduceOnlyOrders = ordersData.orders.filter(order =>
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order.reduceOnly && order.status === 'OPEN'
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);
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console.log(` Found ${reduceOnlyOrders.length} existing SL/TP orders:`);
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reduceOnlyOrders.forEach(order => {
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console.log(` - ${order.orderType}: ${order.side} @ $${order.triggerPrice.toFixed(4)}`);
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});
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} else {
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console.log(' No existing SL/TP orders found');
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}
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console.log('');
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// 3. Create mock AI analysis for optimal levels
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const mockAIAnalysis = {
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recommendation: currentPosition.side.toLowerCase() === 'long' ? 'BUY' : 'SELL',
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confidence: 85,
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reasoning: 'Test DCA scaling with AI-calculated optimal levels',
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stopLoss: {
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price: currentPosition.side.toLowerCase() === 'long'
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? currentPosition.entryPrice * 0.98 // 2% below for long
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: currentPosition.entryPrice * 1.02, // 2% above for short
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reasoning: 'AI-calculated optimal stop loss level'
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},
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takeProfits: {
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tp1: {
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price: currentPosition.side.toLowerCase() === 'long'
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? currentPosition.entryPrice * 1.05 // 5% above for long
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: currentPosition.entryPrice * 0.95, // 5% below for short
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reasoning: 'AI-calculated optimal take profit level'
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}
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}
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};
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console.log('🧠 Mock AI Analysis for DCA:');
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console.log(` Recommendation: ${mockAIAnalysis.recommendation} (${mockAIAnalysis.confidence}%)`);
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console.log(` AI Stop Loss: $${mockAIAnalysis.stopLoss.price.toFixed(4)}`);
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console.log(` AI Take Profit: $${mockAIAnalysis.takeProfits.tp1.price.toFixed(4)}`);
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console.log('');
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// 4. Execute position scaling DCA
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const dcaAmount = 25; // Add $25 to position
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console.log(`💰 Executing Position Scaling DCA: Adding $${dcaAmount}`);
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console.log('🔧 This will:');
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console.log(' 1. Cancel existing SL/TP orders');
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console.log(' 2. Add to position size');
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console.log(' 3. Calculate new average entry price');
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console.log(' 4. Place new SL/TP for ENTIRE scaled position');
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console.log('');
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const scalingResponse = await fetch(`${baseUrl}/api/drift/scale-position`, {
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method: 'POST',
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headers: { 'Content-Type': 'application/json' },
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body: JSON.stringify({
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dcaAmount: dcaAmount,
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analysis: mockAIAnalysis
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})
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});
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const scalingResult = await scalingResponse.json();
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if (scalingResult.success) {
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console.log('✅ POSITION SCALING SUCCESSFUL!');
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console.log('');
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console.log('📊 SCALING RESULTS:');
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console.log(' ═══ BEFORE ═══');
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console.log(` Size: ${scalingResult.scalingResult.originalSize.toFixed(4)} SOL`);
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console.log(` Entry: $${scalingResult.scalingResult.originalEntryPrice.toFixed(4)}`);
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console.log(` Value: $${scalingResult.scalingResult.originalValue.toFixed(2)}`);
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console.log('');
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console.log(' ═══ DCA ADDITION ═══');
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console.log(` Added: ${scalingResult.scalingResult.dcaSize.toFixed(4)} SOL @ $${scalingResult.scalingResult.dcaPrice.toFixed(4)}`);
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console.log(` Value: $${scalingResult.scalingResult.dcaValue.toFixed(2)}`);
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console.log('');
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console.log(' ═══ AFTER (SCALED) ═══');
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console.log(` Size: ${scalingResult.scalingResult.newTotalSize.toFixed(4)} SOL`);
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console.log(` Average: $${scalingResult.scalingResult.newAveragePrice.toFixed(4)}`);
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console.log(` Value: $${scalingResult.scalingResult.newTotalValue.toFixed(2)}`);
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console.log('');
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console.log('🛡️ NEW RISK MANAGEMENT:');
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console.log(` Stop Loss: $${scalingResult.scalingResult.newStopLoss.toFixed(4)} (for ENTIRE position)`);
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console.log(` Take Profit: $${scalingResult.scalingResult.newTakeProfit.toFixed(4)} (for ENTIRE position)`);
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console.log(` Used AI Levels: ${scalingResult.scalingResult.usedAILevels ? 'YES' : 'NO'}`);
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console.log('');
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console.log('🔗 TRANSACTION IDs:');
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console.log(` DCA Trade: ${scalingResult.scalingResult.dcaTxId || 'N/A'}`);
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console.log(` Stop Loss: ${scalingResult.scalingResult.stopLossTxId || 'N/A'}`);
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console.log(` Take Profit: ${scalingResult.scalingResult.takeProfitTxId || 'N/A'}`);
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} else {
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console.log('❌ POSITION SCALING FAILED:');
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console.log(` Error: ${scalingResult.error}`);
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if (scalingResult.details) {
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console.log(` Details: ${scalingResult.details}`);
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}
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}
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console.log('\n=== POSITION SCALING vs FRAGMENTED ORDERS ===');
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console.log('✅ GOOD (Position Scaling):');
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console.log(' • ONE position with adjusted size and average price');
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console.log(' • ONE stop loss order covering entire position');
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console.log(' • ONE take profit order covering entire position');
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console.log(' • Clean order book, unified risk management');
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console.log('');
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console.log('❌ BAD (Fragmented Orders - what caused 24+ orders):');
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console.log(' • Multiple separate positions');
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console.log(' • Multiple separate stop loss orders');
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console.log(' • Multiple separate take profit orders');
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console.log(' • Messy order book, complex risk management');
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console.log('');
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console.log('💡 SOLUTION: Always use position scaling for DCA!');
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console.log('\n=== Test Complete ===');
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} catch (error) {
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console.error('❌ Test failed:', error.message);
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console.error(error.stack);
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}
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}
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// Run test
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console.log('🚀 Starting Position Scaling DCA Test...\n');
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testPositionScalingDCA();
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