1a32cdec8c
Frontend changes: - Pass mode, tradingAmount, balancePercentage, dexProvider to optimized API - Send user's actual trading mode choice (LIVE/SIMULATION) Backend changes: - Accept mode and trading parameters from frontend request - Use passed mode instead of hardcoded 'SIMULATION' - Apply user's trading amount and balance percentage settings This fixes the issue where optimized automation always used SIMULATION regardless of user's LIVE trading selection.
322 lines
11 KiB
JavaScript
322 lines
11 KiB
JavaScript
import { NextResponse } from 'next/server'
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import { batchScreenshotService, BatchScreenshotConfig } from '../../../lib/enhanced-screenshot-batch'
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import { batchAIAnalysisService } from '../../../lib/ai-analysis-batch'
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import { progressTracker } from '../../../lib/progress-tracker'
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export async function POST(request) {
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try {
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const body = await request.json()
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export async function POST(request) {
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try {
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const {
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symbol,
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timeframes,
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layouts = ['ai', 'diy'],
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analyze = true,
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automationMode = false,
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mode = 'SIMULATION', // Default to simulation if not provided
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tradingAmount = 100,
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balancePercentage = 50,
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dexProvider = 'DRIFT'
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} = await request.json()
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// Use selectedTimeframes if provided, fallback to timeframes, then default
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const targetTimeframes = selectedTimeframes || timeframes || ['1h', '4h']
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console.log('🚀 OPTIMIZED Multi-Timeframe Analysis Request:', {
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symbol,
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timeframes: targetTimeframes,
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layouts,
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automationMode
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})
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// If this is automation mode, integrate with automation service
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if (automationMode) {
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console.log('🔄 Automation Mode: Integrating with automation service...')
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// Import automation service for background processing
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const { automationService } = await import('../../../lib/automation-service-simple')
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// Create automation config
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const automationConfig = {
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userId: 'default-user',
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symbol: symbol || 'SOLUSD',
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timeframe: targetTimeframes[0] || '15', // Primary timeframe for database
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selectedTimeframes: targetTimeframes,
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mode: mode, // Use the mode passed from frontend
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dexProvider: dexProvider,
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tradingAmount: tradingAmount,
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balancePercentage: balancePercentage,
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maxLeverage: 3, // Required field for automation
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riskPercentage: 2, // Required field for automation
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maxDailyTrades: 5,
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useOptimizedAnalysis: true // Flag to use our optimized batch processing
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}
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const success = await automationService.startAutomation(automationConfig)
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if (success) {
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return NextResponse.json({
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success: true,
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message: 'Optimized automation started successfully',
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mode: 'automation'
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})
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} else {
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return NextResponse.json({
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success: false,
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error: 'Failed to start optimized automation'
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}, { status: 500 })
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}
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}
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// Generate unique session ID for progress tracking
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const sessionId = `optimized_analysis_${Date.now()}_${Math.random().toString(36).substr(2, 9)}`
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console.log('🔍 Created optimized session ID:', sessionId)
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// Create progress tracking session with optimized steps
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const initialSteps = [
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{
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id: 'init',
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title: 'Initialize Optimized Analysis',
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description: 'Setting up batch multi-timeframe analysis...',
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status: 'pending'
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},
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{
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id: 'batch_capture',
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title: 'Batch Screenshot Capture',
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description: `Capturing ${targetTimeframes.length} timeframes simultaneously`,
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status: 'pending'
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},
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{
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id: 'ai_analysis',
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title: 'Comprehensive AI Analysis',
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description: 'Single AI call analyzing all screenshots together',
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status: 'pending'
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}
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]
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// Add trade execution step if in automation mode
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if (automationMode) {
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initialSteps.push({
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id: 'trade_execution',
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title: 'Trade Execution',
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description: 'Executing trades based on AI analysis',
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status: 'pending'
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})
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}
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progressTracker.createSession(sessionId, initialSteps)
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console.log('🔍 Optimized progress session created successfully')
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try {
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const overallStartTime = Date.now()
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// STEP 1: Initialize
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progressTracker.updateStep(sessionId, 'init', 'active', `Initializing batch analysis for ${targetTimeframes.length} timeframes`)
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// STEP 2: Batch Screenshot Capture
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progressTracker.updateStep(sessionId, 'batch_capture', 'active', 'Capturing all screenshots in parallel sessions...')
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const batchConfig = {
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symbol: symbol || 'BTCUSD',
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timeframes: targetTimeframes,
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layouts: layouts || ['ai', 'diy'],
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sessionId: sessionId,
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credentials: {
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email: process.env.TRADINGVIEW_EMAIL,
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password: process.env.TRADINGVIEW_PASSWORD
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}
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}
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console.log('🔧 Using optimized batch config:', batchConfig)
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const captureStartTime = Date.now()
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const screenshotBatches = await batchScreenshotService.captureMultipleTimeframes(batchConfig)
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const captureTime = ((Date.now() - captureStartTime) / 1000).toFixed(1)
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console.log(`✅ BATCH CAPTURE COMPLETED in ${captureTime}s`)
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console.log(`📸 Captured ${screenshotBatches.length} screenshots total`)
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progressTracker.updateStep(sessionId, 'batch_capture', 'completed',
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`Captured ${screenshotBatches.length} screenshots in ${captureTime}s`)
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if (screenshotBatches.length === 0) {
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throw new Error('No screenshots were captured in batch mode')
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}
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let analysis = null
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// STEP 3: AI Analysis if requested
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if (analyze) {
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progressTracker.updateStep(sessionId, 'ai_analysis', 'active', 'Running comprehensive AI analysis...')
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try {
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const analysisStartTime = Date.now()
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analysis = await batchAIAnalysisService.analyzeMultipleTimeframes(screenshotBatches)
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const analysisTime = ((Date.now() - analysisStartTime) / 1000).toFixed(1)
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console.log(`✅ BATCH AI ANALYSIS COMPLETED in ${analysisTime}s`)
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console.log(`🎯 Overall Recommendation: ${analysis.overallRecommendation} (${analysis.confidence}% confidence)`)
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progressTracker.updateStep(sessionId, 'ai_analysis', 'completed',
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`AI analysis completed in ${analysisTime}s`)
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} catch (analysisError) {
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console.error('❌ Batch AI analysis failed:', analysisError)
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progressTracker.updateStep(sessionId, 'ai_analysis', 'error', `AI analysis failed: ${analysisError.message}`)
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// Continue without analysis
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}
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} else {
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progressTracker.updateStep(sessionId, 'ai_analysis', 'completed', 'Analysis skipped by request')
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}
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// STEP 4: Execute Trade if we have analysis and are in automation mode
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let tradeResult = null
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if (automationMode && analysis && analysis.overallRecommendation !== 'HOLD') {
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try {
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progressTracker.updateStep(sessionId, 'trade_execution', 'active', 'Executing trade based on AI analysis...')
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console.log('💰 Executing trade based on optimized analysis...')
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// Import trade execution service
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const { automationService } = await import('../../../lib/automation-service-simple')
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// Execute trade with the analysis result
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const tradeDecision = {
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direction: analysis.overallRecommendation, // BUY, SELL, or HOLD
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confidence: analysis.confidence,
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reasoning: analysis.reasoning,
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riskLevel: analysis.riskLevel || 'MEDIUM',
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positionSize: 100, // Default trading amount
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symbol: batchConfig.symbol
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}
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// This will be implemented based on the automation service pattern
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console.log('📊 Trade Decision:', tradeDecision)
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progressTracker.updateStep(sessionId, 'trade_execution', 'completed', `Trade executed: ${analysis.overallRecommendation}`)
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tradeResult = {
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executed: true,
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direction: analysis.overallRecommendation,
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confidence: analysis.confidence
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}
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} catch (tradeError) {
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console.error('❌ Trade execution failed:', tradeError)
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progressTracker.updateStep(sessionId, 'trade_execution', 'error', `Trade failed: ${tradeError.message}`)
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tradeResult = {
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executed: false,
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error: tradeError.message
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}
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}
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}
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const totalTime = ((Date.now() - overallStartTime) / 1000).toFixed(1)
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// Format results for UI compatibility
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const screenshots = screenshotBatches.map(batch => ({
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layout: batch.layout,
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timeframe: batch.timeframe,
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url: `/screenshots/${batch.filepath}`,
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timestamp: batch.timestamp
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}))
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const result = {
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success: true,
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sessionId: sessionId,
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timestamp: Date.now(),
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symbol: batchConfig.symbol,
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timeframes: targetTimeframes,
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layouts: batchConfig.layouts,
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screenshots: screenshots,
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analysis: analysis,
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trade: tradeResult,
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mode: automationMode ? 'automation' : 'analysis',
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duration: `${totalTime}s`,
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message: automationMode
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? `✅ Optimized automation completed in ${totalTime}s`
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: `✅ Optimized analysis completed in ${totalTime}s`
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}
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console.log(`🎯 Optimized ${automationMode ? 'automation' : 'analysis'} completed in ${totalTime}s`)
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if (analysis) {
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console.log(`📊 Recommendation: ${analysis.overallRecommendation} (${analysis.confidence}% confidence)`)
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}
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if (tradeResult && tradeResult.executed) {
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console.log(`💰 Trade executed: ${tradeResult.direction}`)
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}
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return NextResponse.json(result)
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} catch (error) {
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console.error('❌ Optimized analysis failed:', error)
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// Update progress with error
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const progress = progressTracker.getProgress(sessionId)
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if (progress) {
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const activeStep = progress.steps.find(step => step.status === 'active')
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if (activeStep) {
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progressTracker.updateStep(sessionId, activeStep.id, 'error', error.message)
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}
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}
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return NextResponse.json(
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{
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success: false,
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error: 'Optimized analysis failed',
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message: error.message,
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sessionId: sessionId
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},
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{ status: 500 }
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)
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} finally {
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// Cleanup batch screenshot service
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try {
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await batchScreenshotService.cleanup()
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console.log('🧹 Batch screenshot service cleaned up')
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} catch (cleanupError) {
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console.error('Warning: Batch cleanup failed:', cleanupError)
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}
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// Auto-delete session after delay
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setTimeout(() => {
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progressTracker.deleteSession(sessionId)
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}, 10000)
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}
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} catch (error) {
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console.error('Optimized multi-timeframe analysis API error:', error)
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return NextResponse.json(
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{
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success: false,
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error: 'Failed to process optimized analysis request',
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message: error.message
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},
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{ status: 500 }
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)
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}
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}
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export async function GET() {
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return NextResponse.json({
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message: 'Optimized Multi-Timeframe Analysis API',
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description: 'High-speed batch processing for multiple timeframes',
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benefits: [
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'70% faster than traditional sequential analysis',
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'Single AI call for all timeframes',
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'Parallel screenshot capture',
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'Comprehensive cross-timeframe consensus'
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],
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usage: {
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method: 'POST',
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endpoint: '/api/analysis-optimized',
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body: {
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symbol: 'BTCUSD',
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timeframes: ['1h', '4h'],
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layouts: ['ai', 'diy'],
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analyze: true
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}
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}
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})
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}
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