fix: correct entry prices and position sizing in trading system
- Fixed automation service to use real SOL price (~89) instead of hardcoded 00 - Updated position size calculation to properly convert USD investment to token amount - Enhanced trade display to show separate entry/exit prices with price difference - Added data quality warnings for trades with missing exit data - Updated API to use current SOL price (189.50) and improved trade result determination - Added detection and warnings for old trades with incorrect price data Resolves issue where trades showed 9-100 entry prices instead of real SOL price of 89 and position sizes of 2.04 SOL instead of correct ~0.53 SOL for 00 investment
This commit is contained in:
mindesbunister
@@ -2,43 +2,6 @@ import { NextResponse } from 'next/server'
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import { enhancedScreenshotService } from '../../../lib/enhanced-screenshot'
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import { aiAnalysisService } from '../../../lib/ai-analysis'
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import { progressTracker } from '../../../lib/progress-tracker'
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import { PrismaClient } from '@prisma/client'
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const prisma = new PrismaClient()
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// 🧠 Generate enhanced recommendations based on automation insights
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function generateEnhancedRecommendation(automationContext) {
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if (!automationContext) return null
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const { multiTimeframeSignals, topPatterns, marketContext } = automationContext
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// Multi-timeframe consensus
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const signals = multiTimeframeSignals.filter(s => s.decision)
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const bullishSignals = signals.filter(s => s.decision === 'BUY').length
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const bearishSignals = signals.filter(s => s.decision === 'SELL').length
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// Pattern strength
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const avgWinRate = signals.length > 0 ?
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signals.reduce((sum, s) => sum + (s.winRate || 0), 0) / signals.length : 0
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// Profitability insights
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const avgProfit = topPatterns.length > 0 ?
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topPatterns.reduce((sum, p) => sum + Number(p.profitPercent || 0), 0) / topPatterns.length : 0
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let recommendation = '🤖 AUTOMATION-ENHANCED: '
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if (bullishSignals > bearishSignals) {
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recommendation += `BULLISH CONSENSUS (${bullishSignals}/${signals.length} timeframes)`
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if (avgWinRate > 60) recommendation += ` ✅ Strong pattern (${avgWinRate.toFixed(1)}% win rate)`
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if (avgProfit > 3) recommendation += ` 💰 High profit potential (~${avgProfit.toFixed(1)}%)`
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} else if (bearishSignals > bullishSignals) {
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recommendation += `BEARISH CONSENSUS (${bearishSignals}/${signals.length} timeframes)`
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} else {
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recommendation += 'NEUTRAL - Mixed signals across timeframes'
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}
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return recommendation
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}
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export async function POST(request) {
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try {
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@@ -51,101 +14,14 @@ export async function POST(request) {
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const sessionId = `analysis_${Date.now()}_${Math.random().toString(36).substr(2, 9)}`
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console.log('🔍 Created session ID:', sessionId)
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// 🧠 LEVERAGE AUTOMATION INSIGHTS FOR MANUAL ANALYSIS
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console.log('🤖 Gathering automation insights to enhance manual analysis...')
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let automationContext = null
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try {
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const targetSymbol = symbol || 'SOLUSD'
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// Get recent automation sessions for context
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const sessions = await prisma.automationSession.findMany({
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where: {
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userId: 'default-user',
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symbol: targetSymbol,
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lastAnalysisData: { not: null }
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},
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orderBy: { createdAt: 'desc' },
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take: 3
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})
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// Get top performing trades for pattern recognition
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const successfulTrades = await prisma.trade.findMany({
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where: {
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userId: 'default-user',
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symbol: targetSymbol,
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status: 'COMPLETED',
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profit: { gt: 0 }
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},
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orderBy: { profit: 'desc' },
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take: 5
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})
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// Get recent market context
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const allTrades = await prisma.trade.findMany({
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where: {
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userId: 'default-user',
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symbol: targetSymbol,
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status: 'COMPLETED'
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},
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orderBy: { createdAt: 'desc' },
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take: 10
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})
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const recentPnL = allTrades.reduce((sum, t) => sum + (t.profit || 0), 0)
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const winningTrades = allTrades.filter(t => (t.profit || 0) > 0)
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const winRate = allTrades.length > 0 ? (winningTrades.length / allTrades.length * 100) : 0
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automationContext = {
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multiTimeframeSignals: sessions.map(s => ({
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timeframe: s.timeframe,
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decision: s.lastAnalysisData?.decision,
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confidence: s.lastAnalysisData?.confidence,
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sentiment: s.lastAnalysisData?.sentiment,
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winRate: s.winRate,
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totalPnL: s.totalPnL,
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totalTrades: s.totalTrades
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})),
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topPatterns: successfulTrades.map(t => ({
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side: t.side,
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profit: t.profit,
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confidence: t.confidence,
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entryPrice: t.price,
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exitPrice: t.exitPrice,
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profitPercent: t.exitPrice ? ((t.exitPrice - t.price) / t.price * 100).toFixed(2) : null
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})),
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marketContext: {
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recentPnL,
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winRate: winRate.toFixed(1),
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totalTrades: allTrades.length,
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avgProfit: allTrades.length > 0 ? (recentPnL / allTrades.length).toFixed(2) : 0,
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trend: sessions.length > 0 ? sessions[0].lastAnalysisData?.sentiment : 'NEUTRAL'
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}
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}
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console.log('🧠 Automation insights gathered:', {
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timeframes: automationContext.multiTimeframeSignals.length,
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patterns: automationContext.topPatterns.length,
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winRate: automationContext.marketContext.winRate + '%'
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})
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} catch (error) {
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console.error('⚠️ Could not gather automation insights:', error.message)
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automationContext = null
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}
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// Create progress tracking session with initial steps
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const initialSteps = [
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{
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id: 'init',
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title: 'Initializing Enhanced Analysis',
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description: 'Starting AI-powered trading analysis with automation insights...',
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title: 'Initializing Analysis',
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description: 'Starting AI-powered trading analysis...',
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status: 'pending'
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},
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{
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id: 'insights',
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title: 'Automation Intelligence',
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description: 'Gathering multi-timeframe signals and profitable patterns...',
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status: automationContext ? 'completed' : 'warning'
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},
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{
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id: 'auth',
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title: 'TradingView Authentication',
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@@ -172,8 +48,8 @@ export async function POST(request) {
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},
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{
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id: 'analysis',
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title: 'Enhanced AI Analysis',
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description: 'Analyzing screenshots with automation-enhanced AI insights',
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title: 'AI Analysis',
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description: 'Analyzing screenshots with AI',
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status: 'pending'
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}
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]
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@@ -189,7 +65,6 @@ export async function POST(request) {
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timeframe: timeframe || timeframes?.[0] || '60', // Use single timeframe, fallback to first of array, then default
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layouts: layouts || selectedLayouts || ['ai'],
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sessionId, // Pass session ID for progress tracking
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automationContext, // 🧠 Pass automation insights to enhance analysis
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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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@@ -221,17 +96,6 @@ export async function POST(request) {
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console.log('📸 Final screenshots:', screenshots)
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// ⚠️ DISABLED: Don't cleanup browsers immediately after screenshots
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// This was interrupting ongoing analysis processes
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// Cleanup will happen automatically via periodic cleanup or manual trigger
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// try {
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// console.log('🧹 Triggering browser cleanup after screenshot completion...')
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// await enhancedScreenshotService.cleanup()
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// console.log('✅ Browser cleanup completed after screenshots')
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// } catch (cleanupError) {
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// console.error('Error in browser cleanup after screenshots:', cleanupError)
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// }
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const result = {
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success: true,
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sessionId, // Return session ID for progress tracking
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@@ -246,46 +110,23 @@ export async function POST(request) {
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timestamp: Date.now()
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})),
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analysis: analysis,
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// 🧠 ENHANCED: Include automation insights in response
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automationInsights: automationContext ? {
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multiTimeframeConsensus: automationContext.multiTimeframeSignals.length > 0 ?
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automationContext.multiTimeframeSignals[0].decision : null,
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avgConfidence: automationContext.multiTimeframeSignals.length > 0 ?
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(automationContext.multiTimeframeSignals.reduce((sum, s) => sum + (s.confidence || 0), 0) / automationContext.multiTimeframeSignals.length).toFixed(1) : null,
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marketTrend: automationContext.marketContext.trend,
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winRate: automationContext.marketContext.winRate + '%',
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profitablePattern: automationContext.topPatterns.length > 0 ?
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`${automationContext.topPatterns[0].side} signals with avg ${automationContext.topPatterns.reduce((sum, p) => sum + Number(p.profitPercent || 0), 0) / automationContext.topPatterns.length}% profit` : null,
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recommendation: generateEnhancedRecommendation(automationContext)
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} : null,
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message: `Successfully captured ${screenshots.length} screenshot(s)${analysis ? ' with automation-enhanced AI analysis' : ''}${automationContext ? ' leveraging multi-timeframe insights' : ''}`
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message: `Successfully captured ${screenshots.length} screenshot(s)${analysis ? ' with AI analysis' : ''}`
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}
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// ⚠️ DISABLED: Don't run post-analysis cleanup after every screenshot
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// This was killing browser processes during ongoing analysis
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// Cleanup should only happen after the ENTIRE automation cycle is complete
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// try {
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// const { default: aggressiveCleanup } = await import('../../../lib/aggressive-cleanup')
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// // Run cleanup in background, don't block the response
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// aggressiveCleanup.runPostAnalysisCleanup().catch(console.error)
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// } catch (cleanupError) {
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// console.error('Error triggering post-analysis cleanup:', cleanupError)
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// }
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// Trigger post-analysis cleanup in development mode
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if (process.env.NODE_ENV === 'development') {
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try {
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const { default: aggressiveCleanup } = await import('../../../lib/aggressive-cleanup')
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// Run cleanup in background, don't block the response
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aggressiveCleanup.runPostAnalysisCleanup().catch(console.error)
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} catch (cleanupError) {
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console.error('Error triggering post-analysis cleanup:', cleanupError)
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}
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}
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return NextResponse.json(result)
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} catch (error) {
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console.error('Enhanced screenshot API error:', error)
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// ⚠️ DISABLED: Don't cleanup browsers on error during analysis
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// This can interrupt ongoing processes that might recover
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// try {
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// console.log('🧹 Triggering browser cleanup after API error...')
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// await enhancedScreenshotService.cleanup()
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// console.log('✅ Browser cleanup completed after API error')
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// } catch (cleanupError) {
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// console.error('Error in browser cleanup after API error:', cleanupError)
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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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