71e1a64b5d
- 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
957 lines
33 KiB
TypeScript
957 lines
33 KiB
TypeScript
import { PrismaClient } from '@prisma/client'
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import { aiAnalysisService, AnalysisResult } from './ai-analysis'
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import { jupiterDEXService } from './jupiter-dex-service'
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import { enhancedScreenshotService } from './enhanced-screenshot-simple'
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import { TradingViewCredentials } from './tradingview-automation'
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import { progressTracker } from './progress-tracker'
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import aggressiveCleanup from './aggressive-cleanup'
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import { analysisCompletionFlag } from './analysis-completion-flag'
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const prisma = new PrismaClient()
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export interface AutomationConfig {
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userId: string
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mode: 'SIMULATION' | 'LIVE'
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symbol: string
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timeframe: string
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tradingAmount: number
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maxLeverage: number
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stopLossPercent: number
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takeProfitPercent: number
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maxDailyTrades: number
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riskPercentage: number
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}
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export interface AutomationStatus {
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isActive: boolean
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mode: 'SIMULATION' | 'LIVE'
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symbol: string
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timeframe: string
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totalTrades: number
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successfulTrades: number
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winRate: number
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totalPnL: number
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lastAnalysis?: Date
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lastTrade?: Date
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nextScheduled?: Date
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errorCount: number
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lastError?: string
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}
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export class AutomationService {
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private isRunning = false
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private config: AutomationConfig | null = null
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private intervalId: NodeJS.Timeout | null = null
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private stats = {
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totalTrades: 0,
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successfulTrades: 0,
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winRate: 0,
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totalPnL: 0,
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errorCount: 0,
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lastError: null as string | null
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}
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async startAutomation(config: AutomationConfig): Promise<boolean> {
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try {
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if (this.isRunning) {
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throw new Error('Automation is already running')
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}
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this.config = config
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this.isRunning = true
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console.log(`🤖 Starting automation for ${config.symbol} ${config.timeframe} in ${config.mode} mode`)
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// Ensure user exists in database
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await prisma.user.upsert({
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where: { id: config.userId },
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update: {},
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create: {
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id: config.userId,
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email: `${config.userId}@example.com`,
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name: config.userId,
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createdAt: new Date(),
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updatedAt: new Date()
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}
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})
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// Delete any existing automation session for this user/symbol/timeframe
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await prisma.automationSession.deleteMany({
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where: {
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userId: config.userId,
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symbol: config.symbol,
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timeframe: config.timeframe
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}
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})
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// Create new automation session in database
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await prisma.automationSession.create({
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data: {
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userId: config.userId,
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status: 'ACTIVE',
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mode: config.mode,
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symbol: config.symbol,
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timeframe: config.timeframe,
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settings: {
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tradingAmount: config.tradingAmount,
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maxLeverage: config.maxLeverage,
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stopLossPercent: config.stopLossPercent,
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takeProfitPercent: config.takeProfitPercent,
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maxDailyTrades: config.maxDailyTrades,
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riskPercentage: config.riskPercentage
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},
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startBalance: config.tradingAmount,
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currentBalance: config.tradingAmount,
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createdAt: new Date(),
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updatedAt: new Date()
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}
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})
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// Start automation cycle
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this.startAutomationCycle()
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return true
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} catch (error) {
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console.error('Failed to start automation:', error)
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this.stats.errorCount++
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this.stats.lastError = error instanceof Error ? error.message : 'Unknown error'
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return false
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}
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}
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private startAutomationCycle(): void {
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if (!this.config) return
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// Get interval in milliseconds based on timeframe
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const intervalMs = this.getIntervalFromTimeframe(this.config.timeframe)
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console.log(`🔄 Starting automation cycle every ${intervalMs/1000} seconds`)
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this.intervalId = setInterval(async () => {
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if (this.isRunning && this.config) {
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await this.runAutomationCycle()
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}
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}, intervalMs)
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// Run first cycle immediately
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this.runAutomationCycle()
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}
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private getIntervalFromTimeframe(timeframe: string): number {
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const intervals: { [key: string]: number } = {
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'1m': 60 * 1000,
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'3m': 3 * 60 * 1000,
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'5m': 5 * 60 * 1000,
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'15m': 15 * 60 * 1000,
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'30m': 30 * 60 * 1000,
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'1h': 60 * 60 * 1000,
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'2h': 2 * 60 * 60 * 1000,
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'4h': 4 * 60 * 60 * 1000,
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'1d': 24 * 60 * 60 * 1000
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}
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return intervals[timeframe] || intervals['1h'] // Default to 1 hour
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}
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private async runAutomationCycle(): Promise<void> {
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if (!this.config) return
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try {
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console.log(`🔍 Running automation cycle for ${this.config.symbol} ${this.config.timeframe}`)
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// Step 1: Check daily trade limit
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const todayTrades = await this.getTodayTradeCount(this.config.userId)
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if (todayTrades >= this.config.maxDailyTrades) {
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console.log(`📊 Daily trade limit reached (${todayTrades}/${this.config.maxDailyTrades})`)
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// Run cleanup even when trade limit is reached
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await this.runPostCycleCleanup('trade_limit_reached')
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return
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}
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// Step 2: Take screenshot and analyze
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const analysisResult = await this.performAnalysis()
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if (!analysisResult) {
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console.log('❌ Analysis failed, skipping cycle')
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// Run cleanup when analysis fails
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await this.runPostCycleCleanup('analysis_failed')
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return
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}
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// Step 3: Store analysis for learning
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await this.storeAnalysisForLearning(analysisResult)
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// Step 4: Update session with latest analysis
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await this.updateSessionWithAnalysis(analysisResult)
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// Step 5: Make trading decision
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const tradeDecision = await this.makeTradeDecision(analysisResult)
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if (!tradeDecision) {
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console.log('📊 No trading opportunity found')
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// Run cleanup when no trading opportunity
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await this.runPostCycleCleanup('no_opportunity')
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return
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}
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// Step 6: Execute trade
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await this.executeTrade(tradeDecision)
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// Run cleanup after successful trade execution
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await this.runPostCycleCleanup('trade_executed')
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} catch (error) {
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console.error('Error in automation cycle:', error)
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this.stats.errorCount++
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this.stats.lastError = error instanceof Error ? error.message : 'Unknown error'
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// Run cleanup on error
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await this.runPostCycleCleanup('error')
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}
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}
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private async runPostCycleCleanup(reason: string): Promise<void> {
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console.log(`🧹 Running post-cycle cleanup (reason: ${reason})`)
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// Longer delay to ensure all analysis processes AND trading decision have finished
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await new Promise(resolve => setTimeout(resolve, 10000)) // 10 seconds
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try {
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// Use the new post-analysis cleanup that respects completion flags
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await aggressiveCleanup.runPostAnalysisCleanup()
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console.log(`✅ Post-cycle cleanup completed for: ${reason}`)
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} catch (error) {
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console.error('Error in post-cycle cleanup:', error)
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}
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}
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private async performAnalysis(): Promise<{
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screenshots: string[]
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analysis: AnalysisResult | null
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} | null> {
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// Generate unique session ID for this analysis
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const sessionId = `automation-${Date.now()}-${Math.random().toString(36).substr(2, 9)}`
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// Mark the start of analysis cycle to prevent cleanup interruption
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analysisCompletionFlag.startAnalysisCycle(sessionId)
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try {
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console.log(`📸 Starting multi-timeframe analysis with dual layouts... (Session: ${sessionId})`)
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// Create progress tracking session
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const progressSteps = [
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{ id: 'init', title: 'Initialize', description: 'Starting multi-timeframe analysis', status: 'pending' as const },
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{ id: 'capture', title: 'Capture', description: 'Capturing screenshots for all timeframes', status: 'pending' as const },
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{ id: 'analysis', title: 'Analysis', description: 'Running AI analysis on screenshots', status: 'pending' as const },
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{ id: 'complete', title: 'Complete', description: 'Analysis complete', status: 'pending' as const }
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]
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progressTracker.createSession(sessionId, progressSteps)
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progressTracker.updateStep(sessionId, 'init', 'active', 'Starting multi-timeframe analysis...')
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// Multi-timeframe analysis: 15m, 1h, 2h, 4h
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const timeframes = ['15', '1h', '2h', '4h']
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const symbol = this.config!.symbol
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console.log(`🔍 Analyzing ${symbol} across timeframes: ${timeframes.join(', ')} with AI + DIY layouts`)
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progressTracker.updateStep(sessionId, 'init', 'completed', `Starting analysis for ${timeframes.length} timeframes`)
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progressTracker.updateStep(sessionId, 'capture', 'active', 'Capturing screenshots...')
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// Analyze each timeframe with both AI and DIY layouts
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const multiTimeframeResults = await this.analyzeMultiTimeframeWithDualLayouts(symbol, timeframes, sessionId)
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if (multiTimeframeResults.length === 0) {
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console.log('❌ No multi-timeframe analysis results')
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progressTracker.updateStep(sessionId, 'capture', 'error', 'No analysis results captured')
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progressTracker.deleteSession(sessionId)
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// Mark analysis as complete to allow cleanup
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analysisCompletionFlag.markAnalysisComplete(sessionId)
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return null
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}
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progressTracker.updateStep(sessionId, 'capture', 'completed', `Captured ${multiTimeframeResults.length} timeframe analyses`)
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progressTracker.updateStep(sessionId, 'analysis', 'active', 'Processing multi-timeframe results...')
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// Process and combine multi-timeframe results
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const combinedResult = this.combineMultiTimeframeAnalysis(multiTimeframeResults)
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if (!combinedResult.analysis) {
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console.log('❌ Failed to combine multi-timeframe analysis')
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progressTracker.updateStep(sessionId, 'analysis', 'error', 'Failed to combine analysis results')
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progressTracker.deleteSession(sessionId)
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// Mark analysis as complete to allow cleanup
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analysisCompletionFlag.markAnalysisComplete(sessionId)
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return null
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}
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console.log(`✅ Multi-timeframe analysis completed: ${combinedResult.analysis.recommendation} with ${combinedResult.analysis.confidence}% confidence`)
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console.log(`📊 Timeframe alignment: ${this.analyzeTimeframeAlignment(multiTimeframeResults)}`)
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progressTracker.updateStep(sessionId, 'analysis', 'completed', `Analysis complete: ${combinedResult.analysis.recommendation}`)
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progressTracker.updateStep(sessionId, 'complete', 'completed', 'Multi-timeframe analysis finished')
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// Clean up session after successful completion
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setTimeout(() => {
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progressTracker.deleteSession(sessionId)
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}, 2000)
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// Mark analysis as complete to allow cleanup
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analysisCompletionFlag.markAnalysisComplete(sessionId)
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return combinedResult
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} catch (error) {
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console.error('Error performing multi-timeframe analysis:', error)
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progressTracker.updateStep(sessionId, 'analysis', 'error', error instanceof Error ? error.message : 'Unknown error')
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setTimeout(() => {
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progressTracker.deleteSession(sessionId)
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}, 5000)
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// Mark analysis as complete even on error to allow cleanup
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analysisCompletionFlag.markAnalysisComplete(sessionId)
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return null
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}
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}
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private async analyzeMultiTimeframeWithDualLayouts(
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symbol: string,
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timeframes: string[],
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sessionId: string
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): Promise<Array<{ symbol: string; timeframe: string; analysis: AnalysisResult | null }>> {
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const results: Array<{ symbol: string; timeframe: string; analysis: AnalysisResult | null }> = []
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for (let i = 0; i < timeframes.length; i++) {
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const timeframe = timeframes[i]
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try {
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console.log(`📊 Analyzing ${symbol} ${timeframe} with AI + DIY layouts... (${i + 1}/${timeframes.length})`)
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// Update progress for timeframe
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progressTracker.updateTimeframeProgress(sessionId, i + 1, timeframes.length, timeframe)
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// Use the dual-layout configuration for each timeframe
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const screenshotConfig = {
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symbol: symbol,
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timeframe: timeframe,
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layouts: ['ai', 'diy'],
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sessionId: sessionId
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}
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const result = await aiAnalysisService.captureAndAnalyzeWithConfig(screenshotConfig)
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if (result.analysis) {
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console.log(`✅ ${timeframe} analysis: ${result.analysis.recommendation} (${result.analysis.confidence}% confidence)`)
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results.push({
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symbol,
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timeframe,
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analysis: result.analysis
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})
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} else {
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console.log(`❌ ${timeframe} analysis failed`)
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results.push({
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symbol,
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timeframe,
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analysis: null
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})
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}
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// Small delay between captures to avoid overwhelming the system
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await new Promise(resolve => setTimeout(resolve, 3000))
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} catch (error) {
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console.error(`Failed to analyze ${symbol} ${timeframe}:`, error)
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results.push({
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symbol,
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timeframe,
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analysis: null
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})
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}
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}
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return results
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}
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private combineMultiTimeframeAnalysis(results: Array<{ symbol: string; timeframe: string; analysis: AnalysisResult | null }>): {
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screenshots: string[]
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analysis: AnalysisResult | null
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} {
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const validResults = results.filter(r => r.analysis !== null)
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if (validResults.length === 0) {
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return { screenshots: [], analysis: null }
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}
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// Get the primary timeframe (1h) as base
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const primaryResult = validResults.find(r => r.timeframe === '1h') || validResults[0]
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const screenshots = validResults.length > 0 ? [primaryResult.timeframe] : []
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// Calculate weighted confidence based on timeframe alignment
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const alignment = this.calculateTimeframeAlignment(validResults)
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const baseAnalysis = primaryResult.analysis!
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// Adjust confidence based on timeframe alignment
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const adjustedConfidence = Math.round(baseAnalysis.confidence * alignment.score)
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// Create combined analysis with multi-timeframe reasoning
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const combinedAnalysis: AnalysisResult = {
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...baseAnalysis,
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confidence: adjustedConfidence,
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reasoning: `Multi-timeframe Dual-Layout Analysis (${results.map(r => r.timeframe).join(', ')}): ${baseAnalysis.reasoning}
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📊 Each timeframe analyzed with BOTH AI layout (RSI, MACD, EMAs) and DIY layout (Stochastic RSI, VWAP, OBV)
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⏱️ Timeframe Alignment: ${alignment.description}
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<EFBFBD> Signal Strength: ${alignment.strength}
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🎯 Confidence Adjustment: ${baseAnalysis.confidence}% → ${adjustedConfidence}% (${alignment.score >= 1 ? 'Enhanced' : 'Reduced'} due to timeframe ${alignment.score >= 1 ? 'alignment' : 'divergence'})
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🔬 Analysis Details:
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${validResults.map(r => `• ${r.timeframe}: ${r.analysis?.recommendation} (${r.analysis?.confidence}% confidence)`).join('\n')}`,
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keyLevels: this.consolidateKeyLevels(validResults),
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marketSentiment: this.consolidateMarketSentiment(validResults)
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}
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return {
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screenshots,
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analysis: combinedAnalysis
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}
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}
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private calculateTimeframeAlignment(results: Array<{ symbol: string; timeframe: string; analysis: AnalysisResult | null }>): {
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score: number
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description: string
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strength: string
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} {
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const recommendations = results.map(r => r.analysis?.recommendation).filter(Boolean)
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const buySignals = recommendations.filter(r => r === 'BUY').length
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const sellSignals = recommendations.filter(r => r === 'SELL').length
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const holdSignals = recommendations.filter(r => r === 'HOLD').length
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const total = recommendations.length
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const maxSignals = Math.max(buySignals, sellSignals, holdSignals)
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const alignmentRatio = maxSignals / total
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let score = 1.0
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let description = ''
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let strength = ''
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if (alignmentRatio >= 0.75) {
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score = 1.2 // Boost confidence
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description = `Strong alignment (${maxSignals}/${total} timeframes agree)`
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strength = 'Strong'
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} else if (alignmentRatio >= 0.5) {
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score = 1.0 // Neutral
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description = `Moderate alignment (${maxSignals}/${total} timeframes agree)`
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strength = 'Moderate'
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} else {
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score = 0.8 // Reduce confidence
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description = `Weak alignment (${maxSignals}/${total} timeframes agree)`
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strength = 'Weak'
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}
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return { score, description, strength }
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}
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private consolidateKeyLevels(results: Array<{ symbol: string; timeframe: string; analysis: AnalysisResult | null }>): any {
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const allLevels = results.map(r => r.analysis?.keyLevels).filter(Boolean)
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if (allLevels.length === 0) return {}
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// Use the 1h timeframe levels as primary, or first available
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const primaryLevels = results.find(r => r.timeframe === '1h')?.analysis?.keyLevels || allLevels[0]
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return {
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...primaryLevels,
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note: `Consolidated from ${allLevels.length} timeframes`
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}
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}
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private consolidateMarketSentiment(results: Array<{ symbol: string; timeframe: string; analysis: AnalysisResult | null }>): 'BULLISH' | 'BEARISH' | 'NEUTRAL' {
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const sentiments = results.map(r => r.analysis?.marketSentiment).filter(Boolean)
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if (sentiments.length === 0) return 'NEUTRAL'
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// Use the 1h timeframe sentiment as primary, or first available
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const primarySentiment = results.find(r => r.timeframe === '1h')?.analysis?.marketSentiment || sentiments[0]
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return primarySentiment || 'NEUTRAL'
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}
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private analyzeTimeframeAlignment(results: Array<{ symbol: string; timeframe: string; analysis: AnalysisResult | null }>): string {
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const recommendations = results.map(r => ({
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timeframe: r.timeframe,
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recommendation: r.analysis?.recommendation,
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confidence: r.analysis?.confidence || 0
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}))
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const summary = recommendations.map(r => `${r.timeframe}: ${r.recommendation} (${r.confidence}%)`).join(', ')
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return summary
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}
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private async storeAnalysisForLearning(result: {
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screenshots: string[]
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analysis: AnalysisResult | null
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}): Promise<void> {
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try {
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if (!result.analysis) return
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await prisma.aILearningData.create({
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data: {
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userId: this.config!.userId,
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symbol: this.config!.symbol,
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timeframe: this.config!.timeframe,
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screenshot: result.screenshots[0] || '',
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analysisData: JSON.stringify(result.analysis),
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marketConditions: JSON.stringify({
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||
marketSentiment: result.analysis.marketSentiment,
|
||
keyLevels: result.analysis.keyLevels,
|
||
timestamp: new Date().toISOString()
|
||
}),
|
||
confidenceScore: result.analysis.confidence,
|
||
createdAt: new Date()
|
||
}
|
||
})
|
||
} catch (error) {
|
||
console.error('Error storing analysis for learning:', error)
|
||
}
|
||
}
|
||
|
||
private async updateSessionWithAnalysis(result: {
|
||
screenshots: string[]
|
||
analysis: AnalysisResult | null
|
||
}): Promise<void> {
|
||
try {
|
||
if (!result.analysis) return
|
||
|
||
// Store the analysis decision in a field that works for now
|
||
const analysisDecision = `${result.analysis.recommendation} with ${result.analysis.confidence}% confidence - ${result.analysis.summary}`
|
||
|
||
// Update the current session with the latest analysis
|
||
await prisma.automationSession.updateMany({
|
||
where: {
|
||
userId: this.config!.userId,
|
||
symbol: this.config!.symbol,
|
||
timeframe: this.config!.timeframe,
|
||
status: 'ACTIVE'
|
||
},
|
||
data: {
|
||
lastAnalysis: new Date(),
|
||
lastError: analysisDecision // Temporarily store analysis here
|
||
}
|
||
})
|
||
|
||
// Also log the analysis for debugging
|
||
console.log('📊 Analysis stored in database:', {
|
||
recommendation: result.analysis.recommendation,
|
||
confidence: result.analysis.confidence,
|
||
summary: result.analysis.summary
|
||
})
|
||
} catch (error) {
|
||
console.error('Failed to update session with analysis:', error)
|
||
}
|
||
}
|
||
|
||
private async makeTradeDecision(result: {
|
||
screenshots: string[]
|
||
analysis: AnalysisResult | null
|
||
}): Promise<any | null> {
|
||
try {
|
||
const analysis = result.analysis
|
||
if (!analysis) return null
|
||
|
||
// Only trade if confidence is high enough
|
||
if (analysis.confidence < 70) {
|
||
console.log(`📊 Confidence too low: ${analysis.confidence}%`)
|
||
return null
|
||
}
|
||
|
||
// Only trade if direction is clear
|
||
if (analysis.recommendation === 'HOLD') {
|
||
console.log('📊 No clear direction signal')
|
||
return null
|
||
}
|
||
|
||
// Calculate position size based on risk percentage
|
||
const positionSize = await this.calculatePositionSize(analysis)
|
||
|
||
return {
|
||
direction: analysis.recommendation,
|
||
confidence: analysis.confidence,
|
||
positionSize,
|
||
stopLoss: this.calculateStopLoss(analysis),
|
||
takeProfit: this.calculateTakeProfit(analysis),
|
||
marketSentiment: analysis.marketSentiment
|
||
}
|
||
|
||
} catch (error) {
|
||
console.error('Error making trade decision:', error)
|
||
return null
|
||
}
|
||
}
|
||
|
||
private async calculatePositionSize(analysis: any): Promise<number> {
|
||
const baseAmount = this.config!.tradingAmount // This is the USD amount to invest
|
||
const riskAdjustment = this.config!.riskPercentage / 100
|
||
const confidenceAdjustment = analysis.confidence / 100
|
||
|
||
// Calculate the USD amount to invest
|
||
const usdAmount = baseAmount * riskAdjustment * confidenceAdjustment
|
||
|
||
// Get current price to convert USD to token amount
|
||
let currentPrice = analysis.entry?.price || analysis.currentPrice
|
||
|
||
if (!currentPrice) {
|
||
try {
|
||
const { default: PriceFetcher } = await import('./price-fetcher')
|
||
currentPrice = await PriceFetcher.getCurrentPrice(this.config?.symbol || 'SOLUSD')
|
||
console.log(`📊 Using current ${this.config?.symbol || 'SOLUSD'} price for position size: $${currentPrice}`)
|
||
} catch (error) {
|
||
console.error('Error fetching price for position size, using fallback:', error)
|
||
currentPrice = this.config?.symbol === 'SOLUSD' ? 189 : 100
|
||
}
|
||
}
|
||
|
||
// Calculate token amount: USD investment / token price
|
||
const tokenAmount = usdAmount / currentPrice
|
||
console.log(`💰 Position calculation: $${usdAmount} ÷ $${currentPrice} = ${tokenAmount.toFixed(4)} tokens`)
|
||
|
||
return tokenAmount
|
||
}
|
||
|
||
private calculateStopLoss(analysis: any): number {
|
||
// Use AI analysis stopLoss if available, otherwise calculate from entry price
|
||
if (analysis.stopLoss?.price) {
|
||
return analysis.stopLoss.price
|
||
}
|
||
|
||
const currentPrice = analysis.entry?.price || 189 // Current SOL price
|
||
const stopLossPercent = this.config!.stopLossPercent / 100
|
||
|
||
if (analysis.recommendation === 'BUY') {
|
||
return currentPrice * (1 - stopLossPercent)
|
||
} else {
|
||
return currentPrice * (1 + stopLossPercent)
|
||
}
|
||
}
|
||
|
||
private calculateTakeProfit(analysis: any): number {
|
||
// Use AI analysis takeProfit if available, otherwise calculate from entry price
|
||
if (analysis.takeProfits?.tp1?.price) {
|
||
return analysis.takeProfits.tp1.price
|
||
}
|
||
|
||
const currentPrice = analysis.entry?.price || 150 // Default SOL price
|
||
const takeProfitPercent = this.config!.takeProfitPercent / 100
|
||
|
||
if (analysis.recommendation === 'BUY') {
|
||
return currentPrice * (1 + takeProfitPercent)
|
||
} else {
|
||
return currentPrice * (1 - takeProfitPercent)
|
||
}
|
||
}
|
||
|
||
private async executeTrade(decision: any): Promise<void> {
|
||
try {
|
||
console.log(`🎯 Executing ${this.config!.mode} trade: ${decision.direction} ${decision.positionSize} ${this.config!.symbol}`)
|
||
|
||
let tradeResult: any
|
||
|
||
if (this.config!.mode === 'SIMULATION') {
|
||
// Execute simulation trade
|
||
tradeResult = await this.executeSimulationTrade(decision)
|
||
} else {
|
||
// Execute live trade via Jupiter
|
||
tradeResult = await this.executeLiveTrade(decision)
|
||
}
|
||
|
||
// Store trade in database
|
||
await this.storeTrade(decision, tradeResult)
|
||
|
||
// Update stats
|
||
this.updateStats(tradeResult)
|
||
|
||
console.log(`✅ Trade executed successfully: ${tradeResult.transactionId || 'SIMULATION'}`)
|
||
|
||
} catch (error) {
|
||
console.error('Error executing trade:', error)
|
||
this.stats.errorCount++
|
||
this.stats.lastError = error instanceof Error ? error.message : 'Trade execution failed'
|
||
}
|
||
}
|
||
|
||
private async executeSimulationTrade(decision: any): Promise<any> {
|
||
// Simulate trade execution with realistic parameters
|
||
let currentPrice = decision.currentPrice
|
||
|
||
// If no current price provided, fetch real price
|
||
if (!currentPrice) {
|
||
try {
|
||
const { default: PriceFetcher } = await import('./price-fetcher')
|
||
currentPrice = await PriceFetcher.getCurrentPrice(this.config?.symbol || 'SOLUSD')
|
||
console.log(`📊 Fetched real ${this.config?.symbol || 'SOLUSD'} price: $${currentPrice}`)
|
||
} catch (error) {
|
||
console.error('Error fetching real price, using fallback:', error)
|
||
// Use a more realistic fallback based on symbol
|
||
currentPrice = this.config?.symbol === 'SOLUSD' ? 189 : 100
|
||
}
|
||
}
|
||
|
||
const slippage = Math.random() * 0.005 // 0-0.5% slippage
|
||
const executionPrice = currentPrice * (1 + (Math.random() > 0.5 ? slippage : -slippage))
|
||
|
||
return {
|
||
transactionId: `SIM_${Date.now()}_${Math.random().toString(36).substr(2, 9)}`,
|
||
executionPrice,
|
||
amount: decision.positionSize,
|
||
direction: decision.direction,
|
||
status: 'COMPLETED',
|
||
timestamp: new Date(),
|
||
fees: decision.positionSize * 0.001, // 0.1% fee
|
||
slippage: slippage * 100
|
||
}
|
||
}
|
||
|
||
private async executeLiveTrade(decision: any): Promise<any> {
|
||
// Execute real trade via Jupiter DEX
|
||
const inputToken = decision.direction === 'BUY' ? 'USDC' : 'SOL'
|
||
const outputToken = decision.direction === 'BUY' ? 'SOL' : 'USDC'
|
||
|
||
const tokens = {
|
||
SOL: 'So11111111111111111111111111111111111111112',
|
||
USDC: 'EPjFWdd5AufqSSqeM2qN1xzybapC8G4wEGGkZwyTDt1v',
|
||
}
|
||
|
||
return await jupiterDEXService.executeSwap(
|
||
tokens[inputToken as keyof typeof tokens],
|
||
tokens[outputToken as keyof typeof tokens],
|
||
decision.positionSize,
|
||
50 // 0.5% slippage
|
||
)
|
||
}
|
||
|
||
private async storeTrade(decision: any, result: any): Promise<void> {
|
||
try {
|
||
await prisma.trade.create({
|
||
data: {
|
||
userId: this.config!.userId,
|
||
symbol: this.config!.symbol,
|
||
side: decision.direction,
|
||
amount: decision.positionSize,
|
||
price: result.executionPrice,
|
||
status: result.status,
|
||
driftTxId: result.transactionId || result.txId,
|
||
fees: result.fees || 0,
|
||
stopLoss: decision.stopLoss,
|
||
takeProfit: decision.takeProfit,
|
||
isAutomated: true,
|
||
tradingMode: this.config!.mode,
|
||
confidence: decision.confidence,
|
||
marketSentiment: decision.marketSentiment,
|
||
createdAt: new Date()
|
||
}
|
||
})
|
||
} catch (error) {
|
||
console.error('Error storing trade:', error)
|
||
}
|
||
}
|
||
|
||
private updateStats(result: any): void {
|
||
this.stats.totalTrades++
|
||
|
||
if (result.status === 'COMPLETED') {
|
||
this.stats.successfulTrades++
|
||
this.stats.winRate = (this.stats.successfulTrades / this.stats.totalTrades) * 100
|
||
|
||
// Update PnL (simplified calculation)
|
||
const pnl = result.amount * 0.01 * (Math.random() > 0.5 ? 1 : -1) // Random PnL for demo
|
||
this.stats.totalPnL += pnl
|
||
}
|
||
}
|
||
|
||
private async getTodayTradeCount(userId: string): Promise<number> {
|
||
const today = new Date()
|
||
today.setHours(0, 0, 0, 0)
|
||
|
||
const count = await prisma.trade.count({
|
||
where: {
|
||
userId,
|
||
isAutomated: true,
|
||
createdAt: {
|
||
gte: today
|
||
}
|
||
}
|
||
})
|
||
|
||
return count
|
||
}
|
||
|
||
async stopAutomation(): Promise<boolean> {
|
||
try {
|
||
this.isRunning = false
|
||
|
||
// Clear the interval if it exists
|
||
if (this.intervalId) {
|
||
clearInterval(this.intervalId)
|
||
this.intervalId = null
|
||
}
|
||
|
||
// Update database session status to STOPPED
|
||
if (this.config) {
|
||
await prisma.automationSession.updateMany({
|
||
where: {
|
||
userId: this.config.userId,
|
||
symbol: this.config.symbol,
|
||
timeframe: this.config.timeframe,
|
||
status: 'ACTIVE'
|
||
},
|
||
data: {
|
||
status: 'STOPPED',
|
||
updatedAt: new Date()
|
||
}
|
||
})
|
||
}
|
||
|
||
this.config = null
|
||
|
||
console.log('🛑 Automation stopped')
|
||
return true
|
||
} catch (error) {
|
||
console.error('Failed to stop automation:', error)
|
||
return false
|
||
}
|
||
}
|
||
|
||
async pauseAutomation(): Promise<boolean> {
|
||
try {
|
||
if (!this.isRunning) {
|
||
return false
|
||
}
|
||
|
||
this.isRunning = false
|
||
console.log('⏸️ Automation paused')
|
||
return true
|
||
} catch (error) {
|
||
console.error('Failed to pause automation:', error)
|
||
return false
|
||
}
|
||
}
|
||
|
||
async resumeAutomation(): Promise<boolean> {
|
||
try {
|
||
if (!this.config) {
|
||
return false
|
||
}
|
||
|
||
this.isRunning = true
|
||
console.log('▶️ Automation resumed')
|
||
return true
|
||
} catch (error) {
|
||
console.error('Failed to resume automation:', error)
|
||
return false
|
||
}
|
||
}
|
||
|
||
async getStatus(): Promise<AutomationStatus | null> {
|
||
try {
|
||
// Get the latest active automation session from database first
|
||
const session = await prisma.automationSession.findFirst({
|
||
where: { status: 'ACTIVE' },
|
||
orderBy: { createdAt: 'desc' }
|
||
})
|
||
|
||
if (!session) {
|
||
return null
|
||
}
|
||
|
||
// If we have a session but automation is not running in memory,
|
||
// it means the server was restarted but the session is still active
|
||
const isActiveInMemory = this.isRunning && this.config !== null
|
||
|
||
// Auto-restart automation if session exists but not running in memory
|
||
if (!isActiveInMemory) {
|
||
console.log('🔄 Found active session but automation not running, attempting auto-restart...')
|
||
await this.autoRestartFromSession(session)
|
||
}
|
||
|
||
return {
|
||
isActive: this.isRunning && this.config !== null,
|
||
mode: session.mode as 'SIMULATION' | 'LIVE',
|
||
symbol: session.symbol,
|
||
timeframe: session.timeframe,
|
||
totalTrades: session.totalTrades,
|
||
successfulTrades: session.successfulTrades,
|
||
winRate: session.winRate,
|
||
totalPnL: session.totalPnL,
|
||
errorCount: session.errorCount,
|
||
lastError: session.lastError || undefined,
|
||
lastAnalysis: session.lastAnalysis || undefined,
|
||
lastTrade: session.lastTrade || undefined,
|
||
nextScheduled: session.nextScheduled || undefined
|
||
}
|
||
} catch (error) {
|
||
console.error('Failed to get automation status:', error)
|
||
return null
|
||
}
|
||
}
|
||
|
||
private async autoRestartFromSession(session: any): Promise<void> {
|
||
try {
|
||
const settings = session.settings || {}
|
||
const config: AutomationConfig = {
|
||
userId: session.userId,
|
||
mode: session.mode,
|
||
symbol: session.symbol,
|
||
timeframe: session.timeframe,
|
||
tradingAmount: settings.tradingAmount || 100,
|
||
maxLeverage: settings.maxLeverage || 3,
|
||
stopLossPercent: settings.stopLossPercent || 2,
|
||
takeProfitPercent: settings.takeProfitPercent || 6,
|
||
maxDailyTrades: settings.maxDailyTrades || 5,
|
||
riskPercentage: settings.riskPercentage || 2
|
||
}
|
||
|
||
await this.startAutomation(config)
|
||
console.log('✅ Automation auto-restarted successfully')
|
||
} catch (error) {
|
||
console.error('Failed to auto-restart automation:', error)
|
||
}
|
||
}
|
||
|
||
async getLearningInsights(userId: string): Promise<{
|
||
totalAnalyses: number
|
||
avgAccuracy: number
|
||
bestTimeframe: string
|
||
worstTimeframe: string
|
||
commonFailures: string[]
|
||
recommendations: string[]
|
||
}> {
|
||
try {
|
||
// For now, return mock data
|
||
return {
|
||
totalAnalyses: 150,
|
||
avgAccuracy: 0.72,
|
||
bestTimeframe: '1h',
|
||
worstTimeframe: '15m',
|
||
commonFailures: [
|
||
'Low confidence predictions',
|
||
'Missed support/resistance levels',
|
||
'Timeframe misalignment'
|
||
],
|
||
recommendations: [
|
||
'Focus on 1h timeframe for better accuracy',
|
||
'Wait for higher confidence signals (>75%)',
|
||
'Use multiple timeframe confirmation'
|
||
]
|
||
}
|
||
} catch (error) {
|
||
console.error('Failed to get learning insights:', error)
|
||
return {
|
||
totalAnalyses: 0,
|
||
avgAccuracy: 0,
|
||
bestTimeframe: 'Unknown',
|
||
worstTimeframe: 'Unknown',
|
||
commonFailures: [],
|
||
recommendations: []
|
||
}
|
||
}
|
||
}
|
||
}
|
||
|
||
export const automationService = new AutomationService()
|