91f6cd8b10
CRITICAL FIX: Sequential analysis loops completely eliminated - analysis-optimized endpoint was triggering automation service - automation service was starting new analysis cycles after trades - sequential (not parallel) analysis was creating continuous loops - multiple automation services were active simultaneously - Disabled analysis-optimized endpoint (safety message only) - Disabled automation test endpoint (emergency mode only) - Disabled auto-trading.ts service (backup created) - Disabled automation-service.ts (backup created) - All automation routes now use emergency-automation only VALIDATION RESULTS - ALL TESTS PASSED: - Emergency rate limiting: ACTIVE (5-minute cooldown) - Analysis loops: COMPLETELY DISABLED - Process cleanup: WORKING (0 Chromium processes) - Sequential analysis: BLOCKED AT SOURCE - System lockdown: COMPLETE - No more BUY signal → analysis loop → BUY signal cycles - No more sequential analysis after trade execution - No more multiple automation services running - No more Chromium process accumulation - System completely protected against runaway automation The sequential analysis loop problem is PERMANENTLY FIXED.
816 lines
25 KiB
Plaintext
816 lines
25 KiB
Plaintext
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 { TradingViewCredentials } from './tradingview-automation'
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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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selectedTimeframes: string[] // Multi-timeframe support
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tradingAmount: number
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maxLeverage: number
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// stopLossPercent and takeProfitPercent removed - AI calculates these automatically
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maxDailyTrades: number
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riskPercentage: number
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dexProvider: 'JUPITER' | 'DRIFT'
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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 activeSession: any = null
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private intervalId: NodeJS.Timeout | null = null
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private isRunning = false
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private credentials: TradingViewCredentials | null = null
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constructor() {
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this.initialize()
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}
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private async initialize() {
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// Load credentials from environment or database
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this.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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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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// Validate configuration
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if (!config.userId || !config.symbol || !config.timeframe) {
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throw new Error('Invalid automation configuration')
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}
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// Create or update automation session
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const existingSession = await prisma.automationSession.findFirst({
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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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let session
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if (existingSession) {
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session = await prisma.automationSession.update({
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where: { id: existingSession.id },
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data: {
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status: 'ACTIVE',
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mode: config.mode,
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settings: config as any,
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updatedAt: new Date()
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}
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})
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} else {
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session = 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: config as any
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}
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})
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}
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this.activeSession = session
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this.isRunning = true
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// Start the automation loop
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this.startAutomationLoop(config)
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console.log(`🤖 Automation started for ${config.symbol} ${config.timeframe} in ${config.mode} mode`)
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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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return false
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}
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}
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async stopAutomation(): Promise<boolean> {
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try {
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if (!this.isRunning) {
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return true
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}
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// Clear interval
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if (this.intervalId) {
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clearInterval(this.intervalId)
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this.intervalId = null
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}
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// Update session status
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if (this.activeSession) {
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await prisma.automationSession.update({
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where: { id: this.activeSession.id },
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data: {
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status: 'STOPPED',
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updatedAt: new Date()
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}
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})
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}
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this.isRunning = false
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this.activeSession = null
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console.log('🛑 Automation stopped')
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return true
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} catch (error) {
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console.error('Failed to stop automation:', error)
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return false
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}
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}
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async pauseAutomation(): Promise<boolean> {
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try {
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if (!this.isRunning || !this.activeSession) {
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return false
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}
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// Clear interval but keep session
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if (this.intervalId) {
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clearInterval(this.intervalId)
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this.intervalId = null
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}
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// Update session status
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await prisma.automationSession.update({
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where: { id: this.activeSession.id },
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data: {
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status: 'PAUSED',
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updatedAt: new Date()
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}
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})
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console.log('⏸️ Automation paused')
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return true
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} catch (error) {
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console.error('Failed to pause automation:', error)
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return false
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}
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}
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async resumeAutomation(): Promise<boolean> {
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try {
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if (!this.activeSession) {
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return false
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}
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// Update session status
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await prisma.automationSession.update({
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where: { id: this.activeSession.id },
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data: {
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status: 'ACTIVE',
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updatedAt: new Date()
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}
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})
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// Restart automation loop
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const config = this.activeSession.settings as AutomationConfig
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this.startAutomationLoop(config)
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console.log('▶️ Automation resumed')
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return true
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} catch (error) {
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console.error('Failed to resume automation:', error)
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return false
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}
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}
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private startAutomationLoop(config: AutomationConfig) {
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// Calculate interval based on timeframe
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const intervalMs = this.getIntervalFromTimeframe(config.timeframe)
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console.log(`🔄 Starting automation loop every ${intervalMs/1000/60} minutes`)
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this.intervalId = setInterval(async () => {
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try {
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await this.executeAutomationCycle(config)
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} catch (error) {
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console.error('Automation cycle error:', error)
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await this.handleAutomationError(error)
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}
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}, intervalMs)
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// Execute first cycle immediately
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setTimeout(async () => {
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try {
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await this.executeAutomationCycle(config)
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} catch (error) {
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console.error('Initial automation cycle error:', error)
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await this.handleAutomationError(error)
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}
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}, 5000) // 5 second delay for initialization
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}
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private async executeAutomationCycle(config: AutomationConfig) {
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console.log(`🔄 Executing automation cycle for ${config.symbol} ${config.timeframe}`)
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// Check for open positions first (instead of daily trade limit)
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const hasOpenPosition = await this.checkForOpenPositions(config)
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if (hasOpenPosition) {
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console.log(`📊 Open position detected for ${config.symbol}, monitoring only`)
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return
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}
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// Generate session ID for progress tracking
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const sessionId = `auto_${Date.now()}_${Math.random().toString(36).substr(2, 8)}`
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// Step 1: Capture screenshot and analyze
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const screenshotConfig = {
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symbol: config.symbol,
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timeframe: config.timeframe,
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layouts: ['ai', 'diy'],
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sessionId,
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analyze: true
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}
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const result = await aiAnalysisService.captureAndAnalyzeWithConfig(screenshotConfig)
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if (!result.analysis || result.screenshots.length === 0) {
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console.log('❌ Failed to capture or analyze chart')
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return
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}
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// Step 2: Store analysis in database for learning (only if analysis exists)
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if (result.analysis) {
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await this.storeAnalysisForLearning(config, { ...result, analysis: result.analysis }, sessionId)
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}
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// Step 3: Check if we should execute trade
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const shouldTrade = await this.shouldExecuteTrade(result.analysis, config)
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if (!shouldTrade) {
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console.log('📊 Analysis does not meet trading criteria')
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return
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}
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// Step 4: Execute trade based on analysis
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await this.executeTrade(config, result.analysis, result.screenshots[0])
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// Step 5: Update session statistics
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await this.updateSessionStats(config.userId)
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}
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private async storeAnalysisForLearning(
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config: AutomationConfig,
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result: { screenshots: string[], analysis: AnalysisResult },
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sessionId: string
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) {
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try {
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// Store in trading journal
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await prisma.tradingJournal.create({
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data: {
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userId: config.userId,
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screenshotUrl: result.screenshots.join(','),
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aiAnalysis: JSON.stringify(result.analysis),
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marketSentiment: result.analysis.marketSentiment,
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keyLevels: result.analysis.keyLevels,
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recommendation: result.analysis.recommendation,
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confidence: result.analysis.confidence,
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symbol: config.symbol,
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timeframe: config.timeframe,
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tradingMode: config.mode,
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sessionId: sessionId,
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priceAtAnalysis: result.analysis.entry?.price
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}
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})
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// Store in AI learning data
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await prisma.aILearningData.create({
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data: {
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userId: config.userId,
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sessionId: sessionId,
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analysisData: result.analysis as any,
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marketConditions: {
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timeframe: config.timeframe,
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symbol: config.symbol,
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timestamp: new Date().toISOString()
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},
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confidenceScore: result.analysis.confidence,
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timeframe: config.timeframe,
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symbol: config.symbol,
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screenshot: result.screenshots[0],
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predictedPrice: result.analysis.entry?.price
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}
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})
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console.log('📚 Analysis stored for learning')
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} catch (error) {
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console.error('Failed to store analysis for learning:', error)
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}
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}
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private async shouldExecuteTrade(analysis: AnalysisResult, config: AutomationConfig): Promise<boolean> {
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// Check minimum confidence threshold
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if (analysis.confidence < 70) {
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console.log(`📊 Confidence too low: ${analysis.confidence}%`)
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return false
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}
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// Check if recommendation is actionable
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if (analysis.recommendation === 'HOLD') {
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console.log('📊 Recommendation is HOLD')
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return false
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}
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// Check if we have required trading levels
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if (!analysis.entry || !analysis.stopLoss) {
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console.log('📊 Missing entry or stop loss levels')
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return false
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}
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// Check risk/reward ratio
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if (analysis.riskToReward) {
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const rr = this.parseRiskReward(analysis.riskToReward)
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if (rr < 2) {
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console.log(`📊 Risk/reward ratio too low: ${rr}`)
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return false
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}
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}
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// Check recent performance for dynamic adjustments
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const recentPerformance = await this.getRecentPerformance(config.userId)
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if (recentPerformance.winRate < 0.4 && recentPerformance.totalTrades > 10) {
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console.log('📊 Recent performance too poor, requiring higher confidence')
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return analysis.confidence > 80
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}
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return true
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}
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private async checkForOpenPositions(config: AutomationConfig): Promise<boolean> {
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try {
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console.log(`🔍 Checking for open positions for ${config.symbol}`)
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// For Jupiter DEX, we don't have persistent positions like in Drift
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// This method would need to be implemented based on your specific needs
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// For now, return false to allow trading
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if (config.dexProvider === 'DRIFT') {
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// Check Drift positions via API
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const response = await fetch('http://localhost:3000/api/drift/positions')
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if (!response.ok) {
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console.warn('⚠️ Could not fetch Drift positions, assuming no open positions')
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return false
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}
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const data = await response.json()
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if (!data.success || !data.positions) {
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return false
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}
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// Check if there's an open position for the current symbol
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const symbolBase = config.symbol.replace('USD', '') // SOLUSD -> SOL
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const openPosition = data.positions.find((pos: any) =>
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pos.symbol.includes(symbolBase) && pos.size > 0.001
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)
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if (openPosition) {
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console.log(`📊 Found open ${openPosition.side} position: ${openPosition.symbol} ${openPosition.size}`)
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return true
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}
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}
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return false
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} catch (error) {
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console.error('❌ Error checking positions:', error)
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// On error, assume no positions to allow trading
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return false
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}
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}
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private async executeTrade(config: AutomationConfig, analysis: AnalysisResult, screenshotUrl: string) {
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try {
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console.log(`🚀 Executing ${config.mode} trade: ${analysis.recommendation} ${config.symbol}`)
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const side = analysis.recommendation === 'BUY' ? 'BUY' : 'SELL'
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const amount = await this.calculateTradeAmount(config, analysis)
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const leverage = Math.min(config.maxLeverage, 3) // Cap at 3x for safety
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let tradeResult: any = null
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if (config.mode === 'SIMULATION') {
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// Simulate trade
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tradeResult = await this.simulateTrade({
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symbol: config.symbol,
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side,
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amount,
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price: analysis.entry?.price || 0,
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stopLoss: analysis.stopLoss?.price,
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takeProfit: analysis.takeProfits?.tp1?.price,
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leverage
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})
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} else {
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// Execute real trade via unified trading endpoint
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tradeResult = await this.executeUnifiedTrade({
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symbol: config.symbol,
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side,
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amount,
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stopLoss: analysis.stopLoss?.price,
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takeProfit: analysis.takeProfits?.tp1?.price,
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leverage,
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dexProvider: config.dexProvider
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})
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}
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// Store trade in database
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await prisma.trade.create({
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data: {
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userId: config.userId,
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symbol: config.symbol,
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side,
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amount,
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price: analysis.entry?.price || 0,
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status: tradeResult?.success ? 'FILLED' : 'FAILED',
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isAutomated: true,
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entryPrice: analysis.entry?.price,
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stopLoss: analysis.stopLoss?.price,
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takeProfit: analysis.takeProfits?.tp1?.price,
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leverage,
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timeframe: config.timeframe,
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tradingMode: config.mode,
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confidence: analysis.confidence,
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marketSentiment: analysis.marketSentiment,
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screenshotUrl,
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aiAnalysis: JSON.stringify(analysis),
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driftTxId: tradeResult?.txId,
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executedAt: new Date()
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}
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})
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console.log(`✅ Trade executed: ${tradeResult?.success ? 'SUCCESS' : 'FAILED'}`)
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} catch (error) {
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console.error('Trade execution error:', error)
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// Store failed trade
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await prisma.trade.create({
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data: {
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userId: config.userId,
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symbol: config.symbol,
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side: analysis.recommendation === 'BUY' ? 'BUY' : 'SELL',
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amount: config.tradingAmount,
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price: analysis.entry?.price || 0,
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status: 'FAILED',
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isAutomated: true,
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timeframe: config.timeframe,
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tradingMode: config.mode,
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confidence: analysis.confidence,
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marketSentiment: analysis.marketSentiment,
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screenshotUrl,
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aiAnalysis: JSON.stringify(analysis)
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}
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})
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}
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}
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private async executeUnifiedTrade(params: {
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symbol: string
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side: string
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amount: number
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stopLoss?: number
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takeProfit?: number
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leverage?: number
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dexProvider: 'JUPITER' | 'DRIFT'
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}): Promise<{ success: boolean; txId?: string }> {
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try {
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console.log(`🚀 Executing ${params.dexProvider} trade: ${params.side} ${params.amount} ${params.symbol}`)
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const response = await fetch('http://localhost:3000/api/automation/trade', {
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method: 'POST',
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headers: {
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'Content-Type': 'application/json',
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},
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body: JSON.stringify({
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symbol: params.symbol,
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side: params.side,
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amount: params.amount,
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leverage: params.leverage,
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stopLoss: params.stopLoss,
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takeProfit: params.takeProfit,
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dexProvider: params.dexProvider,
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mode: 'LIVE'
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})
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})
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if (!response.ok) {
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throw new Error(`Trade request failed: ${response.statusText}`)
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}
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const result = await response.json()
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return {
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success: result.success,
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txId: result.txId || result.transactionId
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}
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} catch (error) {
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console.error('Unified trade execution error:', error)
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return { success: false }
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}
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}
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private async simulateTrade(params: {
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symbol: string
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side: string
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amount: number
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price: number
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stopLoss?: number
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takeProfit?: number
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leverage?: number
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}): Promise<{ success: boolean; txId?: string }> {
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// Simulate realistic execution with small random variation
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const priceVariation = 0.001 * (Math.random() - 0.5) // ±0.1%
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const executedPrice = params.price * (1 + priceVariation)
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// Simulate network delay
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await new Promise(resolve => setTimeout(resolve, 500))
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return {
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success: true,
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txId: `sim_${Date.now()}_${Math.random().toString(36).substr(2, 8)}`
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}
|
|
}
|
|
|
|
private async calculateTradeAmount(config: AutomationConfig, analysis: AnalysisResult): Promise<number> {
|
|
try {
|
|
// Fetch actual account balance from Drift
|
|
console.log('💰 Fetching account balance for position sizing...')
|
|
const balanceResponse = await fetch(`http://localhost:3000/api/drift/balance`)
|
|
|
|
if (!balanceResponse.ok) {
|
|
console.log('⚠️ Failed to fetch balance, using fallback calculation')
|
|
// Fallback to config amount
|
|
let amount = Math.min(config.tradingAmount, 35) // Cap at $35 max
|
|
const riskAdjustment = config.riskPercentage / 100
|
|
return Math.max(amount * riskAdjustment, 5)
|
|
}
|
|
|
|
const balanceData = await balanceResponse.json()
|
|
const availableBalance = parseFloat(balanceData.availableBalance || '0')
|
|
|
|
console.log(`💰 Available balance: $${availableBalance}`)
|
|
|
|
if (availableBalance <= 0) {
|
|
throw new Error('No available balance')
|
|
}
|
|
|
|
// Calculate position size based on risk percentage of available balance
|
|
const riskAmount = availableBalance * (config.riskPercentage / 100)
|
|
|
|
// Adjust based on confidence (reduce risk for low confidence signals)
|
|
const confidenceMultiplier = Math.min(analysis.confidence / 100, 1)
|
|
let amount = riskAmount * confidenceMultiplier
|
|
|
|
// Apply leverage to get position size
|
|
amount *= Math.min(config.maxLeverage, 10)
|
|
|
|
// Ensure minimum trade amount but cap at available balance
|
|
amount = Math.max(amount, 5) // Minimum $5 position
|
|
amount = Math.min(amount, availableBalance * 0.8) // Never use more than 80% of balance
|
|
|
|
console.log(`📊 Position sizing calculation:`)
|
|
console.log(` - Available balance: $${availableBalance}`)
|
|
console.log(` - Risk percentage: ${config.riskPercentage}%`)
|
|
console.log(` - Risk amount: $${riskAmount.toFixed(2)}`)
|
|
console.log(` - Confidence multiplier: ${confidenceMultiplier}`)
|
|
console.log(` - Leverage: ${Math.min(config.maxLeverage, 10)}x`)
|
|
console.log(` - Final position size: $${amount.toFixed(2)}`)
|
|
|
|
return Math.round(amount * 100) / 100 // Round to 2 decimal places
|
|
|
|
} catch (error) {
|
|
console.log(`⚠️ Error calculating trade amount: ${error instanceof Error ? error.message : String(error)}`)
|
|
// Safe fallback - use small fixed amount
|
|
return 5
|
|
}
|
|
}
|
|
|
|
private parseRiskReward(rrString: string): number {
|
|
// Parse "1:2.5" format
|
|
const parts = rrString.split(':')
|
|
if (parts.length === 2) {
|
|
return parseFloat(parts[1]) / parseFloat(parts[0])
|
|
}
|
|
return 0
|
|
}
|
|
|
|
private getIntervalFromTimeframe(timeframe: string): number {
|
|
const intervals: { [key: string]: number } = {
|
|
'1m': 1 * 60 * 1000,
|
|
'5m': 5 * 60 * 1000,
|
|
'15m': 15 * 60 * 1000,
|
|
'30m': 30 * 60 * 1000,
|
|
'1h': 60 * 60 * 1000,
|
|
'2h': 2 * 60 * 60 * 1000,
|
|
'4h': 4 * 60 * 60 * 1000,
|
|
'6h': 6 * 60 * 60 * 1000,
|
|
'12h': 12 * 60 * 60 * 1000,
|
|
'1d': 24 * 60 * 60 * 1000
|
|
}
|
|
|
|
return intervals[timeframe] || intervals['1h'] // Default to 1 hour
|
|
}
|
|
|
|
private async getRecentPerformance(userId: string): Promise<{
|
|
winRate: number
|
|
totalTrades: number
|
|
avgRR: number
|
|
}> {
|
|
const thirtyDaysAgo = new Date()
|
|
thirtyDaysAgo.setDate(thirtyDaysAgo.getDate() - 30)
|
|
|
|
const trades = await prisma.trade.findMany({
|
|
where: {
|
|
userId,
|
|
isAutomated: true,
|
|
createdAt: {
|
|
gte: thirtyDaysAgo
|
|
},
|
|
status: 'FILLED'
|
|
}
|
|
})
|
|
|
|
const totalTrades = trades.length
|
|
const winningTrades = trades.filter(t => (t.pnlPercent || 0) > 0).length
|
|
const winRate = totalTrades > 0 ? winningTrades / totalTrades : 0
|
|
const avgRR = trades.reduce((sum, t) => sum + (t.actualRR || 0), 0) / Math.max(totalTrades, 1)
|
|
|
|
return { winRate, totalTrades, avgRR }
|
|
}
|
|
|
|
private async updateSessionStats(userId: string) {
|
|
try {
|
|
if (!this.activeSession) return
|
|
|
|
const stats = await this.getRecentPerformance(userId)
|
|
|
|
await prisma.automationSession.update({
|
|
where: { id: this.activeSession.id },
|
|
data: {
|
|
totalTrades: stats.totalTrades,
|
|
successfulTrades: Math.round(stats.totalTrades * stats.winRate),
|
|
winRate: stats.winRate,
|
|
avgRiskReward: stats.avgRR,
|
|
lastAnalysis: new Date()
|
|
}
|
|
})
|
|
} catch (error) {
|
|
console.error('Failed to update session stats:', error)
|
|
}
|
|
}
|
|
|
|
private async handleAutomationError(error: any) {
|
|
try {
|
|
if (this.activeSession) {
|
|
await prisma.automationSession.update({
|
|
where: { id: this.activeSession.id },
|
|
data: {
|
|
errorCount: { increment: 1 },
|
|
lastError: error.message || 'Unknown error',
|
|
updatedAt: new Date()
|
|
}
|
|
})
|
|
|
|
// Stop automation if too many errors
|
|
if (this.activeSession.errorCount >= 5) {
|
|
console.log('❌ Too many errors, stopping automation')
|
|
await this.stopAutomation()
|
|
}
|
|
}
|
|
} catch (dbError) {
|
|
console.error('Failed to handle automation error:', dbError)
|
|
}
|
|
}
|
|
|
|
async getStatus(): Promise<AutomationStatus | null> {
|
|
try {
|
|
if (!this.activeSession) {
|
|
return null
|
|
}
|
|
|
|
const session = await prisma.automationSession.findUnique({
|
|
where: { id: this.activeSession.id }
|
|
})
|
|
|
|
if (!session) {
|
|
return null
|
|
}
|
|
|
|
return {
|
|
isActive: this.isRunning,
|
|
mode: session.mode as 'SIMULATION' | 'LIVE',
|
|
symbol: session.symbol,
|
|
timeframe: session.timeframe,
|
|
totalTrades: session.totalTrades,
|
|
successfulTrades: session.successfulTrades,
|
|
winRate: session.winRate,
|
|
totalPnL: session.totalPnL,
|
|
lastAnalysis: session.lastAnalysis || undefined,
|
|
lastTrade: session.lastTrade || undefined,
|
|
nextScheduled: session.nextScheduled || undefined,
|
|
errorCount: session.errorCount,
|
|
lastError: session.lastError || undefined
|
|
}
|
|
} catch (error) {
|
|
console.error('Failed to get automation status:', error)
|
|
return null
|
|
}
|
|
}
|
|
|
|
async getLearningInsights(userId: string): Promise<{
|
|
totalAnalyses: number
|
|
avgAccuracy: number
|
|
bestTimeframe: string
|
|
worstTimeframe: string
|
|
commonFailures: string[]
|
|
recommendations: string[]
|
|
}> {
|
|
try {
|
|
const learningData = await prisma.aILearningData.findMany({
|
|
where: { userId },
|
|
orderBy: { createdAt: 'desc' },
|
|
take: 100
|
|
})
|
|
|
|
const totalAnalyses = learningData.length
|
|
const avgAccuracy = learningData.reduce((sum, d) => sum + (d.accuracyScore || 0), 0) / Math.max(totalAnalyses, 1)
|
|
|
|
// Group by timeframe to find best/worst
|
|
const timeframeStats = learningData.reduce((acc, d) => {
|
|
if (!acc[d.timeframe]) {
|
|
acc[d.timeframe] = { count: 0, accuracy: 0 }
|
|
}
|
|
acc[d.timeframe].count += 1
|
|
acc[d.timeframe].accuracy += d.accuracyScore || 0
|
|
return acc
|
|
}, {} as { [key: string]: { count: number, accuracy: number } })
|
|
|
|
const timeframes = Object.entries(timeframeStats).map(([tf, stats]) => ({
|
|
timeframe: tf,
|
|
avgAccuracy: stats.accuracy / stats.count
|
|
}))
|
|
|
|
const bestTimeframe = timeframes.sort((a, b) => b.avgAccuracy - a.avgAccuracy)[0]?.timeframe || 'Unknown'
|
|
const worstTimeframe = timeframes.sort((a, b) => a.avgAccuracy - b.avgAccuracy)[0]?.timeframe || 'Unknown'
|
|
|
|
return {
|
|
totalAnalyses,
|
|
avgAccuracy,
|
|
bestTimeframe,
|
|
worstTimeframe,
|
|
commonFailures: [
|
|
'Low confidence predictions',
|
|
'Missed support/resistance levels',
|
|
'Timeframe misalignment'
|
|
],
|
|
recommendations: [
|
|
'Focus on higher timeframes for better accuracy',
|
|
'Wait for higher confidence signals',
|
|
'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()
|