📚 COMPREHENSIVE KNOWLEDGE DOCUMENTATION
ADVANCED SYSTEM KNOWLEDGE: - Superior parallel screenshot system (60% performance gain) - AI learning system architecture and decision flow - Orphaned order cleanup integration patterns - Critical technical fixes and troubleshooting guide - Database schema best practices - Memory leak prevention strategies - AI learning system patterns and functions - Error handling best practices for trading systems - Integration patterns for position monitoring - Performance optimization rules - UI/UX consistency requirements - Critical anti-patterns to avoid - Added links to new knowledge base documents - Comprehensive documentation structure - Development guides and best practices - Performance optimizations summary - 60% screenshot performance improvement techniques - AI learning system that adapts trading decisions - Container stability and crash prevention - Frontend-backend consistency requirements - Integration strategies for existing infrastructure This documentation preserves critical insights from complex debugging sessions and provides patterns for future development.
This commit is contained in:
mindesbunister
@@ -0,0 +1,326 @@
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class SafeParallelAutomation {
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private isRunning = false
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private config: any = null
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private intervalId: NodeJS.Timeout | null = null
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private lastAnalysisTime = 0
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// SAFE PARAMETERS - Respect timeframe strategy
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private readonly MIN_ANALYSIS_INTERVAL = 10 * 60 * 1000 // 10 minutes minimum
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private readonly MAX_TRADES_PER_HOUR = 2
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private readonly ANALYSIS_COOLDOWN = 5 * 60 * 1000 // 5 minutes
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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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lastAnalysis: null as string | null,
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nextScheduled: null as string | null,
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nextAnalysisIn: 0,
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analysisInterval: 0,
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currentCycle: 0,
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lastError: null as string | null,
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strategyType: 'General'
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}
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async startSafeAutomation(config: any): Promise<{ success: boolean, message?: string }> {
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try {
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if (this.isRunning) {
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return { success: false, message: 'Safe automation already running' }
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}
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// SAFETY CHECK: Rate limiting
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const now = Date.now()
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if (now - this.lastAnalysisTime < this.ANALYSIS_COOLDOWN) {
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const remaining = Math.ceil((this.ANALYSIS_COOLDOWN - (now - this.lastAnalysisTime)) / 1000)
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return {
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success: false,
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message: 'Safety cooldown: Wait ' + remaining + ' seconds'
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}
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}
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// SAFETY CHECK: Validate timeframes
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if (!config.timeframes || config.timeframes.length === 0) {
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return { success: false, message: 'At least one timeframe required' }
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}
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this.config = config
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this.isRunning = true
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this.lastAnalysisTime = now
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this.stats.currentCycle = 0
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this.stats.strategyType = config.strategyType || 'General'
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console.log('SAFE PARALLEL: Starting for ' + config.symbol)
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console.log('STRATEGY: ' + this.stats.strategyType)
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console.log('TIMEFRAMES: [' + config.timeframes.join(', ') + ']')
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console.log('SAFETY: Max ' + this.MAX_TRADES_PER_HOUR + ' trades/hour')
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// Start with strategy-appropriate interval
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this.startSafeAnalysisCycle()
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return { success: true, message: 'Safe parallel automation started' }
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} catch (error) {
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console.error('Failed to start safe automation:', error)
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return { success: false, message: 'Failed to start automation' }
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}
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}
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private startSafeAnalysisCycle(): void {
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// Get strategy-appropriate interval but enforce minimum safety
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const strategyInterval = this.getStrategyInterval(this.config.timeframes, this.config.strategyType)
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const safeInterval = Math.max(this.MIN_ANALYSIS_INTERVAL, strategyInterval)
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console.log('STRATEGY INTERVAL: ' + (strategyInterval / 1000 / 60) + ' minutes')
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console.log('SAFE INTERVAL: ' + (safeInterval / 1000 / 60) + ' minutes (enforced minimum)')
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this.stats.analysisInterval = safeInterval
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this.stats.nextScheduled = new Date(Date.now() + safeInterval).toISOString()
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this.stats.nextAnalysisIn = safeInterval
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// Set safe interval
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this.intervalId = setInterval(async () => {
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if (this.isRunning && this.config) {
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await this.runSafeAnalysisCycle()
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}
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}, safeInterval)
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// First cycle after delay
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setTimeout(() => {
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if (this.isRunning && this.config) {
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this.runSafeAnalysisCycle()
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}
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}, 30000)
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}
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private async runSafeAnalysisCycle(): Promise<void> {
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try {
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console.log('\nSAFE CYCLE ' + (this.stats.currentCycle + 1) + ': ' + this.config.symbol)
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console.log('STRATEGY: ' + this.stats.strategyType)
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// SAFETY CHECK
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const canTrade = await this.canExecuteTrade()
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if (!canTrade.allowed) {
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console.log('SAFETY BLOCK: ' + canTrade.reason)
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this.updateNextAnalysisTime()
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return
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}
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// PARALLEL ANALYSIS using enhanced screenshot API
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console.log('PARALLEL: Analyzing ' + this.config.timeframes.length + ' timeframes...')
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const analysisResult = await this.performParallelAnalysis()
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if (!analysisResult || !analysisResult.analysis) {
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console.log('Analysis failed, skipping')
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return
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}
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this.stats.lastAnalysis = new Date().toISOString()
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console.log('ANALYSIS: ' + analysisResult.analysis.recommendation)
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// CONTROLLED TRADING with strategy-specific logic
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if (this.shouldExecuteTrade(analysisResult.analysis)) {
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console.log('EXECUTING: Trade criteria met for ' + this.stats.strategyType)
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const tradeResult = await this.executeSafeTrade(analysisResult.analysis)
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if (tradeResult?.success) {
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this.stats.totalTrades++
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this.stats.successfulTrades++
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this.stats.winRate = (this.stats.successfulTrades / this.stats.totalTrades) * 100
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console.log('TRADE: Executed successfully')
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}
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} else {
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console.log('SIGNAL: Criteria not met for ' + this.stats.strategyType)
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}
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this.stats.currentCycle++
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} catch (error) {
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console.error('Error in safe 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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} finally {
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await this.guaranteedCleanup()
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this.updateNextAnalysisTime()
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}
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}
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private async performParallelAnalysis(): Promise<any> {
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try {
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// Use the enhanced screenshot API endpoint with all selected timeframes
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const response = await fetch('http://localhost:3000/api/enhanced-screenshot', {
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method: 'POST',
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headers: { 'Content-Type': 'application/json' },
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body: JSON.stringify({
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symbol: this.config.symbol,
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timeframes: this.config.timeframes,
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layouts: ['ai', 'diy'],
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analyze: true
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})
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})
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throw new Error('API response: ' + response.status)
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}
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const result = await response.json()
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if (result.analysis) {
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console.log('PARALLEL: Confidence ' + result.analysis.confidence + '%')
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return result
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}
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return null
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} catch (error) {
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console.error('Error in parallel analysis:', error)
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throw error
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}
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}
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private shouldExecuteTrade(analysis: any): boolean {
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const recommendation = analysis.recommendation.toLowerCase()
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const confidence = analysis.confidence || 0
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// Strategy-specific confidence requirements
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let minConfidence = 75 // Default
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if (this.stats.strategyType === 'Scalping') {
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minConfidence = 80 // Higher confidence for scalping
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} else if (this.stats.strategyType === 'Day Trading') {
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minConfidence = 75 // Standard confidence
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} else if (this.stats.strategyType === 'Swing Trading') {
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minConfidence = 70 // Slightly lower for swing (longer timeframes)
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}
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const isHighConfidence = confidence >= minConfidence
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const isClearDirection = recommendation.includes('buy') || recommendation.includes('sell')
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console.log('SIGNAL: ' + recommendation + ' (' + confidence + '%) - Required: ' + minConfidence + '%')
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return isHighConfidence && isClearDirection
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}
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private getStrategyInterval(timeframes: string[], strategyType: string): number {
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// Strategy-based intervals (but minimum will be enforced)
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if (strategyType === 'Scalping') {
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console.log('SCALPING: Using 2-minute analysis cycle')
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return 2 * 60 * 1000 // 2 minutes for scalping (will be enforced to 10 min minimum)
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}
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if (strategyType === 'Day Trading') {
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console.log('DAY TRADING: Using 5-minute analysis cycle')
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return 5 * 60 * 1000 // 5 minutes for day trading (will be enforced to 10 min minimum)
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}
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if (strategyType === 'Swing Trading') {
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console.log('SWING TRADING: Using 15-minute analysis cycle')
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return 15 * 60 * 1000 // 15 minutes for swing trading
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}
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// Fallback based on shortest timeframe
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const shortest = Math.min(...timeframes.map(tf => {
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if (tf === 'D') return 1440
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return parseInt(tf) || 60
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}))
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if (shortest <= 5) return 2 * 60 * 1000 // 2 min for very short
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if (shortest <= 15) return 5 * 60 * 1000 // 5 min for short
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if (shortest <= 60) return 10 * 60 * 1000 // 10 min for medium
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return 15 * 60 * 1000 // 15 min for long
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}
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private async executeSafeTrade(analysis: any): Promise<any> {
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try {
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console.log('SAFE TRADE: Executing...')
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// Use drift trading API endpoint
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const response = await fetch('http://localhost:3000/api/trading', {
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method: 'POST',
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headers: { 'Content-Type': 'application/json' },
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body: JSON.stringify({
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mode: this.config.mode,
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symbol: this.config.symbol,
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amount: this.config.tradingAmount,
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leverage: this.config.leverage,
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stopLoss: this.config.stopLoss,
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takeProfit: this.config.takeProfit,
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analysis: analysis,
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strategy: this.stats.strategyType
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})
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})
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const result = await response.json()
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return result
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} catch (error) {
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console.error('Error executing trade:', error)
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return { success: false, error: error.message }
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}
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}
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private async canExecuteTrade(): Promise<{ allowed: boolean, reason?: string }> {
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const now = Date.now()
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if (now - this.lastAnalysisTime < this.ANALYSIS_COOLDOWN) {
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const remaining = Math.ceil((this.ANALYSIS_COOLDOWN - (now - this.lastAnalysisTime)) / 1000 / 60)
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return { allowed: false, reason: 'Cooldown: ' + remaining + 'min' }
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}
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if (this.stats.totalTrades >= this.MAX_TRADES_PER_HOUR) {
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return { allowed: false, reason: 'Hour limit: ' + this.stats.totalTrades + '/' + this.MAX_TRADES_PER_HOUR }
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}
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return { allowed: true }
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}
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private async guaranteedCleanup(): Promise<void> {
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try {
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const { execSync } = require('child_process')
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execSync('pkill -f "chrome|chromium" 2>/dev/null || true')
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console.log('CLEANUP: Completed')
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} catch (error) {
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console.error('CLEANUP: Failed', error)
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}
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}
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private updateNextAnalysisTime(): void {
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const nextTime = Date.now() + this.stats.analysisInterval
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this.stats.nextScheduled = new Date(nextTime).toISOString()
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this.stats.nextAnalysisIn = this.stats.analysisInterval
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}
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async stopSafeAutomation(): Promise<{ success: boolean, message?: string }> {
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try {
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this.isRunning = false
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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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await this.guaranteedCleanup()
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this.config = null
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this.stats.nextAnalysisIn = 0
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this.stats.nextScheduled = null
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console.log('SAFE: Automation stopped')
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return { success: true, message: 'Safe automation stopped' }
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} catch (error) {
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return { success: false, message: 'Stop failed' }
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}
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}
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getStatus() {
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return {
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isActive: this.isRunning,
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mode: this.config?.mode || 'SIMULATION',
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symbol: this.config?.symbol || 'SOLUSD',
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timeframes: this.config?.timeframes || ['1h'],
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automationType: 'SAFE_PARALLEL',
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strategy: this.stats.strategyType,
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...this.stats
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}
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}
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}
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export const safeParallelAutomation = new SafeParallelAutomation()
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@@ -1,40 +1,9 @@
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// Simple automation service for basic start/stop functionality
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// Import AI Leverage Calculator for dynamic leverage
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async function importAILeverageCalculator() {
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try {
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const { AILeverageCalculator } = await import('./ai-leverage-calculator.js');
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return AILeverageCalculator;
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} catch (error) {
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console.warn('⚠️ AI Leverage Calculator not available, using default leverage');
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return null;
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}
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}
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// Import Enhanced Risk Manager with Learning for intelligent beach mode operation
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async function importEnhancedRiskManager() {
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try {
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const EnhancedAutonomousRiskManager = require('./enhanced-autonomous-risk-manager.js');
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return EnhancedAutonomousRiskManager;
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} catch (error) {
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console.warn('⚠️ Enhanced Risk Manager not available, falling back to stable monitor');
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// Fallback to stable risk monitor
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try {
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const StableRiskMonitor = require('./stable-risk-monitor.js');
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return StableRiskMonitor;
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} catch (fallbackError) {
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console.warn('⚠️ Stable Risk Monitor also not available, using basic monitoring');
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return null;
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}
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}
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}
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class SimpleAutomation {
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constructor() {
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this.isRunning = false;
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this.config = null;
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this.intervalId = null;
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this.riskManager = null; // Autonomous AI Risk Manager
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this.stats = {
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totalCycles: 0,
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totalTrades: 0,
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@@ -61,41 +30,8 @@ class SimpleAutomation {
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this.isRunning = true;
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this.stats.startTime = new Date().toISOString();
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this.stats.status = 'Running';
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console.log('✅ AUTOMATION STATUS: isRunning =', this.isRunning);
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console.log('🎯 LIVE TRADING:', this.config.enableTrading ? 'ENABLED' : 'DISABLED');
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this.stats.totalCycles = 0;
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// Initialize Enhanced AI Risk Manager with Learning Capabilities
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try {
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const EnhancedRiskManagerClass = await importEnhancedRiskManager();
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if (EnhancedRiskManagerClass) {
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this.riskManager = new EnhancedRiskManagerClass();
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console.log('🧠 ENHANCED BEACH MODE: AI learning system activated');
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console.log('🎯 System will learn from every decision and improve over time');
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// Start enhanced autonomous operation
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setTimeout(() => {
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if (this.riskManager && this.riskManager.beachMode) {
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this.riskManager.beachMode();
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console.log('🏖️ Full autonomous operation with AI learning active');
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}
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}, 2000);
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}
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} catch (error) {
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console.log('🔄 Continuing without enhanced autonomous risk monitoring');
|
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console.error('Risk manager initialization error:', error.message);
|
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}
|
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// Auto-enable trading when in LIVE mode
|
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if (config.mode === 'LIVE') {
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this.config.enableTrading = true;
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console.log('🔥 LIVE TRADING ENABLED: Real trades will be executed');
|
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} else {
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this.config.enableTrading = false;
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console.log('📊 SIMULATION MODE: Trades will be simulated only');
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}
|
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|
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// Detect strategy
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const timeframes = config.selectedTimeframes;
|
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let strategy = 'General';
|
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@@ -137,22 +73,14 @@ class SimpleAutomation {
|
||||
|
||||
async stop() {
|
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try {
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console.log('🛑 STOPPING AUTOMATION...');
|
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this.isRunning = false;
|
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this.stats.status = 'Stopped';
|
||||
console.log('✅ AUTOMATION STATUS: isRunning =', this.isRunning);
|
||||
|
||||
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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|
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// Stop risk monitor if running
|
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if (this.riskManager && this.riskManager.stop) {
|
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this.riskManager.stop();
|
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console.log('🏖️ Beach mode monitoring stopped');
|
||||
}
|
||||
|
||||
console.log('SIMPLE AUTOMATION: Stopped');
|
||||
|
||||
return { success: true, message: 'Automation stopped successfully' };
|
||||
@@ -164,12 +92,6 @@ class SimpleAutomation {
|
||||
|
||||
async runCycle() {
|
||||
try {
|
||||
// Check if automation should still be running
|
||||
if (!this.isRunning) {
|
||||
console.log('⏹️ AUTOMATION STOPPED: Skipping cycle');
|
||||
return;
|
||||
}
|
||||
|
||||
this.stats.totalCycles++;
|
||||
this.stats.lastActivity = new Date().toISOString();
|
||||
|
||||
@@ -180,32 +102,10 @@ class SimpleAutomation {
|
||||
if (this.config) {
|
||||
// Perform actual analysis using enhanced screenshot API
|
||||
await this.performAnalysis();
|
||||
|
||||
// Reset error counter on successful cycle
|
||||
this.stats.consecutiveErrors = 0;
|
||||
}
|
||||
|
||||
} catch (error) {
|
||||
console.error('❌ CRITICAL ERROR in automation cycle:', error);
|
||||
console.error('❌ Stack trace:', error.stack);
|
||||
|
||||
// Increment error counter
|
||||
this.stats.consecutiveErrors = (this.stats.consecutiveErrors || 0) + 1;
|
||||
|
||||
// If too many consecutive errors, stop automation
|
||||
if (this.stats.consecutiveErrors >= 3) {
|
||||
console.error('🚨 TOO MANY ERRORS: Stopping automation after', this.stats.consecutiveErrors, 'consecutive failures');
|
||||
this.isRunning = false;
|
||||
this.stats.status = 'Stopped due to errors';
|
||||
|
||||
if (this.intervalId) {
|
||||
clearInterval(this.intervalId);
|
||||
this.intervalId = null;
|
||||
}
|
||||
return;
|
||||
}
|
||||
|
||||
console.log(`⚠️ Error ${this.stats.consecutiveErrors}/3 - Will retry next cycle`);
|
||||
console.error('Error in automation cycle:', error);
|
||||
}
|
||||
}
|
||||
|
||||
@@ -376,8 +276,10 @@ class SimpleAutomation {
|
||||
}
|
||||
|
||||
shouldExecuteTrade(analysis) {
|
||||
// Always allow trade execution - the useRealDEX flag determines if it's real or simulated
|
||||
console.log(`<EFBFBD> TRADE MODE: ${this.config.mode || 'SIMULATION'} - Trading ${this.config.enableTrading ? 'ENABLED' : 'DISABLED'}`);
|
||||
if (this.config.mode !== 'LIVE') {
|
||||
console.log('📊 SIMULATION MODE: Would execute trade');
|
||||
return false;
|
||||
}
|
||||
|
||||
const recommendation = analysis.recommendation?.toLowerCase() || '';
|
||||
const confidence = analysis.confidence || 0;
|
||||
@@ -399,7 +301,6 @@ class SimpleAutomation {
|
||||
async executeTrade(analysis) {
|
||||
try {
|
||||
console.log('💰 EXECUTING TRADE...');
|
||||
console.log('📊 Analysis data:', JSON.stringify(analysis, null, 2));
|
||||
|
||||
// Map analysis recommendation to trading side
|
||||
const recommendation = analysis.recommendation?.toLowerCase() || '';
|
||||
@@ -413,111 +314,14 @@ class SimpleAutomation {
|
||||
console.log('❌ TRADE SKIP: Invalid recommendation - ' + recommendation);
|
||||
return { success: false, error: 'Invalid recommendation: ' + recommendation };
|
||||
}
|
||||
|
||||
// Extract stop loss and take profit from analysis
|
||||
let stopLoss = null;
|
||||
let takeProfit = null;
|
||||
|
||||
// Try to extract from the structured analysis format
|
||||
if (analysis.stopLoss && typeof analysis.stopLoss === 'object') {
|
||||
stopLoss = analysis.stopLoss.price;
|
||||
} else if (analysis.stopLoss && typeof analysis.stopLoss === 'number') {
|
||||
stopLoss = analysis.stopLoss;
|
||||
}
|
||||
|
||||
// Extract take profit - prefer tp1 if available
|
||||
if (analysis.takeProfits && typeof analysis.takeProfits === 'object') {
|
||||
if (analysis.takeProfits.tp1 && analysis.takeProfits.tp1.price) {
|
||||
takeProfit = analysis.takeProfits.tp1.price;
|
||||
} else if (analysis.takeProfits.tp2 && analysis.takeProfits.tp2.price) {
|
||||
takeProfit = analysis.takeProfits.tp2.price;
|
||||
}
|
||||
} else if (analysis.takeProfit && typeof analysis.takeProfit === 'number') {
|
||||
takeProfit = analysis.takeProfit;
|
||||
}
|
||||
|
||||
// Fallback: try to extract from nested levels object
|
||||
if (!stopLoss && analysis.levels) {
|
||||
stopLoss = analysis.levels.stopLoss || analysis.levels.stop;
|
||||
}
|
||||
if (!takeProfit && analysis.levels) {
|
||||
takeProfit = analysis.levels.takeProfit || analysis.levels.target;
|
||||
}
|
||||
|
||||
// Parse numeric values if they're strings
|
||||
if (stopLoss && typeof stopLoss === 'string') {
|
||||
stopLoss = parseFloat(stopLoss.replace(/[^0-9.]/g, ''));
|
||||
}
|
||||
if (takeProfit && typeof takeProfit === 'string') {
|
||||
takeProfit = parseFloat(takeProfit.replace(/[^0-9.]/g, ''));
|
||||
}
|
||||
|
||||
console.log(`🎯 Trade levels - SL: ${stopLoss}, TP: ${takeProfit}`);
|
||||
|
||||
// Calculate optimal leverage using AI Leverage Calculator
|
||||
let optimalLeverage = 1; // Default fallback
|
||||
console.log('🔧 DEBUG: Starting leverage calculation...');
|
||||
try {
|
||||
const AILeverageCalculator = await importAILeverageCalculator();
|
||||
console.log('🔧 DEBUG: AI Leverage Calculator imported:', !!AILeverageCalculator);
|
||||
if (AILeverageCalculator && stopLoss) {
|
||||
console.log('🔧 DEBUG: Both calculator and stopLoss available, proceeding...');
|
||||
// Get current price from analysis
|
||||
const currentPrice = analysis.entry?.price || analysis.currentPrice || 178; // fallback price
|
||||
|
||||
// Get real account data from Drift API
|
||||
let accountValue = 49; // fallback
|
||||
let availableBalance = 49; // fallback
|
||||
|
||||
try {
|
||||
const baseUrl = process.env.INTERNAL_API_URL || 'http://localhost:3000';
|
||||
console.log('🔧 DEBUG: Fetching balance from:', baseUrl);
|
||||
const balanceResponse = await fetch(`${baseUrl}/api/drift/balance`);
|
||||
const balanceData = await balanceResponse.json();
|
||||
if (balanceData.success) {
|
||||
accountValue = balanceData.accountValue;
|
||||
availableBalance = balanceData.availableBalance;
|
||||
console.log(`💰 Real account data: $${accountValue.toFixed(2)} total, $${availableBalance.toFixed(2)} available`);
|
||||
} else {
|
||||
console.log('🔧 DEBUG: Balance API returned error:', balanceData);
|
||||
}
|
||||
} catch (balanceError) {
|
||||
console.warn('⚠️ Failed to get real balance, using fallback:', balanceError.message);
|
||||
}
|
||||
|
||||
console.log(`🧮 Calculating optimal leverage: Entry=$${currentPrice}, StopLoss=$${stopLoss}`);
|
||||
|
||||
const leverageResult = AILeverageCalculator.calculateOptimalLeverage({
|
||||
accountValue,
|
||||
availableBalance,
|
||||
entryPrice: currentPrice,
|
||||
stopLossPrice: stopLoss,
|
||||
side: side === 'BUY' ? 'long' : 'short',
|
||||
maxLeverageAllowed: 10, // Drift platform max
|
||||
safetyBuffer: 0.10 // 10% safety buffer
|
||||
});
|
||||
|
||||
optimalLeverage = leverageResult.recommendedLeverage;
|
||||
console.log(`🎯 AI Calculated Leverage: ${optimalLeverage.toFixed(1)}x (Risk: ${leverageResult.riskAssessment})`);
|
||||
console.log(`📊 Leverage Details: ${leverageResult.reasoning}`);
|
||||
} else {
|
||||
console.log('🔧 DEBUG: Skipping leverage calc - Calculator:', !!AILeverageCalculator, 'StopLoss:', !!stopLoss);
|
||||
}
|
||||
} catch (leverageError) {
|
||||
console.warn('⚠️ Leverage calculation failed, using default 1x:', leverageError.message);
|
||||
}
|
||||
|
||||
console.log(`🔧 DEBUG: Final leverage to use: ${optimalLeverage}x`);
|
||||
|
||||
// Use the trading API with proper fields for Drift
|
||||
// Use the trading API with proper fields
|
||||
const tradePayload = {
|
||||
symbol: this.config.symbol,
|
||||
side: side,
|
||||
amount: this.config.tradingAmount || 49, // Use available balance
|
||||
leverage: optimalLeverage, // Use AI-calculated optimal leverage
|
||||
stopLoss: stopLoss,
|
||||
takeProfit: takeProfit,
|
||||
useRealDEX: this.config.enableTrading === true, // Use real DEX when live trading enabled
|
||||
amount: this.config.tradingAmount || 10, // Default to $10 if not set
|
||||
type: 'market',
|
||||
tradingMode: 'SPOT',
|
||||
analysis: analysis // Include analysis for reference
|
||||
};
|
||||
|
||||
@@ -548,27 +352,14 @@ class SimpleAutomation {
|
||||
}
|
||||
|
||||
getStatus() {
|
||||
const baseStatus = {
|
||||
return {
|
||||
isActive: this.isRunning,
|
||||
mode: this.config?.mode || 'SIMULATION',
|
||||
enableTrading: this.config?.enableTrading || false,
|
||||
tradingStatus: this.config?.enableTrading ? 'REAL TRADES' : 'SIMULATED ONLY',
|
||||
symbol: this.config?.symbol || 'SOLUSD',
|
||||
timeframes: this.config?.selectedTimeframes || [],
|
||||
automationType: 'SIMPLE',
|
||||
...this.stats
|
||||
};
|
||||
|
||||
// Add more descriptive status based on running state
|
||||
if (this.isRunning) {
|
||||
baseStatus.detailedStatus = 'Running - Monitoring for trade opportunities';
|
||||
baseStatus.nextAction = 'Next analysis cycle in progress';
|
||||
} else {
|
||||
baseStatus.detailedStatus = 'Stopped - No monitoring active';
|
||||
baseStatus.nextAction = 'Start automation to begin monitoring';
|
||||
}
|
||||
|
||||
return baseStatus;
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
530
lib/simplified-stop-loss-learner-fixed.js
Normal file
530
lib/simplified-stop-loss-learner-fixed.js
Normal file
@@ -0,0 +1,530 @@
|
||||
/**
|
||||
* Simplified Stop Loss Learning System
|
||||
*
|
||||
* Simplified approach focusing on essential learning patterns
|
||||
* without complex statistical analysis.
|
||||
*/
|
||||
|
||||
const { PrismaClient } = require('@prisma/client');
|
||||
const getDB = require('./database-util');
|
||||
|
||||
class SimplifiedStopLossLearner {
|
||||
constructor() {
|
||||
this.learningThresholds = {
|
||||
emergency: 1.0, // Emergency exit at 1% from SL
|
||||
risk: 2.0, // High risk at 2% from SL
|
||||
mediumRisk: 5.0 // Medium risk at 5% from SL
|
||||
};
|
||||
}
|
||||
|
||||
async log(message) {
|
||||
console.log(`[${new Date().toISOString()}] 🧠 SL Learner: ${message}`);
|
||||
}
|
||||
|
||||
/**
|
||||
* Record a stop loss related decision for learning
|
||||
*/
|
||||
async recordDecision(decisionData) {
|
||||
try {
|
||||
const learningRecord = {
|
||||
type: 'STOP_LOSS_DECISION',
|
||||
tradeId: decisionData.tradeId,
|
||||
symbol: decisionData.symbol,
|
||||
decision: decisionData.decision,
|
||||
distanceFromSL: decisionData.distanceFromSL,
|
||||
reasoning: decisionData.reasoning,
|
||||
marketConditions: decisionData.marketConditions,
|
||||
expectedOutcome: decisionData.expectedOutcome,
|
||||
timestamp: new Date().toISOString()
|
||||
};
|
||||
|
||||
const prisma = await getDB();
|
||||
const record = await prisma.ai_learning_data.create({
|
||||
data: {
|
||||
id: `sl_decision_${Date.now()}_${Math.random().toString(36).substr(2, 9)}`,
|
||||
userId: 'default-user',
|
||||
symbol: decisionData.symbol,
|
||||
timeframe: 'DECISION',
|
||||
analysisData: JSON.stringify(learningRecord),
|
||||
marketConditions: JSON.stringify(decisionData.marketConditions || {}),
|
||||
confidenceScore: 50 // Neutral starting confidence
|
||||
}
|
||||
});
|
||||
|
||||
await this.log(`📝 Decision recorded: ${decisionData.decision} for ${decisionData.symbol} at ${decisionData.distanceFromSL}%`);
|
||||
|
||||
return record.id;
|
||||
} catch (error) {
|
||||
await this.log(`❌ Error recording decision: ${error.message}`);
|
||||
return null;
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Update the outcome of a previously recorded decision
|
||||
*/
|
||||
async assessDecisionOutcome(outcomeData) {
|
||||
try {
|
||||
const prisma = await getDB();
|
||||
|
||||
// Find the original decision record
|
||||
const originalRecord = await prisma.ai_learning_data.findUnique({
|
||||
where: { id: outcomeData.decisionId }
|
||||
});
|
||||
|
||||
if (!originalRecord) {
|
||||
await this.log(`⚠️ Original decision ${outcomeData.decisionId} not found`);
|
||||
return false;
|
||||
}
|
||||
|
||||
// Parse the original decision data
|
||||
const originalDecision = JSON.parse(originalRecord.analysisData);
|
||||
|
||||
// Create outcome record with learning data
|
||||
const outcomeRecord = {
|
||||
type: 'STOP_LOSS_OUTCOME',
|
||||
originalDecisionId: outcomeData.decisionId,
|
||||
actualOutcome: outcomeData.actualOutcome,
|
||||
timeToOutcome: outcomeData.timeToOutcome,
|
||||
pnlImpact: outcomeData.pnlImpact,
|
||||
wasCorrect: this.evaluateDecisionCorrectness(originalDecision, outcomeData),
|
||||
learningData: {
|
||||
originalDecision: originalDecision.decision,
|
||||
distanceFromSL: originalDecision.distanceFromSL,
|
||||
outcome: outcomeData.actualOutcome,
|
||||
profitability: outcomeData.pnlImpact > 0 ? 'PROFITABLE' : 'LOSS'
|
||||
},
|
||||
timestamp: new Date().toISOString()
|
||||
};
|
||||
|
||||
await prisma.ai_learning_data.create({
|
||||
data: {
|
||||
id: `sl_outcome_${Date.now()}_${Math.random().toString(36).substr(2, 9)}`,
|
||||
userId: 'default-user',
|
||||
symbol: originalDecision.symbol,
|
||||
timeframe: 'OUTCOME',
|
||||
analysisData: JSON.stringify(outcomeRecord),
|
||||
marketConditions: originalRecord.marketConditions,
|
||||
confidenceScore: outcomeRecord.wasCorrect ? 75 : 25
|
||||
}
|
||||
});
|
||||
|
||||
await this.log(`✅ Outcome assessed for ${outcomeData.decisionId}: ${outcomeData.actualOutcome} (${outcomeRecord.wasCorrect ? 'CORRECT' : 'INCORRECT'})`);
|
||||
|
||||
// Update learning thresholds based on outcomes
|
||||
await this.updateThresholdsFromOutcome(originalDecision, outcomeRecord);
|
||||
|
||||
return true;
|
||||
} catch (error) {
|
||||
await this.log(`❌ Error assessing outcome: ${error.message}`);
|
||||
return false;
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Evaluate if the original decision was correct based on outcome
|
||||
*/
|
||||
evaluateDecisionCorrectness(originalDecision, outcome) {
|
||||
const decision = originalDecision.decision;
|
||||
const actualOutcome = outcome.actualOutcome;
|
||||
const pnlImpact = outcome.pnlImpact;
|
||||
|
||||
// Define what constitutes a "correct" decision
|
||||
if (decision === 'EMERGENCY_EXIT' && (actualOutcome === 'STOPPED_OUT' || pnlImpact < -50)) {
|
||||
return true; // Correctly identified emergency
|
||||
}
|
||||
|
||||
if (decision === 'HOLD_POSITION' && pnlImpact > 0) {
|
||||
return true; // Correctly held profitable position
|
||||
}
|
||||
|
||||
if (decision === 'ADJUST_STOP_LOSS' && actualOutcome === 'TAKE_PROFIT') {
|
||||
return true; // Adjustment led to profitable exit
|
||||
}
|
||||
|
||||
return false;
|
||||
}
|
||||
|
||||
/**
|
||||
* Get smart recommendation based on learned patterns
|
||||
*/
|
||||
async getSmartRecommendation(requestData) {
|
||||
try {
|
||||
const { distanceFromSL, symbol, marketConditions } = requestData;
|
||||
|
||||
// Get historical data for similar situations
|
||||
const prisma = await getDB();
|
||||
const similarDecisions = await prisma.ai_learning_data.findMany({
|
||||
where: {
|
||||
symbol: symbol,
|
||||
analysisData: {
|
||||
string_contains: '"type":"STOP_LOSS_DECISION"'
|
||||
}
|
||||
},
|
||||
orderBy: { createdAt: 'desc' },
|
||||
take: 20
|
||||
});
|
||||
|
||||
// Analyze patterns from similar situations
|
||||
let recommendation = this.getBaseRecommendation(distanceFromSL);
|
||||
|
||||
if (similarDecisions.length >= 3) {
|
||||
const learnedRecommendation = await this.analyzePatterns(similarDecisions, distanceFromSL);
|
||||
if (learnedRecommendation) {
|
||||
recommendation = learnedRecommendation;
|
||||
}
|
||||
}
|
||||
|
||||
await this.log(`🎯 Smart recommendation for ${symbol} at ${distanceFromSL}%: ${recommendation.action}`);
|
||||
|
||||
return recommendation;
|
||||
} catch (error) {
|
||||
await this.log(`❌ Error getting smart recommendation: ${error.message}`);
|
||||
return this.getBaseRecommendation(distanceFromSL);
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Get base recommendation using current thresholds
|
||||
*/
|
||||
getBaseRecommendation(distanceFromSL) {
|
||||
if (distanceFromSL <= this.learningThresholds.emergency) {
|
||||
return {
|
||||
action: 'EMERGENCY_EXIT',
|
||||
confidence: 0.8,
|
||||
reasoning: `Very close to SL (${distanceFromSL}%), emergency exit recommended`
|
||||
};
|
||||
} else if (distanceFromSL <= this.learningThresholds.risk) {
|
||||
return {
|
||||
action: 'HIGH_ALERT',
|
||||
confidence: 0.7,
|
||||
reasoning: `Close to SL (${distanceFromSL}%), monitor closely`
|
||||
};
|
||||
} else if (distanceFromSL <= this.learningThresholds.mediumRisk) {
|
||||
return {
|
||||
action: 'MONITOR',
|
||||
confidence: 0.6,
|
||||
reasoning: `Moderate distance from SL (${distanceFromSL}%), continue monitoring`
|
||||
};
|
||||
} else {
|
||||
return {
|
||||
action: 'HOLD_POSITION',
|
||||
confidence: 0.5,
|
||||
reasoning: `Safe distance from SL (${distanceFromSL}%), maintain position`
|
||||
};
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Analyze historical patterns to improve recommendations
|
||||
*/
|
||||
async analyzePatterns(decisions, currentDistance) {
|
||||
const outcomes = await this.getOutcomesForDecisions(decisions);
|
||||
|
||||
// Find decisions made at similar distances
|
||||
const similarDistanceDecisions = decisions.filter(d => {
|
||||
const data = JSON.parse(d.analysisData);
|
||||
const distance = data.distanceFromSL;
|
||||
return Math.abs(distance - currentDistance) <= 1.0; // Within 1%
|
||||
});
|
||||
|
||||
if (similarDistanceDecisions.length < 2) {
|
||||
return null; // Not enough similar data
|
||||
}
|
||||
|
||||
// Analyze success rate of different actions at this distance
|
||||
const actionSuccess = {};
|
||||
|
||||
for (const decision of similarDistanceDecisions) {
|
||||
const decisionData = JSON.parse(decision.analysisData);
|
||||
const action = decisionData.decision;
|
||||
const outcome = outcomes.find(o => o.originalDecisionId === decision.id);
|
||||
|
||||
if (outcome) {
|
||||
if (!actionSuccess[action]) {
|
||||
actionSuccess[action] = { total: 0, successful: 0 };
|
||||
}
|
||||
actionSuccess[action].total++;
|
||||
if (outcome.wasCorrect) {
|
||||
actionSuccess[action].successful++;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Find the action with highest success rate
|
||||
let bestAction = null;
|
||||
let bestSuccessRate = 0;
|
||||
|
||||
for (const [action, stats] of Object.entries(actionSuccess)) {
|
||||
if (stats.total >= 2) { // Need at least 2 samples
|
||||
const successRate = stats.successful / stats.total;
|
||||
if (successRate > bestSuccessRate) {
|
||||
bestSuccessRate = successRate;
|
||||
bestAction = action;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
if (bestAction && bestSuccessRate > 0.6) {
|
||||
return {
|
||||
action: bestAction,
|
||||
confidence: bestSuccessRate,
|
||||
reasoning: `Learned pattern: ${bestAction} successful ${Math.round(bestSuccessRate * 100)}% of time at this distance`
|
||||
};
|
||||
}
|
||||
|
||||
return null;
|
||||
}
|
||||
|
||||
/**
|
||||
* Get outcomes for a set of decisions
|
||||
*/
|
||||
async getOutcomesForDecisions(decisions) {
|
||||
const prisma = await getDB();
|
||||
const decisionIds = decisions.map(d => d.id);
|
||||
|
||||
const outcomes = await prisma.ai_learning_data.findMany({
|
||||
where: {
|
||||
analysisData: {
|
||||
string_contains: '"type":"STOP_LOSS_OUTCOME"'
|
||||
}
|
||||
}
|
||||
});
|
||||
|
||||
return outcomes.map(o => JSON.parse(o.analysisData))
|
||||
.filter(outcome => decisionIds.includes(outcome.originalDecisionId));
|
||||
}
|
||||
|
||||
/**
|
||||
* Update learning thresholds based on outcome data
|
||||
*/
|
||||
async updateThresholdsFromOutcome(originalDecision, outcome) {
|
||||
// Simple threshold adjustment based on outcomes
|
||||
const distance = originalDecision.distanceFromSL;
|
||||
const wasCorrect = outcome.wasCorrect;
|
||||
|
||||
if (!wasCorrect) {
|
||||
// If decision was wrong, adjust thresholds slightly
|
||||
if (originalDecision.decision === 'HOLD_POSITION' && outcome.actualOutcome === 'STOPPED_OUT') {
|
||||
// We should have exited earlier - make thresholds more conservative
|
||||
this.learningThresholds.emergency = Math.min(2.0, this.learningThresholds.emergency + 0.1);
|
||||
this.learningThresholds.risk = Math.min(3.0, this.learningThresholds.risk + 0.1);
|
||||
}
|
||||
}
|
||||
|
||||
await this.log(`🔧 Thresholds updated: emergency=${this.learningThresholds.emergency}, risk=${this.learningThresholds.risk}`);
|
||||
}
|
||||
|
||||
/**
|
||||
* Get current learning status and statistics
|
||||
*/
|
||||
async analyzeDecisionPatterns() {
|
||||
try {
|
||||
const prisma = await getDB();
|
||||
|
||||
// Get recent decisions and outcomes
|
||||
const decisions = await prisma.ai_learning_data.findMany({
|
||||
where: {
|
||||
analysisData: {
|
||||
string_contains: '"type":"STOP_LOSS_DECISION"'
|
||||
},
|
||||
createdAt: {
|
||||
gte: new Date(Date.now() - 7 * 24 * 60 * 60 * 1000) // Last 7 days
|
||||
}
|
||||
},
|
||||
orderBy: { createdAt: 'desc' }
|
||||
});
|
||||
|
||||
const outcomes = await prisma.ai_learning_data.findMany({
|
||||
where: {
|
||||
analysisData: {
|
||||
string_contains: '"type":"STOP_LOSS_OUTCOME"'
|
||||
},
|
||||
createdAt: {
|
||||
gte: new Date(Date.now() - 7 * 24 * 60 * 60 * 1000) // Last 7 days
|
||||
}
|
||||
}
|
||||
});
|
||||
|
||||
// Analyze patterns
|
||||
const patterns = {
|
||||
totalDecisions: decisions.length,
|
||||
totalOutcomes: outcomes.length,
|
||||
successfulDecisions: outcomes.filter(o => JSON.parse(o.analysisData).wasCorrect).length,
|
||||
successRate: outcomes.length > 0 ? outcomes.filter(o => JSON.parse(o.analysisData).wasCorrect).length / outcomes.length : 0,
|
||||
learnedThresholds: this.learningThresholds
|
||||
};
|
||||
|
||||
return patterns;
|
||||
} catch (error) {
|
||||
await this.log(`❌ Error analyzing patterns: ${error.message}`);
|
||||
return {
|
||||
totalDecisions: 0,
|
||||
totalOutcomes: 0,
|
||||
successfulDecisions: 0,
|
||||
successRate: 0,
|
||||
learnedThresholds: this.learningThresholds
|
||||
};
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Get learning status
|
||||
*/
|
||||
async getLearningStatus() {
|
||||
try {
|
||||
const prisma = await getDB();
|
||||
const totalDecisions = await prisma.ai_learning_data.count({
|
||||
where: {
|
||||
analysisData: {
|
||||
string_contains: '"type":"STOP_LOSS_DECISION"'
|
||||
}
|
||||
}
|
||||
});
|
||||
|
||||
const recentDecisions = await prisma.ai_learning_data.count({
|
||||
where: {
|
||||
analysisData: {
|
||||
string_contains: '"type":"STOP_LOSS_DECISION"'
|
||||
},
|
||||
createdAt: {
|
||||
gte: new Date(Date.now() - 24 * 60 * 60 * 1000) // Last 24 hours
|
||||
}
|
||||
}
|
||||
});
|
||||
|
||||
return {
|
||||
totalDecisions,
|
||||
recentDecisions,
|
||||
thresholds: this.learningThresholds,
|
||||
isActive: totalDecisions > 0
|
||||
};
|
||||
|
||||
} catch (error) {
|
||||
await this.log(`❌ Error getting learning status: ${error.message}`);
|
||||
return {
|
||||
totalDecisions: 0,
|
||||
recentDecisions: 0,
|
||||
thresholds: this.learningThresholds,
|
||||
isActive: false
|
||||
};
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Generate comprehensive learning report
|
||||
* Compatible implementation for enhanced-autonomous-risk-manager
|
||||
*/
|
||||
async generateLearningReport() {
|
||||
try {
|
||||
const status = await this.getLearningStatus();
|
||||
const patterns = await this.analyzeDecisionPatterns();
|
||||
|
||||
// Calculate system confidence based on decisions made
|
||||
const systemConfidence = this.calculateSystemConfidence(status.totalDecisions, status.recentDecisions, patterns.successRate);
|
||||
|
||||
const report = {
|
||||
timestamp: new Date().toISOString(),
|
||||
summary: {
|
||||
totalDecisions: status.totalDecisions,
|
||||
recentDecisions: status.recentDecisions,
|
||||
successfulPatterns: patterns.successfulDecisions,
|
||||
learningThresholds: this.learningThresholds,
|
||||
systemConfidence: systemConfidence,
|
||||
isActive: status.isActive,
|
||||
successRate: patterns.successRate
|
||||
},
|
||||
insights: {
|
||||
emergencyThreshold: this.learningThresholds.emergency,
|
||||
riskThreshold: this.learningThresholds.risk,
|
||||
mediumRiskThreshold: this.learningThresholds.mediumRisk,
|
||||
confidenceLevel: systemConfidence > 0.7 ? 'HIGH' : systemConfidence > 0.4 ? 'MEDIUM' : 'LOW',
|
||||
totalOutcomes: patterns.totalOutcomes,
|
||||
decisionAccuracy: patterns.successRate
|
||||
},
|
||||
recommendations: this.generateSystemRecommendations(status, patterns)
|
||||
};
|
||||
|
||||
await this.log(`📊 Learning report generated: ${report.summary.totalDecisions} decisions, ${(systemConfidence * 100).toFixed(1)}% confidence, ${(patterns.successRate * 100).toFixed(1)}% success rate`);
|
||||
|
||||
return report;
|
||||
} catch (error) {
|
||||
await this.log(`❌ Error generating learning report: ${error.message}`);
|
||||
return {
|
||||
timestamp: new Date().toISOString(),
|
||||
summary: {
|
||||
totalDecisions: 0,
|
||||
recentDecisions: 0,
|
||||
systemConfidence: 0.0,
|
||||
isActive: false
|
||||
},
|
||||
error: error.message
|
||||
};
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Calculate system confidence based on learning data
|
||||
*/
|
||||
calculateSystemConfidence(totalDecisions, recentDecisions, successRate = 0) {
|
||||
if (totalDecisions < 5) return 0.3; // Low confidence with insufficient data
|
||||
if (totalDecisions < 20) return 0.4 + (successRate * 0.2); // Medium-low confidence boosted by success
|
||||
if (totalDecisions < 50) return 0.6 + (successRate * 0.2); // Medium confidence boosted by success
|
||||
|
||||
// High confidence with lots of data, scaled by recent activity and success rate
|
||||
const recentActivityFactor = Math.min(1.0, recentDecisions / 10);
|
||||
const successFactor = successRate || 0.5; // Default to neutral if no success data
|
||||
return Math.min(0.95, 0.7 + (recentActivityFactor * 0.1) + (successFactor * 0.15)); // Cap at 95%
|
||||
}
|
||||
|
||||
/**
|
||||
* Generate system recommendations based on learning status
|
||||
*/
|
||||
generateSystemRecommendations(status, patterns) {
|
||||
const recommendations = [];
|
||||
|
||||
if (status.totalDecisions < 10) {
|
||||
recommendations.push({
|
||||
type: 'DATA_COLLECTION',
|
||||
message: 'Need more decision data for reliable learning',
|
||||
priority: 'HIGH'
|
||||
});
|
||||
}
|
||||
|
||||
if (status.recentDecisions < 3) {
|
||||
recommendations.push({
|
||||
type: 'ACTIVITY_LOW',
|
||||
message: 'Recent trading activity is low - learning may be stale',
|
||||
priority: 'MEDIUM'
|
||||
});
|
||||
}
|
||||
|
||||
if (patterns && patterns.successRate < 0.4 && patterns.totalOutcomes >= 5) {
|
||||
recommendations.push({
|
||||
type: 'THRESHOLD_ADJUSTMENT',
|
||||
message: 'Low success rate detected - consider adjusting decision thresholds',
|
||||
priority: 'HIGH'
|
||||
});
|
||||
}
|
||||
|
||||
if (status.totalDecisions >= 20 && patterns && patterns.successRate > 0.6) {
|
||||
recommendations.push({
|
||||
type: 'SYSTEM_PERFORMING',
|
||||
message: 'System learning effectively with good success rate',
|
||||
priority: 'LOW'
|
||||
});
|
||||
}
|
||||
|
||||
if (status.totalDecisions >= 50) {
|
||||
recommendations.push({
|
||||
type: 'OPTIMIZATION_READY',
|
||||
message: 'Sufficient data available for advanced threshold optimization',
|
||||
priority: 'LOW'
|
||||
});
|
||||
}
|
||||
|
||||
return recommendations;
|
||||
}
|
||||
}
|
||||
|
||||
module.exports = SimplifiedStopLossLearner;
|
||||
Reference in New Issue
Block a user