trading_bot_v3/lib/simplified-stop-loss-learner.js.backup

#!/usr/bin/env node

/**
 * Simplified Stop Loss Decision Learning System
 * 
 * Uses existing AILearningData schema for learning integration
 */

const { getDB } = require('./db');

class SimplifiedStopLossLearner {
  constructor() {
    this.decisionHistory = [];
    this.learningThresholds = {
      emergencyDistance: 1.0,
      highRiskDistance: 2.0,
      mediumRiskDistance: 5.0
    };
  }

  async log(message) {
    const timestamp = new Date().toISOString();
    console.log(`[${timestamp}] 🧠 SL Learner: ${message}`);
  }

  /**
   * Record an AI decision for learning (using existing schema)
   */
  async recordDecision(decisionData) {
    try {
      const decision = {
        userId: 'system', // System decisions
        analysisData: {
          type: 'STOP_LOSS_DECISION',
          decision: decisionData.decision,
          reasoning: decisionData.reasoning,
          confidence: decisionData.confidence,
          distanceFromSL: decisionData.distanceFromSL,
          marketConditions: decisionData.marketConditions || {},
          timestamp: new Date().toISOString()
        },
        marketConditions: decisionData.marketConditions || {},
        timeframe: decisionData.timeframe || '1h',
        symbol: decisionData.symbol || 'SOLUSD'
      };

      const prisma = await getDB();
      const record = await prisma.ai_learning_data.create({
        data: decision
      });

      await this.log(`📝 Recorded decision ${record.id} for learning: ${decisionData.decision}`);
      this.decisionHistory.push(decision);
      return record.id;

    } catch (error) {
      await this.log(`❌ Error recording decision: ${error.message}`);
      return null;
    }
  }

  /**
   * Update decision outcome for learning
   */
  async updateDecisionOutcome(decisionId, outcomeData) {
    try {
      const prisma = await getDB();
      await prisma.ai_learning_data.update({
        where: { id: decisionId },
        data: {
          outcome: outcomeData.outcome,
          actualPrice: outcomeData.price,
          feedbackData: {
            outcome: outcomeData.outcome,
            pnlImpact: outcomeData.pnlImpact,
            timeToOutcome: outcomeData.timeToOutcome,
            wasCorrect: outcomeData.wasCorrect,
            learningScore: outcomeData.learningScore
          },
          updatedAt: new Date()
        }
      });

      await this.log(`✅ Updated decision ${decisionId} with outcome: ${outcomeData.outcome}`);
    } catch (error) {
      await this.log(`❌ Error updating decision outcome: ${error.message}`);
    }
  }

  /**
   * Analyze historical decisions for patterns
   */
  async analyzeDecisionPatterns() {
    try {
      const prisma = await getDB();
      const decisions = await prisma.ai_learning_data.findMany({
        where: {
          analysisData: {
            string_contains: '"type":"STOP_LOSS_DECISION"'
          }
        },
        orderBy: { createdAt: 'desc' },
        take: 50
      });

      if (decisions.length === 0) {
        await this.log(`📊 No stop loss decisions found for pattern analysis`);
        return this.learningThresholds;
      }

      // Basic pattern analysis
      const patterns = {
        emergencyDecisions: decisions.filter(d => 
          d.analysisData?.distanceFromSL < 1.0
        ),
        highRiskDecisions: decisions.filter(d => 
          d.analysisData?.distanceFromSL >= 1.0 && 
          d.analysisData?.distanceFromSL < 2.0
        ),
        successfulExits: decisions.filter(d => 
          d.outcome === 'PROFIT' || d.outcome === 'BREAK_EVEN'
        )
      };

      await this.log(`📊 Analyzed ${decisions.length} decisions. Emergency: ${patterns.emergencyDecisions.length}, High Risk: ${patterns.highRiskDecisions.length}, Successful: ${patterns.successfulExits.length}`);

      // Update thresholds based on success rates
      if (patterns.successfulExits.length > 5) {
        const avgSuccessDistance = patterns.successfulExits
          .map(d => d.analysisData?.distanceFromSL || 2.0)
          .reduce((a, b) => a + b, 0) / patterns.successfulExits.length;
        
        this.learningThresholds.emergencyDistance = Math.max(0.5, avgSuccessDistance - 1.0);
        this.learningThresholds.highRiskDistance = Math.max(1.0, avgSuccessDistance);
      }

      return this.learningThresholds;

    } catch (error) {
      await this.log(`❌ Error analyzing decision patterns: ${error.message}`);
      return this.learningThresholds;
    }
  }

  /**
   * Generate smart recommendation based on learning (alias for compatibility)
   */
  async getSmartRecommendation(currentSituation) {
    return await this.generateSmartRecommendation(currentSituation);
  }

  /**
   * Generate smart recommendation based on learning
   */
  async generateSmartRecommendation(currentSituation) {
    try {
      const patterns = await this.analyzeDecisionPatterns();
      const { distanceFromSL, marketConditions, position } = currentSituation;

      // Find similar situations
      const prisma = await getDB();
      const similarDecisions = await prisma.ai_learning_data.findMany({
        where: {
          analysisData: {
            string_contains: '"type":"STOP_LOSS_DECISION"'
          },
          symbol: position?.symbol || 'SOLUSD'
        },
        orderBy: { createdAt: 'desc' },
        take: 20
      });

      let recommendation = 'HOLD';
      let confidence = 0.5;
      let reasoning = 'Default decision based on distance thresholds';

      if (distanceFromSL < patterns.emergencyDistance) {
        recommendation = 'EMERGENCY_EXIT';
        confidence = 0.9;
        reasoning = `Critical proximity (${distanceFromSL}%) to stop loss requires immediate action`;
      } else if (distanceFromSL < patterns.highRiskDistance) {
        recommendation = 'ENHANCED_MONITORING';
        confidence = 0.7;
        reasoning = `High risk zone (${distanceFromSL}%) - increased monitoring and preparation for exit`;
      } else if (distanceFromSL < patterns.mediumRiskDistance) {
        recommendation = 'MONITOR';
        confidence = 0.6;
        reasoning = `Medium risk zone (${distanceFromSL}%) - standard monitoring`;
      }

      // Adjust based on similar situations
      const successfulSimilar = similarDecisions.filter(d => 
        d.outcome === 'PROFIT' || d.outcome === 'BREAK_EVEN'
      );

      if (successfulSimilar.length > 0) {
        const avgSuccessAction = successfulSimilar
          .map(d => d.analysisData?.decision)
          .filter(Boolean);
        
        if (avgSuccessAction.length > 0) {
          const mostSuccessfulAction = avgSuccessAction
            .reduce((a, b, _, arr) => 
              arr.filter(v => v === a).length >= arr.filter(v => v === b).length ? a : b
            );
          
          if (mostSuccessfulAction !== recommendation) {
            reasoning += `. Learning suggests ${mostSuccessfulAction} based on ${successfulSimilar.length} similar situations`;
            confidence = Math.min(0.95, confidence + 0.1);
          }
        }
      }

      await this.log(`🎯 Smart recommendation: ${recommendation} (${Math.round(confidence * 100)}% confidence)`);

      return {
        recommendation,
        confidence,
        reasoning,
        learnedThresholds: patterns
      };

    } catch (error) {
      await this.log(`❌ Error generating smart recommendation: ${error.message}`);
      return {
        recommendation: 'HOLD',
        confidence: 0.5,
        reasoning: `Default decision - learning system error: ${error.message}`,
        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
      };
    }
  }
}

module.exports = SimplifiedStopLossLearner;