diff --git a/app/api/analytics/tp-sl-optimization/route.ts b/app/api/analytics/tp-sl-optimization/route.ts
new file mode 100644
index 0000000..0697c2c
--- /dev/null
+++ b/app/api/analytics/tp-sl-optimization/route.ts
@@ -0,0 +1,319 @@
+/**
+ * TP/SL Optimization API Endpoint
+ * 
+ * Analyzes historical trades using MAE/MFE data to recommend optimal TP/SL levels
+ * GET /api/analytics/tp-sl-optimization
+ */
+
+import { NextRequest, NextResponse } from 'next/server'
+import { getPrismaClient } from '@/lib/database/trades'
+
+export interface TPSLOptimizationResponse {
+  success: boolean
+  analysis?: {
+    totalTrades: number
+    winningTrades: number
+    losingTrades: number
+    winRate: number
+    avgWin: number
+    avgLoss: number
+    profitFactor: number
+    
+    // MAE/MFE Analysis
+    maeAnalysis: {
+      avgMAE: number
+      medianMAE: number
+      percentile25MAE: number
+      percentile75MAE: number
+      worstMAE: number
+    }
+    
+    mfeAnalysis: {
+      avgMFE: number
+      medianMFE: number
+      percentile25MFE: number
+      percentile75MFE: number
+      bestMFE: number
+    }
+    
+    // Current Configuration Performance
+    currentLevels: {
+      tp1Percent: number
+      tp2Percent: number
+      slPercent: number
+      tp1HitRate: number
+      tp2HitRate: number
+      slHitRate: number
+      moneyLeftOnTable: number // Sum of (MFE - realized P&L) for winning trades
+    }
+    
+    // Recommendations
+    recommendations: {
+      optimalTP1: number // 50% of avg MFE
+      optimalTP2: number // 80% of avg MFE
+      optimalSL: number // 70% of avg MAE (tighter to catch losers early)
+      
+      reasoning: {
+        tp1: string
+        tp2: string
+        sl: string
+      }
+      
+      projectedImpact: {
+        expectedWinRateChange: number
+        expectedProfitFactorChange: number
+        estimatedProfitImprovement: number // % improvement in total P&L
+      }
+    }
+    
+    // Detailed Trade Stats
+    tradesByOutcome: {
+      tp1Exits: number
+      tp2Exits: number
+      slExits: number
+      manualExits: number
+    }
+  }
+  error?: string
+}
+
+export async function GET(request: NextRequest): Promise<NextResponse<TPSLOptimizationResponse>> {
+  try {
+    const prisma = getPrismaClient()
+    
+    // Get all closed trades with MAE/MFE data
+    const trades = await prisma.trade.findMany({
+      where: {
+        status: 'closed',
+        maxFavorableExcursion: { not: null },
+        maxAdverseExcursion: { not: null },
+      },
+      orderBy: {
+        entryTime: 'desc',
+      },
+    })
+    
+    if (trades.length < 10) {
+      return NextResponse.json({
+        success: false,
+        error: `Insufficient data: Only ${trades.length} trades found. Need at least 10 trades with MAE/MFE data for meaningful analysis.`,
+      })
+    }
+    
+    console.log(`📊 Analyzing ${trades.length} trades for TP/SL optimization`)
+    
+    // Separate winning and losing trades
+    const winningTrades = trades.filter(t => (t.realizedPnL || 0) > 0)
+    const losingTrades = trades.filter(t => (t.realizedPnL || 0) <= 0)
+    
+    // Calculate basic stats
+    const totalPnL = trades.reduce((sum, t) => sum + (t.realizedPnL || 0), 0)
+    const avgWin = winningTrades.length > 0 
+      ? winningTrades.reduce((sum, t) => sum + (t.realizedPnL || 0), 0) / winningTrades.length 
+      : 0
+    const avgLoss = losingTrades.length > 0
+      ? Math.abs(losingTrades.reduce((sum, t) => sum + (t.realizedPnL || 0), 0) / losingTrades.length)
+      : 0
+    const winRate = (winningTrades.length / trades.length) * 100
+    const profitFactor = avgLoss > 0 ? (avgWin * winningTrades.length) / (avgLoss * losingTrades.length) : 0
+    
+    // MAE Analysis (how far price moved against us)
+    const maeValues = trades
+      .map(t => t.maxAdverseExcursion!)
+      .filter(v => v !== null && v !== undefined)
+      .sort((a, b) => a - b)
+    
+    const avgMAE = maeValues.reduce((sum, v) => sum + v, 0) / maeValues.length
+    const medianMAE = maeValues[Math.floor(maeValues.length / 2)]
+    const percentile25MAE = maeValues[Math.floor(maeValues.length * 0.25)]
+    const percentile75MAE = maeValues[Math.floor(maeValues.length * 0.75)]
+    const worstMAE = Math.min(...maeValues)
+    
+    // MFE Analysis (how far price moved in our favor)
+    const mfeValues = trades
+      .map(t => t.maxFavorableExcursion!)
+      .filter(v => v !== null && v !== undefined)
+      .sort((a, b) => b - a)
+    
+    const avgMFE = mfeValues.reduce((sum, v) => sum + v, 0) / mfeValues.length
+    const medianMFE = mfeValues[Math.floor(mfeValues.length / 2)]
+    const percentile25MFE = mfeValues[Math.floor(mfeValues.length * 0.75)] // Reverse for MFE
+    const percentile75MFE = mfeValues[Math.floor(mfeValues.length * 0.25)]
+    const bestMFE = Math.max(...mfeValues)
+    
+    // Current configuration analysis (extract from first trade's config snapshot)
+    const sampleConfig: any = trades[0]?.configSnapshot || {}
+    const currentTP1 = sampleConfig.takeProfit1Percent || 0.4
+    const currentTP2 = sampleConfig.takeProfit2Percent || 0.7
+    const currentSL = sampleConfig.stopLossPercent || -1.1
+    
+    // Calculate hit rates for current levels
+    const tp1Hits = trades.filter(t => {
+      const mfe = t.maxFavorableExcursion || 0
+      return mfe >= currentTP1
+    }).length
+    
+    const tp2Hits = trades.filter(t => {
+      const mfe = t.maxFavorableExcursion || 0
+      return mfe >= currentTP2
+    }).length
+    
+    const slHits = trades.filter(t => {
+      const mae = t.maxAdverseExcursion || 0
+      return mae <= currentSL
+    }).length
+    
+    const tp1HitRate = (tp1Hits / trades.length) * 100
+    const tp2HitRate = (tp2Hits / trades.length) * 100
+    const slHitRate = (slHits / trades.length) * 100
+    
+    // Calculate "money left on table" - how much profit we didn't capture
+    const moneyLeftOnTable = winningTrades.reduce((sum, t) => {
+      const mfe = t.maxFavorableExcursion || 0
+      const realizedPct = ((t.realizedPnL || 0) / t.positionSizeUSD) * 100
+      const leftOnTable = Math.max(0, mfe - realizedPct)
+      return sum + (leftOnTable * t.positionSizeUSD / 100)
+    }, 0)
+    
+    // Calculate optimal levels
+    const optimalTP1 = avgMFE * 0.5  // Capture 50% of avg move
+    const optimalTP2 = avgMFE * 0.8  // Capture 80% of avg move
+    const optimalSL = avgMAE * 0.7   // Exit at 70% of avg adverse move (tighter to minimize losses)
+    
+    // Trade outcome breakdown
+    const tp1Exits = trades.filter(t => t.exitReason === 'TP1').length
+    const tp2Exits = trades.filter(t => t.exitReason === 'TP2').length
+    const slExits = trades.filter(t => 
+      t.exitReason === 'SL' || t.exitReason === 'SOFT_SL' || t.exitReason === 'HARD_SL'
+    ).length
+    const manualExits = trades.filter(t => 
+      t.exitReason === 'manual' || t.exitReason === 'emergency'
+    ).length
+    
+    // Projected impact calculation
+    // Simulate what would have happened with optimal levels
+    let projectedWins = 0
+    let projectedLosses = 0
+    let projectedTotalPnL = 0
+    
+    trades.forEach(t => {
+      const mfe = t.maxFavorableExcursion || 0
+      const mae = t.maxAdverseExcursion || 0
+      
+      // Would SL have been hit first with optimal level?
+      if (mae <= optimalSL) {
+        projectedLosses++
+        projectedTotalPnL += optimalSL * t.positionSizeUSD / 100
+      }
+      // Would TP1 have been hit?
+      else if (mfe >= optimalTP1) {
+        projectedWins++
+        // Assume 50% exit at TP1, 50% continues to TP2 or SL
+        const tp1PnL = optimalTP1 * t.positionSizeUSD * 0.5 / 100
+        
+        if (mfe >= optimalTP2) {
+          const tp2PnL = optimalTP2 * t.positionSizeUSD * 0.5 / 100
+          projectedTotalPnL += tp1PnL + tp2PnL
+        } else {
+          // TP2 not hit, remaining 50% exits at breakeven or small profit
+          projectedTotalPnL += tp1PnL
+        }
+      }
+    })
+    
+    const projectedWinRate = (projectedWins / trades.length) * 100
+    const expectedWinRateChange = projectedWinRate - winRate
+    
+    const projectedProfitFactor = projectedLosses > 0
+      ? (projectedWins * avgWin) / (projectedLosses * avgLoss)
+      : 0
+    const expectedProfitFactorChange = projectedProfitFactor - profitFactor
+    
+    const estimatedProfitImprovement = totalPnL > 0
+      ? ((projectedTotalPnL - totalPnL) / totalPnL) * 100
+      : 0
+    
+    // Build response
+    const analysis: TPSLOptimizationResponse = {
+      success: true,
+      analysis: {
+        totalTrades: trades.length,
+        winningTrades: winningTrades.length,
+        losingTrades: losingTrades.length,
+        winRate,
+        avgWin,
+        avgLoss,
+        profitFactor,
+        
+        maeAnalysis: {
+          avgMAE,
+          medianMAE,
+          percentile25MAE,
+          percentile75MAE,
+          worstMAE,
+        },
+        
+        mfeAnalysis: {
+          avgMFE,
+          medianMFE,
+          percentile25MFE,
+          percentile75MFE,
+          bestMFE,
+        },
+        
+        currentLevels: {
+          tp1Percent: currentTP1,
+          tp2Percent: currentTP2,
+          slPercent: currentSL,
+          tp1HitRate,
+          tp2HitRate,
+          slHitRate,
+          moneyLeftOnTable,
+        },
+        
+        recommendations: {
+          optimalTP1,
+          optimalTP2,
+          optimalSL,
+          
+          reasoning: {
+            tp1: `Set at ${optimalTP1.toFixed(2)}% (50% of avg MFE ${avgMFE.toFixed(2)}%). This captures early profits while letting winners run. Current hit rate: ${tp1HitRate.toFixed(1)}%`,
+            tp2: `Set at ${optimalTP2.toFixed(2)}% (80% of avg MFE ${avgMFE.toFixed(2)}%). This captures most of the move before reversal. Current hit rate: ${tp2HitRate.toFixed(1)}%`,
+            sl: `Set at ${optimalSL.toFixed(2)}% (70% of avg MAE ${avgMAE.toFixed(2)}%). Tighter stop to minimize losses on bad trades. Current hit rate: ${slHitRate.toFixed(1)}%`,
+          },
+          
+          projectedImpact: {
+            expectedWinRateChange,
+            expectedProfitFactorChange,
+            estimatedProfitImprovement,
+          },
+        },
+        
+        tradesByOutcome: {
+          tp1Exits,
+          tp2Exits,
+          slExits,
+          manualExits,
+        },
+      },
+    }
+    
+    console.log('✅ TP/SL optimization analysis complete')
+    console.log('   Current: TP1=' + currentTP1 + '% TP2=' + currentTP2 + '% SL=' + currentSL + '%')
+    console.log('   Optimal: TP1=' + optimalTP1.toFixed(2) + '% TP2=' + optimalTP2.toFixed(2) + '% SL=' + optimalSL.toFixed(2) + '%')
+    console.log('   Projected improvement: ' + estimatedProfitImprovement.toFixed(1) + '%')
+    
+    return NextResponse.json(analysis)
+    
+  } catch (error) {
+    console.error('❌ TP/SL optimization error:', error)
+    
+    return NextResponse.json(
+      {
+        success: false,
+        error: 'Failed to analyze trades: ' + (error as Error).message,
+      },
+      { status: 500 }
+    )
+  }
+}
diff --git a/scripts/test-analytics.sh b/scripts/test-analytics.sh
new file mode 100755
index 0000000..67ade9d
--- /dev/null
+++ b/scripts/test-analytics.sh
@@ -0,0 +1,15 @@
+#!/bin/bash
+
+# Test TP/SL Optimization Analytics Endpoint
+# Usage: ./scripts/test-analytics.sh
+
+echo "🔍 Testing TP/SL Optimization Analytics..."
+echo ""
+
+RESPONSE=$(curl -s http://localhost:3001/api/analytics/tp-sl-optimization)
+
+# Pretty print JSON response
+echo "$RESPONSE" | jq '.' || echo "$RESPONSE"
+
+echo ""
+echo "✅ Test complete"