{
  "name": "Pattern Analysis - Win Rate by Hour",
  "nodes": [
    {
      "parameters": {},
      "id": "1a2b3c4d-5e6f-7a8b-9c0d-1e2f3a4b5c6d",
      "name": "Manual Trigger",
      "type": "n8n-nodes-base.manualTrigger",
      "typeVersion": 1,
      "position": [240, 300]
    },
    {
      "parameters": {
        "operation": "executeQuery",
        "query": "-- Win rate analysis by hour of day\nSELECT \n    EXTRACT(HOUR FROM \"entryTime\") as hour,\n    COUNT(*) as total_trades,\n    COUNT(CASE WHEN \"realizedPnL\" > 0 THEN 1 END) as wins,\n    COUNT(CASE WHEN \"realizedPnL\" < 0 THEN 1 END) as losses,\n    ROUND(COUNT(CASE WHEN \"realizedPnL\" > 0 THEN 1 END)::numeric / NULLIF(COUNT(*), 0) * 100, 2) as win_rate_pct,\n    ROUND(SUM(\"realizedPnL\")::numeric, 2) as total_pnl,\n    ROUND(AVG(\"realizedPnL\")::numeric, 4) as avg_pnl\nFROM \"Trade\"\nWHERE status = 'closed'\n    AND \"isTestTrade\" = false\n    AND \"entryTime\" >= NOW() - INTERVAL '30 days'\nGROUP BY EXTRACT(HOUR FROM \"entryTime\")\nORDER BY hour;"
      },
      "id": "2b3c4d5e-6f7a-8b9c-0d1e-2f3a4b5c6d7e",
      "name": "Query Hourly Performance",
      "type": "n8n-nodes-base.postgres",
      "typeVersion": 2.4,
      "position": [460, 300],
      "credentials": {
        "postgres": {
          "id": "1",
          "name": "Trading Bot Database"
        }
      }
    },
    {
      "parameters": {
        "operation": "executeQuery",
        "query": "-- Win rate by day of week\nSELECT \n    EXTRACT(DOW FROM \"entryTime\") as day_of_week,\n    CASE EXTRACT(DOW FROM \"entryTime\")\n        WHEN 0 THEN 'Sunday'\n        WHEN 1 THEN 'Monday'\n        WHEN 2 THEN 'Tuesday'\n        WHEN 3 THEN 'Wednesday'\n        WHEN 4 THEN 'Thursday'\n        WHEN 5 THEN 'Friday'\n        WHEN 6 THEN 'Saturday'\n    END as day_name,\n    COUNT(*) as total_trades,\n    COUNT(CASE WHEN \"realizedPnL\" > 0 THEN 1 END) as wins,\n    ROUND(COUNT(CASE WHEN \"realizedPnL\" > 0 THEN 1 END)::numeric / NULLIF(COUNT(*), 0) * 100, 2) as win_rate_pct,\n    ROUND(SUM(\"realizedPnL\")::numeric, 2) as total_pnl\nFROM \"Trade\"\nWHERE status = 'closed'\n    AND \"isTestTrade\" = false\n    AND \"entryTime\" >= NOW() - INTERVAL '30 days'\nGROUP BY EXTRACT(DOW FROM \"entryTime\")\nORDER BY day_of_week;"
      },
      "id": "3c4d5e6f-7a8b-9c0d-1e2f-3a4b5c6d7e8f",
      "name": "Query Daily Performance",
      "type": "n8n-nodes-base.postgres",
      "typeVersion": 2.4,
      "position": [460, 500],
      "credentials": {
        "postgres": {
          "id": "1",
          "name": "Trading Bot Database"
        }
      }
    },
    {
      "parameters": {
        "operation": "executeQuery",
        "query": "-- Hold time vs profitability\nSELECT \n    CASE \n        WHEN \"holdTimeSeconds\" < 300 THEN '0-5 min'\n        WHEN \"holdTimeSeconds\" < 900 THEN '5-15 min'\n        WHEN \"holdTimeSeconds\" < 1800 THEN '15-30 min'\n        WHEN \"holdTimeSeconds\" < 3600 THEN '30-60 min'\n        WHEN \"holdTimeSeconds\" < 7200 THEN '1-2 hours'\n        ELSE '2+ hours'\n    END as hold_time_bucket,\n    COUNT(*) as trades,\n    COUNT(CASE WHEN \"realizedPnL\" > 0 THEN 1 END) as wins,\n    ROUND(COUNT(CASE WHEN \"realizedPnL\" > 0 THEN 1 END)::numeric / NULLIF(COUNT(*), 0) * 100, 2) as win_rate_pct,\n    ROUND(AVG(\"realizedPnL\")::numeric, 4) as avg_pnl,\n    ROUND(SUM(\"realizedPnL\")::numeric, 2) as total_pnl\nFROM \"Trade\"\nWHERE status = 'closed'\n    AND \"isTestTrade\" = false\n    AND \"holdTimeSeconds\" IS NOT NULL\n    AND \"entryTime\" >= NOW() - INTERVAL '30 days'\nGROUP BY \n    CASE \n        WHEN \"holdTimeSeconds\" < 300 THEN '0-5 min'\n        WHEN \"holdTimeSeconds\" < 900 THEN '5-15 min'\n        WHEN \"holdTimeSeconds\" < 1800 THEN '15-30 min'\n        WHEN \"holdTimeSeconds\" < 3600 THEN '30-60 min'\n        WHEN \"holdTimeSeconds\" < 7200 THEN '1-2 hours'\n        ELSE '2+ hours'\n    END\nORDER BY \n    CASE \n        WHEN hold_time_bucket = '0-5 min' THEN 1\n        WHEN hold_time_bucket = '5-15 min' THEN 2\n        WHEN hold_time_bucket = '15-30 min' THEN 3\n        WHEN hold_time_bucket = '30-60 min' THEN 4\n        WHEN hold_time_bucket = '1-2 hours' THEN 5\n        ELSE 6\n    END;"
      },
      "id": "4d5e6f7a-8b9c-0d1e-2f3a-4b5c6d7e8f9a",
      "name": "Query Hold Time Analysis",
      "type": "n8n-nodes-base.postgres",
      "typeVersion": 2.4,
      "position": [460, 700],
      "credentials": {
        "postgres": {
          "id": "1",
          "name": "Trading Bot Database"
        }
      }
    },
    {
      "parameters": {
        "jsCode": "const hourly = $('Query Hourly Performance').all().map(item => item.json);\nconst daily = $('Query Daily Performance').all().map(item => item.json);\nconst holdTime = $('Query Hold Time Analysis').all().map(item => item.json);\n\n// Find best and worst hours\nconst sortedHours = [...hourly].sort((a, b) => b.win_rate_pct - a.win_rate_pct);\nconst bestHours = sortedHours.slice(0, 3);\nconst worstHours = sortedHours.slice(-3).reverse();\n\n// Find best day\nconst sortedDays = [...daily].sort((a, b) => b.win_rate_pct - a.win_rate_pct);\nconst bestDay = sortedDays[0];\nconst worstDay = sortedDays[sortedDays.length - 1];\n\n// Find optimal hold time\nconst sortedHoldTime = [...holdTime].sort((a, b) => b.avg_pnl - a.avg_pnl);\nconst optimalHoldTime = sortedHoldTime[0];\n\n// Generate insights\nconst insights = {\n  hourly: {\n    bestHours: bestHours.map(h => `${h.hour}:00 (${h.win_rate_pct}% win rate, ${h.total_trades} trades)`),\n    worstHours: worstHours.map(h => `${h.hour}:00 (${h.win_rate_pct}% win rate, ${h.total_trades} trades)`),\n    recommendation: bestHours.length > 0 ? `Focus trading around ${bestHours[0].hour}:00-${bestHours[2].hour}:00` : 'Need more data'\n  },\n  daily: {\n    bestDay: bestDay ? `${bestDay.day_name} (${bestDay.win_rate_pct}% win rate)` : 'N/A',\n    worstDay: worstDay ? `${worstDay.day_name} (${worstDay.win_rate_pct}% win rate)` : 'N/A',\n    recommendation: bestDay && worstDay ? `Trade more on ${bestDay.day_name}, avoid ${worstDay.day_name}` : 'Need more data'\n  },\n  holdTime: {\n    optimal: optimalHoldTime ? `${optimalHoldTime.hold_time_bucket} (avg P&L: $${optimalHoldTime.avg_pnl})` : 'N/A',\n    recommendation: optimalHoldTime ? `Target exits in ${optimalHoldTime.hold_time_bucket} range` : 'Need more data'\n  },\n  rawData: {\n    hourly,\n    daily,\n    holdTime\n  }\n};\n\nreturn [{ json: insights }];"
      },
      "id": "5e6f7a8b-9c0d-1e2f-3a4b-5c6d7e8f9a0b",
      "name": "Generate Insights",
      "type": "n8n-nodes-base.code",
      "typeVersion": 2,
      "position": [680, 300]
    }
  ],
  "connections": {
    "Manual Trigger": {
      "main": [
        [
          {
            "node": "Query Hourly Performance",
            "type": "main",
            "index": 0
          },
          {
            "node": "Query Daily Performance",
            "type": "main",
            "index": 0
          },
          {
            "node": "Query Hold Time Analysis",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Query Hourly Performance": {
      "main": [
        [
          {
            "node": "Generate Insights",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Query Daily Performance": {
      "main": [
        [
          {
            "node": "Generate Insights",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Query Hold Time Analysis": {
      "main": [
        [
          {
            "node": "Generate Insights",
            "type": "main",
            "index": 0
          }
        ]
      ]
    }
  },
  "pinData": {},
  "settings": {
    "executionOrder": "v1"
  },
  "staticData": null,
  "tags": [],
  "triggerCount": 0,
  "updatedAt": "2025-10-27T00:00:00.000Z",
  "versionId": "1"
}