{ "id": "U7DxutTrX9dYZsYT", "meta": { "templateCredsSetupCompleted": true }, "name": "AI-GROK-4 COE Price Tracker with Smart Buy/Wait Recommendations", "tags": [], "nodes": [ { "id": "432d1c6a-17ae-44a4-adce-06152589e2f1", "name": "Schedule Trigger - Bi-Weekly COE Scraping", "type": "n8n-nodes-base.scheduleTrigger", "position": [ 112, 464 ], "parameters": { "rule": { "interval": [ { "field": "cronExpression", "expression": "0 20 * * 3" } ] } }, "typeVersion": 1.2 }, { "id": "c6e3d1b9-c26d-4007-a2b2-6303b0f637c7", "name": "Scrape COE Data from OneMotoring", "type": "n8n-nodes-base.httpRequest", "disabled": true, "position": [ 400, 432 ], "parameters": { "url": "https://onemotoring.lta.gov.sg/content/onemotoring/home/buying/upfront-vehicle-costs/certificate-of-entitlement--coe-.html", "options": { "timeout": 30000 } }, "typeVersion": 4.2 }, { "id": "13595ddf-b04a-405a-a432-bcdc24b4d442", "name": "Extract COE Price Data", "type": "n8n-nodes-base.code", "position": [ 608, 576 ], "parameters": { "jsCode": "const html = $input.item.json.data;\nconst cheerio = require('cheerio');\nconst $ = cheerio.load(html);\n\nconst categories = ['Category A', 'Category B', 'Category C', 'Category D', 'Category E'];\nconst coeData = [];\nconst biddingDate = new Date().toISOString().split('T')[0];\n\n$('table tbody tr').each((index, row) => {\n const cells = $(row).find('td');\n if (cells.length >= 4) {\n const category = $(cells[0]).text().trim();\n const quotaPremium = parseFloat($(cells[1]).text().replace(/[^0-9.]/g, ''));\n const quotaAvailable = parseInt($(cells[2]).text().replace(/[^0-9]/g, ''));\n const bidsReceived = parseInt($(cells[3]).text().replace(/[^0-9]/g, ''));\n \n if (categories.some(cat => category.includes(cat))) {\n coeData.push({\n biddingDate,\n category,\n quotaPremium,\n quotaAvailable,\n bidsReceived,\n timestamp: new Date().toISOString(),\n biddingRound: new Date().getDate() <= 15 ? 1 : 2\n });\n }\n }\n});\n\nif (coeData.length === 0) {\n throw new Error('No COE data extracted - possible website structure change');\n}\n\nreturn coeData.map(item => ({ json: item }));" }, "typeVersion": 2 }, { "id": "7230f728-2dd1-435c-a96a-e3e843eac3e4", "name": "Store in Google Sheets", "type": "n8n-nodes-base.googleSheets", "position": [ 1136, 576 ], "parameters": { "columns": { "value": { "category": "={{ $json.category }}", "timestamp": "={{ $json.timestamp }}", "biddingDate": "={{ $json.biddingDate }}", "biddingRound": "={{ $json.biddingRound }}", "bidsReceived": "={{ $json.bidsReceived }}", "quotaPremium": "={{ $json.quotaPremium }}", "quotaAvailable": "={{ $json.quotaAvailable }}" }, "mappingMode": "defineBelow" }, "options": {}, "operation": "append", "sheetName": { "__rl": true, "mode": "name", "value": "COE Historical Data" }, "documentId": { "__rl": true, "mode": "id", "value": "{{ $json.spreadsheetId }}" } }, "credentials": { "googleSheetsOAuth2Api": { "name": "" } }, "typeVersion": 4.5 }, { "id": "9747556f-348f-42cd-b254-80b1cf954789", "name": "Retrieve Historical COE Data", "type": "n8n-nodes-base.googleSheets", "position": [ 1376, 784 ], "parameters": { "options": {}, "sheetName": { "__rl": true, "mode": "name", "value": "COE Historical Data" }, "documentId": { "__rl": true, "mode": "id", "value": "={{ $json.spreadsheetId }}" } }, "credentials": { "googleSheetsOAuth2Api": { "name": "" } }, "typeVersion": 4.5 }, { "id": "8b3ef486-ec56-4eab-be32-69adfa2509ce", "name": "Calculate Technical Indicators", "type": "n8n-nodes-base.code", "position": [ 1584, 768 ], "parameters": { "jsCode": "const items = $input.all();\nconst categories = ['Category A', 'Category B', 'Category C', 'Category D', 'Category E'];\n\nconst processedData = {};\n\ncategories.forEach(category => {\n const categoryData = items.filter(item => \n item.json.category && item.json.category.includes(category)\n ).sort((a, b) => new Date(a.json.biddingDate) - new Date(b.json.biddingDate));\n \n if (categoryData.length < 4) {\n processedData[category] = { error: 'Insufficient data' };\n return;\n }\n \n const prices = categoryData.map(item => item.json.quotaPremium);\n const latestPrice = prices[prices.length - 1];\n \n const ma4 = prices.slice(-4).reduce((a, b) => a + b, 0) / 4;\n const ma12 = prices.length >= 12 ? prices.slice(-12).reduce((a, b) => a + b, 0) / 12 : ma4;\n const ma26 = prices.length >= 26 ? prices.slice(-26).reduce((a, b) => a + b, 0) / 26 : ma12;\n \n const rateOfChange = prices.length >= 5 ? ((latestPrice - prices[prices.length - 5]) / prices[prices.length - 5]) * 100 : 0;\n \n const variance = prices.slice(-12).reduce((sum, price) => sum + Math.pow(price - ma12, 2), 0) / Math.min(12, prices.length);\n const volatility = Math.sqrt(variance);\n \n const now = new Date();\n const month = now.getMonth() + 1;\n const quarter = Math.ceil(month / 3);\n const isCNYPeriod = month === 1 || month === 2;\n const isYearEnd = month === 11 || month === 12;\n const biddingRound = categoryData[categoryData.length - 1].json.biddingRound || 1;\n \n processedData[category] = {\n latestPrice,\n ma4,\n ma12,\n ma26,\n rateOfChange,\n volatility,\n month,\n quarter,\n isCNYPeriod,\n isYearEnd,\n biddingRound,\n historicalPrices: prices.slice(-26),\n dataPoints: categoryData.length\n };\n});\n\nreturn [{ json: processedData }];" }, "typeVersion": 2 }, { "id": "62064423-11e3-4c12-890d-39febc727275", "name": "Prepare AI Prediction Prompt", "type": "n8n-nodes-base.set", "disabled": true, "position": [ 1888, 576 ], "parameters": { "options": {}, "assignments": { "assignments": [ { "id": "1", "name": "predictionPrompt", "type": "string", "value": "=You are a Singapore COE market analysis expert. Analyze the following COE data and provide detailed price predictions:\n\n{{ Object.keys($json).map(category => {\n const data = $json[category];\n return `${category}:\n- Latest Price: $${data.latestPrice}\n- 4-week MA: $${data.ma4?.toFixed(0)}\n- 12-week MA: $${data.ma12?.toFixed(0)}\n- 26-week MA: $${data.ma26?.toFixed(0)}\n- Rate of Change: ${data.rateOfChange?.toFixed(2)}%\n- Volatility: $${data.volatility?.toFixed(0)}\n- Current Period: Q${data.quarter}, Month ${data.month}\n- CNY Period: ${data.isCNYPeriod}\n- Year-End Period: ${data.isYearEnd}\n- Historical Prices (last 26 weeks): ${data.historicalPrices?.join(', ')}`;\n}).join('\\n\\n') }}\n\nProvide:\n1. Price predictions for next 6 bidding rounds (3 months) for each category\n2. Confidence levels (High/Medium/Low) for each prediction\n3. Market trend analysis (uptrend/downtrend/consolidation)\n4. Seasonal factors impact\n5. Risk assessment\n\nFormat as JSON:\n{\n \"predictions\": {\n \"Category A\": [{\"round\": 1, \"predictedPrice\": 95000, \"confidence\": \"High\", \"lower\": 92000, \"upper\": 98000}, ...],\n ...\n },\n \"trends\": {\"Category A\": \"uptrend\", ...},\n \"seasonalImpact\": \"High impact expected due to...\",\n \"riskLevel\": \"Medium\"\n}" } ] } }, "typeVersion": 3.4 }, { "id": "4fc3d93a-3ccc-40bd-bf7f-bc3f235d2f2c", "name": "AI Agent - COE Analysis", "type": "@n8n/n8n-nodes-langchain.agent", "position": [ 2640, 544 ], "parameters": { "text": "={{ $json.predictionPrompt }}", "options": {} }, "typeVersion": 1.7 }, { "id": "55d5e431-97e4-4094-8339-8490b48b8f0d", "name": "Generate Buy Recommendations", "type": "n8n-nodes-base.code", "position": [ 3200, 752 ], "parameters": { "jsCode": "const technicalData = $('Calculate Technical Indicators').first().json;\nconst aiPrediction = $input.first().json;\n\nconst categories = Object.keys(technicalData);\nconst recommendations = {};\n\ncategories.forEach(category => {\n const tech = technicalData[category];\n if (tech.error) {\n recommendations[category] = { action: 'Insufficient Data', reasoning: tech.error };\n return;\n }\n \n let score = 50;\n let reasoning = [];\n \n if (tech.latestPrice < tech.ma12) {\n score += 15;\n reasoning.push('Price below 12-week average (bullish signal)');\n } else if (tech.latestPrice > tech.ma12 * 1.1) {\n score -= 15;\n reasoning.push('Price significantly above moving average (overheated)');\n }\n \n if (tech.rateOfChange < -5) {\n score += 20;\n reasoning.push(`Strong downward momentum (${tech.rateOfChange.toFixed(1)}%)`);\n } else if (tech.rateOfChange > 10) {\n score -= 20;\n reasoning.push(`Strong upward momentum (${tech.rateOfChange.toFixed(1)}%) - avoid buying`);\n }\n \n if (tech.isCNYPeriod) {\n score -= 10;\n reasoning.push('CNY period typically sees higher prices');\n }\n \n if (tech.isYearEnd) {\n score -= 5;\n reasoning.push('Year-end period - slightly elevated demand');\n }\n \n if (tech.volatility > tech.ma12 * 0.15) {\n score -= 10;\n reasoning.push('High volatility - uncertain market conditions');\n }\n \n let action, timingAdvice, potentialSavings;\n if (score >= 70) {\n action = 'BUY NOW';\n timingAdvice = 'Strong buying opportunity. Market conditions favorable.';\n potentialSavings = 0;\n } else if (score >= 50) {\n action = 'MONITOR CLOSELY';\n timingAdvice = 'Market at neutral position. Consider waiting 2-4 weeks.';\n potentialSavings = Math.round(tech.latestPrice * 0.03);\n } else {\n action = 'WAIT';\n const weeksToWait = score < 30 ? 8 : 4;\n timingAdvice = `Wait ${weeksToWait} weeks. Market showing unfavorable conditions.`;\n potentialSavings = Math.round(tech.latestPrice * 0.08);\n }\n \n recommendations[category] = {\n action,\n score,\n currentPrice: tech.latestPrice,\n timingAdvice,\n potentialSavings,\n reasoning: reasoning.join('; '),\n trend: tech.rateOfChange > 5 ? 'Uptrend' : tech.rateOfChange < -5 ? 'Downtrend' : 'Consolidation',\n volatilityLevel: tech.volatility > tech.ma12 * 0.15 ? 'High' : tech.volatility > tech.ma12 * 0.08 ? 'Medium' : 'Low'\n };\n});\n\nconst bestCategory = Object.entries(recommendations)\n .filter(([_, rec]) => !rec.reasoning.includes('Insufficient'))\n .sort((a, b) => b[1].score - a[1].score)[0];\n\nreturn [{\n json: {\n recommendations,\n bestCategory: bestCategory ? bestCategory[0] : 'None',\n bestCategoryScore: bestCategory ? bestCategory[1].score : 0,\n analysisDate: new Date().toISOString(),\n marketOverview: {\n averageVolatility: Object.values(recommendations)\n .filter(r => r.volatilityLevel)\n .reduce((sum, r) => sum + (r.volatilityLevel === 'High' ? 3 : r.volatilityLevel === 'Medium' ? 2 : 1), 0) / categories.length,\n dominantTrend: Object.values(recommendations)\n .filter(r => r.trend)\n .reduce((acc, r) => {\n acc[r.trend] = (acc[r.trend] || 0) + 1;\n return acc;\n }, {})\n }\n }\n}];" }, "typeVersion": 2 }, { "id": "fe4d8755-6dab-4329-b760-9d22b9236d75", "name": "Format HTML Report", "type": "n8n-nodes-base.code", "disabled": true, "position": [ 3728, 224 ], "parameters": { "jsCode": "const recommendations = $input.first().json.recommendations;\nconst bestCategory = $input.first().json.bestCategory;\nconst analysisDate = new Date($input.first().json.analysisDate).toLocaleDateString('en-SG');\n\nlet htmlReport = `\n\n\n\n \n\n\n
\n

\ud83d\ude97 Singapore COE Price Analysis & Recommendations

\n

Analysis Date: ${analysisDate}

\n \n
\n

\u2b50 Best Category to Consider

\n

${bestCategory}

\n

Highest recommendation score based on current market conditions

\n
\n`;\n\nObject.entries(recommendations).forEach(([category, rec]) => {\n if (rec.reasoning && rec.reasoning.includes('Insufficient')) {\n htmlReport += `\n
\n

${category}

\n

${rec.reasoning}

\n
\n `;\n return;\n }\n \n const cardClass = rec.action === 'BUY NOW' ? 'buy-now' : rec.action === 'WAIT' ? 'wait' : 'monitor';\n const actionClass = rec.action === 'BUY NOW' ? 'buy' : rec.action === 'WAIT' ? 'wait' : 'monitor';\n \n htmlReport += `\n
\n

${category}

\n
${rec.action === 'BUY NOW' ? '\u2705' : rec.action === 'WAIT' ? '\u23f8\ufe0f' : '\ud83d\udc40'} ${rec.action}
\n
$${rec.currentPrice.toLocaleString('en-SG')}
\n
\n Score: ${rec.score}/100\n
\n
\n Trend: ${rec.trend}\n
\n
\n Volatility: ${rec.volatilityLevel}\n
\n

Timing Advice: ${rec.timingAdvice}

\n ${rec.potentialSavings > 0 ? `
\ud83d\udcb0 Potential Savings: $${rec.potentialSavings.toLocaleString('en-SG')}
` : ''}\n
\n Analysis:
\n ${rec.reasoning}\n
\n
\n `;\n});\n\nhtmlReport += `\n
\n

This analysis is based on historical COE data and market trends. Actual prices may vary.

\n

Data source: LTA OneMotoring Portal | Generated by n8n Automation

\n
\n
\n\n\n`;\n\nreturn [{ json: { htmlReport, subject: `COE Analysis Report - ${analysisDate}` } }];" }, "typeVersion": 2 }, { "id": "1576e59a-54ba-452c-938c-547410701201", "name": "Send Email Report", "type": "n8n-nodes-base.emailSend", "position": [ 3952, 384 ], "parameters": { "options": {}, "subject": "={{ $json.subject }}", "toEmail": "user@example.com", "fromEmail": "user@example.com" }, "typeVersion": 2.1 }, { "id": "9e03ad9e-4bbb-4fbc-9ba5-4af04a292f67", "name": "Check for Buy Opportunities", "type": "n8n-nodes-base.if", "position": [ 3392, 368 ], "parameters": { "options": {}, "conditions": { "options": { "leftValue": "", "caseSensitive": true, "typeValidation": "strict" }, "combinator": "or", "conditions": [ { "id": "1", "operator": { "type": "string", "operation": "equals" }, "leftValue": "={{ $json.recommendations['Category A'].action }}", "rightValue": "BUY NOW" }, { "id": "2", "operator": { "type": "string", "operation": "equals" }, "leftValue": "={{ $json.recommendations['Category B'].action }}", "rightValue": "BUY NOW" } ] } }, "typeVersion": 2.1 }, { "id": "3229f007-f192-4562-8827-d4ddf27fa55d", "name": "Send Telegram Alert", "type": "n8n-nodes-base.telegram", "position": [ 3728, 368 ], "parameters": { "text": "=\ud83d\udea8 *COE BUY ALERT* \ud83d\udea8\n\nOptimal buying opportunity detected!\n\n{{ Object.entries($json.recommendations)\n .filter(([_, rec]) => rec.action === 'BUY NOW')\n .map(([cat, rec]) => `*${cat}*: $${rec.currentPrice.toLocaleString('en-SG')}\\nScore: ${rec.score}/100\\n${rec.timingAdvice}`)\n .join('\\n\\n') }}\n\nView full report in your email.", "chatId": "{{ $('Schedule Trigger - Bi-Weekly COE Scraping').item.json.telegramChatId }}", "additionalFields": { "parse_mode": "Markdown" } }, "typeVersion": 1.2 }, { "id": "1483eee4-c8b9-459e-bc32-06daa16fed6e", "name": "Generate Dashboard Summary", "type": "n8n-nodes-base.set", "position": [ 3392, 1328 ], "parameters": { "options": {}, "assignments": { "assignments": [ { "id": "1", "name": "dashboardSummary", "type": "string", "value": "=## Singapore COE Dashboard - {{ new Date().toLocaleDateString('en-SG') }}\n\n### Current Prices\n{{ Object.entries($('Generate Buy Recommendations').first().json.recommendations)\n .map(([cat, rec]) => `**${cat}**: $${rec.currentPrice?.toLocaleString('en-SG') || 'N/A'}`)\n .join('\\n') }}\n\n### Top Recommendation\n**{{ $('Generate Buy Recommendations').first().json.bestCategory }}** (Score: {{ $('Generate Buy Recommendations').first().json.bestCategoryScore }}/100)\n\n### Market Overview\n- Dominant Trend: {{ Object.entries($('Generate Buy Recommendations').first().json.marketOverview.dominantTrend).sort((a,b) => b[1] - a[1])[0][0] }}\n- Average Volatility: {{ $('Generate Buy Recommendations').first().json.marketOverview.averageVolatility > 2.5 ? 'High' : $('Generate Buy Recommendations').first().json.marketOverview.averageVolatility > 1.5 ? 'Medium' : 'Low' }}\n\n### Action Items\n{{ Object.entries($('Generate Buy Recommendations').first().json.recommendations)\n .map(([cat, rec]) => `- **${cat}**: ${rec.action} - ${rec.timingAdvice}`)\n .join('\\n') }}" } ] } }, "typeVersion": 3.4 }, { "id": "e8bc3153-c4ff-4a6a-a99d-48bd76e89479", "name": "OpenRouter Chat Model", "type": "@n8n/n8n-nodes-langchain.lmChatOpenRouter", "position": [ 2656, 752 ], "parameters": { "model": "z-ai/glm-4.5", "options": {} }, "credentials": { "openRouterApi": { "name": "" } }, "typeVersion": 1 }, { "id": "06db176e-bf96-42d6-a1b0-7a896a4f3b32", "name": "Airtable - Store COE Data", "type": "n8n-nodes-base.airtable", "position": [ 1120, 720 ], "parameters": { "base": { "__rl": true, "mode": "list", "value": "" }, "table": { "__rl": true, "mode": "name", "value": "COE Historical Data" }, "operation": "append" }, "typeVersion": 2 }, { "id": "852583f1-d3e8-4178-8bd2-041dff8976cd", "name": "Data Validation Check", "type": "n8n-nodes-base.if", "position": [ 848, 672 ], "parameters": { "options": {}, "conditions": { "options": { "leftValue": "", "caseSensitive": true, "typeValidation": "strict" }, "combinator": "and", "conditions": [ { "id": "1", "operator": { "type": "number", "operation": "gt" }, "leftValue": "={{ $json.quotaPremium }}", "rightValue": 0 }, { "id": "2", "operator": { "type": "string", "operation": "notEmpty" }, "leftValue": "={{ $json.category }}", "rightValue": "" } ] } }, "typeVersion": 2.1 }, { "id": "6e63dc80-1123-4e59-bbdb-ada1db7fd8f8", "name": "Error Handler - Retry Scraping", "type": "n8n-nodes-base.noOp", "position": [ 1120, 912 ], "parameters": {}, "typeVersion": 1 }, { "id": "b9dad53a-81b4-4bf1-83f5-53561e2e8f04", "name": "Scrape with Retry Logic", "type": "n8n-nodes-base.code", "position": [ 400, 624 ], "parameters": { "jsCode": "const maxRetries = 3;\nconst retryDelay = 5000;\n\nfor (let attempt = 1; attempt <= maxRetries; attempt++) {\n try {\n const response = await this.helpers.httpRequest({\n method: 'GET',\n url: 'https://onemotoring.lta.gov.sg/content/onemotoring/home/buying/upfront-vehicle-costs/certificate-of-entitlement--coe-.html',\n timeout: 30000\n });\n \n if (response && response.length > 1000) {\n return [{ json: { data: response, success: true, attempts: attempt } }];\n }\n } catch (error) {\n if (attempt === maxRetries) {\n throw new Error(`Scraping failed after ${maxRetries} attempts: ${error.message}`);\n }\n await new Promise(resolve => setTimeout(resolve, retryDelay * attempt));\n }\n}\n\nthrow new Error('Scraping failed: Invalid response');" }, "typeVersion": 2 }, { "id": "213dea78-48e3-4b49-9d97-2db358bd2a90", "name": "Extract Economic Indicators", "type": "n8n-nodes-base.httpRequest", "position": [ 1728, 912 ], "parameters": { "url": "https://eservices.mas.gov.sg/api/action/datastore/search.json?resource_id=key-economic-indicators", "options": { "timeout": 15000 } }, "typeVersion": 4.2 }, { "id": "b78e4004-1b1f-4916-9b2f-16583905b966", "name": "Process Economic Data", "type": "n8n-nodes-base.code", "position": [ 1920, 912 ], "parameters": { "jsCode": "const economicData = $input.first().json;\nconst coeData = $('Calculate Technical Indicators').first().json;\n\nlet gdpGrowth = 0;\nlet interestRate = 0;\n\ntry {\n if (economicData && economicData.result && economicData.result.records) {\n const records = economicData.result.records;\n const latestGdp = records.find(r => r.indicator === 'GDP Growth');\n const latestRate = records.find(r => r.indicator === 'Prime Lending Rate');\n \n gdpGrowth = latestGdp ? parseFloat(latestGdp.value) : 0;\n interestRate = latestRate ? parseFloat(latestRate.value) : 0;\n }\n} catch (error) {\n gdpGrowth = 0;\n interestRate = 0;\n}\n\nreturn [{\n json: {\n ...coeData,\n economicIndicators: {\n gdpGrowth,\n interestRate,\n impactScore: gdpGrowth > 3 ? 'Positive' : gdpGrowth < 1 ? 'Negative' : 'Neutral'\n }\n }\n}];" }, "typeVersion": 2 }, { "id": "452a530e-af62-4a66-bedc-9416449bf4e2", "name": "Enhanced AI Prompt with Economics", "type": "n8n-nodes-base.set", "position": [ 2400, 848 ], "parameters": { "options": {}, "assignments": { "assignments": [ { "id": "1", "name": "enhancedPrompt", "type": "string", "value": "=You are a Singapore automotive market economist specializing in COE price forecasting.\n\n**Historical COE Data:**\n{{ Object.keys($json).filter(k => k !== 'economicIndicators').map(category => {\n const data = $json[category];\n return `${category}:\n- Latest: $${data.latestPrice}\n- 4w MA: $${data.ma4?.toFixed(0)} | 12w MA: $${data.ma12?.toFixed(0)} | 26w MA: $${data.ma26?.toFixed(0)}\n- ROC: ${data.rateOfChange?.toFixed(2)}% | Volatility: $${data.volatility?.toFixed(0)}\n- Period: Q${data.quarter}, Month ${data.month} (CNY: ${data.isCNYPeriod}, YE: ${data.isYearEnd})`;\n}).join('\\n\\n') }}\n\n**Economic Context:**\n- GDP Growth: {{ $json.economicIndicators.gdpGrowth }}%\n- Interest Rate: {{ $json.economicIndicators.interestRate }}%\n- Economic Outlook: {{ $json.economicIndicators.impactScore }}\n\n**Task:** Provide 6-round price predictions (3 months) for each category with:\n1. Predicted prices with 95% confidence intervals (lower/upper bounds)\n2. Confidence levels: High (>80%), Medium (60-80%), Low (<60%)\n3. Trend classification: Uptrend/Downtrend/Consolidation\n4. Key risk factors (economic shocks, policy changes, seasonal effects)\n5. Best-case, worst-case, most-likely scenarios\n\n**Output Format (strict JSON):**\n{\n \"predictions\": {\n \"Category A\": [\n {\"round\": 1, \"predictedPrice\": 95000, \"confidence\": \"High\", \"lower\": 92000, \"upper\": 98000, \"scenario\": \"most-likely\"},\n {\"round\": 1, \"predictedPrice\": 88000, \"confidence\": \"Medium\", \"lower\": 85000, \"upper\": 91000, \"scenario\": \"best-case\"},\n {\"round\": 1, \"predictedPrice\": 102000, \"confidence\": \"Medium\", \"lower\": 98000, \"upper\": 106000, \"scenario\": \"worst-case\"}\n ]\n },\n \"trends\": {\"Category A\": \"uptrend\"},\n \"riskFactors\": [\"CNY demand spike\", \"Interest rate hikes\"],\n \"economicImpact\": \"Moderate upward pressure from GDP growth\"\n}" } ] } }, "typeVersion": 3.4 }, { "id": "68c8abd0-8c25-4591-94c9-4222364d640f", "name": "Multi-Scenario Analysis", "type": "n8n-nodes-base.code", "position": [ 2976, 816 ], "parameters": { "jsCode": "const aiPrediction = $input.first().json;\nconst categories = Object.keys(aiPrediction.predictions || {});\n\nconst scenarioAnalysis = {};\n\ncategories.forEach(category => {\n const predictions = aiPrediction.predictions[category] || [];\n \n const mostLikely = predictions.filter(p => p.scenario === 'most-likely');\n const bestCase = predictions.filter(p => p.scenario === 'best-case');\n const worstCase = predictions.filter(p => p.scenario === 'worst-case');\n \n const avgMostLikely = mostLikely.reduce((sum, p) => sum + p.predictedPrice, 0) / mostLikely.length;\n const avgBestCase = bestCase.reduce((sum, p) => sum + p.predictedPrice, 0) / bestCase.length;\n const avgWorstCase = worstCase.reduce((sum, p) => sum + p.predictedPrice, 0) / worstCase.length;\n \n scenarioAnalysis[category] = {\n mostLikely: { avg: Math.round(avgMostLikely), predictions: mostLikely },\n bestCase: { avg: Math.round(avgBestCase), predictions: bestCase },\n worstCase: { avg: Math.round(avgWorstCase), predictions: worstCase },\n savingsPotential: Math.round(avgWorstCase - avgBestCase),\n optimalRound: bestCase.reduce((min, p) => p.predictedPrice < min.predictedPrice ? p : min, bestCase[0]).round\n };\n});\n\nreturn [{ json: { scenarioAnalysis, riskFactors: aiPrediction.riskFactors } }];" }, "typeVersion": 2 }, { "id": "2ca62cf3-3060-4086-be65-4eb6202576f3", "name": "ROI Calculator", "type": "n8n-nodes-base.code", "position": [ 3392, 656 ], "parameters": { "jsCode": "const recommendations = $('Generate Buy Recommendations').first().json.recommendations;\nconst scenarios = $input.first().json.scenarioAnalysis;\n\nconst categories = Object.keys(recommendations).filter(k => !recommendations[k].reasoning?.includes('Insufficient'));\nconst roiAnalysis = {};\n\ncategories.forEach(category => {\n const rec = recommendations[category];\n const scenario = scenarios[category];\n \n const carPrice = category.includes('A') ? 80000 : category.includes('B') ? 120000 : 50000;\n const loanRate = 0.028;\n const loanTenure = 7;\n const depreciation = carPrice * 0.6;\n \n const coeNow = rec.currentPrice;\n const coeBestCase = scenario.bestCase.avg;\n const coeWorstCase = scenario.worstCase.avg;\n \n const totalCostNow = coeNow + carPrice + (carPrice * loanRate * loanTenure) - depreciation;\n const totalCostBest = coeBestCase + carPrice + (carPrice * loanRate * loanTenure) - depreciation;\n const totalCostWorst = coeWorstCase + carPrice + (carPrice * loanRate * loanTenure) - depreciation;\n \n roiAnalysis[category] = {\n buyNowCost: Math.round(totalCostNow),\n bestCaseWaitCost: Math.round(totalCostBest),\n worstCaseWaitCost: Math.round(totalCostWorst),\n potentialSavings: Math.round(totalCostNow - totalCostBest),\n potentialLoss: Math.round(totalCostWorst - totalCostNow),\n recommendation: totalCostNow < totalCostBest ? 'Buy Now - Lower Total Cost' : `Wait ${scenario.optimalRound * 2} weeks - Save $${Math.round(totalCostNow - totalCostBest)}`,\n breakdownNow: {\n coe: coeNow,\n carPrice,\n interestPaid: Math.round(carPrice * loanRate * loanTenure),\n depreciation: Math.round(depreciation)\n }\n };\n});\n\nreturn [{ json: { roiAnalysis } }];" }, "typeVersion": 2 }, { "id": "053b7e99-50c7-45ce-846c-e4d1f5d6ec2a", "name": "Check Significant Price Movement", "type": "n8n-nodes-base.if", "position": [ 3392, 944 ], "parameters": { "options": {}, "conditions": { "options": { "leftValue": "", "caseSensitive": true, "typeValidation": "strict" }, "combinator": "or", "conditions": [ { "id": "1", "operator": { "type": "number", "operation": "gt" }, "leftValue": "={{ Math.abs($json.recommendations['Category A']?.rateOfChange || 0) }}", "rightValue": 10 } ] } }, "typeVersion": 2.1 }, { "id": "069afeb1-8f92-4475-ac73-8be8a376be8d", "name": "Urgent Market Alert", "type": "n8n-nodes-base.telegram", "position": [ 3728, 944 ], "parameters": { "text": "=\ud83d\udea8 *URGENT: Significant COE Price Movement Detected* \ud83d\udea8\n\n{{ Object.entries($json.recommendations)\n .filter(([_, rec]) => Math.abs(rec.rateOfChange || 0) > 10)\n .map(([cat, rec]) => `*${cat}*: ${rec.rateOfChange > 0 ? '\ud83d\udcc8' : '\ud83d\udcc9'} ${Math.abs(rec.rateOfChange).toFixed(1)}% change\\nCurrent: $${rec.currentPrice.toLocaleString('en-SG')}\\nAction: ${rec.action}`)\n .join('\\n\\n') }}\n\nImmediate review recommended.", "chatId": "{{ $('Schedule Trigger - Bi-Weekly COE Scraping').item.json.telegramChatId }}", "additionalFields": { "parse_mode": "Markdown" } }, "typeVersion": 1.2 }, { "id": "e2540cb4-60ad-4689-8e3e-f791fb8fbd0c", "name": "Daily Prediction Update Trigger", "type": "n8n-nodes-base.scheduleTrigger", "position": [ 1120, 1104 ], "parameters": { "rule": { "interval": [ { "field": "cronExpression", "expression": "0 9 * * *" } ] } }, "typeVersion": 1.2 }, { "id": "dc79a47d-0b19-4a1c-94e0-30c983fccbf0", "name": "Check Market Volatility", "type": "n8n-nodes-base.if", "position": [ 3392, 1136 ], "parameters": { "options": {}, "conditions": { "options": { "leftValue": "", "caseSensitive": true, "typeValidation": "strict" }, "combinator": "and", "conditions": [ { "id": "1", "operator": { "type": "number", "operation": "gt" }, "leftValue": "={{ $json.marketOverview.averageVolatility }}", "rightValue": 2.5 } ] } }, "typeVersion": 2.1 }, { "id": "df67db2f-8535-4d28-a838-2ebe6d4c4b25", "name": "Enhanced Dashboard Report", "type": "n8n-nodes-base.code", "position": [ 3728, 576 ], "parameters": { "jsCode": "const recommendations = $('Generate Buy Recommendations').first().json.recommendations;\nconst scenarios = $('Multi-Scenario Analysis').first().json.scenarioAnalysis;\nconst roi = $('ROI Calculator').first().json.roiAnalysis;\nconst analysisDate = new Date().toLocaleDateString('en-SG');\n\nlet htmlReport = `\n\n\n\n \n\n\n
\n

\ud83d\ude97 Singapore COE Comprehensive Market Analysis

\n

Analysis Date: ${analysisDate}

\n \n
\n

\u2b50 Best Category - Data-Driven Recommendation

\n

${$('Generate Buy Recommendations').first().json.bestCategory}

\n

Highest overall score: ${$('Generate Buy Recommendations').first().json.bestCategoryScore}/100

\n
\n`;\n\nObject.entries(recommendations).forEach(([category, rec]) => {\n if (rec.reasoning && rec.reasoning.includes('Insufficient')) {\n htmlReport += `

${category}

${rec.reasoning}

`;\n return;\n }\n \n const cardClass = rec.action === 'BUY NOW' ? 'buy-now' : rec.action === 'WAIT' ? 'wait' : 'monitor';\n const actionClass = rec.action === 'BUY NOW' ? 'buy' : rec.action === 'WAIT' ? 'wait' : 'monitor';\n const scenario = scenarios[category];\n const roiData = roi[category];\n \n htmlReport += `\n
\n

${category}

\n
${rec.action === 'BUY NOW' ? '\u2705' : rec.action === 'WAIT' ? '\u23f8\ufe0f' : '\ud83d\udc40'} ${rec.action}
\n
$${rec.currentPrice.toLocaleString('en-SG')}
\n
Score: ${rec.score}/100
\n
Trend: ${rec.trend}
\n
Volatility: ${rec.volatilityLevel}
\n

Timing Advice: ${rec.timingAdvice}

\n \n

\ud83d\udcb0 Total Cost of Ownership Analysis

\n
\n

Buy Now Total Cost: $${roiData.buyNowCost.toLocaleString('en-SG')}

\n

Best-Case Wait Cost: $${roiData.bestCaseWaitCost.toLocaleString('en-SG')}

\n

Potential Savings: $${roiData.potentialSavings.toLocaleString('en-SG')}

\n

Risk (Worst Case): -$${roiData.potentialLoss.toLocaleString('en-SG')}

\n

ROI Recommendation: ${roiData.recommendation}

\n
\n \n

\ud83d\udcca Price Prediction Scenarios (Next 3 Months)

\n
\n
\n

Best Case \ud83d\udfe2

\n

$${scenario.bestCase.avg.toLocaleString('en-SG')}

\n

Optimal Round: ${scenario.optimalRound}

\n
\n
\n

Most Likely \ud83d\udd35

\n

$${scenario.mostLikely.avg.toLocaleString('en-SG')}

\n

Baseline forecast

\n
\n
\n

Worst Case \ud83d\udd34

\n

$${scenario.worstCase.avg.toLocaleString('en-SG')}

\n

Maximum risk exposure

\n
\n
\n \n ${rec.potentialSavings > 0 ? `
\ud83d\udcb0 Potential Timing Savings: $${rec.potentialSavings.toLocaleString('en-SG')}
` : ''}\n
\n `;\n});\n\nhtmlReport += `\n
\n \ud83d\udcc8 Interactive price charts and trend visualization would appear here
\n (Integrate with Chart.js or similar library for production)\n
\n \n
\n

Risk Factors: ${$('Multi-Scenario Analysis').first().json.riskFactors?.join(', ') || 'None identified'}

\n

This analysis combines historical data, AI predictions, and economic indicators. Market conditions may change rapidly.

\n

Data source: LTA OneMotoring Portal & MAS Economic Indicators | Powered by n8n + AI

\n
\n
\n\n\n`;\n\nreturn [{ json: { htmlReport, subject: `COE Comprehensive Analysis - ${analysisDate}` } }];" }, "typeVersion": 2 }, { "id": "48ef07ff-cf0a-4f8c-b86e-4b3ad76f48b8", "name": "Merge Economic Data", "type": "n8n-nodes-base.merge", "position": [ 2176, 848 ], "parameters": { "mode": "combine", "options": {} }, "typeVersion": 3 }, { "id": "87a9c973-2735-436f-b42c-aa7d9ac6ef28", "name": "Alternative Category Suggester", "type": "n8n-nodes-base.code", "position": [ 3728, 768 ], "parameters": { "jsCode": "const recommendations = $input.first().json.recommendations;\nconst categories = Object.keys(recommendations).filter(k => !recommendations[k].reasoning?.includes('Insufficient'));\n\nconst alternatives = {};\n\ncategories.forEach(primaryCategory => {\n const primaryRec = recommendations[primaryCategory];\n const alternativeOptions = [];\n \n categories.forEach(altCategory => {\n if (altCategory === primaryCategory) return;\n \n const altRec = recommendations[altCategory];\n const priceDiff = altRec.currentPrice - primaryRec.currentPrice;\n const scoreDiff = altRec.score - primaryRec.score;\n \n if (scoreDiff > 10 || (scoreDiff > 0 && Math.abs(priceDiff) < 10000)) {\n alternativeOptions.push({\n category: altCategory,\n priceDiff: Math.round(priceDiff),\n scoreDiff,\n reasoning: scoreDiff > 15 ? 'Significantly better market conditions' : 'Comparable conditions with different pricing'\n });\n }\n });\n \n alternatives[primaryCategory] = alternativeOptions.sort((a, b) => b.scoreDiff - a.scoreDiff);\n});\n\nreturn [{ json: { alternatives } }];" }, "typeVersion": 2 }, { "id": "9c13a0c9-5903-4649-9ddb-60a31d11e9f0", "name": "Sticky Note", "type": "n8n-nodes-base.stickyNote", "position": [ 272, 0 ], "parameters": { "width": 1408, "height": 352, "content": "## Introduction\nAutomates Singapore COE price tracking with AI forecasts and buy/wait recommendations. Weekly scraping collects LTA data, enriches with economic indicators, predicts 6-month trends, and alerts users via Telegram/email\u2014helping car buyers and fleet managers make data-driven purchase decisions while avoiding manual tracking.\n\n## How it Works\nWeekly trigger scrapes LTA COE \u2192 validates \u2192 stores in Google Sheets \u2192 calculates indicators \u2192 AI forecasts trends \u2192 multi-scenario analysis \u2192 generates buy/wait signals \u2192 sends actionable alerts.\n\n## Setup Steps\n1. Add OpenAI/NVIDIA API credentials in n8n\n2. Authenticate Google Sheets and create spreadsheet\n3. Configure Telegram bot or Gmail SMTP\n4. Set weekly trigger (Thursday 9AM SGT post-bidding)\n5. Adjust alert thresholds in conditional nodes\n\n\n\n\n\n\n\n\n" }, "typeVersion": 1 }, { "id": "3d015ecf-03a3-4892-83ac-3f053d099a60", "name": "Sticky Note1", "type": "n8n-nodes-base.stickyNote", "position": [ 1712, 0 ], "parameters": { "color": 4, "width": 672, "height": 240, "content": "## Prerequisites\nOpenAI/NVIDIA API key, Google Sheets access, Telegram bot token or Gmail, basic COE category understanding\n\n## Use Cases\nFirst-time buyers timing purchases, fleet operators coordinating bulk acquisitions" }, "typeVersion": 1 }, { "id": "e9250a46-284e-4031-beb5-3fe4db34171c", "name": "Sticky Note2", "type": "n8n-nodes-base.stickyNote", "position": [ 2416, 0 ], "parameters": { "color": 6, "width": 528, "height": 208, "content": "## Customization\nAdd SMS alerts via Twilio, integrate loan calculators for total cost analysis\n\n## Benefits\nSaves 5+ hours monthly, captures 10\u201318% price dips, provides predictive insights (potential $10K\u2013$25K savings)\n \n" }, "typeVersion": 1 }, { "id": "f15a58ad-3b4c-479b-ab7d-5fde49487373", "name": "Sticky Note3", "type": "n8n-nodes-base.stickyNote", "position": [ 64, 368 ], "parameters": { "color": 5, "width": 224, "height": 496, "content": "## Weekly COE Check Trigger\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nLaunches workflow every Thursday 9AM SGT, right after LTA bidding closes\nWhy: Captures price data at the moment it matters most\u2014fresh results mean your forecast starts with the latest market signal before prices shift" }, "typeVersion": 1 }, { "id": "9d882ef7-7853-4912-96bb-bd09d2cbc505", "name": "Sticky Note4", "type": "n8n-nodes-base.stickyNote", "position": [ 304, 368 ], "parameters": { "color": 5, "width": 448, "height": 592, "content": "## Scrape LTA COE Data\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFetches official COE results with retry logic to handle connection issues\nWhy: LTA is the authoritative source; automated scraping eliminates manual checking while retry logic ensures you never miss a bidding round due to network hiccups" }, "typeVersion": 1 }, { "id": "e61894a5-ab0c-4556-91eb-0e41990e75e6", "name": "Sticky Note5", "type": "n8n-nodes-base.stickyNote", "position": [ 768, 368 ], "parameters": { "color": 5, "height": 480, "content": "## Validate Data Completeness\nChecks that all COE categories arrived and flags any anomalies or missing records\nWhy: Bad data breaks forecasts\u2014catching incomplete records here prevents misleading buy/wait signals downstream" }, "typeVersion": 1 }, { "id": "45d278ef-4c62-4bbd-baf5-6e479d1f0afa", "name": "Sticky Note6", "type": "n8n-nodes-base.stickyNote", "position": [ 1024, 368 ], "parameters": { "color": 5, "width": 288, "height": 896, "content": "## Store Data\nAppends timestamped COE records to your spreadsheet with category breakdown\nWhy: Creates permanent audit trail; you can always trace back why a recommendation was made, and historical data feeds better AI predictions over time" }, "typeVersion": 1 }, { "id": "a2381f74-7fa3-4eb8-8537-6a906b577f30", "name": "Sticky Note7", "type": "n8n-nodes-base.stickyNote", "position": [ 1328, 384 ], "parameters": { "color": 5, "width": 214, "height": 720, "content": "## Fetch Historical Baseline\nRetrieves previous 12+ months of COE data to establish trends\nWhy: AI needs context\u2014knowing if prices are historically high or low makes 6-month forecasts meaningful, not just noise\n" }, "typeVersion": 1 }, { "id": "58ce40c0-5080-4b0f-8080-333fe72d8fa2", "name": "Sticky Note8", "type": "n8n-nodes-base.stickyNote", "position": [ 1568, 384 ], "parameters": { "color": 5, "width": 1312, "height": 720, "content": "## Market Indicators & AI Forecasting- Processing\n\nBuilds key indicators (averages, volatility, seasonality, trends) and uses them to predict COE prices 6 months out.\n**Why:** Converts raw data into fast, clear signals and forecasts so users can judge market direction and decide if waiting helps or hurts.\n\n" }, "typeVersion": 1 }, { "id": "877a0f53-544d-4193-aabc-0cab7547e3ae", "name": "Sticky Note9", "type": "n8n-nodes-base.stickyNote", "position": [ 2096, 688 ], "parameters": { "color": 7, "width": 480, "height": 352, "content": "\n## Merge Economic Indicators\nEnriches COE data with interest rates, vehicle import taxes, fuel costs, and market sentiment\nWhy: COE doesn't exist in a vacuum\u2014economic context explains why prices move; better forecasts come from understanding the full picture" }, "typeVersion": 1 }, { "id": "9e1a93a8-4fd9-443a-bb9f-35c5cb7461e4", "name": "Sticky Note10", "type": "n8n-nodes-base.stickyNote", "position": [ 2896, 384 ], "parameters": { "color": 5, "width": 272, "height": 672, "content": "## Multi-Scenario Analysis\n\nGenerates three outcomes: best case (wait scenario), base case (realistic), worst case (buy now)\nWhy: Removes false certainty\u2014you see the range of possibilities and can decide your own risk tolerance instead of following one rigid signal" }, "typeVersion": 1 }, { "id": "852fb988-0a1b-49b2-a690-30052e911113", "name": "Sticky Note11", "type": "n8n-nodes-base.stickyNote", "position": [ 3200, 16 ], "parameters": { "color": 5, "width": 416, "height": 1488, "content": "## Threshold Comparison & COE Dashboard\n\nChecks forecasts against your custom buy/wait rules, then compiles prices, predictions, recommendations, and confidence levels into a single dashboard.\n**Why:** Supports different decision styles\u2014whether it\u2019s \u201cbuy if >15% savings expected\u201d or \u201cwait if volatility <8%\u201d\u2014and gives stakeholders one clear view without digging through spreadsheets.\n" }, "typeVersion": 1 }, { "id": "20ea90b4-1802-4093-bb10-bc9910441ea0", "name": "Sticky Note12", "type": "n8n-nodes-base.stickyNote", "position": [ 3632, 16 ], "parameters": { "color": 5, "width": 464, "height": 1088, "content": "## Send Telegram/Email Alerts\nDelivers your recommendation, key price changes, and next review date via your preferred channel\nWhy: Information reaches you where you check it\u2014whether you're a solo buyer or coordinating fleet-wide purchases, you stay informed without manual monitoring\n" }, "typeVersion": 1 } ], "active": false, "settings": { "executionOrder": "v1" }, "versionId": "aea095b0-d476-4f66-afab-fe7253eca58e", "connections": { "ROI Calculator": { "main": [ [ { "node": "Enhanced Dashboard Report", "type": "main", "index": 0 }, { "node": "Alternative Category Suggester", "type": "main", "index": 0 } ] ] }, "Format HTML Report": { "main": [ [ { "node": "Send Email Report", "type": "main", "index": 0 } ] ] }, "Merge Economic Data": { "main": [ [ { "node": "Enhanced AI Prompt with Economics", "type": "main", "index": 0 } ] ] }, "Data Validation Check": { "main": [ [ { "node": "Store in Google Sheets", "type": "main", "index": 0 }, { "node": "Airtable - Store COE Data", "type": "main", "index": 0 } ], [ { "node": "Error Handler - Retry Scraping", "type": "main", "index": 0 } ] ] }, "OpenRouter Chat Model": { "ai_languageModel": [ [ { "node": "AI Agent - COE Analysis", "type": "ai_languageModel", "index": 0 } ] ] }, "Process Economic Data": { "main": [ [ { "node": "Merge Economic Data", "type": "main", "index": 1 } ] ] }, "Extract COE Price Data": { "main": [ [ { "node": "Store in Google Sheets", "type": "main", "index": 0 }, { "node": "Data Validation Check", "type": "main", "index": 0 } ] ] }, "Store in Google Sheets": { "main": [ [ { "node": "Retrieve Historical COE Data", "type": "main", "index": 0 } ] ] }, "AI Agent - COE Analysis": { "main": [ [ { "node": "Generate Buy Recommendations", "type": "main", "index": 0 }, { "node": "Multi-Scenario Analysis", "type": "main", "index": 0 } ] ] }, "Multi-Scenario Analysis": { "main": [ [ { "node": "Generate Buy Recommendations", "type": "main", "index": 0 } ] ] }, "Scrape with Retry Logic": { "main": [ [ { "node": "Extract COE Price Data", "type": "main", "index": 0 } ] ] }, "Enhanced Dashboard Report": { "main": [ [ { "node": "Send Email Report", "type": "main", "index": 0 } ] ] }, "Check for Buy Opportunities": { "main": [ [ { "node": "Send Telegram Alert", "type": "main", "index": 0 } ] ] }, "Extract Economic Indicators": { "main": [ [ { "node": "Process Economic Data", "type": "main", "index": 0 } ] ] }, "Generate Buy Recommendations": { "main": [ [ { "node": "Format HTML Report", "type": "main", "index": 0 }, { "node": "Check for Buy Opportunities", "type": "main", "index": 0 }, { "node": "Generate Dashboard Summary", "type": "main", "index": 0 }, { "node": "ROI Calculator", "type": "main", "index": 0 }, { "node": "Check Significant Price Movement", "type": "main", "index": 0 }, { "node": "Check Market Volatility", "type": "main", "index": 0 } ] ] }, "Prepare AI Prediction Prompt": { "main": [ [ { "node": "AI Agent - COE Analysis", "type": "main", "index": 0 } ] ] }, "Retrieve Historical COE Data": { "main": [ [ { "node": "Calculate Technical Indicators", "type": "main", "index": 0 } ] ] }, "Calculate Technical Indicators": { "main": [ [ { "node": "Prepare AI Prediction Prompt", "type": "main", "index": 0 }, { "node": "Merge Economic Data", "type": "main", "index": 0 }, { "node": "Extract Economic Indicators", "type": "main", "index": 0 } ] ] }, "Daily Prediction Update Trigger": { "main": [ [ { "node": "Retrieve Historical COE Data", "type": "main", "index": 0 } ] ] }, "Check Significant Price Movement": { "main": [ [ { "node": "Urgent Market Alert", "type": "main", "index": 0 } ] ] }, "Scrape COE Data from OneMotoring": { "main": [ [ { "node": "Extract COE Price Data", "type": "main", "index": 0 } ] ] }, "Enhanced AI Prompt with Economics": { "main": [ [ { "node": "AI Agent - COE Analysis", "type": "main", "index": 0 } ] ] }, "Schedule Trigger - Bi-Weekly COE Scraping": { "main": [ [ { "node": "Scrape COE Data from OneMotoring", "type": "main", "index": 0 }, { "node": "Scrape with Retry Logic", "type": "main", "index": 0 } ] ] } } }