{ "name": "Portfolio Intelligence Agent - Complete with RAG", "nodes": [ { "parameters": {}, "id": "a88b636d-ab6a-4cae-ab74-8da295e86bd7", "name": "Manual Trigger", "type": "n8n-nodes-base.manualTrigger", "typeVersion": 1, "position": [ -144, -32 ] }, { "parameters": { "rule": { "interval": [ { "field": "cronExpression", "expression": "0 17 * * 1-5" } ] } }, "id": "25ad4113-8252-45e3-a57c-2229140b3566", "name": "Schedule Trigger", "type": "n8n-nodes-base.scheduleTrigger", "typeVersion": 1.2, "position": [ -144, 176 ] }, { "parameters": { "filePath": "/path/to/user/Humanitarians/Portfolio_Intelligence_Agent/data/holdings.json" }, "id": "3237e868-434f-43f3-aaea-9d3c5b47a492", "name": "Read Holdings File", "type": "n8n-nodes-base.readBinaryFile", "typeVersion": 1, "position": [ 128, 80 ] }, { "parameters": { "jsCode": "const items = $input.all();\nif (!items || items.length === 0) {\n throw new Error('No input items');\n}\n\nconst item = items[0];\nconst binaryData = item.binary;\n\nif (!binaryData) {\n throw new Error('No binary data found');\n}\n\nconst binaryPropertyName = Object.keys(binaryData)[0];\nconst buffer = Buffer.from(binaryData[binaryPropertyName].data, 'base64');\n\nconst jsonString = buffer.toString('utf-8');\nlet holdings = JSON.parse(jsonString);\n\nif (Array.isArray(holdings) && holdings.length > 0 && Array.isArray(holdings[0])) {\n holdings = holdings[0];\n}\n\nif (!Array.isArray(holdings)) {\n throw new Error('Holdings must be an array');\n}\n\nreturn holdings.map(holding => ({\n json: holding\n}));" }, "id": "2b39de8d-129c-4b3b-a79c-9b5bfd3d1c6d", "name": "Parse JSON Holdings", "type": "n8n-nodes-base.code", "typeVersion": 2, "position": [ 336, 80 ] }, { "parameters": { "jsCode": "// Get all holdings directly from the previous node\nconst inputItems = $input.all();\n\n// Extract the json from each item\nconst holdings = inputItems.map(item => item.json);\n\nconsole.log('Holdings received:', holdings.length);\nconsole.log('First holding:', JSON.stringify(holdings[0]));\n\nif (!holdings || holdings.length === 0) {\n throw new Error('No holdings data');\n}\n\n// Calculate total value\nconst totalValue = holdings.reduce((sum, h) => {\n const val = parseFloat(h.value);\n console.log(`Adding ${h.ticker}: $${val}`);\n return sum + val;\n}, 0);\n\nconsole.log('Total Value:', totalValue);\n\n// Add weights to each holding\nconst enrichedHoldings = holdings.map(h => {\n const weight = h.value / totalValue;\n return {\n ...h,\n weight: parseFloat(weight.toFixed(4))\n };\n});\n\n// Find top holding\nconst sorted = [...enrichedHoldings].sort((a, b) => b.value - a.value);\nconst topHolding = sorted[0];\n\n// Calculate sector concentrations\nconst sectorWeights = {};\nenrichedHoldings.forEach(h => {\n if (!sectorWeights[h.sector]) {\n sectorWeights[h.sector] = 0;\n }\n sectorWeights[h.sector] += h.weight;\n});\n\nconsole.log(`Portfolio Total Value: $${totalValue.toFixed(2)}`);\nconsole.log(`Top Holding: ${topHolding.ticker} (${(topHolding.weight * 100).toFixed(1)}%)`);\n\nreturn {\n json: {\n date: holdings[0].date,\n total_value: parseFloat(totalValue.toFixed(2)),\n holdings: enrichedHoldings,\n top_holding: topHolding.ticker,\n top_holding_weight: topHolding.weight,\n sector_weights: sectorWeights\n }\n};" }, "id": "df8962cf-5ba7-4fe9-bbe6-0f28cd713a35", "name": "Calculate Portfolio Metrics", "type": "n8n-nodes-base.code", "typeVersion": 2, "position": [ 816, 80 ] }, { "parameters": { "filePath": "/path/to/user/Humanitarians/Portfolio_Intelligence_Agent/data/daily_summaries.csv" }, "id": "e171cc65-2fde-43d5-ba40-8135560dd8aa", "name": "Read Previous Summaries", "type": "n8n-nodes-base.readBinaryFile", "typeVersion": 1, "position": [ 1664, 80 ] }, { "parameters": { "jsCode": "// Get all holdings\nconst allHoldings = $('Parse JSON Holdings').all();\n\nif (!allHoldings || allHoldings.length === 0) {\n throw new Error('No holdings found');\n}\n\nconst today = new Date().toISOString().split('T')[0];\n\n// Fetch all prices in parallel\nconst pricePromises = allHoldings.map(async (holdingItem) => {\n const holding = holdingItem.json;\n const ticker = holding.ticker;\n \n try {\n const url = `https://query1.finance.yahoo.com/v8/finance/chart/${ticker}?interval=1d&range=1d`;\n const response = await this.helpers.request({\n method: 'GET',\n url: url,\n json: true\n });\n \n const price = response.chart.result[0].meta.regularMarketPrice;\n const value = holding.quantity * price;\n \n return {\n date: today,\n ticker: ticker,\n quantity: holding.quantity,\n sector: holding.sector,\n price: parseFloat(price.toFixed(2)),\n value: parseFloat(value.toFixed(2))\n };\n \n } catch (error) {\n console.log(`Error fetching ${ticker}: ${error.message}`);\n return null;\n }\n});\n\n// Wait for all requests to complete\nconst results = await Promise.all(pricePromises);\n\n// Filter out any failed requests\nconst validResults = results.filter(r => r !== null);\n\nconsole.log(`Successfully fetched ${validResults.length} out of ${allHoldings.length} prices`);\n\n// Return all results as separate items\nreturn validResults.map(item => ({ json: item }));" }, "type": "n8n-nodes-base.code", "typeVersion": 2, "position": [ 576, 80 ], "id": "0c62adc3-b714-44c7-ba42-b5722563fad0", "name": "Fetch & Extract Stock Prices" }, { "parameters": { "jsCode": "// Get today's data from previous node\nconst todayData = $input.item.json;\nconst todayValue = parseFloat(todayData.total_value);\n\nconsole.log('Today Value:', todayValue);\n\n// Initialize variables\nlet yesterdayValue = null;\nlet summaries = [];\nlet patterns = [];\n\n// Check if this node received binary data from readBinaryFile\n// We need to look at the items array to find the binary data\nconst allItems = $input.all();\n\n// Find the item with binary data (the CSV file)\nlet binaryItem = null;\nfor (const item of allItems) {\n if (item.binary) {\n binaryItem = item;\n break;\n }\n}\n\nif (binaryItem && binaryItem.binary) {\n try {\n const binaryPropertyName = Object.keys(binaryItem.binary)[0];\n const buffer = Buffer.from(binaryItem.binary[binaryPropertyName].data, 'base64');\n const csvText = buffer.toString('utf-8').trim();\n const lines = csvText.split('\\n').filter(line => line.trim());\n\n console.log(`CSV lines found: ${lines.length}`);\n\n if (lines.length > 1) {\n // Parse summaries (skip header)\n for (let i = 1; i < lines.length; i++) {\n const line = lines[i];\n const parts = line.split(',');\n \n if (parts.length >= 3) {\n summaries.push({\n date: parts[0],\n total_value: parseFloat(parts[1]),\n daily_return: parseFloat(parts[2])\n });\n }\n }\n \n // Get yesterday's value (last entry)\n if (summaries.length > 0) {\n yesterdayValue = summaries[summaries.length - 1].total_value;\n console.log('Yesterday value found:', yesterdayValue);\n }\n \n // Extract patterns from recent summaries\n const recentSummaries = summaries.slice(-7);\n \n let consecutiveGains = 0;\n let consecutiveLosses = 0;\n \n for (let i = recentSummaries.length - 1; i >= 0; i--) {\n const ret = recentSummaries[i].daily_return;\n if (ret > 0) {\n consecutiveGains++;\n if (consecutiveLosses > 0) break;\n } else if (ret < 0) {\n consecutiveLosses++;\n if (consecutiveGains > 0) break;\n }\n }\n \n if (consecutiveGains >= 3) {\n patterns.push(`Portfolio gained for ${consecutiveGains} consecutive days`);\n }\n if (consecutiveLosses >= 3) {\n patterns.push(`Portfolio declined for ${consecutiveLosses} consecutive days`);\n }\n }\n } catch (error) {\n console.log('Error parsing summaries:', error.message);\n }\n}\n\n// Calculate daily return\nlet dailyReturn = 0;\nlet dailyReturnPct = 0;\n\nif (yesterdayValue && yesterdayValue > 0) {\n dailyReturn = (todayValue - yesterdayValue) / yesterdayValue;\n dailyReturnPct = dailyReturn * 100;\n console.log(`Return: ${dailyReturnPct.toFixed(2)}%`);\n} else {\n console.log('First run - no previous data');\n}\n\n// Prepare learnings\nconst learnings = [];\n\n// Learning from historical analysis\nif (todayData.historical_analysis && todayData.historical_analysis.days_tracked > 0) {\n const ha = todayData.historical_analysis;\n learnings.push({\n category: 'historical_performance',\n content: `Portfolio tracked ${ha.days_tracked} days with ${ha.total_return_pct}% return`,\n relevance: 'high'\n });\n}\n\n// Learning from patterns\npatterns.forEach(pattern => {\n learnings.push({\n category: 'pattern',\n content: pattern,\n relevance: 'medium'\n });\n});\n\n// Learning from significant movement\nif (Math.abs(dailyReturnPct) > 2) {\n learnings.push({\n category: 'observation',\n content: `Significant ${dailyReturnPct > 0 ? 'gain' : 'decline'} of ${Math.abs(dailyReturnPct).toFixed(2)}% observed`,\n relevance: 'medium'\n });\n}\n\n// Learning from concentration\nconst topWeight = parseFloat(todayData.top_holding_weight);\nif (topWeight > 0.35) {\n learnings.push({\n category: 'observation',\n content: `${todayData.top_holding} concentration at ${(topWeight * 100).toFixed(1)}%`,\n relevance: 'high'\n });\n}\n\n// Format as CSV\nlet kbCsv = '';\nif (learnings.length > 0) {\n const today = todayData.date;\n const csvRows = learnings.map((learning, idx) => {\n const id = Date.now() + idx;\n const cleanContent = learning.content.replace(/\"/g, '\"\"');\n return `${id},${today},${learning.category},\"${cleanContent}\",${learning.relevance}`;\n });\n \n kbCsv = csvRows.join('\\n') + '\\n';\n}\n\nconsole.log(`Prepared ${learnings.length} learnings`);\n\nreturn {\n json: {\n ...todayData,\n daily_return: dailyReturn.toFixed(4),\n daily_return_pct: dailyReturnPct.toFixed(2),\n yesterday_value: yesterdayValue,\n summary_count: summaries.length,\n recent_patterns: patterns,\n kb_update_csv: kbCsv,\n kb_updates_count: learnings.length\n }\n};" }, "type": "n8n-nodes-base.code", "typeVersion": 2, "position": [ 1904, 80 ], "id": "598765b2-a7e6-4ec9-b58c-3908d2ebfde7", "name": "Parse Summaries CSV" }, { "parameters": { "jsCode": "// Get today's portfolio data from Calculate Portfolio Metrics\nconst portfolioData = $('Calculate Portfolio Metrics').item.json;\nconst todayValue = parseFloat(portfolioData.total_value);\n\nconsole.log('Today Value:', todayValue);\n\n// Parse the summaries CSV from binary data\nlet yesterdayValue = null;\nlet summaries = [];\nlet patterns = [];\n\nconst allItems = $input.all();\nlet binaryItem = null;\n\n// Find the item with binary data (the CSV file we just read)\nfor (const item of allItems) {\n if (item.binary) {\n binaryItem = item;\n break;\n }\n}\n\nif (binaryItem && binaryItem.binary) {\n try {\n const binaryPropertyName = Object.keys(binaryItem.binary)[0];\n const buffer = Buffer.from(binaryItem.binary[binaryPropertyName].data, 'base64');\n const csvText = buffer.toString('utf-8').trim();\n const lines = csvText.split('\\n').filter(line => line.trim());\n\n console.log(`CSV lines found: ${lines.length}`);\n\n if (lines.length > 1) {\n // Parse summaries (skip header)\n for (let i = 1; i < lines.length; i++) {\n const parts = lines[i].split(',');\n if (parts.length >= 3) {\n summaries.push({\n date: parts[0],\n total_value: parseFloat(parts[1]),\n daily_return: parseFloat(parts[2])\n });\n }\n }\n \n // Get yesterday's value (last entry)\n if (summaries.length > 0) {\n yesterdayValue = summaries[summaries.length - 1].total_value;\n console.log('Yesterday value:', yesterdayValue);\n }\n \n // Extract patterns from recent summaries (last 7 days)\n const recentSummaries = summaries.slice(-7);\n let consecutiveGains = 0;\n let consecutiveLosses = 0;\n \n for (let i = recentSummaries.length - 1; i >= 0; i--) {\n const ret = recentSummaries[i].daily_return;\n if (ret > 0) {\n consecutiveGains++;\n if (consecutiveLosses > 0) break;\n } else if (ret < 0) {\n consecutiveLosses++;\n if (consecutiveGains > 0) break;\n }\n }\n \n if (consecutiveGains >= 3) {\n patterns.push(`Portfolio gained for ${consecutiveGains} consecutive days`);\n }\n if (consecutiveLosses >= 3) {\n patterns.push(`Portfolio declined for ${consecutiveLosses} consecutive days`);\n }\n }\n } catch (error) {\n console.log('Error parsing summaries:', error.message);\n }\n}\n\n// Calculate daily return\nlet dailyReturn = 0;\nlet dailyReturnPct = 0;\n\nif (yesterdayValue && yesterdayValue > 0) {\n dailyReturn = (todayValue - yesterdayValue) / yesterdayValue;\n dailyReturnPct = dailyReturn * 100;\n \n console.log(`Today: $${todayValue.toFixed(2)}`);\n console.log(`Yesterday: $${yesterdayValue.toFixed(2)}`);\n console.log(`Change: $${(todayValue - yesterdayValue).toFixed(2)}`);\n console.log(`Daily Return: ${dailyReturnPct.toFixed(2)}%`);\n} else {\n console.log('No previous data - this is the first run');\n console.log(`Today's Value: $${todayValue.toFixed(2)}`);\n}\n\n// Extract learnings for knowledge base\nconst learnings = [];\nconst today = portfolioData.date;\n\n// Learning 1: From detected patterns\npatterns.forEach(pattern => {\n learnings.push({\n category: 'pattern',\n content: pattern,\n relevance: 'medium'\n });\n});\n\n// Learning 2: Significant movement\nif (Math.abs(dailyReturnPct) > 2) {\n learnings.push({\n category: 'observation',\n content: `Significant ${dailyReturnPct > 0 ? 'gain' : 'decline'} of ${Math.abs(dailyReturnPct).toFixed(2)}% observed on ${today}`,\n relevance: 'medium'\n });\n}\n\n// Learning 3: Concentration risk\nconst topWeight = parseFloat(portfolioData.top_holding_weight);\nif (topWeight > 0.35) {\n learnings.push({\n category: 'observation',\n content: `${portfolioData.top_holding} concentration at ${(topWeight * 100).toFixed(1)}%, approaching rebalancing threshold`,\n relevance: 'high'\n });\n}\n\n// Format learnings as CSV\nlet kbCsv = '';\nif (learnings.length > 0) {\n const csvRows = learnings.map((learning, idx) => {\n const id = Date.now() + idx;\n const cleanContent = learning.content.replace(/\"/g, '\"\"');\n return `${id},${today},${learning.category},\"${cleanContent}\",${learning.relevance}`;\n });\n kbCsv = csvRows.join('\\n') + '\\n';\n}\n\nconsole.log(`Prepared ${learnings.length} learnings for knowledge base`);\n\n// Return complete data\nreturn {\n json: {\n date: portfolioData.date,\n total_value: todayValue.toFixed(2),\n daily_return: dailyReturn.toFixed(4),\n daily_return_pct: dailyReturnPct.toFixed(2),\n top_holding: portfolioData.top_holding,\n top_holding_weight: portfolioData.top_holding_weight,\n sector_weights: portfolioData.sector_weights,\n holdings: portfolioData.holdings,\n \n // Historical context fields\n yesterday_value: yesterdayValue,\n summary_count: summaries.length,\n recent_patterns: patterns,\n \n // Knowledge base update fields\n kb_update_csv: kbCsv,\n kb_updates_count: Number(learnings.length) // Explicitly convert to number\n }\n};" }, "id": "73ed63b8-9273-41be-9257-78dea0c8efe3", "name": "Calculate Daily Return", "type": "n8n-nodes-base.code", "typeVersion": 2, "position": [ 2128, 80 ] }, { "parameters": { "fileSelector": "/path/to/user/Humanitarians/Portfolio_Intelligence_Agent/data/knowledge_base.csv", "options": {} }, "type": "n8n-nodes-base.readWriteFile", "typeVersion": 1, "position": [ 2368, 80 ], "id": "9bb20683-2985-4f3c-b91a-ca3893619e62", "name": "Read Knowledge Base1" }, { "parameters": { "jsCode": "const items = $input.all();\n\nif (!items || items.length === 0 || !items[0].binary) {\n console.log('No knowledge base found');\n return [{ json: { knowledge: [] } }];\n}\n\ntry {\n const binaryPropertyName = Object.keys(items[0].binary)[0];\n const buffer = Buffer.from(items[0].binary[binaryPropertyName].data, 'base64');\n const csvText = buffer.toString('utf-8').trim();\n\n const lines = csvText.split('\\n').filter(line => line.trim());\n\n if (lines.length <= 1) {\n console.log('Knowledge base is empty');\n return [{ json: { knowledge: [] } }];\n }\n\n // Parse header\n const header = lines[0].split(',');\n\n // Parse CSV with quote handling\n const parseCSVLine = (line) => {\n const result = [];\n let current = '';\n let inQuotes = false;\n \n for (let i = 0; i < line.length; i++) {\n const char = line[i];\n \n if (char === '\"') {\n inQuotes = !inQuotes;\n } else if (char === ',' && !inQuotes) {\n result.push(current.replace(/^\"|\"$/g, ''));\n current = '';\n } else {\n current += char;\n }\n }\n result.push(current.replace(/^\"|\"$/g, ''));\n return result;\n };\n\n // Parse all data rows\n const data = [];\n for (let i = 1; i < lines.length; i++) {\n const values = parseCSVLine(lines[i]);\n const obj = {};\n \n for (let j = 0; j < header.length; j++) {\n obj[header[j]] = values[j];\n }\n \n data.push(obj);\n }\n\n console.log(`Loaded ${data.length} knowledge base entries`);\n\n // Return as a single item with knowledge array inside json\n return [{ json: { knowledge: data } }];\n \n} catch (error) {\n console.log('Error parsing knowledge base:', error.message);\n return [{ json: { knowledge: [] } }];\n}" }, "type": "n8n-nodes-base.code", "typeVersion": 2, "position": [ 2592, 80 ], "id": "e166fd5c-a1c0-481e-a02f-237f10646462", "name": "Parse Knowledge Base CSV" }, { "parameters": { "fileSelector": "/path/to/user/Humanitarians/Portfolio_Intelligence_Agent/data/portfolio_history.csv", "options": {} }, "type": "n8n-nodes-base.readWriteFile", "typeVersion": 1, "position": [ 1024, 80 ], "id": "f61acb4d-9707-4eec-88ba-2b414f97598b", "name": "Read Portfolio History CSV" }, { "parameters": { "jsCode": "const items = $input.all();\n\nif (!items || items.length === 0 || !items[0].binary) {\n console.log('No portfolio history found');\n return [{ json: { history: [] } }];\n}\n\ntry {\n const binaryPropertyName = Object.keys(items[0].binary)[0];\n const buffer = Buffer.from(items[0].binary[binaryPropertyName].data, 'base64');\n const csvText = buffer.toString('utf-8').trim();\n\n const lines = csvText.split('\\n').filter(line => line.trim());\n\n if (lines.length <= 1) {\n console.log('History is empty');\n return [{ json: { history: [] } }];\n }\n\n // Parse CSV\n const header = lines[0].split(',');\n const data = [];\n \n for (let i = 1; i < lines.length; i++) {\n const values = lines[i].split(',');\n const obj = {};\n \n for (let j = 0; j < header.length; j++) {\n obj[header[j]] = values[j];\n }\n \n data.push(obj);\n }\n\n console.log(`Loaded ${data.length} historical records`);\n\n return [{ json: { history: data } }];\n \n} catch (error) {\n console.log('Error parsing history:', error.message);\n return [{ json: { history: [] } }];\n}" }, "type": "n8n-nodes-base.code", "typeVersion": 2, "position": [ 1232, 80 ], "id": "4fcf8068-f3af-47f9-b3d4-d27505e7c357", "name": "Parse Portfolio History Data" }, { "parameters": { "jsCode": "// Get today's portfolio data from earlier node\nconst todayData = $('Calculate Portfolio Metrics').item.json;\n\n// Validate today's data exists\nif (!todayData) {\n throw new Error('Cannot find Calculate Portfolio Metrics node output');\n}\n\nconst todayValue = parseFloat(todayData.total_value);\nconst todayHoldings = todayData.holdings || [];\n\nconsole.log('Today Value:', todayValue);\nconsole.log('Today Holdings:', todayHoldings.length);\n\n// Parse the binary CSV file\nconst items = $input.all();\n\nlet history = [];\n\nif (items && items.length > 0 && items[0].binary) {\n try {\n const binaryPropertyName = Object.keys(items[0].binary)[0];\n const buffer = Buffer.from(items[0].binary[binaryPropertyName].data, 'base64');\n const csvText = buffer.toString('utf-8').trim();\n\n const lines = csvText.split('\\n').filter(line => line.trim());\n\n if (lines.length > 1) {\n const header = lines[0].split(',');\n \n for (let i = 1; i < lines.length; i++) {\n const values = lines[i].split(',');\n const obj = {};\n \n for (let j = 0; j < header.length && j < values.length; j++) {\n obj[header[j]] = values[j];\n }\n \n history.push(obj);\n }\n }\n } catch (error) {\n console.log('Error parsing history:', error.message);\n }\n}\n\nconsole.log(`Loaded ${history.length} historical records`);\n\n// Build comparative analysis\nlet analysis = {\n days_tracked: 0,\n first_value: null,\n total_return_pct: '0.00',\n avg_daily_value: 0,\n max_value: todayValue,\n min_value: todayValue,\n volatility: '0.00',\n holdings_comparison: []\n};\n\nif (history.length > 0) {\n const dates = [...new Set(history.map(h => h.date))];\n analysis.days_tracked = dates.length;\n \n const dailyTotals = {};\n history.forEach(h => {\n if (!dailyTotals[h.date]) {\n dailyTotals[h.date] = 0;\n }\n dailyTotals[h.date] += parseFloat(h.value) || 0;\n });\n \n const values = Object.values(dailyTotals).filter(v => !isNaN(v) && v > 0);\n \n if (values.length > 0) {\n analysis.first_value = values[0];\n analysis.avg_daily_value = values.reduce((a, b) => a + b, 0) / values.length;\n analysis.max_value = Math.max(...values, todayValue);\n analysis.min_value = Math.min(...values, todayValue);\n \n if (analysis.first_value > 0) {\n analysis.total_return_pct = ((todayValue - analysis.first_value) / analysis.first_value * 100).toFixed(2);\n }\n \n const mean = analysis.avg_daily_value;\n const variance = values.reduce((sum, val) => sum + Math.pow(val - mean, 2), 0) / values.length;\n analysis.volatility = Math.sqrt(variance).toFixed(2);\n }\n \n // FIXED: Check if todayHoldings exists and is an array before forEach\n if (Array.isArray(todayHoldings) && todayHoldings.length > 0) {\n todayHoldings.forEach(holding => {\n const historicalRecords = history.filter(h => h.ticker === holding.ticker);\n \n if (historicalRecords.length > 0) {\n const prices = historicalRecords.map(h => parseFloat(h.price)).filter(p => !isNaN(p));\n \n if (prices.length > 0) {\n const avgHistoricalPrice = prices.reduce((sum, p) => sum + p, 0) / prices.length;\n const priceChange = ((holding.price - avgHistoricalPrice) / avgHistoricalPrice * 100).toFixed(2);\n \n analysis.holdings_comparison.push({\n ticker: holding.ticker,\n current_price: holding.price,\n avg_historical_price: avgHistoricalPrice.toFixed(2),\n price_change_pct: priceChange\n });\n }\n }\n });\n } else {\n console.log('Warning: No holdings found in today data');\n }\n}\n\nconsole.log(`Analysis complete:`);\nconsole.log(`- Days tracked: ${analysis.days_tracked}`);\nconsole.log(`- Total Return: ${analysis.total_return_pct}%`);\nconsole.log(`- Volatility: $${analysis.volatility}`);\nconsole.log(`- Holdings compared: ${analysis.holdings_comparison.length}`);\n\nreturn {\n json: {\n ...todayData,\n historical_analysis: analysis\n }\n};" }, "type": "n8n-nodes-base.code", "typeVersion": 2, "position": [ 1440, 80 ], "id": "969d9d00-2c12-4d10-a46d-b88922881e08", "name": "Build Portfolio Analysis" }, { "parameters": { "jsCode": "// The previous node (Parse Knowledge Base CSV) returns { knowledge: [...] }\n// We need to get BOTH the knowledge AND the portfolio data\n\nconst allItems = $input.all();\n\n// Initialize\nlet knowledge = [];\nlet portfolioData = null;\n\nconsole.log('Total items received:', allItems.length);\n\n// Strategy: Look through all items to find what we need\nfor (const item of allItems) {\n const data = item.json;\n \n // Check if this item has knowledge base\n if (data.knowledge && Array.isArray(data.knowledge)) {\n knowledge = data.knowledge;\n console.log('Found knowledge:', knowledge.length, 'entries');\n }\n \n // Check if this item has portfolio data\n if (data.total_value && data.daily_return !== undefined) {\n portfolioData = data;\n console.log('Found portfolio data');\n }\n}\n\n// If we didn't find portfolio data in items, try to get it from a previous node\nif (!portfolioData) {\n console.log('Portfolio data not in current items, fetching from Calculate Daily Return node');\n portfolioData = $('Calculate Daily Return').item.json;\n}\n\n// Validate we have what we need\nif (!portfolioData) {\n throw new Error('Cannot find portfolio data. Check that Calculate Daily Return node executed successfully.');\n}\n\nif (!knowledge || knowledge.length === 0) {\n console.log('Warning: Knowledge base is empty');\n // Continue anyway with empty context\n return {\n json: {\n ...portfolioData,\n rag_context: '',\n context_count: 0,\n context_categories: '',\n context_used: false\n }\n };\n}\n\n// Extract portfolio metrics\nconst currentReturn = parseFloat(portfolioData.daily_return);\nconst returnPct = parseFloat(portfolioData.daily_return_pct);\nconst topHolding = portfolioData.top_holding;\nconst topWeight = parseFloat(portfolioData.top_holding_weight);\n\nconsole.log(`Analyzing: Return ${returnPct}%, Top ${topHolding} (${(topWeight*100).toFixed(1)}%)`);\n\n// Score each knowledge entry for relevance\nconst scoredContext = knowledge.map(k => {\n let score = 0;\n \n // Base relevance score\n if (k.relevance_score === 'high') score += 3;\n else if (k.relevance_score === 'medium') score += 2;\n else score += 1;\n \n const content = (k.content || '').toLowerCase();\n \n // Large movement boost\n if (Math.abs(currentReturn) > 0.02 && content.includes('pattern')) {\n score += 5;\n }\n \n // Concentration risk boost\n if (topWeight > 0.35 && content.includes('concentration')) {\n score += 5;\n }\n \n // Top holding mention boost\n if (content.includes(topHolding.toLowerCase())) {\n score += 3;\n }\n \n // Recovery/drawdown boost\n if (currentReturn < 0 && (content.includes('recovery') || content.includes('drawdown'))) {\n score += 4;\n }\n \n // User preference boost\n if (k.category === 'user_preference') {\n score += 2;\n }\n \n return { ...k, rag_score: score };\n});\n\n// Sort by score and take top 7\nconst topContext = scoredContext\n .sort((a, b) => b.rag_score - a.rag_score)\n .slice(0, 7);\n\n// Format for LLM\nconst contextString = topContext\n .map((k, idx) => `${idx + 1}. [${k.category}] ${k.content}`)\n .join('\\n');\n\nconst categories = [...new Set(topContext.map(k => k.category))];\n\nconsole.log(`Retrieved ${topContext.length} relevant insights`);\nconsole.log(`Top categories: ${categories.join(', ')}`);\n\nreturn {\n json: {\n ...portfolioData,\n rag_context: contextString,\n context_count: topContext.length,\n context_categories: categories.join(','),\n context_used: topContext.length > 0\n }\n};" }, "type": "n8n-nodes-base.code", "typeVersion": 2, "position": [ 2800, 80 ], "id": "fe716f42-588c-4863-909f-05370778625c", "name": "RAG-Retrieve Context" }, { "parameters": { "jsCode": "// Get Groq API response\nconst groqResponse = $input.item.json;\n\n// Extract the AI-generated text\nconst summaryText = groqResponse.choices[0].message.content;\n\n// Clean for CSV\nconst cleanSummary = summaryText\n .replace(/\\n/g, ' ')\n .replace(/\"/g, '\"\"')\n .trim();\n\n// SAFE: Get portfolio data from all previous items\nconst allItems = $input.all();\nlet portfolioData = null;\n\n// Search through all items for portfolio data\nfor (const item of allItems) {\n if (item.json.total_value && item.json.daily_return !== undefined && item.json.holdings) {\n portfolioData = item.json;\n break;\n }\n}\n\n// If not found in items, try common node names\nif (!portfolioData) {\n try {\n portfolioData = $('Calculate Daily Return').item.json;\n } catch (e) {\n console.log('Could not find Calculate Daily Return node');\n }\n}\n\nif (!portfolioData) {\n try {\n portfolioData = $('Parse Summaries & Calculate Return').item.json;\n } catch (e) {\n console.log('Could not find Parse Summaries node');\n }\n}\n\nif (!portfolioData) {\n throw new Error('Cannot find portfolio data. Available fields: ' + Object.keys($input.item.json).join(', '));\n}\n\nconsole.log('โœ“ AI Summary extracted');\nconsole.log(' Summary length:', summaryText.length);\nconsole.log(' Portfolio date:', portfolioData.date);\nconsole.log(' Portfolio value:', portfolioData.total_value);\n\nreturn {\n json: {\n date: portfolioData.date,\n total_value: portfolioData.total_value,\n daily_return: portfolioData.daily_return,\n daily_return_pct: portfolioData.daily_return_pct,\n top_holding: portfolioData.top_holding,\n top_holding_weight: portfolioData.top_holding_weight,\n holdings: portfolioData.holdings,\n sector_weights: portfolioData.sector_weights,\n \n // AI summaries\n ai_summary: cleanSummary,\n ai_summary_raw: summaryText,\n \n // RAG metadata\n rag_context_count: portfolioData.context_count || 0,\n rag_used: portfolioData.context_used || false\n }\n};" }, "type": "n8n-nodes-base.code", "typeVersion": 2, "position": [ 3232, 80 ], "id": "3a9274ba-dc5d-4023-9e11-6fb802d70664", "name": "Extract AI Summary" }, { "parameters": { "fieldToSplitOut": "holdings", "options": {} }, "type": "n8n-nodes-base.splitOut", "typeVersion": 1, "position": [ 3536, 80 ], "id": "79a25fc1-b674-4e21-8dfe-e3b3cae4dde4", "name": "Split Out" }, { "parameters": { "method": "POST", "url": "https://api.groq.com/openai/v1/chat/completions", "sendHeaders": true, "headerParameters": { "parameters": [ { "name": "Authorization", "value": "REPLACE_WITH_AUTHORIZATION" }, { "name": "Content-Type", "value": "application/json" } ] }, "sendBody": true, "specifyBody": "json", "jsonBody": "{\n \"model\": \"llama-3.3-70b-versatile\",\n \"max_tokens\": 1500,\n \"temperature\": 0.7,\n \"messages\": [\n {\n \"role\": \"system\",\n \"content\": \"You are Mycroft, an AI portfolio analyst. Provide 4-5 concise, educational bullet points about portfolio performance and risk.\"\n },\n {\n \"role\": \"user\",\n \"content\": \"Analyze this portfolio and provide insights.\"\n }\n ]\n}", "options": {} }, "type": "n8n-nodes-base.httpRequest", "typeVersion": 4.2, "position": [ 3008, 80 ], "id": "a38a9a9f-2a96-4852-ad51-cf9fba166078", "name": "Generate AI Summary (Groq)" }, { "parameters": { "sendTo": "", "subject": "==Mycroft Portfolio Report - {{ $json.date }}", "message": "==\n\n\n \n\n\n
\n
\n

๐Ÿ“Š Portfolio Intelligence Report

\n

{{ $json.date }}

\n
\n \n
\n
\n

Portfolio Value

\n

${{ $json.total_value }}

\n
\n \n
\n

Daily Return

\n

= 0 ? 'positive' : 'negative' }}\">\n {{ parseFloat($json.daily_return) >= 0 ? '+' : '' }}{{ $json.daily_return_pct }}%\n

\n
\n \n
\n

Top Holding

\n

{{ $json.top_holding }}

\n

\n {{ (parseFloat($json.top_holding_weight) * 100).toFixed(1) }}% of portfolio\n

\n
\n \n
\n

๐Ÿค– AI Analysis

\n

{{ $json.ai_summary_raw }}

\n \n {{ $json.rag_used ? '
๐Ÿง  Enhanced with ' + $json.rag_context_count + ' historical insights
' : '' }}\n
\n
\n \n
\n

Mycroft Investment Intelligence Framework

\n

RAG-Enhanced AI Portfolio Analysis

\n

Using AI to Invest in AI: Building and Learning Together

\n
\n
\n\n", "options": {} }, "type": "n8n-nodes-base.gmail", "typeVersion": 2.1, "position": [ 3568, -144 ], "id": "5b5fee0b-782e-429e-a5e1-8fec407e14ab", "name": "Send a message" }, { "parameters": { "operation": "write", "fileName": "/path/to/user/Humanitarians/Portfolio_Intelligence_Agent/data/portfolio_history.csv", "options": { "append": true } }, "type": "n8n-nodes-base.readWriteFile", "typeVersion": 1, "position": [ 3952, 80 ], "id": "cf19347f-0c1e-4652-969a-067badb9bafd", "name": "Append Portfolio History1" }, { "parameters": { "options": { "fileName": "holdings.csv" } }, "type": "n8n-nodes-base.convertToFile", "typeVersion": 1.1, "position": [ 3744, 80 ], "id": "0a03ad42-68ef-4d8d-8b67-7e65dfea17b5", "name": "Convert Holdings to CSV" }, { "parameters": { "options": { "fileName": "summary.csv" } }, "type": "n8n-nodes-base.convertToFile", "typeVersion": 1.1, "position": [ 3552, -368 ], "id": "9bcb8699-cb30-4c2c-80aa-169741ea4bc3", "name": "Convert Summary to CSV" }, { "parameters": { "operation": "write", "fileName": "/path/to/user/Humanitarians/Portfolio_Intelligence_Agent/data/daily_summaries.csv", "options": { "append": true } }, "type": "n8n-nodes-base.readWriteFile", "typeVersion": 1, "position": [ 3776, -368 ], "id": "0777df25-77b7-4e64-a6ce-2ee5605d18c1", "name": "Append Daily Summary" }, { "id": "896acf1d-039d-509b-90cf-69a7bd5aabe9", "name": "Publication ยท Overview and setup", "type": "n8n-nodes-base.stickyNote", "typeVersion": 1, "position": [ -944, -368 ], "parameters": { "content": "# Portfolio Intelligence Agent - Complete with RAG\n\n## Purpose and structure\nThis folder contains the **Portfolio Intelligence Agent - Complete with RAG** workflow. Its executable nodes use `code`, `convertToFile`, `gmail`, `httpRequest`, `manualTrigger`, `readBinaryFile`, `readWriteFile`, `scheduleTrigger`, `splitOut`. The table and connection map below are derived from the JSON, not an inferred guarantee of behavior.\n\n## Setup\nImport inactive. Configure your own credentials and replace REPLACE_WITH_* values. Review external destinations and Code nodes before running.\n\n## Entry points\nManual Trigger, Schedule Trigger\n\n## Verification\nSanitized source candidate; not runtime-tested. This overview is generated from node metadata. Read the folder README for the connection map, credential types, and license.\n\n## Attribution\nNik Bear Brown; rights confirmed 2026-10-19\nMIT; preserve source notices.", "width": 700, "height": 800, "color": 1 } } ], "connections": { "Manual Trigger": { "main": [ [ { "node": "Read Holdings File", "type": "main", "index": 0 } ] ] }, "Schedule Trigger": { "main": [ [ { "node": "Read Holdings File", "type": "main", "index": 0 } ] ] }, "Read Holdings File": { "main": [ [ { "node": "Parse JSON Holdings", "type": "main", "index": 0 } ] ] }, "Parse JSON Holdings": { "main": [ [ { "node": "Fetch & Extract Stock Prices", "type": "main", "index": 0 } ] ] }, "Calculate Portfolio Metrics": { "main": [ [ { "node": "Read Portfolio History CSV", "type": "main", "index": 0 } ] ] }, "Read Previous Summaries": { "main": [ [ { "node": "Parse Summaries CSV", "type": "main", "index": 0 } ] ] }, "Fetch & Extract Stock Prices": { "main": [ [ { "node": "Calculate Portfolio Metrics", "type": "main", "index": 0 } ] ] }, "Parse Summaries CSV": { "main": [ [ { "node": "Calculate Daily Return", "type": "main", "index": 0 } ] ] }, "Calculate Daily Return": { "main": [ [ { "node": "Read Knowledge Base1", "type": "main", "index": 0 } ] ] }, "Read Knowledge Base1": { "main": [ [ { "node": "Parse Knowledge Base CSV", "type": "main", "index": 0 } ] ] }, "Read Portfolio History CSV": { "main": [ [ { "node": "Parse Portfolio History Data", "type": "main", "index": 0 } ] ] }, "Parse Portfolio History Data": { "main": [ [ { "node": "Build Portfolio Analysis", "type": "main", "index": 0 } ] ] }, "Build Portfolio Analysis": { "main": [ [ { "node": "Read Previous Summaries", "type": "main", "index": 0 } ] ] }, "Parse Knowledge Base CSV": { "main": [ [ { "node": "RAG-Retrieve Context", "type": "main", "index": 0 } ] ] }, "RAG-Retrieve Context": { "main": [ [ { "node": "Generate AI Summary (Groq)", "type": "main", "index": 0 } ] ] }, "Extract AI Summary": { "main": [ [ { "node": "Split Out", "type": "main", "index": 0 }, { "node": "Convert Summary to CSV", "type": "main", "index": 0 }, { "node": "Send a message", "type": "main", "index": 0 } ] ] }, "Generate AI Summary (Groq)": { "main": [ [ { "node": "Extract AI Summary", "type": "main", "index": 0 } ] ] }, "Split Out": { "main": [ [ { "node": "Convert Holdings to CSV", "type": "main", "index": 0 } ] ] }, "Convert Holdings to CSV": { "main": [ [ { "node": "Append Portfolio History1", "type": "main", "index": 0 } ] ] }, "Convert Summary to CSV": { "main": [ [ { "node": "Append Daily Summary", "type": "main", "index": 0 } ] ] }, "Append Daily Summary": { "main": [ [] ] } }, "active": false, "settings": { "executionOrder": "v1" } }