{ "name": "Retail Investor Anxiety Index", "nodes": [ { "parameters": { "rule": { "interval": [ { "field": "hours", "hoursInterval": 6 } ] } }, "id": "a4a3b16b-8fc6-4e54-bdba-d25442570e64", "name": "Schedule Trigger", "type": "n8n-nodes-base.scheduleTrigger", "typeVersion": 1.2, "position": [ -4544, 464 ] }, { "parameters": { "path": "run-pipeline", "responseMode": "responseNode", "options": {} }, "id": "manual-run-trigger", "name": "Manual Run Trigger", "type": "n8n-nodes-base.webhook", "typeVersion": 2, "position": [ -4544, 224 ] }, { "parameters": { "url": "https://www.reddit.com/r/wallstreetbets.json?limit=25", "sendHeaders": true, "headerParameters": { "parameters": [ { "name": "User-Agent", "value": "AnxietyBot/1.0 (by /u/YOUR_REDDIT_USERNAME)" } ] }, "options": {} }, "id": "f03819f7-f736-47b3-ae60-fb992db20538", "name": "Fetch WSB", "type": "n8n-nodes-base.httpRequest", "typeVersion": 4.2, "position": [ -4304, 320 ] }, { "parameters": { "url": "https://www.reddit.com/r/investing.json?limit=25", "sendHeaders": true, "headerParameters": { "parameters": [ { "name": "User-Agent", "value": "AnxietyBot/1.0 (by /u/YOUR_REDDIT_USERNAME)" } ] }, "options": {} }, "id": "ea1668c9-e213-4b91-ad6d-73d3aec46f12", "name": "Fetch Investing", "type": "n8n-nodes-base.httpRequest", "typeVersion": 4.2, "position": [ -4304, 464 ] }, { "parameters": { "url": "https://www.reddit.com/r/stocks.json?limit=25", "sendHeaders": true, "headerParameters": { "parameters": [ { "name": "User-Agent", "value": "AnxietyBot/1.0 (by /u/YOUR_REDDIT_USERNAME)" } ] }, "options": {} }, "id": "9a98f91f-7ae8-4740-a79b-61a161121338", "name": "Fetch Stocks", "type": "n8n-nodes-base.httpRequest", "typeVersion": 4.2, "position": [ -4304, 592 ] }, { "parameters": { "numberInputs": 3 }, "id": "b93a1db2-0273-40e7-8618-54a8ff34f097", "name": "Merge Reddit Feeds", "type": "n8n-nodes-base.merge", "typeVersion": 3, "position": [ -4064, 464 ] }, { "parameters": { "jsCode": "// Node 1A: Raw Merge & Structural Clean\nconst allItems = [];\nfor (const item of $input.all()) {\n const body = item.json;\n let posts = [];\n if (body && body.data && Array.isArray(body.data.children)) {\n posts = body.data.children;\n }\n for (const child of posts) {\n const d = child.data;\n if (!d) continue;\n const title = (d.title || '').trim();\n if (!title) continue;\n allItems.push({\n id: d.id || null,\n title: title,\n subreddit: (d.subreddit || '').toLowerCase(),\n score: typeof d.score === 'number' ? d.score : null,\n numComments: typeof d.num_comments === 'number' ? d.num_comments : null,\n createdUtc: typeof d.created_utc === 'number' ? d.created_utc : null,\n upvoteRatio: typeof d.upvote_ratio === 'number' ? d.upvote_ratio : null\n });\n }\n}\nconst seen = new Set();\nconst deduped = [];\nfor (const post of allItems) {\n if (post.id && !seen.has(post.id)) {\n seen.add(post.id);\n deduped.push(post);\n } else if (!post.id) {\n deduped.push(post);\n }\n}\nreturn deduped.map(post => ({ json: post }));\n" }, "id": "3ff5e7f8-c7a3-4b76-aab5-fe632c2d1553", "name": "1A: Raw Merge & Structural Clean", "type": "n8n-nodes-base.code", "typeVersion": 2, "position": [ -3808, 464 ] }, { "parameters": { "jsCode": "// Node 1B: Linguistic & Relevance Filter\n// FIX 1: Extract both $TICKER and bare uppercase tickers\nconst COMMON_WORDS = new Set([\n 'I','A','AI','IT','US','UK','THE','AND','FOR',\n 'IPO','CEO','GDP','FED','IMF','ETF','SEC','IRA',\n 'BUT','NOT','ARE','ALL','NEW','NOW','GET','GOT'\n]);\n\nconst output = [];\nfor (const item of $input.all()) {\n const post = item.json;\n let title = post.title || '';\n let cleanTitle = title.toLowerCase();\n\n // Extract $TICKER format\n const dollarTickers = title.match(/\\$[A-Z]{1,5}\\b/g) || [];\n\n // Extract standalone uppercase tickers (2-5 chars), excluding common words\n const upperTickers = title.match(/\\b[A-Z]{2,5}\\b/g) || [];\n const filteredUpper = upperTickers.filter(t => !COMMON_WORDS.has(t));\n\n // Merge and deduplicate\n const allTickers = [\n ...dollarTickers.map(t => t.replace('$', '')),\n ...filteredUpper\n ];\n const tickers = [...new Set(allTickers)];\n\n // Clean title for NLP (strip $ tickers, URLs, emoji, special chars)\n cleanTitle = cleanTitle.replace(/\\$[a-z]{1,5}\\b/g, '').trim();\n cleanTitle = cleanTitle.replace(/https?:\\/\\/[^\\s]+/g, '').trim();\n cleanTitle = cleanTitle.replace(/[\\u{1F600}-\\u{1F64F}\\u{1F300}-\\u{1F5FF}\\u{1F680}-\\u{1F6FF}\\u{1F700}-\\u{1F77F}\\u{1F780}-\\u{1F7FF}\\u{1F800}-\\u{1F8FF}\\u{1F900}-\\u{1F9FF}\\u{1FA00}-\\u{1FA6F}\\u{1FA70}-\\u{1FAFF}\\u{2600}-\\u{26FF}\\u{2700}-\\u{27BF}\\u{FE00}-\\u{FE0F}\\u{1F1E0}-\\u{1F1FF}]/gu, '').trim();\n cleanTitle = cleanTitle.replace(/[^a-z0-9 '\\-?]/g, ' ').replace(/\\s+/g, ' ').trim();\n\n const words = cleanTitle.split(' ').filter(w => w.length > 0);\n const wordCount = words.length;\n if (wordCount < 4) continue;\n\n const isQuestion = title.trimEnd().endsWith('?');\n output.push({ json: { id: post.id, title: post.title, cleanTitle, subreddit: post.subreddit, tickers, isQuestion, wordCount, score: post.score, upvoteRatio: post.upvoteRatio } });\n}\nreturn output;\n" }, "id": "60b8e0f9-3c96-4d73-909b-b2c4c3ab99f5", "name": "1B: Linguistic & Relevance Filter", "type": "n8n-nodes-base.code", "typeVersion": 2, "position": [ -3552, 464 ] }, { "parameters": { "jsCode": "// Keyword Pre-Scorer\nconst FEAR_HIGH = [\n 'crash','collapse','margin call','panic','bankrupt',\n 'recession','going to lose','lose it all','losing everything',\n 'worst quarter','get out now','catastrophe','obliterated',\n 'devastating','rekt','wiped out','capitulate','capitulation',\n 'blood bath','bloodbath','implode','imploding'\n];\nconst FEAR_MID = [\n 'dump','sell','down','loss','scared','bubble','warn',\n 'fear','correction','pullback','volatile','volatility',\n 'selloff','sell off','bleeding','hurt','pain','worried',\n 'concerning','tariff','war','conflict','crisis','inflation',\n 'layoffs','overvalued','bearish','bear','puts','short',\n 'hedge','defensive','caution','risk','downside','drop',\n 'fell','fallen','sinking','tanking','tank','worst'\n];\nconst GREED_HIGH = [\n 'moon','all in','yolo','lambo','tendies',\n 'to the moon','diamond hands','sending it',\n 'short squeeze','squeeze','ape'\n];\nconst GREED_MID = [\n 'buy','bull','pump','gain','profit','green','up',\n 'rally','recover','bounce','dip','buy the dip',\n 'bullish','calls','upside','growth','opportunity',\n 'ripping','mooning','printing','loaded'\n];\nconst AMPLIFIERS = [\n 'huge','massive','insane','breaking','historic',\n 'unprecedented','extreme','critical','urgent',\n 'serious','major','significant'\n];\nfunction countMatches(text, phrases) {\n let count = 0;\n for (const phrase of phrases) {\n const escaped = phrase.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\$&');\n const pattern = phrase.includes(' ') ? new RegExp(escaped,'g') : new RegExp('\\\\b'+escaped+'\\\\b','g');\n const hits = text.match(pattern);\n if (hits) count += hits.length;\n }\n return count;\n}\nconst output = [];\nfor (const item of $input.all()) {\n const post = item.json;\n const text = post.cleanTitle || '';\n const fearHighCount = countMatches(text, FEAR_HIGH);\n const fearMidCount = countMatches(text, FEAR_MID);\n const greedHighCount = countMatches(text, GREED_HIGH);\n const greedMidCount = countMatches(text, GREED_MID);\n const ampCount = countMatches(text, AMPLIFIERS);\n const fearPoints = (fearHighCount * 20) + (fearMidCount * 10);\n const greedPoints = (greedHighCount * 20) + (greedMidCount * 10);\n let rawScore = 50 + fearPoints - greedPoints;\n if (ampCount > 0) {\n const deviation = rawScore - 50;\n rawScore = 50 + deviation * Math.pow(1.2, ampCount);\n }\n const keywordScore = Math.round(Math.min(100, Math.max(0, rawScore)));\n const totalSignal = fearHighCount + fearMidCount + greedHighCount + greedMidCount;\n const keywordConfidence = totalSignal === 0 ? 'low' : totalSignal <= 2 ? 'medium' : 'high';\n const zone = keywordScore >= 61 ? 'CLEAR_FEAR' : keywordScore >= 40 ? 'AMBIGUOUS' : 'CLEAR_GREED';\n output.push({ json: { ...post, keywordScore, keywordConfidence, zone } });\n}\nreturn output;\n" }, "id": "8a3ea511-b0c5-4edb-a325-9923670735ab", "name": "Keyword Pre-Scorer", "type": "n8n-nodes-base.code", "typeVersion": 2, "position": [ -3296, 464 ] }, { "parameters": { "jsCode": "// Aggregate for Claude\n// Uses workflow static data instead of fs module\nconst posts = $input.all().map(item => item.json);\n\n// Read previousTopics from static data (no fs required)\nconst staticData = $getWorkflowStaticData('global');\nconst log = staticData.anxietyLog || [];\nconst previousTopics = log.length > 0\n ? (log[log.length - 1].narrativeVelocity || [])\n : [];\n\nconst tickersBySubreddit = {};\nfor (const post of posts) {\n const sub = post.subreddit || 'unknown';\n if (!tickersBySubreddit[sub]) tickersBySubreddit[sub] = {};\n for (const ticker of (post.tickers || [])) {\n tickersBySubreddit[sub][ticker] = (tickersBySubreddit[sub][ticker] || 0) + 1;\n }\n}\n\nreturn [{\n json: {\n allPosts: posts,\n previousTopics,\n tickersBySubreddit\n }\n}];\n" }, "id": "8c1431df-9b33-44ea-83a4-e6099add1ee2", "name": "Aggregate for Claude", "type": "n8n-nodes-base.code", "typeVersion": 2, "position": [ -3040, 464 ] }, { "parameters": { "promptType": "define", "text": "=You are a financial sentiment analyzer scoring Reddit posts from r/wallstreetbets, r/investing, r/stocks.\n\nScore each post title for investor fear/anxiety 0-100:\n 0-20 = strong greed/euphoria (YOLO, moon, massive gains)\n 21-40 = mild optimism (buy the dip, bullish, opportunity)\n 41-60 = neutral or genuine question with no emotional charge\n 61-80 = mild fear (worried, concerned, volatile, correction)\n 81-100 = strong panic (losing everything, crash, worst ever, rekt)\n\nCRITICAL RULES:\n- Be decisive. Do NOT cluster scores around 50.\n- A post about large losses must score 75+\n- A post about market crashes or worst quarters must score 70+\n- A post expressing strong bullish conviction must score 25 or below\n- A neutral factual question with no fear or greed scores 45-55\n- Posts mentioning war, geopolitical risk, or economic crisis score 65+\n- Use the full 0-100 range across the batch of posts\n\nUse the exact id provided. Do not renumber.\nReturn ONLY a JSON array: [{ \"id\": , \"score\": }]\n\nPosts: {{ JSON.stringify($json.allPosts.map(p => ({ id: p.id, title: p.cleanTitle }))) }}", "messages": { "messageValues": [ { "message": "You are a financial sentiment analyzer. Always respond with valid JSON only. No markdown, no explanation." } ] } }, "id": "3d612907-b07c-47f2-9ccc-723dd7cbd410", "name": "Claude 1: Anxiety Score", "type": "@n8n/n8n-nodes-langchain.chainLlm", "typeVersion": 1.4, "position": [ -2784, 224 ] }, { "parameters": { "promptType": "define", "text": "=Extract the top 10 emerging topics or themes from these Reddit post titles.\nCompare their frequency to the previous run topics provided.\nIf previousTopics is empty, this is the first run — set previousCount to 0 for all topics and set velocityScore based on how dominant each topic is in the current titles (high mention count = higher velocity, scale 0-100).\nReturn ONLY a JSON array:\n[{ \"topic\": , \"currentCount\": , \"previousCount\": , \"velocityScore\": }]\n\nCurrent titles: {{ JSON.stringify($json.allPosts.map(p => p.cleanTitle)) }}\nPrevious topics: {{ JSON.stringify($json.previousTopics || []) }}", "messages": { "messageValues": [ { "message": "You are a trend analyst for financial social media. Always respond with valid JSON only. No markdown, no explanation." } ] } }, "id": "85fcbbab-71a8-46b2-8961-72c350ec176f", "name": "Claude 2: Narrative Velocity", "type": "@n8n/n8n-nodes-langchain.chainLlm", "typeVersion": 1.4, "position": [ -2784, 512 ] }, { "parameters": { "promptType": "define", "text": "=You are a market pattern detector. Analyze these Reddit post titles from 3 subreddits and identify stocks, companies, or themes discussed across multiple communities.\n\nLook beyond $ tickers — find company names, ETFs, indices, macro themes discussed in multiple subreddits.\n\nExamples to look for: Nvidia/NVDA, Apple/AAPL, Microsoft/MSFT, Tesla/TSLA, SPY, QQQ, META, oil, gold, Fed, AI stocks, tech stocks, China stocks, defense stocks etc.\n\nSet isHerd = true if topic appears in 2 or more subreddits.\nAlways return at least 3-5 results even if isHerd is false for some — show the top cross-subreddit themes regardless.\n\nReturn ONLY JSON array:\n[{ \n \"ticker\": , \n \"mentions\": { \"wallstreetbets\": , \"investing\": , \"stocks\": }, \n \"isHerd\": \n}]\nsorted by total mentions descending.\n\nwallstreetbets posts: {{ JSON.stringify($json.allPosts.filter(p => p.subreddit === 'wallstreetbets').map(p => p.cleanTitle)) }}\ninvesting posts: {{ JSON.stringify($json.allPosts.filter(p => p.subreddit === 'investing').map(p => p.cleanTitle)) }}\nstocks posts: {{ JSON.stringify($json.allPosts.filter(p => p.subreddit === 'stocks').map(p => p.cleanTitle)) }}", "messages": { "messageValues": [ { "message": "You are a market pattern detector analyzing retail investor behavior. Always respond with valid JSON only. No markdown, no explanation." } ] } }, "id": "4a5aedec-53a2-44a0-a0d2-30ce9b63d2e4", "name": "Claude 3: Herd Detection", "type": "@n8n/n8n-nodes-langchain.chainLlm", "typeVersion": 1.4, "position": [ -2784, 816 ] }, { "parameters": { "promptType": "define", "text": "=Score assertive vs uncertain language in these Reddit post titles.\nAssertive: bold claims, definitive statements, strong calls to action.\nUncertain: questions, hedging, maybe/should I/not sure language.\nReturn ONLY JSON:\n{\n \"convictionScore\": ,\n \"uncertaintyScore\": ,\n \"ratio\": ,\n \"dominantPostTitles\": []\n}\nScores must sum to 100.\n\nTitles with question flags: {{ JSON.stringify($json.allPosts.map(p => ({ title: p.cleanTitle, isQuestion: p.isQuestion }))) }}", "messages": { "messageValues": [ { "message": "You are a linguistic analyst specializing in investor sentiment. Always respond with valid JSON only. No markdown, no explanation." } ] } }, "id": "b29d133a-892d-4c7b-908e-84a57efbd86a", "name": "Claude 4: Conviction vs Uncertainty", "type": "@n8n/n8n-nodes-langchain.chainLlm", "typeVersion": 1.4, "position": [ -2784, 1120 ] }, { "parameters": { "promptType": "define", "text": "=Classify the current retail investor crowd narrative into exactly one stage:\nEuphoria, Complacency, Anxiety, Denial, Panic, or Hope.\n\nStage definitions:\n- Euphoria: extreme optimism, everyone is winning, FOMO language\n- Complacency: calm, passive, low urgency\n- Anxiety: worried but not panicking, lots of questions and hedging\n- Denial: ignoring bad signs, rationalizing losses\n- Panic: extreme fear, selling everything, catastrophic language\n- Hope: cautious optimism after a down period, early recovery language\n\nReturn ONLY JSON:\n{\n \"stage\": ,\n \"confidence\": ,\n \"reasoning\": \n}\n\nTitles: {{ JSON.stringify($json.allPosts.map(p => p.cleanTitle)) }}", "messages": { "messageValues": [ { "message": "You are a behavioral finance expert analyzing crowd psychology. Always respond with valid JSON only. No markdown, no explanation." } ] } }, "id": "c9b9a110-d401-4177-89ae-4bc849f29fc6", "name": "Claude 5: Crowd Cycle Stage", "type": "@n8n/n8n-nodes-langchain.chainLlm", "typeVersion": 1.4, "position": [ -2784, 1424 ] }, { "parameters": { "model": { "__rl": true, "value": "REPLACE_WITH_RESOURCE", "mode": "list" }, "options": { "maxTokensToSample": 1000 } }, "id": "94f01430-aca7-48da-8f1f-5874accf622e", "name": "Anthropic Model 1", "type": "@n8n/n8n-nodes-langchain.lmChatAnthropic", "typeVersion": 1.3, "position": [ -2784, 64 ] }, { "parameters": { "model": { "__rl": true, "value": "REPLACE_WITH_RESOURCE", "mode": "list" }, "options": { "maxTokensToSample": 1000 } }, "id": "b083823c-f04a-404d-9ad5-ebd4f6fb948e", "name": "Anthropic Model 2", "type": "@n8n/n8n-nodes-langchain.lmChatAnthropic", "typeVersion": 1.3, "position": [ -2784, 352 ] }, { "parameters": { "model": { "__rl": true, "value": "REPLACE_WITH_RESOURCE", "mode": "list" }, "options": { "maxTokensToSample": 1000 } }, "id": "ee2f7ede-b7f9-4dd8-a1e5-bd7d2639fbb8", "name": "Anthropic Model 3", "type": "@n8n/n8n-nodes-langchain.lmChatAnthropic", "typeVersion": 1.3, "position": [ -2784, 656 ] }, { "parameters": { "model": { "__rl": true, "value": "REPLACE_WITH_RESOURCE", "mode": "list" }, "options": { "maxTokensToSample": 1000 } }, "id": "86a33294-6d27-4be6-9de0-e79fe3eeaec0", "name": "Anthropic Model 4", "type": "@n8n/n8n-nodes-langchain.lmChatAnthropic", "typeVersion": 1.3, "position": [ -2784, 960 ] }, { "parameters": { "model": { "__rl": true, "value": "REPLACE_WITH_RESOURCE", "mode": "list" }, "options": { "maxTokensToSample": 1000 } }, "id": "f5072312-5c01-4e04-943f-ea804d4f36e3", "name": "Anthropic Model 5", "type": "@n8n/n8n-nodes-langchain.lmChatAnthropic", "typeVersion": 1.3, "position": [ -2784, 1264 ] }, { "parameters": { "numberInputs": 5 }, "id": "f99efd49-f1f8-4122-b0ed-76b429901d93", "name": "Merge Claude Outputs", "type": "n8n-nodes-base.merge", "typeVersion": 3, "position": [ -2336, 832 ] }, { "parameters": { "jsCode": "// Signal Aggregator\nfunction parseLlmOutput(text) {\n if (!text) return null;\n try {\n const clean = (text + '')\n .replace(/^```[\\w]*\\n?/m, '')\n .replace(/\\n?```$/m, '')\n .trim();\n return JSON.parse(clean);\n } catch (e) {\n return null;\n }\n}\n\nconst items = $input.all();\n\nlet anxietyScores = null;\nlet narrativeVelocity = null;\nlet herdDetection = null;\nlet conviction = null;\nlet cycleStage = null;\n\nfor (const item of items) {\n const parsed = parseLlmOutput(item.json.text);\n if (parsed === null) continue;\n\n if (Array.isArray(parsed) && parsed.length > 0) {\n const first = parsed[0];\n if ('score' in first && 'id' in first) {\n anxietyScores = parsed;\n } else if ('velocityScore' in first || 'currentCount' in first) {\n narrativeVelocity = parsed;\n } else if ('ticker' in first || 'isHerd' in first) {\n herdDetection = parsed;\n }\n } else if (parsed && typeof parsed === 'object' && !Array.isArray(parsed)) {\n if ('convictionScore' in parsed || 'uncertaintyScore' in parsed) {\n conviction = parsed;\n } else if ('stage' in parsed || 'confidence' in parsed) {\n cycleStage = parsed;\n }\n }\n}\n\nanxietyScores = anxietyScores || [];\nnarrativeVelocity = narrativeVelocity || [];\nherdDetection = herdDetection || [];\nconviction = conviction || {};\ncycleStage = cycleStage || {};\n\nconst aggregateData = $('Aggregate for Claude').first().json;\nconst allPosts = (aggregateData.allPosts || []).map(p => ({ ...p }));\n\nconst scoreMap = {};\nfor (const item of anxietyScores) {\n if (item.id != null) scoreMap[String(item.id)] = Number(item.score);\n}\n\nconst scoredPosts = allPosts\n .map(post => {\n const claudeScore = scoreMap[String(post.id)];\n const finalScore = claudeScore !== undefined ? claudeScore : post.keywordScore;\n return { ...post, finalScore };\n })\n .filter(p => typeof p.finalScore === 'number');\n\nconst averageAnxietyScore = scoredPosts.length\n ? Math.round(scoredPosts.reduce((s, p) => s + p.finalScore, 0) / scoredPosts.length)\n : 50;\n\nconst avg = arr =>\n arr.length ? Math.round(arr.reduce((s, v) => s + v, 0) / arr.length) : 50;\n\nconst subBuckets = { wallstreetbets: [], investing: [], stocks: [] };\nfor (const post of scoredPosts) {\n if (subBuckets[post.subreddit]) subBuckets[post.subreddit].push(post.finalScore);\n}\nconst subredditScores = {\n wallstreetbets: avg(subBuckets.wallstreetbets),\n investing: avg(subBuckets.investing),\n stocks: avg(subBuckets.stocks),\n};\n\nconst top3FearfulPosts = [...scoredPosts]\n .sort((a, b) => b.finalScore - a.finalScore)\n .slice(0, 3)\n .map(p => ({ title: p.cleanTitle, score: p.finalScore }));\n\nreturn [{\n json: {\n anxietyScores,\n narrativeVelocity,\n herdDetection,\n conviction,\n cycleStage,\n averageAnxietyScore,\n anxietyScore: averageAnxietyScore,\n subredditScores,\n top3FearfulPosts,\n timestamp: new Date().toISOString(),\n date: new Date().toISOString().split('T')[0],\n postCount: allPosts.length,\n }\n}];\n" }, "id": "8d5c740c-cecf-49b7-ab27-aa3ccb2e7bcd", "name": "Signal Aggregator", "type": "n8n-nodes-base.code", "typeVersion": 2, "position": [ -2128, 528 ] }, { "parameters": { "jsCode": "// Persist to File — uses workflow static data instead of fs module\nconst staticData = $getWorkflowStaticData('global');\nconst data = $input.first().json;\nlet log = staticData.anxietyLog || [];\n\nconst entry = {\n date: data.date,\n timestamp: data.timestamp,\n postCount: data.postCount,\n anxietyScore: data.anxietyScore,\n subredditScores: data.subredditScores,\n top3FearfulPosts: data.top3FearfulPosts,\n narrativeVelocity: data.narrativeVelocity,\n herdDetection: data.herdDetection,\n conviction: data.conviction,\n cycleStage: data.cycleStage\n};\n\nlog.push(entry);\n\nconst cutoff = new Date();\ncutoff.setDate(cutoff.getDate() - 7);\nlog = log.filter(e => new Date(e.timestamp) >= cutoff);\n\nstaticData.anxietyLog = log;\n\nreturn [{ json: { ...data, log } }];\n" }, "id": "6679100f-3e06-4d76-9253-f567f50239fa", "name": "Persist to File", "type": "n8n-nodes-base.code", "typeVersion": 2, "position": [ -1904, 528 ] }, { "parameters": { "jsCode": "// Generate Quickchart URLs\nconst BASE = 'https://quickchart.io/chart?c=';\nconst data = $input.first().json;\nconst log = data.log || [];\nconst last7 = log.slice(-7);\nconst chart1 = { type: 'line', data: { labels: last7.map(e => e.date), datasets: [{ label: 'Anxiety Score', data: last7.map(e => e.anxietyScore), borderColor: '#E24B4A', backgroundColor: 'rgba(226,75,74,0.2)', fill: true, tension: 0.3 }] }, options: { scales: { y: { min: 0, max: 100 } }, plugins: { legend: { display: true } } } };\nconst anxietyChartUrl = BASE + encodeURIComponent(JSON.stringify(chart1));\nconst velocityItems = (Array.isArray(data.narrativeVelocity) ? data.narrativeVelocity : []).slice(0, 10);\nconst chart2 = { type: 'horizontalBar', data: { labels: velocityItems.map(t => t.topic), datasets: [{ label: 'Velocity Score', data: velocityItems.map(t => t.velocityScore), backgroundColor: '#EF9F27' }] }, options: { scales: { x: { min: 0, max: 100 } } } };\nconst velocityChartUrl = BASE + encodeURIComponent(JSON.stringify(chart2));\nconst herdTickers = (Array.isArray(data.herdDetection) ? data.herdDetection : []).filter(t => t.isHerd);\nconst chart3 = { type: 'bar', data: { labels: herdTickers.map(t => t.ticker), datasets: [ { label: 'wallstreetbets', data: herdTickers.map(t => (t.mentions||{}).wallstreetbets||0), backgroundColor: '#E24B4A' }, { label: 'investing', data: herdTickers.map(t => (t.mentions||{}).investing||0), backgroundColor: '#378ADD' }, { label: 'stocks', data: herdTickers.map(t => (t.mentions||{}).stocks||0), backgroundColor: '#639922' } ] } };\nconst herdChartUrl = BASE + encodeURIComponent(JSON.stringify(chart3));\nconst conv = data.conviction || { convictionScore: 50, uncertaintyScore: 50 };\nconst chart4 = { type: 'doughnut', data: { labels: ['Conviction','Uncertainty'], datasets: [{ data: [conv.convictionScore||0, conv.uncertaintyScore||0], backgroundColor: ['#378ADD','#B4B2A9'] }] } };\nconst convictionChartUrl = BASE + encodeURIComponent(JSON.stringify(chart4));\nreturn [{ json: { ...data, anxietyChartUrl, velocityChartUrl, herdChartUrl, convictionChartUrl } }];\n" }, "id": "58736250-ec2c-4ec3-8c57-c28783cb7092", "name": "Generate Quickchart URLs", "type": "n8n-nodes-base.code", "typeVersion": 2, "position": [ -1680, 528 ] }, { "parameters": { "jsCode": "// Write output.json — stores in static data instead of filesystem\nconst staticData = $getWorkflowStaticData('global');\nconst data = $input.first().json;\nconst cs = data.cycleStage || {};\nconst conv = data.conviction || {};\nconst herd = Array.isArray(data.herdDetection) ? data.herdDetection : [];\n\nconst output = {\n latestDate: data.date,\n postCount: data.postCount,\n anxietyScore: data.anxietyScore,\n subredditScores: data.subredditScores,\n top3FearfulPosts: data.top3FearfulPosts,\n narrativeVelocity: Array.isArray(data.narrativeVelocity) ? data.narrativeVelocity : [],\n herdDetection: herd,\n anxietyChartUrl: data.anxietyChartUrl,\n velocityChartUrl: data.velocityChartUrl,\n herdChartUrl: data.herdChartUrl,\n convictionChartUrl: data.convictionChartUrl,\n cycleStage: cs.stage || null,\n cycleConfidence: cs.confidence || null,\n cycleReasoning: cs.reasoning || null,\n herdTickers: herd.filter(t => t.isHerd).map(t => t.ticker),\n convictionScore: conv.convictionScore ?? null,\n uncertaintyScore: conv.uncertaintyScore ?? null,\n convictionRatio: conv.ratio ?? null,\n dominantPostTitles: conv.dominantPostTitles || []\n};\n\nstaticData.latestOutput = output;\n\nreturn [{ json: output }];\n" }, "id": "c1c41868-39eb-425a-b71a-2af4365e15ab", "name": "Write output.json", "type": "n8n-nodes-base.code", "typeVersion": 2, "position": [ -1424, 528 ] }, { "parameters": { "method": "POST", "url": "YOUR_SUPABASE_URL/rest/v1/anxiety_runs", "sendHeaders": true, "headerParameters": { "parameters": [ { "name": "apikey", "value": "REPLACE_WITH_APIKEY" }, { "name": "Authorization", "value": "REPLACE_WITH_AUTHORIZATION" }, { "name": "Content-Type", "value": "application/json" }, { "name": "Prefer", "value": "return=minimal" } ] }, "sendBody": true, "bodyParameters": { "parameters": [ { "name": "date", "value": "={{ $json.latestDate }}" }, { "name": "timestamp", "value": "={{ $json.latestDate }}" }, { "name": "post_count", "value": "={{ $json.postCount }}" }, { "name": "anxiety_score", "value": "={{ $json.anxietyScore }}" }, { "name": "subreddit_scores", "value": "={{ JSON.stringify($json.subredditScores) }}" }, { "name": "top3_fearful_posts", "value": "={{ JSON.stringify($json.top3FearfulPosts) }}" }, { "name": "narrative_velocity", "value": "={{ JSON.stringify($json.narrativeVelocity) }}" }, { "name": "herd_detection", "value": "={{ JSON.stringify($json.herdDetection) }}" }, { "name": "conviction", "value": "={{ JSON.stringify({ convictionScore: $json.convictionScore, uncertaintyScore: $json.uncertaintyScore, ratio: $json.convictionRatio, dominantPostTitles: $json.dominantPostTitles }) }}" }, { "name": "cycle_stage", "value": "={{ JSON.stringify({ stage: $json.cycleStage, confidence: $json.cycleConfidence, reasoning: $json.cycleReasoning }) }}" }, { "name": "anxiety_chart_url", "value": "={{ $json.anxietyChartUrl }}" }, { "name": "velocity_chart_url", "value": "={{ $json.velocityChartUrl }}" }, { "name": "herd_chart_url", "value": "={{ $json.herdChartUrl }}" }, { "name": "conviction_chart_url", "value": "={{ $json.convictionChartUrl }}" }, { "name": "herd_tickers", "value": "={{ JSON.stringify($json.herdTickers) }}" }, { "name": "conviction_score", "value": "={{ $json.convictionScore }}" }, { "name": "uncertainty_score", "value": "={{ $json.uncertaintyScore }}" }, { "name": "conviction_ratio", "value": "={{ $json.convictionRatio }}" }, { "name": "dominant_post_titles", "value": "={{ JSON.stringify($json.dominantPostTitles) }}" } ] }, "options": {} }, "id": "b7a6850c-7b62-4c64-b952-1fb1815bac37", "name": "Insert to Supabase", "type": "n8n-nodes-base.httpRequest", "typeVersion": 4.2, "position": [ -1152, 528 ] }, { "parameters": { "respondWith": "json", "responseBody": "={{ JSON.stringify({ status: 'success', message: 'Pipeline completed', anxietyScore: $('Write output.json').first().json.anxietyScore, date: $('Write output.json').first().json.latestDate, postCount: $('Write output.json').first().json.postCount }) }}", "options": {} }, "id": "pipeline-response", "name": "Pipeline Response", "type": "n8n-nodes-base.respondToWebhook", "typeVersion": 1, "position": [ -896, 528 ] }, { "id": "dashboard-webhook", "name": "Dashboard Webhook", "type": "n8n-nodes-base.webhook", "typeVersion": 2, "position": [ -4544, -200 ], "parameters": { "path": "dashboard", "responseMode": "responseNode", "options": {} } }, { "id": "build-dashboard-response", "name": "Build Dashboard Response", "type": "n8n-nodes-base.code", "typeVersion": 2, "position": [ -4304, -200 ], "parameters": { "jsCode": "const html = `\n\n\n \n \n Retail Investor Anxiety Index\n \n \n\n\n\n \n
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Anxiety Index

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\n\n \n\n\n`\nreturn [{ json: { html } }]" } }, { "id": "serve-dashboard-html", "name": "Serve Dashboard HTML", "type": "n8n-nodes-base.respondToWebhook", "typeVersion": 1, "position": [ -4064, -200 ], "parameters": { "respondWith": "text", "responseBody": "={{ $json.html }}", "options": {} } }, { "id": "92c63f63-64a6-50b0-9d6a-d2b03f80b927", "name": "Publication · Overview and setup", "type": "n8n-nodes-base.stickyNote", "typeVersion": 1, "position": [ -5344, -200 ], "parameters": { "content": "# Retail Investor Anxiety Index\n\n## Purpose and structure\nThis folder contains the **Retail Investor Anxiety Index** workflow. Its executable nodes use `chainLlm`, `code`, `httpRequest`, `lmChatAnthropic`, `merge`, `respondToWebhook`, `scheduleTrigger`, `webhook`. 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\nSchedule Trigger, Manual Run Trigger, Dashboard Webhook\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": { "Schedule Trigger": { "main": [ [ { "node": "Fetch WSB", "type": "main", "index": 0 }, { "node": "Fetch Investing", "type": "main", "index": 0 }, { "node": "Fetch Stocks", "type": "main", "index": 0 } ] ] }, "Manual Run Trigger": { "main": [ [ { "node": "Fetch WSB", "type": "main", "index": 0 }, { "node": "Fetch Investing", "type": "main", "index": 0 }, { "node": "Fetch Stocks", "type": "main", "index": 0 } ] ] }, "Fetch WSB": { "main": [ [ { "node": "Merge Reddit Feeds", "type": "main", "index": 0 } ] ] }, "Fetch Investing": { "main": [ [ { "node": "Merge Reddit Feeds", "type": "main", "index": 1 } ] ] }, "Fetch Stocks": { "main": [ [ { "node": "Merge Reddit Feeds", "type": "main", "index": 2 } ] ] }, "Merge Reddit Feeds": { "main": [ [ { "node": "1A: Raw Merge & Structural Clean", "type": "main", "index": 0 } ] ] }, "1A: Raw Merge & Structural Clean": { "main": [ [ { "node": "1B: Linguistic & Relevance Filter", "type": "main", "index": 0 } ] ] }, "1B: Linguistic & Relevance Filter": { "main": [ [ { "node": "Keyword Pre-Scorer", "type": "main", "index": 0 } ] ] }, "Keyword Pre-Scorer": { "main": [ [ { "node": "Aggregate for Claude", "type": "main", "index": 0 } ] ] }, "Aggregate for Claude": { "main": [ [ { "node": "Claude 1: Anxiety Score", "type": "main", "index": 0 }, { "node": "Claude 2: Narrative Velocity", "type": "main", "index": 0 }, { "node": "Claude 3: Herd Detection", "type": "main", "index": 0 }, { "node": "Claude 4: Conviction vs Uncertainty", "type": "main", "index": 0 }, { "node": "Claude 5: Crowd Cycle Stage", "type": "main", "index": 0 } ] ] }, "Claude 1: Anxiety Score": { "main": [ [ { "node": "Merge Claude Outputs", "type": "main", "index": 0 } ] ] }, "Claude 2: Narrative Velocity": { "main": [ [ { "node": "Merge Claude Outputs", "type": "main", "index": 1 } ] ] }, "Claude 3: Herd Detection": { "main": [ [ { "node": "Merge Claude Outputs", "type": "main", "index": 2 } ] ] }, "Claude 4: Conviction vs Uncertainty": { "main": [ [ { "node": "Merge Claude Outputs", "type": "main", "index": 3 } ] ] }, "Claude 5: Crowd Cycle Stage": { "main": [ [ { "node": "Merge Claude Outputs", "type": "main", "index": 4 } ] ] }, "Merge Claude Outputs": { "main": [ [ { "node": "Signal Aggregator", "type": "main", "index": 0 } ] ] }, "Signal Aggregator": { "main": [ [ { "node": "Persist to File", "type": "main", "index": 0 } ] ] }, "Persist to File": { "main": [ [ { "node": "Generate Quickchart URLs", "type": "main", "index": 0 } ] ] }, "Generate Quickchart URLs": { "main": [ [ { "node": "Write output.json", "type": "main", "index": 0 } ] ] }, "Write output.json": { "main": [ [ { "node": "Insert to Supabase", "type": "main", "index": 0 } ] ] }, "Insert to Supabase": { "main": [ [ { "node": "Pipeline Response", "type": "main", "index": 0 } ] ] }, "Anthropic Model 1": { "ai_languageModel": [ [ { "node": "Claude 1: Anxiety Score", "type": "ai_languageModel", "index": 0 } ] ] }, "Anthropic Model 2": { "ai_languageModel": [ [ { "node": "Claude 2: Narrative Velocity", "type": "ai_languageModel", "index": 0 } ] ] }, "Anthropic Model 3": { "ai_languageModel": [ [ { "node": "Claude 3: Herd Detection", "type": "ai_languageModel", "index": 0 } ] ] }, "Anthropic Model 4": { "ai_languageModel": [ [ { "node": "Claude 4: Conviction vs Uncertainty", "type": "ai_languageModel", "index": 0 } ] ] }, "Anthropic Model 5": { "ai_languageModel": [ [ { "node": "Claude 5: Crowd Cycle Stage", "type": "ai_languageModel", "index": 0 } ] ] }, "Dashboard Webhook": { "main": [ [ { "node": "Build Dashboard Response", "type": "main", "index": 0 } ] ] }, "Build Dashboard Response": { "main": [ [ { "node": "Serve Dashboard HTML", "type": "main", "index": 0 } ] ] } }, "active": false, "settings": { "executionOrder": "v1" } }