{ "name": "Factor Loading Report - Extended User Workflow (sanitized template)", "nodes": [ { "parameters": {}, "id": "00dc83c8-01d4-45cd-b1f9-b06d307c7d19", "name": "Manual Trigger (One-Time Report)", "type": "n8n-nodes-base.manualTrigger", "typeVersion": 1, "position": [ 1216, 608 ] }, { "parameters": { "rule": { "interval": [ { "field": "cronExpression", "expression": "0 7 * * 2-6" } ] } }, "id": "0f92c9db-89ce-4a14-aa67-930e7f1c1f5f", "name": "Schedule Trigger (Daily 07:00 NY)", "type": "n8n-nodes-base.scheduleTrigger", "typeVersion": 1.2, "position": [ 1216, 800 ] }, { "parameters": { "assignments": { "assignments": [ { "name": "runMode", "value": "once", "type": "string" } ] }, "options": {} }, "id": "0c6cad55-d9a8-4d7d-90f0-9dc1e8831abb", "name": "Set Mode = Once", "type": "n8n-nodes-base.set", "typeVersion": 3.4, "position": [ 1440, 608 ] }, { "parameters": { "assignments": { "assignments": [ { "name": "runMode", "value": "daily", "type": "string" } ] }, "options": {} }, "id": "29ef8fb6-fe5c-46a4-8c5b-c33ad143d848", "name": "Set Mode = Daily", "type": "n8n-nodes-base.set", "typeVersion": 3.4, "position": [ 1440, 800 ] }, { "parameters": { "assignments": { "assignments": [ { "name": "delivery", "value": "logs", "type": "string" }, { "name": "telegramChatId", "value": "", "type": "string" }, { "name": "emailTo", "value": "", "type": "string" }, { "name": "brevoSenderName", "value": "", "type": "string" }, { "name": "brevoSenderEmail", "value": "", "type": "string" }, { "name": "brevoApiKey", "value": "", "type": "string" }, { "name": "portfolioSource", "value": "manual", "type": "string" }, { "name": "manualPortfolio", "value": "[{\"ticker\":\"SPY\",\"shares\":10},{\"ticker\":\"PLTR\",\"shares\":5}]", "type": "string" }, { "name": "manualCash", "value": "100", "type": "string" }, { "name": "subscriptionStartDate", "value": "", "type": "string" }, { "name": "limeAuthType", "value": "credentials", "type": "string" }, { "name": "limeToken", "value": "", "type": "string" }, { "name": "limeClientId", "value": "", "type": "string" }, { "name": "limeClientSecret", "value": "", "type": "string" }, { "name": "limeUsername", "value": "", "type": "string" }, { "name": "limePassword", "value": "", "type": "string" }, { "name": "limeAccountNumber", "value": "", "type": "string" }, { "name": "limeIncludeCash", "value": "false", "type": "string" } ] }, "includeOtherFields": true, "options": {} }, "id": "1eebbf3e-cd57-4e00-9237-e3ef0ac03f03", "name": "Configuration", "type": "n8n-nodes-base.set", "typeVersion": 3.4, "position": [ 1664, 704 ] }, { "parameters": { "language": "pythonNative", "pythonCode": "# Validate the configuration captured by Configuration or Normalize Webhook Input.\nimport json\n\nitem = _items[0][\"json\"]\nerrors = []\n\nrun_mode = (item.get(\"runMode\") or \"once\").strip().lower()\nif run_mode not in (\"once\", \"daily\"):\n errors.append(\"runMode must be 'once' or 'daily'.\")\n\ndelivery = (item.get(\"delivery\") or \"\").strip().lower()\nif delivery not in (\"telegram\", \"email\", \"logs\", \"webhook\"):\n errors.append(\"delivery must be 'telegram', 'email', 'logs' or 'webhook'.\")\n\nif delivery == \"telegram\" and not str(item.get(\"telegramChatId\") or \"\").strip():\n errors.append(\"telegramChatId is required for Telegram delivery.\")\n\nif delivery == \"email\":\n email_to = (item.get(\"emailTo\") or \"\").strip()\n email_from = (item.get(\"brevoSenderEmail\") or \"\").strip()\n brevo_key = (item.get(\"brevoApiKey\") or \"\").strip()\n if \"@\" not in email_to:\n errors.append(\"emailTo is required for Email delivery.\")\n if \"@\" not in email_from:\n errors.append(\"brevoSenderEmail is required for Email delivery.\")\n if not brevo_key:\n errors.append(\"brevoApiKey is required for Email delivery.\")\n\nportfolio_source = (item.get(\"portfolioSource\") or \"\").strip().lower()\nif portfolio_source not in (\"manual\", \"lime\", \"csv\"):\n errors.append(\"portfolioSource must be 'manual', 'lime' or 'csv'.\")\n\nif portfolio_source == \"manual\":\n raw_portfolio = item.get(\"manualPortfolio\") or \"[]\"\n try:\n parsed = json.loads(raw_portfolio) if isinstance(raw_portfolio, str) else raw_portfolio\n if not isinstance(parsed, list) or not parsed:\n errors.append(\"manualPortfolio must be a non-empty JSON array of {ticker, shares}.\")\n else:\n for entry in parsed:\n if \"ticker\" not in entry or \"shares\" not in entry:\n errors.append(\"Each manualPortfolio entry needs 'ticker' and 'shares'.\")\n break\n except Exception as exc:\n errors.append(f\"manualPortfolio is not valid JSON: {exc}\")\n\nif portfolio_source == \"csv\":\n portfolio_csv = (item.get(\"portfolioCsv\") or \"\").strip()\n if not portfolio_csv:\n errors.append(\"portfolioCsv is required when portfolioSource is 'csv'.\")\n\nif portfolio_source == \"lime\":\n auth_type = (item.get(\"limeAuthType\") or \"\").strip().lower()\n if auth_type not in (\"bearer\", \"jwt\", \"credentials\"):\n errors.append(\"limeAuthType must be 'bearer', 'jwt' or 'credentials'.\")\n if auth_type == \"bearer\" and run_mode == \"daily\":\n errors.append(\n \"Bearer tokens expire every night, so they cannot drive a daily \"\n \"subscription. Use 'jwt' (long-lived) or 'credentials'.\"\n )\n if auth_type in (\"bearer\", \"jwt\") and not str(item.get(\"limeToken\") or \"\").strip():\n errors.append(\"limeToken is required when limeAuthType is 'bearer' or 'jwt'.\")\n if auth_type == \"credentials\":\n for fld in (\"limeClientId\", \"limeClientSecret\", \"limeUsername\", \"limePassword\"):\n if not str(item.get(fld) or \"\").strip():\n errors.append(f\"{fld} is required when limeAuthType is 'credentials'.\")\n if not str(item.get(\"limeAccountNumber\") or \"\").strip():\n errors.append(\"limeAccountNumber is required for Lime portfolios.\")\n\nif errors:\n return [{\n \"json\": {\n **item,\n \"configError\": True,\n \"configErrorMessage\": \"Configuration error(s): \" + \" | \".join(errors),\n }\n }]\n\nreturn [{\"json\": {**item, \"configError\": False, \"configErrorMessage\": \"\"}}]" }, "id": "de37d941-d5f5-4896-a382-66e8cd401457", "name": "Validate Configuration", "type": "n8n-nodes-base.code", "typeVersion": 2, "position": [ 1888, 560 ] }, { "parameters": { "conditions": { "options": { "caseSensitive": true, "leftValue": "", "typeValidation": "strict", "version": 3 }, "conditions": [ { "id": "17fb2324-f9b3-4962-bdcd-36cd073e1076", "leftValue": "={{ $json.configError == true }}", "rightValue": "", "operator": { "type": "boolean", "operation": "true", "singleValue": true } } ], "combinator": "and" }, "options": {} }, "id": "acb2888d-5b72-498d-8903-63366ea84f48", "name": "Config Error?", "type": "n8n-nodes-base.if", "typeVersion": 2.3, "position": [ 2112, 560 ] }, { "parameters": { "language": "pythonNative", "pythonCode": "# For \"once\" runs we always continue. For \"daily\" runs we use the official\n# NYSE schedule produced by `pandas_market_calendars`, which transparently\n# handles weekends, scheduled holidays and unscheduled closures across all\n# years. If yesterday (New York calendar) was not a trading day the\n# execution stops here (no MCP call, no report, no notification).\n\nfrom datetime import datetime, timedelta, timezone\n\nimport pandas as pd\nimport pandas_market_calendars as mcal\n\nitem = _items[0][\"json\"]\n\nif (item.get(\"runMode\") or \"once\").lower() != \"daily\":\n return [{\"json\": item}]\n\ntry:\n from zoneinfo import ZoneInfo\n ny_now = datetime.now(ZoneInfo(\"America/New_York\"))\nexcept Exception:\n # Fallback when tzdata is not bundled: approximate NY = UTC-4\n # (correct during DST, off by 1h during standard time). Since we only\n # need a calendar date and the trigger fires at 07:00 NY (well into\n # the day in NY), the small offset cannot push us across midnight.\n ny_now = datetime.now(timezone.utc) - timedelta(hours=4)\n\nyesterday = (ny_now - timedelta(days=1)).date()\n\nnyse = mcal.get_calendar(\"NYSE\")\nschedule = nyse.schedule(\n start_date=(yesterday - timedelta(days=10)).strftime(\"%Y-%m-%d\"),\n end_date=yesterday.strftime(\"%Y-%m-%d\"),\n)\ntrading_days = {d.date() for d in pd.to_datetime(schedule.index)}\n\nif yesterday not in trading_days:\n # Yesterday was not a NYSE trading day; nothing to do.\n return []\n\nreturn [{\"json\": {**item, \"tradingDayChecked\": yesterday.strftime(\"%Y-%m-%d\")}}]" }, "id": "cfeb1059-a7ab-4882-915d-a56838ad4ca0", "name": "Trading Day Check", "type": "n8n-nodes-base.code", "typeVersion": 2, "position": [ 2336, 672 ] }, { "parameters": { "language": "pythonNative", "pythonCode": "# Build the effective portfolio that will be sent to the factor calculator.\n#\n# Sources:\n# manual — manualPortfolio JSON (+ optional manualCash)\n# csv — portfolioCsv text: 2 columns (asset, position); optional header row\n# lime — Lime API positions (+ optional cash)\nimport csv\nimport io\nimport json\nimport time\nimport urllib.parse\nimport urllib.request\nfrom datetime import datetime\n\nimport pandas as pd\nimport yfinance as yf\n\nitem = _items[0][\"json\"]\nsource = (item.get(\"portfolioSource\") or \"manual\").strip().lower()\nrun_mode = (item.get(\"runMode\") or \"once\").strip().lower()\n\n\ndef http_json(url, method=\"GET\", headers=None, data=None, timeout=30):\n req = urllib.request.Request(url, method=method)\n req.add_header(\"Accept\", \"application/json\")\n for k, v in (headers or {}).items():\n req.add_header(k, v)\n if data is not None and not isinstance(data, (bytes, bytearray)):\n data = data.encode(\"utf-8\")\n with urllib.request.urlopen(req, data=data, timeout=timeout) as resp:\n return json.loads(resp.read().decode())\n\n\ndef lime_token_from_credentials(cfg):\n body = urllib.parse.urlencode({\n \"grant_type\": \"password\",\n \"client_id\": cfg[\"limeClientId\"],\n \"client_secret\": cfg[\"limeClientSecret\"],\n \"username\": cfg[\"limeUsername\"],\n \"password\": cfg[\"limePassword\"],\n })\n resp = http_json(\n \"https://auth.lime.co/connect/token\",\n method=\"POST\",\n headers={\"Content-Type\": \"application/x-www-form-urlencoded\"},\n data=body,\n )\n return resp[\"access_token\"]\n\n\ndef resolve_lime_auth_header(cfg):\n auth_type = (cfg.get(\"limeAuthType\") or \"\").strip().lower()\n if auth_type == \"credentials\":\n token = lime_token_from_credentials(cfg)\n return f\"Bearer {token}\"\n token = (cfg.get(\"limeToken\") or \"\").strip()\n if auth_type == \"bearer\":\n return f\"Bearer {token}\"\n return token\n\n\ndef lime_get_balances(auth_header):\n return http_json(\n \"https://api.lime.co/accounts\",\n headers={\"Authorization\": auth_header},\n )\n\n\ndef lime_get_positions(auth_header, account_number):\n encoded = urllib.parse.quote(account_number, safe=\"\")\n return http_json(\n f\"https://api.lime.co/accounts/{encoded}/positions\",\n headers={\"Authorization\": auth_header},\n )\n\n\ndef apply_split_adjustment(portfolio, start_date):\n if not start_date:\n return portfolio\n try:\n anchor = datetime.strptime(start_date, \"%Y-%m-%d\").date()\n except ValueError:\n return portfolio\n adjusted = []\n for entry in portfolio:\n ticker = entry[\"ticker\"]\n shares = float(entry[\"shares\"])\n ratio = 1.0\n for attempt in range(3):\n try:\n splits = yf.Ticker(ticker).splits\n if splits is not None and len(splits) > 0:\n idx = splits.index\n if getattr(idx, \"tz\", None) is not None:\n idx = idx.tz_localize(None)\n splits = pd.Series(splits.values, index=idx)\n mask = splits.index.date > anchor\n relevant = splits[mask]\n if len(relevant) > 0:\n ratio = float(relevant.prod())\n break\n except Exception:\n if attempt < 2:\n time.sleep(2)\n adjusted.append({\"ticker\": ticker, \"shares\": shares * ratio})\n return adjusted\n\n\ndef parse_portfolio_csv(text):\n text = (text or \"\").strip()\n if not text:\n raise ValueError(\"portfolioCsv is empty\")\n first_line = text.splitlines()[0]\n delimiter = \";\" if first_line.count(\";\") > first_line.count(\",\") else \",\"\n rows = []\n for row in csv.reader(io.StringIO(text), delimiter=delimiter):\n if not row or not any(str(c).strip() for c in row):\n continue\n rows.append(row)\n if not rows:\n raise ValueError(\"portfolioCsv has no data rows\")\n start = 0\n try:\n float(str(rows[0][1]).strip().replace(\",\", \".\"))\n except (ValueError, IndexError):\n start = 1\n portfolio = []\n for row in rows[start:]:\n if len(row) < 2:\n continue\n ticker = str(row[0]).strip().upper()\n if not ticker:\n continue\n shares = float(str(row[1]).strip().replace(\",\", \".\"))\n portfolio.append({\"ticker\": ticker, \"shares\": shares})\n if not portfolio:\n raise ValueError(\"portfolioCsv has no valid asset/position rows\")\n return portfolio\n\n\ndef read_cash(cfg):\n for key in (\"manualCash\", \"cash\"):\n raw = cfg.get(key)\n if raw in (None, \"\"):\n continue\n try:\n return float(raw)\n except (TypeError, ValueError):\n pass\n return 0.0\n\n\nportfolio = []\ncash = 0.0\nportfolio_source_detail = source\n\nif source == \"manual\":\n portfolio = json.loads(item.get(\"manualPortfolio\") or \"[]\")\n if not isinstance(portfolio, list) or not portfolio:\n return [{\n \"json\": {\n **item,\n \"buildError\": True,\n \"buildErrorMessage\": \"Manual portfolio is empty or invalid.\",\n }\n }]\n cash = read_cash(item)\n if run_mode == \"daily\":\n portfolio = apply_split_adjustment(\n portfolio, item.get(\"subscriptionStartDate\")\n )\n\nelif source == \"csv\":\n try:\n portfolio = parse_portfolio_csv(item.get(\"portfolioCsv\") or \"\")\n except Exception as exc:\n return [{\n \"json\": {\n **item,\n \"buildError\": True,\n \"buildErrorMessage\": f\"Invalid portfolioCsv: {exc}\",\n }\n }]\n cash = read_cash(item)\n if run_mode == \"daily\":\n portfolio = apply_split_adjustment(\n portfolio, item.get(\"subscriptionStartDate\")\n )\n\nelse: # lime\n try:\n auth_header = resolve_lime_auth_header(item)\n except Exception as exc:\n return [{\n \"json\": {\n **item,\n \"buildError\": True,\n \"buildErrorMessage\": f\"Failed to obtain Lime token: {exc}\",\n }\n }]\n\n account_number = (item.get(\"limeAccountNumber\") or \"\").strip()\n try:\n balances = lime_get_balances(auth_header)\n except Exception as exc:\n return [{\n \"json\": {\n **item,\n \"buildError\": True,\n \"buildErrorMessage\": f\"Lime /accounts call failed: {exc}\",\n }\n }]\n\n account_balance = next(\n (acc for acc in balances if acc.get(\"account_number\") == account_number),\n None,\n )\n if account_balance is None:\n return [{\n \"json\": {\n **item,\n \"buildError\": True,\n \"buildErrorMessage\": (\n f\"Account {account_number} not found in Lime balances. \"\n f\"Available: {[a.get('account_number') for a in balances]}\"\n ),\n }\n }]\n\n try:\n positions = lime_get_positions(auth_header, account_number)\n except Exception as exc:\n return [{\n \"json\": {\n **item,\n \"buildError\": True,\n \"buildErrorMessage\": f\"Lime positions call failed: {exc}\",\n }\n }]\n\n include_cash = str(item.get(\"limeIncludeCash\") or \"false\").strip().lower() in (\n \"true\", \"1\", \"yes\",\n )\n if include_cash:\n try:\n cash = float(account_balance.get(\"cash\") or 0)\n except (TypeError, ValueError):\n cash = 0.0\n else:\n cash = 0.0\n\n EXCLUDED_SECURITY_TYPES = {\"option\", \"strategy\"}\n skipped_types = []\n for pos in positions:\n sec_type = (pos.get(\"security_type\") or \"\").lower()\n if sec_type in EXCLUDED_SECURITY_TYPES:\n skipped_types.append(sec_type)\n continue\n symbol = pos.get(\"symbol\")\n qty = pos.get(\"quantity\")\n if not symbol or qty in (None, 0):\n continue\n try:\n portfolio.append({\"ticker\": str(symbol), \"shares\": float(qty)})\n except (TypeError, ValueError):\n continue\n\n if not portfolio:\n return [{\n \"json\": {\n **item,\n \"buildError\": True,\n \"buildErrorMessage\": (\n \"No equity positions returned by Lime for this account. \"\n f\"Raw positions count: {len(positions)}; \"\n f\"skipped (option/strategy): {len(skipped_types)}; \"\n f\"first raw position: {positions[0] if positions else 'none'}\"\n ),\n }\n }]\n\nreturn [{\n \"json\": {\n **item,\n \"buildError\": False,\n \"buildErrorMessage\": \"\",\n \"portfolio\": json.dumps(portfolio),\n \"cash\": cash,\n \"portfolioSourceDetail\": portfolio_source_detail,\n }\n}]" }, "id": "9006af89-16bf-4a06-985b-2e290f924131", "name": "Build Portfolio (Manual / Lime)", "type": "n8n-nodes-base.code", "typeVersion": 2, "position": [ 2560, 672 ] }, { "parameters": { "conditions": { "options": { "caseSensitive": true, "leftValue": "", "typeValidation": "strict", "version": 3 }, "conditions": [ { "id": "274f0627-465a-4a1b-adca-9aa191e6a0b6", "leftValue": "={{ $json.buildError == true }}", "rightValue": "", "operator": { "type": "boolean", "operation": "true", "singleValue": true } } ], "combinator": "and" }, "options": {} }, "id": "c85cc97e-116d-4d08-ba5c-e628b33bdf05", "name": "Build Error?", "type": "n8n-nodes-base.if", "typeVersion": 2.3, "position": [ 2784, 672 ] }, { "parameters": { "conditions": { "options": { "caseSensitive": true, "leftValue": "", "typeValidation": "strict", "version": 3 }, "conditions": [ { "id": "eda038b2-6301-4fff-a620-45093793ec7c", "leftValue": "={{ $json.calcError == true }}", "rightValue": "", "operator": { "type": "boolean", "operation": "true", "singleValue": true } } ], "combinator": "and" }, "options": {} }, "id": "042e74eb-453a-4f28-bfee-c108eae46215", "name": "Calc Error?", "type": "n8n-nodes-base.if", "typeVersion": 2.3, "position": [ 4800, 768 ] }, { "parameters": { "rules": { "values": [ { "conditions": { "options": { "caseSensitive": true, "leftValue": "", "typeValidation": "strict", "version": 2 }, "conditions": [ { "id": "ab5d0dcf-716b-4827-afdc-7a386d7f2d09", "leftValue": "={{ $json.delivery }}", "rightValue": "telegram", "operator": { "type": "string", "operation": "equals" } } ], "combinator": "and" }, "renameOutput": true, "outputKey": "telegram" }, { "conditions": { "options": { "caseSensitive": true, "leftValue": "", "typeValidation": "strict", "version": 2 }, "conditions": [ { "id": "239a2e45-56e5-47d6-a6df-f112341144ba", "leftValue": "={{ $json.delivery }}", "rightValue": "email", "operator": { "type": "string", "operation": "equals" } } ], "combinator": "and" }, "renameOutput": true, "outputKey": "email" }, { "conditions": { "options": { "caseSensitive": true, "leftValue": "", "typeValidation": "strict", "version": 2 }, "conditions": [ { "id": "7382e4e1-1a79-40c7-9e64-e868cd4460a6", "leftValue": "={{ $json.delivery }}", "rightValue": "logs", "operator": { "type": "string", "operation": "equals" } } ], "combinator": "and" }, "renameOutput": true, "outputKey": "logs" }, { "conditions": { "options": { "caseSensitive": true, "leftValue": "", "typeValidation": "strict", "version": 2 }, "conditions": [ { "id": "3e97072c-2ca2-4939-97c2-22beca7c24c0", "leftValue": "={{ $json.delivery }}", "rightValue": "webhook", "operator": { "type": "string", "operation": "equals", "name": "filter.operator.equals" } } ], "combinator": "and" }, "renameOutput": true, "outputKey": "webhook" } ] }, "options": {} }, "id": "7d54af61-35c2-41b8-9992-4528e08904d2", "name": "Route Delivery", "type": "n8n-nodes-base.switch", "typeVersion": 3.2, "position": [ 5024, 784 ] }, { "parameters": { "operation": "sendPhoto", "chatId": "", "binaryData": true, "binaryPropertyName": "table_image", "additionalFields": { "caption": "={{ $json.caption }}" } }, "id": "e730e3a6-5177-449f-96d2-5f3ae008c85c", "name": "Telegram - Send Photo", "type": "n8n-nodes-base.telegram", "typeVersion": 1.2, "position": [ 5248, 528 ] }, { "parameters": { "jsCode": "// Assemble the JSON body sent to Brevo. Keeping this in a dedicated Code\n// node makes the HTTP Request configuration trivial (single body field).\nconst item = $input.first().json;\nconst html = `\n

Factor Loading Report

\n

Report date: ${item.report_date}

\n

Portfolio: ${item.portfolio_desc}

\n

Loadings & Alpha

\n ${item.table_html}\n

\n 1-Month window: ${item.data_points_1m} trading days |\n 3-Month window: ${item.data_points_3m} trading days |\n 6-Month window: ${item.data_points_6m} trading days |\n 1-Year window: ${item.data_points_1y} trading days\n

\n ${item.insufficient_note ? `

${item.insufficient_note}

` : \"\"}\n

\n Note: Alpha is the intercept from a regression of the\n portfolio's daily excess return (total return minus the risk-free rate)\n on the Fama-French five factors (Mkt-RF, SMB, HML, RMW, CMA). It\n represents the average daily abnormal return not explained by the\n factor model.\n

\n

Report generated automatically on ${item.report_timestamp}

\n`;\n\nconst payload = {\n sender: {\n name: item.brevoSenderName || \"Factor Loading Bot\",\n email: item.brevoSenderEmail,\n },\n to: [{ email: item.emailTo }],\n subject: item.subject,\n htmlContent: html,\n attachment: [\n {\n name: item.file_name,\n content: item.table_image_base64,\n },\n ],\n};\n\nreturn [{ json: { brevoApiKey: item.brevoApiKey, brevoPayload: payload, ...item } }];\n" }, "id": "1c104c9e-99a6-4e76-b11d-7d8b9faa7bf4", "name": "Build Brevo Payload", "type": "n8n-nodes-base.code", "typeVersion": 2, "position": [ 5248, 720 ] }, { "parameters": { "method": "POST", "url": "https://api.brevo.com/v3/smtp/email", "sendHeaders": true, "headerParameters": { "parameters": [ { "name": "api-key", "value": "" }, { "name": "accept", "value": "application/json" }, { "name": "content-type", "value": "application/json" } ] }, "sendBody": true, "specifyBody": "json", "jsonBody": "={{ JSON.stringify($json.brevoPayload) }}", "options": {} }, "id": "cb7ec3b0-11af-4e5b-adcf-7263aca59b0f", "name": "Brevo - Send Email", "type": "n8n-nodes-base.httpRequest", "typeVersion": 4.2, "position": [ 5472, 720 ] }, { "parameters": { "language": "pythonNative", "pythonCode": "# \"logs\" delivery channel - the user picked this option to avoid setting\n# up a Telegram bot or a Brevo account. The report is exposed in three\n# ways so it can be inspected without leaving n8n:\n# 1. an ASCII table printed to the n8n execution log via print();\n# 2. the same fields (plus the ASCII table) returned as JSON so they\n# appear in the node's \"Output\" panel;\n# 3. the original PNG image passed through as binary - n8n previews it\n# inline in the \"Binary\" tab of the same Output panel.\nitem = _items[0][\"json\"]\nbinary_in = _items[0].get(\"binary\") or {}\n\ntable_text = item.get(\"table_text\") or \"(table text unavailable)\"\n\nlines = [\n \"============== Fama-French Factor Loading Report ==============\",\n f\"Report date : {item.get('report_date')}\",\n f\"Run mode : {item.get('runMode')}\",\n f\"Portfolio source: {item.get('portfolioSourceDetail') or item.get('portfolioSource')}\",\n f\"Portfolio : {item.get('portfolio_desc')}\",\n f\"Cash : {item.get('cash')}\",\n f\"Observations : \"\n f\"1M={item.get('data_points_1m')} | 3M={item.get('data_points_3m')} | \"\n f\"6M={item.get('data_points_6m')} | 1Y={item.get('data_points_1y')}\",\n \"\",\n \"Loadings & Alpha:\",\n table_text,\n]\nif item.get(\"insufficient_note\"):\n lines.append(\"\")\n lines.append(item[\"insufficient_note\"])\nlines.append(\"\")\nlines.append(\n \"PNG version of the same table is available in the 'Binary' tab \"\n \"of this node's output (key: table_image).\"\n)\nlines.append(\"===============================================================\")\n\nfor line in lines:\n print(line)\n\nreturn [{\n \"json\": {\n \"report_date\": item.get(\"report_date\"),\n \"runMode\": item.get(\"runMode\"),\n \"portfolio\": item.get(\"portfolio_desc\"),\n \"cash\": item.get(\"cash\"),\n \"caption\": item.get(\"caption\"),\n \"table_text\": table_text,\n \"insufficient_note\": item.get(\"insufficient_note\"),\n \"data_points_1m\": item.get(\"data_points_1m\"),\n \"data_points_3m\": item.get(\"data_points_3m\"),\n \"data_points_6m\": item.get(\"data_points_6m\"),\n \"data_points_1y\": item.get(\"data_points_1y\"),\n },\n \"binary\": binary_in,\n}]" }, "id": "58756ee9-6977-4bc1-b666-960d05a5def5", "name": "Log Report to n8n Logs", "type": "n8n-nodes-base.code", "typeVersion": 2, "position": [ 5248, 912 ] }, { "parameters": { "jsCode": "// Compose a user-friendly error message and decide where to send it.\n// The previous nodes always pass the whole item through, so we have access\n// to the original configuration (delivery, telegramChatId, ...) here.\nconst item = $input.first().json;\n\nconst reason =\n item.configErrorMessage ||\n item.buildErrorMessage ||\n item.calcErrorMessage ||\n \"Unknown error\";\n\nreturn [{\n json: {\n ...item,\n errorText: `Factor Loading Report could not be generated.\\n${reason}`,\n },\n}];\n" }, "id": "3488bf43-b928-4dfa-ad2f-7f047168856f", "name": "Format Error Message", "type": "n8n-nodes-base.code", "typeVersion": 2, "position": [ 5024, 224 ] }, { "parameters": { "rules": { "values": [ { "conditions": { "options": { "caseSensitive": true, "leftValue": "", "typeValidation": "strict", "version": 2 }, "conditions": [ { "id": "643d1316-b062-4e3b-8584-d7678f2f34c4", "leftValue": "={{ $json.delivery }}", "rightValue": "telegram", "operator": { "type": "string", "operation": "equals" } } ], "combinator": "and" }, "renameOutput": true, "outputKey": "telegram" }, { "conditions": { "options": { "caseSensitive": true, "leftValue": "", "typeValidation": "strict", "version": 2 }, "conditions": [ { "id": "1d0603ca-e682-4000-af94-c91cc78b6871", "leftValue": "={{ $json.delivery }}", "rightValue": "email", "operator": { "type": "string", "operation": "equals" } } ], "combinator": "and" }, "renameOutput": true, "outputKey": "email" }, { "conditions": { "options": { "caseSensitive": true, "leftValue": "", "typeValidation": "strict", "version": 2 }, "conditions": [ { "id": "d58b8bf7-4008-4336-816d-c536d319f955", "leftValue": "={{ $json.delivery }}", "rightValue": "logs", "operator": { "type": "string", "operation": "equals" } } ], "combinator": "and" }, "renameOutput": true, "outputKey": "logs" }, { "conditions": { "options": { "caseSensitive": true, "leftValue": "", "typeValidation": "strict", "version": 2 }, "conditions": [ { "id": "afe44d1d-5bde-45b4-a196-0c25928a0a06", "leftValue": "={{ $json.delivery }}", "rightValue": "webhook", "operator": { "type": "string", "operation": "equals", "name": "filter.operator.equals" } } ], "combinator": "and" }, "renameOutput": true, "outputKey": "webhook" } ] }, "options": {} }, "id": "0b09b218-d1da-4552-9b7c-20c340239517", "name": "Route Error Delivery", "type": "n8n-nodes-base.switch", "typeVersion": 3.2, "position": [ 5472, 192 ] }, { "parameters": { "chatId": "", "text": "={{ $json.errorText }}", "additionalFields": { "appendAttribution": false } }, "id": "e1236098-6f20-4c70-987a-95da10ee56db", "name": "Telegram - Send Error", "type": "n8n-nodes-base.telegram", "typeVersion": 1.2, "position": [ 5696, 0 ] }, { "parameters": { "method": "POST", "url": "https://api.brevo.com/v3/smtp/email", "sendHeaders": true, "headerParameters": { "parameters": [ { "name": "api-key", "value": "" }, { "name": "accept", "value": "application/json" }, { "name": "content-type", "value": "application/json" } ] }, "sendBody": true, "specifyBody": "json", "jsonBody": "={{ JSON.stringify({sender:{name: $json.brevoSenderName || 'Factor Loading Bot', email: $json.brevoSenderEmail},to:[{email: $json.emailTo}],subject:'Factor Loading Report - error',htmlContent:'
' + $json.errorText + '
'}) }}", "options": {} }, "id": "08ebcf42-7b9b-486e-9a51-bbc8f5b9ba01", "name": "Brevo - Send Error", "type": "n8n-nodes-base.httpRequest", "typeVersion": 4.2, "position": [ 5696, 192 ] }, { "parameters": { "language": "pythonNative", "pythonCode": "# \"logs\" delivery channel for failures: surface the error inside the\n# n8n execution log so the user sees it without configuring Telegram\n# or Brevo. The node also fails the execution so it shows up red.\nitem = _items[0][\"json\"]\nmsg = item.get(\"errorText\") or \"Factor Loading Report failed.\"\nprint(\"============== Fama-French Factor Loading - ERROR ==============\")\nprint(msg)\nprint(\"=================================================================\")\nraise Exception(msg)\n" }, "id": "2c7ee086-d796-4d05-8fd5-1c20436dd59c", "name": "Log Error to n8n Logs", "type": "n8n-nodes-base.code", "typeVersion": 2, "position": [ 5696, 384 ] }, { "parameters": { "content": "## Fama-French Factor Loading Report — User Workflow\n\nThis workflow generates a Fama-French five-factor loading and alpha report for\n**one portfolio** and delivers it either on demand or after every NYSE trading\nday. Everything is configured directly inside this workflow — no database is\nrequired.\n\n### 1. Pick how to run the workflow\n\n* **One-time report** — click *Execute workflow* (uses the `Manual Trigger`\n node). Any portfolio source (manual / Lime Bearer / Lime JWT / Lime\n credentials) is allowed.\n* **Daily report** — activate the workflow. The `Schedule Trigger` fires at\n `07:00 America/New_York` Tuesday-Saturday, asks `pandas_market_calendars`\n whether yesterday was actually a NYSE trading day, and only then calls the\n Fama-French MCP. Daily runs do **not** support Lime *Bearer* tokens because\n they expire every night — use *JWT* or *credentials* instead. Demo accounts\n only issue Bearer tokens, so daily reports on a demo account require the\n *credentials* option.\n\n> The trading-day check imports `pandas_market_calendars`; the n8n Python\n> Code node must have it available. If you self-host n8n, install it once\n> with `pip install pandas_market_calendars` inside the same environment\n> that runs the Code node.\n", "height": 900, "width": 720 }, "id": "02d52b47-a43e-45d3-8992-43fc7594edc0", "name": "Instructions", "type": "n8n-nodes-base.stickyNote", "typeVersion": 1, "position": [ 0, 0 ] }, { "parameters": { "content": "### Lime API quick reference\n\n* Auth (password flow):\n `POST https://auth.lime.co/connect/token`\n body: `grant_type=password&client_id=...&client_secret=...&username=...&password=...`\n* Balances: `GET https://api.lime.co/accounts`\n* Positions: `GET https://api.lime.co/accounts/{account_number}/positions`\n* Header convention used by this workflow:\n * `bearer` → `Authorization: Bearer `\n * `jwt` → `Authorization: ` (no prefix)\n * `credentials` → token fetched on demand, then `Authorization: Bearer `\n", "height": 360, "width": 716 }, "id": "2bfbecc6-795c-4710-bbd7-71cc81826957", "name": "Lime Reference", "type": "n8n-nodes-base.stickyNote", "typeVersion": 1, "position": [ 0, 2160 ] }, { "parameters": { "content": "### 2. Fill in the **Configuration** Set node\n\nOpen the node called **Configuration** and adjust the values to your case:\n\n| Field | Description |\n|-------|-------------|\n| `delivery` | `telegram`, `email` or `logs`. Use `logs` to skip Telegram / Brevo entirely (see step 5). |\n| `telegramChatId` | Numeric chat id where the report image will be posted. |\n| `emailTo` | Recipient email address (Brevo delivery). |\n| `brevoApiKey` | Brevo v3 API key. Create at https://app.brevo.com/ → SMTP & API → API Keys. |\n| `brevoSenderEmail` | Verified sender address in Brevo. |\n| `brevoSenderName` | Display name shown to the recipient. |\n| `portfolioSource` | `manual` or `lime`. |\n| `manualPortfolio` | JSON array, e.g. `[{\"ticker\":\"SPY\",\"shares\":10},{\"ticker\":\"GLD\",\"shares\":5}]`. |\n| `manualCash` | Cash amount included in the portfolio (USD). |\n| `subscriptionStartDate` | `YYYY-MM-DD`. Used only for manual + daily runs to roll forward share counts for splits that happened after this date. Leave empty to disable split adjustment. |\n| `limeAuthType` | `bearer`, `jwt` or `credentials`. |\n| `limeToken` | Bearer or JWT token (when `limeAuthType` is `bearer` or `jwt`). |\n| `limeClientId`, `limeClientSecret`, `limeUsername`, `limePassword` | Used when `limeAuthType` is `credentials`; a fresh access token is requested before every run. |\n| `limeAccountNumber` | The Lime account, format ``. |\n| `limeIncludeCash` | `true` to add Lime cash balance to the portfolio value before regression, `false` to ignore cash. |\n\n### 3. How to create your Telegram bot\n\n1. In Telegram open `@BotFather` → `/newbot` → choose a name → save the token.\n2. Start a conversation with your new bot and send any message.\n3. Visit `https://api.telegram.org/bot/getUpdates` and copy\n `result[].message.chat.id` — that's the value of `telegramChatId`.\n4. In n8n, open the **Telegram - Send Photo** node, create a credential of\n type *Telegram API* with the token from step 1, and select it.\n\n### 4. How to set up Brevo email delivery\n\n1. Sign up at https://www.brevo.com/ (free tier supports 300 emails/day).\n2. In *Senders, Domains & Dedicated IPs*, add and verify the address you put\n into `brevoSenderEmail`.\n3. In *SMTP & API → API Keys*, create a v3 API key and paste it into\n `brevoApiKey`.\n4. No further setup is needed — the **Brevo - Send Email** node uses an HTTP\n Request, so no n8n credential is required.", "height": 900, "width": 720 }, "id": "0aa79c21-2a66-4482-85f9-0077cc0b5db7", "name": "Instructions1", "type": "n8n-nodes-base.stickyNote", "typeVersion": 1, "position": [ 0, 448 ] }, { "parameters": { "content": "### 5. No Telegram / Brevo? Use `delivery = logs`\n\nSet `delivery = logs` in the **Configuration** node to skip both Telegram and\nBrevo. The **Log Report to n8n Logs** node will then:\n\n* `print()` a human-readable summary (report date, portfolio, loadings caption,\n insufficient-history note, HTML table) to the workflow's execution log; open\n any execution from the *Executions* tab and click the node to read it.\n* return the same fields as JSON output, so they are also visible in the\n node's output panel.\n\nThis is the easiest way to try the workflow end-to-end: only the n8n instance\nitself is needed, no external accounts.\n\n### 6. Lime broker integration\n\n* **Bearer token** — copy from the Lime web app. Rotates daily at 03:00 ET,\n works for one-time reports only. Demo accounts can only issue Bearer tokens.\n* **JWT token** — long-lived, works for both one-time and daily runs.\n* **Credentials** — `client_id`, `client_secret`, `username` and `password`.\n The workflow requests a fresh Bearer at `https://auth.lime.co/connect/token`\n on every run, which is the only way to run daily reports against a demo\n account.\n* `limeIncludeCash = true` lets the regression treat your cash balance as a\n zero-return asset, which damps loadings proportionally to cash weight. Set\n it to `false` to compute loadings on the equity portion only.\n\n### 7. Schedule / time zone\n\nThe workflow `settings.timezone` is already set to `America/New_York`, so the\ncron expression `0 7 * * 2-6` fires at 07:00 New York time on Tuesday through\nSaturday regardless of the n8n instance default. Open *Settings* and confirm\nthe value if you ever clone the workflow.\n\n### 8. MCP credentials\n\nThe three MCP nodes (`MCP - Get FF Factors1`, `MCP - Get Factor Result`,\n`MCP - Get Loadings & Alpha`) call the public Fama-French MCP endpoint at\n``.\nIf your n8n instance has the [n8n-nodes-mcp](https://www.npmjs.com/package/n8n-nodes-mcp)\ncommunity node installed, open each MCP node once and either link an existing\n*MCP Client (HTTP Streamable)* credential or create a new one (the endpoint\ndoes not require authentication, an empty credential is enough).\n\n### 9. What gets sent\n\nFor both Telegram and email the report contains a PNG of the loadings &\nalpha table (1M / 3M / 6M / 1Y windows for `Mkt-RF`, `SMB`, `HML`, `RMW`,\n`CMA`), a portfolio descriptor, the report date, and a note if any ticker has\nless than one year of history.\n", "height": 900, "width": 720 }, "id": "f03b7de5-dca7-46f0-8a89-eec57d5bb6f6", "name": "Instructions2", "type": "n8n-nodes-base.stickyNote", "typeVersion": 1, "position": [ 0, 1264 ] }, { "parameters": { "connectionType": "http", "uriOverride": "", "operation": "executeTool", "toolName": "result_get", "toolParameters": "={{ JSON.stringify({ request: { _skill_contract_acknowledged: 'read-and-understood', result_id: $json.result.structuredContent.result.result_id } }) }}" }, "id": "17046433-a6e9-41c1-b4d9-b843bc3f2950", "name": "MCP - Get Factor Result", "type": "n8n-nodes-mcp.mcpClient", "typeVersion": 1, "position": [ 3232, 816 ] }, { "parameters": { "language": "pythonNative", "pythonCode": "# Build aligned return windows for the downstream MCP regression node.\n#\n# Manual portfolios — gross P&L (Variant 1):\n# net_t = sum(shares_i * price_i,t) + cash\n# gross_t = sum(|shares_i * price_i,t|) + cash\n# r_t = (net_t - net_{t-1}) / gross_{t-1}\n#\n# Lime with limeIncludeCash — legacy net return (gross = net):\n# gross_t = net_t # avoids double-counting shorts via cash in the denominator\n#\n# All other cases — gross P&L (Variant 1):\n# gross_t = sum(|shares_i * price_i,t|) + cash\nimport json\nimport time\nfrom datetime import datetime, timedelta\n\nimport pandas as pd\nimport yfinance as yf\n\nitem = _items[0][\"json\"]\nrun_mode = (item.get(\"runMode\") or \"once\").strip().lower()\nportfolio_source = (item.get(\"portfolioSource\") or \"manual\").strip().lower()\nlime_include_cash = str(item.get(\"limeIncludeCash\") or \"false\").strip().lower() in (\n \"true\", \"1\", \"yes\",\n)\nportfolio = json.loads(item.get(\"portfolio\") or \"[]\")\ncash = float(item.get(\"cash\") or 0)\n\nmcp_inner = (item.get(\"result\") or {}).get(\"structuredContent\", {}).get(\"result\", {})\npayload = mcp_inner.get(\"payload\")\nif payload is None and isinstance(mcp_inner, list):\n payload = mcp_inner\nif not payload:\n return [{\"json\": {**item, \"calcError\": True,\n \"calcErrorMessage\": \"MCP factor payload is missing on the merged item.\"}}]\n\nfactors_table = payload[0]\nfactor_dates = [r[\"Date\"] for r in factors_table[\"rows\"]]\nfactor_dates_set = set(factor_dates)\nlatest_factor_date = pd.to_datetime(factor_dates[-1]).date()\n\nif run_mode == \"daily\":\n expected_yesterday = (datetime.utcnow() - timedelta(days=1)).date()\n if latest_factor_date < expected_yesterday:\n return [{\"json\": {**item, \"calcError\": True,\n \"calcErrorMessage\": (\n \"Yesterday's Fama-French factors are not yet published \"\n \"by the MCP service. The workflow will retry on the \"\n \"next scheduled run.\")}}]\n anchor_date = expected_yesterday\nelse:\n anchor_date = latest_factor_date\n\ndownload_start = (anchor_date - timedelta(days=400)).strftime(\"%Y-%m-%d\")\ndownload_end = (anchor_date + timedelta(days=1)).strftime(\"%Y-%m-%d\")\n\ntickers = [p[\"ticker\"] for p in portfolio]\nshares = [p[\"shares\"] for p in portfolio]\nprice_series = []\nvalid_tickers = []\nvalid_shares = []\nticker_lengths = {}\n\nfor ticker, share_count in zip(tickers, shares):\n for attempt in range(1, 6):\n try:\n data = yf.download(\n ticker,\n start=download_start,\n end=download_end,\n auto_adjust=True,\n progress=False,\n )\n if data.empty:\n raise ValueError(f\"Empty data for {ticker}\")\n prices = data[\"Close\"]\n if isinstance(prices, pd.DataFrame):\n prices = prices[ticker] if ticker in prices.columns else prices.iloc[:, 0]\n prices = prices.dropna()\n if prices.empty:\n raise ValueError(f\"All NaN for {ticker}\")\n prices.index = prices.index.tz_localize(None)\n prices.name = ticker\n price_series.append(prices)\n valid_tickers.append(ticker)\n valid_shares.append(share_count)\n ticker_lengths[ticker] = len(prices)\n break\n except Exception:\n if attempt < 5:\n time.sleep(2)\n\nif not price_series:\n return [{\"json\": {**item, \"calcError\": True,\n \"calcErrorMessage\": \"Failed to download price history for any ticker.\"}}]\n\nprices_df = pd.concat(price_series, axis=1, join=\"outer\").sort_index()\nprices_df = prices_df.ffill().dropna()\n\nposition_value = pd.Series(0.0, index=prices_df.index)\ngross_positions = pd.Series(0.0, index=prices_df.index)\nfor i, ticker in enumerate(valid_tickers):\n if ticker in prices_df.columns:\n leg = valid_shares[i] * prices_df[ticker]\n position_value += leg\n gross_positions += leg.abs()\n\nnet_value = position_value + cash\nif portfolio_source == \"lime\" and lime_include_cash:\n gross_value = net_value\nelse:\n gross_value = gross_positions + cash\n\npnl = net_value.diff()\nprior_gross = gross_value.shift(1)\nvalid = prior_gross > 0\nportfolio_returns = (pnl[valid] / prior_gross[valid])\nportfolio_returns = portfolio_returns.replace([float(\"inf\"), float(\"-inf\")], pd.NA).dropna()\nportfolio_returns = portfolio_returns[portfolio_returns.index.date <= anchor_date]\n\naligned = [\n (d.strftime(\"%Y-%m-%d\"), float(v))\n for d, v in zip(portfolio_returns.index.date, portfolio_returns.values)\n if d.strftime(\"%Y-%m-%d\") in factor_dates_set\n]\nif not aligned:\n return [{\"json\": {**item, \"calcError\": True,\n \"calcErrorMessage\": \"Portfolio returns do not overlap with the MCP factor calendar.\"}}]\n\ninsufficient_tickers = [t for t, n in ticker_lengths.items() if n < 252]\nwindows = {\"1M\": 21, \"3M\": 63, \"6M\": 126, \"1Y\": 252}\noutputs = []\nfor label, n_days in windows.items():\n window = aligned[-n_days:]\n rets_payload = {\n \"rows\": [{\"index\": d, \"value\": v} for d, v in window],\n \"columns\": [\"index\", \"value\"],\n \"row_count\": len(window),\n }\n outputs.append({\n \"json\": {\n **item,\n \"calcError\": False,\n \"window_label\": label,\n \"window_from\": window[0][0] if window else None,\n \"window_to\": window[-1][0] if window else None,\n \"window_n_obs\": len(window),\n \"rets_payload\": rets_payload,\n \"insufficient_tickers\": insufficient_tickers,\n \"anchor_date\": anchor_date.strftime(\"%Y-%m-%d\"),\n }\n })\nreturn outputs" }, "id": "43e85fd7-309c-4660-b14d-f8f52c65f416", "name": "Prepare Regression Windows", "type": "n8n-nodes-base.code", "typeVersion": 2, "position": [ 3680, 752 ] }, { "parameters": { "conditions": { "options": { "caseSensitive": true, "leftValue": "", "typeValidation": "strict", "version": 3 }, "conditions": [ { "id": "b6e6f55f-03ff-441a-bd1c-b72c6d967d3d", "leftValue": "={{ $json.calcError == true }}", "rightValue": "", "operator": { "type": "boolean", "operation": "true", "singleValue": true } } ], "combinator": "and" }, "options": {} }, "id": "f3040019-9681-45f1-bcf4-99b61170d45a", "name": "Regression Ready?", "type": "n8n-nodes-base.if", "typeVersion": 2.3, "position": [ 3904, 752 ] }, { "parameters": { "connectionType": "http", "uriOverride": "", "operation": "executeTool", "toolName": "get-loadings-and-alpha", "toolParameters": "={{ JSON.stringify({ request: { _skill_contract_acknowledged: 'read-and-understood', rets: $json.rets_payload, from_date: $json.window_from, to_date: $json.window_to, rets_are_excess: false } }) }}" }, "id": "00543f22-e810-4fbf-be4b-439f74e1d83c", "name": "MCP - Get Loadings & Alpha", "type": "n8n-nodes-mcp.mcpClient", "typeVersion": 1, "position": [ 4352, 960 ], "executeOnce": false }, { "parameters": { "language": "pythonNative", "pythonCode": "# Merge four MCP regression results and render the delivery artifacts.\nimport base64\nimport io\nimport json\nfrom datetime import datetime\n\nimport matplotlib.pyplot as plt\nimport pandas as pd\n\nif not _items:\n return [{\"json\": {\"calcError\": True, \"calcErrorMessage\": \"No regression items received.\"}}]\n\nfactor_names = [\"Mkt-RF\", \"SMB\", \"HML\", \"RMW\", \"CMA\"]\nwindows = {\"1M\": 21, \"3M\": 63, \"6M\": 126, \"1Y\": 252}\nWINDOW_ORDER = [\"1M\", \"3M\", \"6M\", \"1Y\"]\nN_OBS_TO_LABEL = {21: \"1M\", 63: \"3M\", 126: \"6M\", 252: \"1Y\"}\n\nregression_results = {\n label: {\"alpha\": None, \"loadings\": {f: None for f in factor_names}, \"n_obs\": 0}\n for label in windows\n}\n\n\ndef extract_regression(j):\n result = j.get(\"result\") or {}\n if result.get(\"isError\"):\n return None\n inner = (result.get(\"structuredContent\") or {}).get(\"result\")\n if isinstance(inner, dict) and inner.get(\"loadings\") is not None:\n return inner\n sc = result.get(\"structuredContent\") or {}\n if isinstance(sc.get(\"loadings\"), dict):\n return sc\n return None\n\n\ndef resolve_label(j, idx, inner):\n label = j.get(\"window_label\")\n if label in regression_results:\n return label\n if inner:\n n_obs = inner.get(\"n_obs\")\n if n_obs in N_OBS_TO_LABEL:\n return N_OBS_TO_LABEL[n_obs]\n if idx < len(WINDOW_ORDER):\n return WINDOW_ORDER[idx]\n return None\n\n\nbase = {}\nfor it in _items:\n j = it[\"json\"]\n for key, val in j.items():\n if key == \"result\":\n continue\n if val is not None and val != \"\" and key not in base:\n base[key] = val\n\nfor idx, it in enumerate(_items):\n j = it[\"json\"]\n inner = extract_regression(j)\n\n if inner is None:\n label = resolve_label(j, idx, None)\n if label in regression_results:\n regression_results[label][\"n_obs\"] = j.get(\"window_n_obs\", 0)\n continue\n\n label = resolve_label(j, idx, inner)\n if label not in regression_results:\n continue\n\n regression_results[label] = {\n \"alpha\": inner.get(\"alpha\"),\n \"loadings\": {f: (inner.get(\"loadings\") or {}).get(f) for f in factor_names},\n \"n_obs\": inner.get(\"n_obs\", j.get(\"window_n_obs\", 0)),\n }\n\nportfolio = []\ncash = 0.0\ninsufficient_tickers = []\nraw_portfolio = base.get(\"portfolio\")\nif raw_portfolio:\n try:\n portfolio = json.loads(raw_portfolio) if isinstance(raw_portfolio, str) else raw_portfolio\n if not isinstance(portfolio, list):\n portfolio = []\n except Exception:\n portfolio = []\ntry:\n cash = float(base.get(\"cash\") or 0)\nexcept (TypeError, ValueError):\n cash = 0.0\ninsufficient_tickers = base.get(\"insufficient_tickers\") or []\nif isinstance(insufficient_tickers, str):\n try:\n insufficient_tickers = json.loads(insufficient_tickers)\n except Exception:\n insufficient_tickers = []\n\n\ndef fmt_html(val):\n if val is None:\n return \" N/A \"\n return f\"-{-val:.4f}\" if val < 0 else f\" {val:.4f}\"\n\n\ndef fmt_plain(val):\n return \"N/A\" if val is None else f\"{val:.4f}\"\n\n\ndef fmt_signed(val):\n if val is None:\n return \" N/A \"\n return f\"{val:+.4f}\"\n\n\nrows_html = \"\"\nfor f in factor_names:\n row = f\"{f}\"\n for label in windows:\n loading = regression_results[label][\"loadings\"].get(f)\n row += (\n \"{fmt_html(loading)}\"\n )\n row += \"\"\n rows_html += row\n\nalpha_row = \"Alpha (daily)\"\nfor label in windows:\n alpha = regression_results[label][\"alpha\"]\n alpha_row += (\n \"{fmt_html(alpha)}\"\n )\nalpha_row += \"\"\nrows_html += alpha_row\n\ntable_html = (\n ''\n \"\"\n \"\"\n \"\"\n \"\"\n \"\"\n f\"{rows_html}
Factor1-Month Loading3-Month Loading6-Month Loading1-Year Loading
\"\n)\n\ntable_data = []\nfor f in factor_names:\n table_data.append([f] + [fmt_plain(regression_results[lab][\"loadings\"].get(f)) for lab in windows])\ntable_data.append([\"Alpha (daily)\"] + [fmt_plain(regression_results[lab][\"alpha\"]) for lab in windows])\ndf_table = pd.DataFrame(table_data, columns=[\"Factor\"] + list(windows.keys()))\n\nheader_cells = [\"Factor\"] + list(windows.keys())\nrows_text = []\nfor f in factor_names:\n rows_text.append([f] + [fmt_signed(regression_results[lab][\"loadings\"].get(f)) for lab in windows])\nrows_text.append([\"Alpha (daily)\"] + [fmt_signed(regression_results[lab][\"alpha\"]) for lab in windows])\ncol_widths = [\n max(len(str(row[i])) for row in ([header_cells] + rows_text))\n for i in range(len(header_cells))\n]\n\n\ndef _fmt_row(row):\n return \" | \".join(str(cell).ljust(col_widths[i]) for i, cell in enumerate(row))\n\n\nseparator = \"-+-\".join(\"-\" * w for w in col_widths)\ntable_text = \"\\n\".join([_fmt_row(header_cells), separator] + [_fmt_row(r) for r in rows_text])\n\nfig, ax = plt.subplots(figsize=(12, 0.5 * len(df_table) + 1.5))\nax.axis(\"off\")\nax.set_title(\"Loadings & Alpha\", fontsize=14, fontweight=\"bold\", pad=10)\ntable = ax.table(\n cellText=df_table.values,\n colLabels=df_table.columns,\n cellLoc=\"center\",\n loc=\"upper center\",\n)\ntable.auto_set_font_size(False)\ntable.set_fontsize(12)\ntable.scale(1.2, 1.2)\nplt.subplots_adjust(top=0.85)\nbuf = io.BytesIO()\nplt.savefig(buf, format=\"png\", dpi=150, bbox_inches=\"tight\")\nbuf.seek(0)\nplt.close(fig)\ntable_image_b64 = base64.b64encode(buf.read()).decode(\"utf-8\")\nbuf.close()\n\nportfolio_desc = \", \".join(f'{p[\"ticker\"]}:{p[\"shares\"]}' for p in portfolio)\nif cash:\n portfolio_desc += f\" | Cash: {cash}\"\nif not portfolio_desc:\n portfolio_desc = \"(portfolio metadata not in MCP items)\"\n\ninsufficient_note = \"\"\nif insufficient_tickers:\n insufficient_note = (\n \"Insufficient history (< 1 year) for: \"\n + \", \".join(str(t) for t in insufficient_tickers)\n + \". Longer-term loadings may be based on fewer observations.\"\n )\n\ntoday = datetime.utcnow().date()\nreport_date = today.strftime(\"%Y-%m-%d\")\nreport_ts = datetime.utcnow().strftime(\"%Y-%m-%d %H:%M UTC\")\nfile_name = f\"factor_loading_report_{report_date}.png\"\ndata_points_text = (\n f\"1-Month window: {regression_results['1M']['n_obs']} trading days | \"\n f\"3-Month window: {regression_results['3M']['n_obs']} trading days | \"\n f\"6-Month window: {regression_results['6M']['n_obs']} trading days | \"\n f\"1-Year window: {regression_results['1Y']['n_obs']} trading days\"\n)\ncaption = (\n \"Factor Loading Report\\n\"\n f\"Report date: {report_date}\\n\"\n f\"Portfolio: {portfolio_desc}\\n\\n\"\n f\"{data_points_text}\"\n)\nif insufficient_note:\n caption += f\"\\n\\n{insufficient_note}\"\n\nclean = {\n k: v for k, v in base.items()\n if k not in (\n \"result\", \"rets_payload\", \"window_label\", \"window_from\", \"window_to\",\n \"window_n_obs\", \"insufficient_tickers\", \"anchor_date\",\n )\n}\n\nreturn [{\n \"json\": {\n **clean,\n \"calcError\": False,\n \"calcErrorMessage\": \"\",\n \"report_date\": report_date,\n \"report_timestamp\": report_ts,\n \"portfolio_desc\": portfolio_desc,\n \"cash\": cash,\n \"table_html\": table_html,\n \"table_text\": table_text,\n \"table_image_base64\": table_image_b64,\n \"file_name\": file_name,\n \"caption\": caption,\n \"subject\": f\"Factor Loading Report - {report_date}\",\n \"insufficient_note\": insufficient_note,\n \"data_points_1m\": regression_results[\"1M\"][\"n_obs\"],\n \"data_points_3m\": regression_results[\"3M\"][\"n_obs\"],\n \"data_points_6m\": regression_results[\"6M\"][\"n_obs\"],\n \"data_points_1y\": regression_results[\"1Y\"][\"n_obs\"],\n \"loadings_1m\": regression_results[\"1M\"][\"loadings\"],\n \"loadings_3m\": regression_results[\"3M\"][\"loadings\"],\n \"loadings_6m\": regression_results[\"6M\"][\"loadings\"],\n \"loadings_1y\": regression_results[\"1Y\"][\"loadings\"],\n \"alpha_1m\": regression_results[\"1M\"][\"alpha\"],\n \"alpha_3m\": regression_results[\"3M\"][\"alpha\"],\n \"alpha_6m\": regression_results[\"6M\"][\"alpha\"],\n \"alpha_1y\": regression_results[\"1Y\"][\"alpha\"],\n },\n \"binary\": {\n \"table_image\": {\n \"data\": table_image_b64,\n \"mimeType\": \"image/png\",\n \"fileName\": file_name,\n }\n },\n}]" }, "id": "d35cb860-f906-4086-9e3e-f96e5dbbf3af", "name": "Assemble Report", "type": "n8n-nodes-base.code", "typeVersion": 2, "position": [ 4352, 768 ] }, { "parameters": { "mode": "combine", "combineBy": "combineByPosition", "options": {} }, "id": "cd3f0a6a-2a71-4eef-b7c9-0964c6f174b6", "name": "Merge Portfolio + Factors1", "type": "n8n-nodes-base.merge", "typeVersion": 3.2, "position": [ 3456, 752 ] }, { "parameters": { "options": {} }, "id": "dde2b7ab-3cfe-48c4-9294-b6089cec6421", "name": "Loop Over Items (MCP)", "type": "n8n-nodes-base.splitInBatches", "typeVersion": 3, "position": [ 4128, 912 ] }, { "parameters": { "connectionType": "http", "uriOverride": "", "operation": "executeTool", "toolName": "get-proxy-ff-factors", "toolParameters": "={{ JSON.stringify({ request: { _skill_contract_acknowledged: 'read-and-understood', from_date: $now.minus({days: 400}).toFormat('yyyy-MM-dd') } }) }}" }, "id": "95d9294a-2f26-4df9-8027-bf6a619917c5", "name": "MCP - Get FF Factors", "type": "n8n-nodes-mcp.mcpClient", "typeVersion": 1, "position": [ 3008, 816 ] }, { "parameters": { "httpMethod": "POST", "path": "factor-loading-report-advanced", "responseMode": "responseNode", "options": { "rawBody": false } }, "id": "088ff627-643b-4edf-8441-b9c369916983", "name": "Webhook Trigger", "type": "n8n-nodes-base.webhook", "typeVersion": 2, "position": [ 768, 368 ] }, { "parameters": { "language": "pythonNative", "pythonCode": "# Map a Webhook POST body (JSON or multipart) to the same shape as Configuration.\n#\n# CSV portfolio sources (priority):\n# 1. portfolioCsv in JSON / multipart form fields\n# 2. portfolioCsvExtracted from Extract From File (uploaded CSV file)\n# 3. raw request body when Content-Type is text/csv (Raw Body on)\n#\n# Uploaded files are read by Extract From File (JS can pass binary; Python cannot).\n# Aliases: cash -> manualCash. delivery defaults to \"webhook\".\nimport csv\nimport io\nimport json\nimport re\n\nitem = _items[0]\nbody = dict(item.get(\"json\") or {})\n\n\ndef _looks_like_csv_text(text):\n if not text or not isinstance(text, str):\n return False\n lines = [ln.strip() for ln in text.strip().splitlines() if ln.strip()]\n if not lines:\n return False\n comma_lines = sum(1 for ln in lines if \",\" in ln or \";\" in ln)\n return comma_lines >= max(1, len(lines) // 2)\n\n\ndef _csv_has_valid_rows(text):\n if not _looks_like_csv_text(text):\n return False\n first_line = text.strip().splitlines()[0]\n delimiter = \";\" if first_line.count(\";\") > first_line.count(\",\") else \",\"\n rows = []\n for row in csv.reader(io.StringIO(text.strip()), delimiter=delimiter):\n if not row or not any(str(cell).strip() for cell in row):\n continue\n rows.append(row)\n if not rows:\n return False\n start = 0\n try:\n float(str(rows[0][1]).strip().replace(\",\", \".\"))\n except (ValueError, IndexError):\n start = 1\n for row in rows[start:]:\n if len(row) < 2:\n continue\n ticker = str(row[0]).strip()\n if not ticker:\n continue\n try:\n float(str(row[1]).strip().replace(\",\", \".\"))\n except (TypeError, ValueError):\n continue\n return True\n return False\n\n\ndef _get_content_type(headers):\n if not isinstance(headers, dict):\n return \"\"\n for key, value in headers.items():\n if str(key).lower() == \"content-type\" and value not in (None, \"\"):\n return str(value).split(\";\", 1)[0].strip().lower()\n return \"\"\n\n\ndef _parse_webhook_body(data):\n raw_body = data.get(\"body\") if isinstance(data.get(\"body\"), str) else None\n json_parsed = False\n\n if isinstance(data.get(\"body\"), dict):\n nested = data.pop(\"body\")\n if isinstance(nested, dict):\n data.update(nested)\n\n if isinstance(data.get(\"body\"), str):\n raw_body = data[\"body\"]\n try:\n parsed = json.loads(raw_body)\n except Exception:\n parsed = None\n if isinstance(parsed, dict):\n data.update(parsed)\n json_parsed = True\n\n return raw_body, json_parsed\n\n\ndef _collect_csv_candidates(data, raw_body, json_parsed):\n candidates = []\n\n explicit = (data.get(\"portfolioCsv\") or \"\").strip()\n if explicit and _csv_has_valid_rows(explicit):\n candidates.append((\"fields\", explicit))\n\n extracted = (data.get(\"portfolioCsvExtracted\") or \"\").strip()\n if extracted and _csv_has_valid_rows(extracted):\n candidates.append((\"extracted\", extracted))\n\n content_type = _get_content_type(data.get(\"headers\") or {})\n if content_type == \"text/csv\" and raw_body and not json_parsed:\n raw_csv = raw_body.strip()\n if _csv_has_valid_rows(raw_csv):\n candidates.append((\"raw_body\", raw_csv))\n elif raw_body and not json_parsed and _csv_has_valid_rows(raw_body):\n candidates.append((\"raw_body\", raw_body.strip()))\n\n return candidates\n\n\ndef _pick_portfolio_csv(candidates):\n priority = (\"fields\", \"extracted\", \"raw_body\")\n by_source = {source: text for source, text in candidates}\n for source in priority:\n if source in by_source:\n return by_source[source]\n return \"\"\n\n\nraw_body, json_parsed = _parse_webhook_body(body)\nportfolio_csv = _pick_portfolio_csv(_collect_csv_candidates(body, raw_body, json_parsed))\nif portfolio_csv:\n body[\"portfolioCsv\"] = portfolio_csv\n body.setdefault(\"portfolioSource\", \"csv\")\n\nbody.pop(\"_csvBinaryReady\", None)\nbody.pop(\"portfolioCsvExtracted\", None)\n\nif body.get(\"cash\") not in (None, \"\") and not str(body.get(\"manualCash\") or \"\").strip():\n body[\"manualCash\"] = body[\"cash\"]\n\nbody.setdefault(\"runMode\", \"once\")\nbody.setdefault(\"delivery\", \"webhook\")\nbody[\"triggerSource\"] = \"webhook\"\n\nif body.get(\"manualPortfolio\") is not None and not isinstance(body.get(\"manualPortfolio\"), str):\n body[\"manualPortfolio\"] = json.dumps(body[\"manualPortfolio\"])\n\nreturn [{\"json\": body}]" }, "id": "e1d823c0-2142-4193-bebb-801fb28fea0c", "name": "Normalize Webhook Input", "type": "n8n-nodes-base.code", "typeVersion": 2, "position": [ 1664, 448 ] }, { "parameters": { "language": "pythonNative", "pythonCode": "# Shape a JSON body for the Respond to Webhook node (success path).\nimport base64\n\nitem = _items[0][\"json\"]\nbinary_in = _items[0].get(\"binary\") or {}\n\nout = {\n \"ok\": True,\n \"report_date\": item.get(\"report_date\"),\n \"runMode\": item.get(\"runMode\"),\n \"portfolioSource\": item.get(\"portfolioSource\"),\n \"portfolio\": item.get(\"portfolio_desc\"),\n \"cash\": item.get(\"cash\"),\n \"caption\": item.get(\"caption\"),\n \"table_text\": item.get(\"table_text\"),\n \"insufficient_note\": item.get(\"insufficient_note\"),\n \"data_points_1m\": item.get(\"data_points_1m\"),\n \"data_points_3m\": item.get(\"data_points_3m\"),\n \"data_points_6m\": item.get(\"data_points_6m\"),\n \"data_points_1y\": item.get(\"data_points_1y\"),\n \"loadings_1m\": item.get(\"loadings_1m\"),\n \"loadings_3m\": item.get(\"loadings_3m\"),\n \"loadings_6m\": item.get(\"loadings_6m\"),\n \"loadings_1y\": item.get(\"loadings_1y\"),\n \"alpha_1m\": item.get(\"alpha_1m\"),\n \"alpha_3m\": item.get(\"alpha_3m\"),\n \"alpha_6m\": item.get(\"alpha_6m\"),\n \"alpha_1y\": item.get(\"alpha_1y\"),\n}\n\n# Optional: uncomment the block below to include the report PNG as base64 in the webhook JSON response.\n#img = binary_in.get(\"table_image\")\n#if img and img.get(\"data\"):\n# out[\"table_image_base64\"] = img[\"data\"]\n# out[\"table_image_mime\"] = img.get(\"mimeType\") or \"image/png\"\n\nreturn [{\"json\": out}]\n" }, "id": "11bc606b-ffee-4fd0-a9c8-5a3b5a929b64", "name": "Prepare Webhook Response", "type": "n8n-nodes-base.code", "typeVersion": 2, "position": [ 5248, 1104 ] }, { "parameters": { "language": "pythonNative", "pythonCode": "# Shape a JSON body for the Respond to Webhook node (error path).\nitem = _items[0][\"json\"]\nreason = (\n item.get(\"errorText\")\n or item.get(\"configErrorMessage\")\n or item.get(\"buildErrorMessage\")\n or item.get(\"calcErrorMessage\")\n or \"Unknown error\"\n)\nreturn [{\"json\": {\"ok\": False, \"error\": reason}}]\n" }, "id": "3de683cb-ea9b-41e1-8946-5d577ffc20af", "name": "Prepare Webhook Error Response", "type": "n8n-nodes-base.code", "typeVersion": 2, "position": [ 5696, 576 ] }, { "parameters": { "respondWith": "json", "responseBody": "={{ $json }}", "options": {} }, "id": "3d0f3dac-0151-424c-b265-6a1ae8fdd3d3", "name": "Respond to Webhook", "type": "n8n-nodes-base.respondToWebhook", "typeVersion": 1.1, "position": [ 5472, 1104 ] }, { "parameters": { "respondWith": "json", "responseBody": "={{ $json }}", "options": {} }, "id": "c46a48bb-ead5-478a-b42d-86327a99fb97", "name": "Respond to Webhook (Error)", "type": "n8n-nodes-base.respondToWebhook", "typeVersion": 1.1, "position": [ 5920, 576 ] }, { "parameters": { "assignments": { "assignments": [ { "name": "delivery", "value": "logs", "type": "string" }, { "name": "telegramChatId", "value": "", "type": "string" }, { "name": "emailTo", "value": "", "type": "string" }, { "name": "brevoSenderName", "value": "", "type": "string" }, { "name": "brevoSenderEmail", "value": "", "type": "string" }, { "name": "brevoApiKey", "value": "", "type": "string" }, { "name": "runMode", "value": "={{ $('Validate Configuration').first().json.runMode }}", "type": "string" }, { "name": "portfolioSource", "value": "manual", "type": "string" }, { "name": "portfolio", "value": "={{ $('Build Portfolio (Manual / Lime)').first().json.portfolio }}", "type": "string" }, { "name": "cash", "value": "={{ $('Build Portfolio (Manual / Lime)').first().json.cash }}", "type": "number" }, { "name": "portfolio_desc", "value": "={{ (() => { const bp = $('Build Portfolio (Manual / Lime)').first().json; const pf = JSON.parse(bp.portfolio || '[]'); const desc = pf.map(p => p.ticker + ':' + p.shares).join(', '); const cash = parseFloat(bp.cash || 0); return cash ? desc + ' | Cash: ' + cash : desc; })() }}", "type": "string" }, { "name": "insufficient_tickers", "value": "={{ $('Prepare Regression Windows').first().json.insufficient_tickers || [] }}", "type": "array" }, { "name": "insufficient_note", "value": "={{ (() => { const t = $('Prepare Regression Windows').first().json.insufficient_tickers || []; return t.length ? 'Insufficient history (< 1 year) for: ' + t.join(', ') + '. Longer-term loadings may be based on fewer observations.' : ''; })() }}", "type": "string" }, { "name": "caption", "value": "={{ (() => { const bp = $('Build Portfolio (Manual / Lime)').first().json; const pf = JSON.parse(bp.portfolio || '[]'); let desc = pf.map(p => p.ticker + ':' + p.shares).join(', '); const cash = parseFloat(bp.cash || 0); if (cash) desc += ' | Cash: ' + cash; const j = $json; const t = $('Prepare Regression Windows').first().json.insufficient_tickers || []; const note = t.length ? '\\n\\nInsufficient history (< 1 year) for: ' + t.join(', ') + '. Longer-term loadings may be based on fewer observations.' : ''; return 'Factor Loading Report\\nReport date: ' + j.report_date + '\\nPortfolio: ' + desc + '\\n\\n1-Month window: ' + j.data_points_1m + ' trading days | 3-Month window: ' + j.data_points_3m + ' trading days | 6-Month window: ' + j.data_points_6m + ' trading days | 1-Year window: ' + j.data_points_1y + ' trading days' + note; })() }}", "type": "string" } ] }, "includeOtherFields": true, "options": {} }, "id": "3c0a36e5-9bc0-46c1-9d84-f010e5bc8197", "name": "Attach Configuration", "type": "n8n-nodes-base.set", "typeVersion": 3.4, "position": [ 4576, 768 ] }, { "parameters": { "assignments": { "assignments": [ { "name": "delivery", "value": "logs", "type": "string" }, { "name": "telegramChatId", "value": "", "type": "string" }, { "name": "emailTo", "value": "", "type": "string" }, { "name": "brevoSenderName", "value": "", "type": "string" }, { "name": "brevoSenderEmail", "value": "", "type": "string" }, { "name": "brevoApiKey", "value": "", "type": "string" }, { "name": "runMode", "value": "={{ $('Validate Configuration').first().json.runMode }}", "type": "string" }, { "name": "portfolioSource", "value": "manual", "type": "string" } ] }, "includeOtherFields": true, "options": {} }, "id": "5f737d92-36b8-4739-ae8e-644198feeced", "name": "Attach Configuration (Error)", "type": "n8n-nodes-base.set", "typeVersion": 3.4, "position": [ 5248, 224 ] }, { "parameters": { "jsCode": "// Copy any uploaded CSV binary field to a fixed name for Extract From File.\n// Python Code nodes cannot read binary bytes (filesystem-v2); JS can pass binary through.\nconst item = $input.first();\nconst json = { ...(item.json || {}) };\nconst binary = { ...(item.binary || {}) };\n\nif (json.body && typeof json.body === 'object' && !Array.isArray(json.body)) {\n Object.assign(json, json.body);\n delete json.body;\n} else if (json.body && typeof json.body === 'string') {\n try {\n Object.assign(json, JSON.parse(json.body));\n } catch (e) {\n // keep raw body for optional text/csv handling downstream\n }\n}\n\nconst uploadKeys = ['portfolioFile', 'portfolioCsv', 'file', 'data'];\nlet hasCsvBinary = false;\n\nfor (const key of uploadKeys) {\n if (item.binary?.[key]) {\n binary.portfolioUpload = item.binary[key];\n hasCsvBinary = true;\n break;\n }\n}\n\njson._csvBinaryReady = hasCsvBinary;\n\nreturn [{ json, binary }];\n" }, "id": "b55e5f57-e64f-48bc-a802-928d6975c8d1", "name": "Consolidate CSV Binary", "type": "n8n-nodes-base.code", "typeVersion": 2, "position": [ 992, 368 ] }, { "parameters": { "conditions": { "options": { "caseSensitive": true, "leftValue": "", "typeValidation": "strict", "version": 2 }, "conditions": [ { "id": "b2c3d4e5-2222-4333-8444-555566667777", "leftValue": "={{ $json._csvBinaryReady }}", "rightValue": "", "operator": { "type": "boolean", "operation": "true", "singleValue": true } } ], "combinator": "and" }, "options": {} }, "id": "7bee71d3-4995-44c1-9d3c-da32dc561385", "name": "CSV File Uploaded?", "type": "n8n-nodes-base.if", "typeVersion": 2.2, "position": [ 1216, 368 ] }, { "parameters": { "operation": "text", "binaryPropertyName": "portfolioUpload", "destinationKey": "portfolioCsvExtracted", "options": { "encoding": "utf8", "stripBOM": true, "keepSource": "json" } }, "id": "3bcb71fa-0618-4ba0-86df-7f53f9e4a567", "name": "Extract CSV File", "type": "n8n-nodes-base.extractFromFile", "typeVersion": 1, "position": [ 1504, 272 ] } ], "connections": { "Manual Trigger (One-Time Report)": { "main": [ [ { "node": "Set Mode = Once", "type": "main", "index": 0 } ] ] }, "Schedule Trigger (Daily 07:00 NY)": { "main": [ [ { "node": "Set Mode = Daily", "type": "main", "index": 0 } ] ] }, "Set Mode = Once": { "main": [ [ { "node": "Configuration", "type": "main", "index": 0 } ] ] }, "Set Mode = Daily": { "main": [ [ { "node": "Configuration", "type": "main", "index": 0 } ] ] }, "Configuration": { "main": [ [ { "node": "Validate Configuration", "type": "main", "index": 0 } ] ] }, "Validate Configuration": { "main": [ [ { "node": "Config Error?", "type": "main", "index": 0 } ] ] }, "Config Error?": { "main": [ [ { "node": "Format Error Message", "type": "main", "index": 0 } ], [ { "node": "Trading Day Check", "type": "main", "index": 0 } ] ] }, "Trading Day Check": { "main": [ [ { "node": "Build Portfolio (Manual / Lime)", "type": "main", "index": 0 } ] ] }, "Build Portfolio (Manual / Lime)": { "main": [ [ { "node": "Build Error?", "type": "main", "index": 0 } ] ] }, "Build Error?": { "main": [ [ { "node": "Format Error Message", "type": "main", "index": 0 } ], [ { "node": "MCP - Get FF Factors", "type": "main", "index": 0 }, { "node": "Merge Portfolio + Factors1", "type": "main", "index": 0 } ] ] }, "Calc Error?": { "main": [ [ { "node": "Format Error Message", "type": "main", "index": 0 } ], [ { "node": "Route Delivery", "type": "main", "index": 0 } ] ] }, "Route Delivery": { "main": [ [ { "node": "Telegram - Send Photo", "type": "main", "index": 0 } ], [ { "node": "Build Brevo Payload", "type": "main", "index": 0 } ], [ { "node": "Log Report to n8n Logs", "type": "main", "index": 0 } ], [ { "node": "Prepare Webhook Response", "type": "main", "index": 0 } ] ] }, "Build Brevo Payload": { "main": [ [ { "node": "Brevo - Send Email", "type": "main", "index": 0 } ] ] }, "Format Error Message": { "main": [ [ { "node": "Attach Configuration (Error)", "type": "main", "index": 0 } ] ] }, "Route Error Delivery": { "main": [ [ { "node": "Telegram - Send Error", "type": "main", "index": 0 } ], [ { "node": "Brevo - Send Error", "type": "main", "index": 0 } ], [ { "node": "Log Error to n8n Logs", "type": "main", "index": 0 } ], [ { "node": "Prepare Webhook Error Response", "type": "main", "index": 0 } ] ] }, "MCP - Get Factor Result": { "main": [ [ { "node": "Merge Portfolio + Factors1", "type": "main", "index": 1 } ] ] }, "Prepare Regression Windows": { "main": [ [ { "node": "Regression Ready?", "type": "main", "index": 0 } ] ] }, "Regression Ready?": { "main": [ [ { "node": "Format Error Message", "type": "main", "index": 0 } ], [ { "node": "Loop Over Items (MCP)", "type": "main", "index": 0 } ] ] }, "MCP - Get Loadings & Alpha": { "main": [ [ { "node": "Loop Over Items (MCP)", "type": "main", "index": 0 } ] ] }, "Merge Portfolio + Factors1": { "main": [ [ { "node": "Prepare Regression Windows", "type": "main", "index": 0 } ] ] }, "Assemble Report": { "main": [ [ { "node": "Attach Configuration", "type": "main", "index": 0 } ] ] }, "Loop Over Items (MCP)": { "main": [ [ { "node": "Assemble Report", "type": "main", "index": 0 } ], [ { "node": "MCP - Get Loadings & Alpha", "type": "main", "index": 0 } ] ] }, "MCP - Get FF Factors": { "main": [ [ { "node": "MCP - Get Factor Result", "type": "main", "index": 0 } ] ] }, "Webhook Trigger": { "main": [ [ { "node": "Consolidate CSV Binary", "type": "main", "index": 0 } ] ] }, "Prepare Webhook Response": { "main": [ [ { "node": "Respond to Webhook", "type": "main", "index": 0 } ] ] }, "Prepare Webhook Error Response": { "main": [ [ { "node": "Respond to Webhook (Error)", "type": "main", "index": 0 } ] ] }, "Normalize Webhook Input": { "main": [ [ { "node": "Validate Configuration", "type": "main", "index": 0 } ] ] }, "Attach Configuration": { "main": [ [ { "node": "Calc Error?", "type": "main", "index": 0 } ] ] }, "Attach Configuration (Error)": { "main": [ [ { "node": "Route Error Delivery", "type": "main", "index": 0 } ] ] }, "Consolidate CSV Binary": { "main": [ [ { "node": "CSV File Uploaded?", "type": "main", "index": 0 } ] ] }, "CSV File Uploaded?": { "main": [ [ { "node": "Extract CSV File", "type": "main", "index": 0 } ], [ { "node": "Normalize Webhook Input", "type": "main", "index": 0 } ] ] }, "Extract CSV File": { "main": [ [ { "node": "Normalize Webhook Input", "type": "main", "index": 0 } ] ] } }, "settings": { "executionOrder": "v1", "binaryMode": "separate", "timeSavedMode": "fixed", "timezone": "America/New_York", "callerPolicy": "workflowsFromSameOwner", "availableInMCP": false, "executionTimeout": 600 }, "nodeGroups": [], "__quantx_template_note": "Sanitized public template. Credentials, private recipients, private webhook identifiers, and literal secrets were removed. Configure MCP and delivery credentials inside your own n8n instance." }