# EDGAR Intelligence — SEC Filings, AI Forecasts & Analysis (`datasiphon/sec-edgar-poller`) Actor Monitor 30+ SEC filing types, forecast revenue with AI, and get plain-English analysis of material events. Built for VC, PE, hedge funds, corp dev, and family offices. - **URL**: https://apify.com/datasiphon/sec-edgar-poller.md - **Developed by:** [Kashif Ali](https://apify.com/datasiphon) (community) - **Categories:** Automation, News - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing Pay per usage This Actor is paid per platform usage. The Actor is free to use, and you only pay for the Apify platform usage, which gets cheaper the higher subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-usage ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## EDGAR Intelligence — SEC Monitoring That Finds What Moves Markets > Monitor 30+ SEC filing types across your entire portfolio. Turn on forecasts to see where revenue is going. Turn on AI analysis to get a one-paragraph summary of every material event. All from one input. --- **An 8-K landed at 2:14 PM last Thursday.** A PE analyst had 3 hours before the Monday morning investment committee meeting. The filing disclosed a material contract loss at a portfolio company. Without it, the team would have walked into that meeting with stale numbers. **EDGAR Intelligence catches what manual tracking misses.** Pick a company, a search term, or just watch the SEC's live feed. The Actor ingests filings across 30+ form types — 8-K, 10-K, 10-Q, Form D, S-1, 13-F — and returns structured JSON with full text. **Need to know where revenue is heading?** Turn on TimesFM forecasting. The model reads 8–12 quarters of history and returns a probability-weighted range for the next year. No spreadsheet modeling. No manual data entry. **Need to know what an 8-K actually says — in one paragraph?** Turn on AI analysis. An 8-K about a director resignation becomes: > *"The CFO resigned effective immediately; no replacement named. Material risk of leadership vacuum during Q3 fundraising."* Not 20 pages. One paragraph. --- #### This is built for: → VC associates covering 100+ portfolio companies → PE analysts running quarterly monitoring → Hedge fund teams tracking every position → Corp dev scanning for acquisition targets → Family offices keeping a pulse on holdings #### This is NOT built for: → Engineers who want to build their own SEC pipeline → PDF readers who prefer 20-page filings to one-paragraph summaries --- #### Setup takes 60 seconds. 1. Paste your EDGAR identity (required by the SEC) 2. Pick your mode — company filings, RSS feed, manual CIK search, or full-text search 3. Toggle forecasts and AI analysis on or off 4. Run Output lands as clean JSON. Every filing. Every enrichment. One call's worth of work. > *"I've been manually checking EDGAR for 6 years. This does in 60 seconds what took me 3 hours a week."* — PE Analyst at a $3B fund --- #### Run it on Apify now. **Free tier:** 50 filings total per user, one-time trial. After that, contact `datasiphon@keystonerev.com` for unlimited access. First run is free. No subscription commitment. No setup call. --- ### Appendix — Technical Summary #### Modes | Mode | What it does | |------|-------------| | `latest_feed` | Poll SEC EDGAR RSS for recent filings | | `company_search` | Fetch filings by ticker / CIK / company name | | `form_specific` | Focus on one form type with filters | | `full_text_search` | Search EDGAR full-text index by keyword | #### Output Fields | Field | Description | |-------|-------------| | `form_type` | SEC form type | | `company_name` | Issuer name | | `cik` | 10-digit SEC CIK | | `filing_date` | Date filed | | `accession_number` | SEC accession (unique ID) | | `filing_text` | Full text extracted and cleaned | | `structured_data` | Filing-specific typed data | | `forecast_data` | TimesFM predictions (if enabled) | | `semantic_analysis` | AI-written summary + impact rating (if enabled) | | `edgar_tools_version` | Actor version | #### Stack edgartools v5.37 — TimesFM v2 (opt-in) — Groq Llama-3-70B (opt-in) — Apify #### SEC Compliance - `set_identity()` called before every SEC request - Rate-limited (1 req/100ms) via edgartools - User-Agent set from `edgarIdentity` #### License MIT --- *Built by Keystone Revenue Systems* # Actor input Schema ## `edgarIdentity` (type: `string`): Your name and email for SEC EDGAR compliance. Format: 'Your Name / your.email@domain.com'. Required by SEC policy — the Actor will not start without this. ## `mode` (type: `string`): What to fetch. 'latest_feed' polls SEC for recent filings (default). 'company_search' fetches filings for specified companies (tickers/CIKs/name). 'form_specific' focuses on one form type. 'full_text_search' queries EDGAR's full-text index by keyword. ## `formTypes` (type: `array`): SEC form types to poll or search. Used in latest_feed and company_search modes. ## `formType` (type: `string`): Single form type for form_specific mode. ## `searchType` (type: `string`): How to identify companies in company_search mode. ## `tickers` (type: `array`): Stock ticker symbols for company_search mode (e.g., AAPL, MSFT, TSLA). ## `ciks` (type: `array`): SEC Central Index Key numbers (10-digit). Used in company_search mode. ## `companyName` (type: `string`): Search by company name (partial match). Used in company_search mode. ## `searchQuery` (type: `string`): Keyword or phrase for full_text_search mode. Supports AND/OR/NOT and quotes. ## `datePreset` (type: `string`): Quick date range selector. Overrides dateFrom/dateTo. ## `dateFrom` (type: `string`): Start date. Overridden by datePreset. ## `dateTo` (type: `string`): End date. Overridden by datePreset. ## `maxFilings` (type: `integer`): Maximum number of filings to return. In company_search mode, this is per-company. Max 1000. ## `includeFilingText` (type: `boolean`): Fetch and include the cleaned text of each filing. ## `maxTextLength` (type: `integer`): Maximum characters of filing text per record. ## `includeStructuredData` (type: `boolean`): Parse structured data via edgartools (typed objects per form type). ## `outputFormat` (type: `string`): Preferred output format. ## `incrementalMode` (type: `boolean`): Only return filings newer than the last run. Uses Apify KV Store. ## `delaySeconds` (type: `number`): Seconds to wait between filing fetches. SEC recommends 1 req/100ms. ## `proxyConfiguration` (type: `object`): Proxy settings. SEC EDGAR does not require proxies. ## `enablePredictiveForecast` (type: `boolean`): [v2.0] Run Google TimesFM AI on 10-K/10-Q financial data to predict next 4 quarters of revenue. Requires timesfm[torch] to be installed (see README). Can use significant memory (~2 GB). ## `forecastHorizon` (type: `integer`): Number of future quarters to forecast. TimesFM supports up to 256 periods, but 4 is standard for next-year outlook. ## `groqApiKey` (type: `string`): [v2.0] Groq API key for AI-powered narrative analysis of filings (8-K summaries, Form D round classification, forecast commentary). Sourced from Apify Secrets if not provided here. ## `enableSemanticAnalysis` (type: `boolean`): [v2.0] Generate narrative intelligence for filings using Groq Llama-3. Requires groqApiKey. Produces filing summaries, material impact assessments, and forecast commentary. ## Actor input object example ```json { "edgarIdentity": "Kashif / kashif@dialnorth.com", "mode": "latest_feed", "formTypes": [ "D", "8-K" ], "formType": "4", "searchType": "ticker", "tickers": [ "AAPL" ], "ciks": [], "companyName": "", "searchQuery": "artificial intelligence", "datePreset": "", "dateFrom": "", "dateTo": "", "maxFilings": 100, "includeFilingText": true, "maxTextLength": 8000, "includeStructuredData": true, "outputFormat": "json", "incrementalMode": false, "delaySeconds": 1, "proxyConfiguration": { "useApifyProxy": false }, "enablePredictiveForecast": false, "forecastHorizon": 4, "enableSemanticAnalysis": false } ```` # Actor output Schema ## `results` (type: `string`): All retrieved SEC filing records with optional forecast and semantic analysis fields. ## `forecastView` (type: `string`): 10-K/10-Q filings with TimesFM AI financial forecasts (when enabled). ## `semanticView` (type: `string`): Filings with Groq AI-generated narrative analysis (when enabled). # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "edgarIdentity": "Kashif / kashif@dialnorth.com", "mode": "latest_feed", "formTypes": [ "D", "8-K" ], "formType": "4", "searchType": "ticker", "tickers": [ "AAPL" ], "searchQuery": "artificial intelligence", "datePreset": "", "outputFormat": "json" }; // Run the Actor and wait for it to finish const run = await client.actor("datasiphon/sec-edgar-poller").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "edgarIdentity": "Kashif / kashif@dialnorth.com", "mode": "latest_feed", "formTypes": [ "D", "8-K", ], "formType": "4", "searchType": "ticker", "tickers": ["AAPL"], "searchQuery": "artificial intelligence", "datePreset": "", "outputFormat": "json", } # Run the Actor and wait for it to finish run = client.actor("datasiphon/sec-edgar-poller").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "edgarIdentity": "Kashif / kashif@dialnorth.com", "mode": "latest_feed", "formTypes": [ "D", "8-K" ], "formType": "4", "searchType": "ticker", "tickers": [ "AAPL" ], "searchQuery": "artificial intelligence", "datePreset": "", "outputFormat": "json" }' | apify call datasiphon/sec-edgar-poller --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=datasiphon/sec-edgar-poller", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "EDGAR Intelligence — SEC Filings, AI Forecasts & Analysis", "description": "Monitor 30+ SEC filing types, forecast revenue with AI, and get plain-English analysis of material events. Built for VC, PE, hedge funds, corp dev, and family offices.", "version": "2.0", "x-build-id": "O6enhGAVQ0wCJHa69" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/datasiphon~sec-edgar-poller/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-datasiphon-sec-edgar-poller", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/datasiphon~sec-edgar-poller/runs": { "post": { "operationId": "runs-sync-datasiphon-sec-edgar-poller", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/datasiphon~sec-edgar-poller/run-sync": { "post": { "operationId": "run-sync-datasiphon-sec-edgar-poller", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "required": [ "edgarIdentity" ], "properties": { "edgarIdentity": { "title": "SEC EDGAR Identity (REQUIRED)", "type": "string", "description": "Your name and email for SEC EDGAR compliance. Format: 'Your Name / your.email@domain.com'. Required by SEC policy — the Actor will not start without this." }, "mode": { "title": "Mode", "enum": [ "latest_feed", "company_search", "form_specific", "full_text_search" ], "type": "string", "description": "What to fetch. 'latest_feed' polls SEC for recent filings (default). 'company_search' fetches filings for specified companies (tickers/CIKs/name). 'form_specific' focuses on one form type. 'full_text_search' queries EDGAR's full-text index by keyword.", "default": "latest_feed" }, "formTypes": { "title": "Form Types (multi-select)", "type": "array", "description": "SEC form types to poll or search. Used in latest_feed and company_search modes.", "items": { "type": "string", "enum": [ "D", "D/A", "8-K", "8-K/A", "4", "4/A", "3", "3/A", "5", "5/A", "13F-HR", "13F-HR/A", "N-PORT", "N-PORT/A", "S-1", "S-1/A", "S-4", "S-4/A", "SC 13D", "SC 13D/A", "SC 13G", "SC 13G/A", "10-K", "10-K/A", "10-Q", "10-Q/A", "DEF 14A", "20-F", "20-F/A", "6-K" ] }, "default": [ "D", "8-K" ] }, "formType": { "title": "Form Type (single)", "enum": [ "4", "3", "5", "8-K", "13F-HR", "N-PORT", "D", "S-1", "S-4", "SC 13D", "SC 13G", "10-K", "10-Q", "DEF 14A", "20-F", "6-K" ], "type": "string", "description": "Single form type for form_specific mode.", "default": "4" }, "searchType": { "title": "Search By", "enum": [ "ticker", "cik", "companyName" ], "type": "string", "description": "How to identify companies in company_search mode.", "default": "ticker" }, "tickers": { "title": "Ticker Symbols", "type": "array", "description": "Stock ticker symbols for company_search mode (e.g., AAPL, MSFT, TSLA).", "items": { "type": "string" }, "default": [] }, "ciks": { "title": "CIK Numbers", "type": "array", "description": "SEC Central Index Key numbers (10-digit). Used in company_search mode.", "items": { "type": "string" }, "default": [] }, "companyName": { "title": "Company Name Search", "type": "string", "description": "Search by company name (partial match). Used in company_search mode.", "default": "" }, "searchQuery": { "title": "Full-Text Search Query", "type": "string", "description": "Keyword or phrase for full_text_search mode. Supports AND/OR/NOT and quotes.", "default": "" }, "datePreset": { "title": "Date Preset", "enum": [ "", "last7Days", "last30Days", "last90Days", "lastQuarter", "lastYear", "yearToDate" ], "type": "string", "description": "Quick date range selector. Overrides dateFrom/dateTo.", "default": "" }, "dateFrom": { "title": "Date From", "type": "string", "description": "Start date. Overridden by datePreset.", "default": "" }, "dateTo": { "title": "Date To", "type": "string", "description": "End date. Overridden by datePreset.", "default": "" }, "maxFilings": { "title": "Max Filings", "minimum": 0, "maximum": 1000, "type": "integer", "description": "Maximum number of filings to return. In company_search mode, this is per-company. Max 1000.", "default": 100 }, "includeFilingText": { "title": "Include Filing Text", "type": "boolean", "description": "Fetch and include the cleaned text of each filing.", "default": true }, "maxTextLength": { "title": "Max Text Length (chars)", "minimum": 1000, "maximum": 100000, "type": "integer", "description": "Maximum characters of filing text per record.", "default": 8000 }, "includeStructuredData": { "title": "Include Structured Data", "type": "boolean", "description": "Parse structured data via edgartools (typed objects per form type).", "default": true }, "outputFormat": { "title": "Output Format", "enum": [ "json", "csv", "xlsx" ], "type": "string", "description": "Preferred output format.", "default": "json" }, "incrementalMode": { "title": "Incremental Mode", "type": "boolean", "description": "Only return filings newer than the last run. Uses Apify KV Store.", "default": false }, "delaySeconds": { "title": "Delay Between Requests (seconds)", "minimum": 0.1, "maximum": 10, "type": "number", "description": "Seconds to wait between filing fetches. SEC recommends 1 req/100ms.", "default": 1 }, "proxyConfiguration": { "title": "Proxy Configuration", "type": "object", "description": "Proxy settings. SEC EDGAR does not require proxies.", "default": { "useApifyProxy": false } }, "enablePredictiveForecast": { "title": "Enable Predictive Forecast (TimesFM)", "type": "boolean", "description": "[v2.0] Run Google TimesFM AI on 10-K/10-Q financial data to predict next 4 quarters of revenue. Requires timesfm[torch] to be installed (see README). Can use significant memory (~2 GB).", "default": false }, "forecastHorizon": { "title": "Forecast Horizon (quarters)", "minimum": 1, "maximum": 16, "type": "integer", "description": "Number of future quarters to forecast. TimesFM supports up to 256 periods, but 4 is standard for next-year outlook.", "default": 4 }, "groqApiKey": { "title": "Groq API Key (for semantic analysis)", "type": "string", "description": "[v2.0] Groq API key for AI-powered narrative analysis of filings (8-K summaries, Form D round classification, forecast commentary). Sourced from Apify Secrets if not provided here." }, "enableSemanticAnalysis": { "title": "Enable Semantic Analysis (Groq)", "type": "boolean", "description": "[v2.0] Generate narrative intelligence for filings using Groq Llama-3. Requires groqApiKey. Produces filing summaries, material impact assessments, and forecast commentary.", "default": false } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```