SEC EDGAR Corporate Filings & 10-K / 8-K Extractor
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from $1.05 / 1,000 item scrapeds
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SEC EDGAR Corporate Filings & 10-K / 8-K Extractor
Extract real-time corporate SEC filings including 10-K, 10-Q, 8-K, Form 4, and S-1 by company ticker or CIK. Extract accession numbers, filing dates, and document URLs.
Pricing
from $1.05 / 1,000 item scrapeds
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LiteZero
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🏛️ SEC EDGAR Financial Filings & 10-K / 8-K Extractor
Extract official US Securities and Exchange Commission (SEC EDGAR) corporate filings including annual reports (10-K), quarterly reports (10-Q), material events (8-K), and insider trading forms with direct interactive document URLs and metadata.
🥊 Why This Actor Beats the Competition
| Feature | 🏛️ SEC EDGAR Filings Extractor | Complex Financial Data APIs | Legacy SEC Web Crawlers |
|---|---|---|---|
| Speed | Instant REST JSON extraction (< 1 sec) | Slow heavy data pipelines | Fragile table scrapers (5-15s) |
| Pricing | Pay-Per-Result ($1.50 / 1k filings) | $500–$2,000/mo enterprise fees | Expensive server compute bills |
| Direct Document Links | Generated direct links to .htm and inline XBRL | Only raw text or PDF | Broken links due to complex SEC paths |
| Ticker & CIK Resolution | Built-in ticker-to-CIK mapping for all major stocks | Requires manual CIK search | Often fails on ticker lookup |
| SEC Rate Compliance | Auto-throttled to respect SEC's 10 req/s ceiling | Prone to 429 IP bans | Frequently blocked by SEC firewalls |
🛠️ Step-by-Step: How to Use
Method 1: Apify Console (No-Code GUI)
- Enter one or more stock tickers or 10-digit CIK numbers (e.g.
AAPL,MSFT,NVDA,TSLA). - Select desired form types (default:
10-K,10-Q,8-K). - Set
maxFilingsPerCompanyand click Start.
Method 2: Python SDK
from apify_client import ApifyClientclient = ApifyClient("YOUR_APIFY_TOKEN")run_input = {"tickersOrCiks": ["AAPL", "NVDA"],"formTypes": ["10-K", "10-Q"],"maxFilingsPerCompany": 5}run = client.actor("litezero/sec-edgar-filings-scraper").call(run_input=run_input)for item in client.dataset(run["defaultDatasetId"]).iterate_items():print(f"[{item['ticker']}] Form {item['formType']} filed on {item['filingDate']}: {item['primaryDocumentUrl']}")
Method 3: Node.js SDK
import { ApifyClient } from 'apify-client';const client = new ApifyClient({ token: 'YOUR_APIFY_TOKEN' });const run = await client.actor('litezero/sec-edgar-filings-scraper').call({tickersOrCiks: ['TSLA', 'AMZN'],formTypes: ['8-K'],maxFilingsPerCompany: 10,});const { items } = await client.dataset(run.defaultDatasetId).listItems();console.log(items);
Method 4: cURL / REST API
curl --request POST \--url "https://api.apify.com/v2/acts/litezero~sec-edgar-filings-scraper/runs?token=YOUR_APIFY_TOKEN" \--header "Content-Type: application/json" \--data '{"tickersOrCiks": ["AAPL"], "formTypes": ["10-K"]}'
📥 Input Parameters
| Parameter | Type | Default | Description |
|---|---|---|---|
tickersOrCiks | Array | ["AAPL", "TSLA"] | List of stock tickers (e.g. NVDA, MSFT) or numeric CIKs. |
formTypes | Array | ["10-K", "10-Q", "8-K"] | Specific SEC forms to retrieve. Leave empty for all forms. |
maxFilingsPerCompany | Integer | 10 | Number of most recent filings to fetch per company. |
📤 Sample Output JSON
{"companyName": "Apple Inc.","cik": "0000320193","ticker": "AAPL","formType": "10-K","filingDate": "2025-11-01","reportDate": "2025-09-28","accessionNumber": "0000320193-25-000106","primaryDocumentUrl": "https://www.sec.gov/Archives/edgar/data/320193/000032019325000106/aapl-20250928.htm","filingSummaryUrl": "https://www.sec.gov/ix?doc=/Archives/edgar/data/320193/000032019325000106/aapl-20250928.htm","primaryDocDescription": "10-K","fileSize": 12849021,"sic": "3571","sicDescription": "Electronic Computers","fiscalYearEnd": "0930","scrapedAt": "2026-10-02T16:45:00.000Z"}
💡 Top Commercial Use Cases
- Automated Earnings Alert Bots: Detect new 10-K and 10-Q filings seconds after submission to extract guidance tables.
- Material Event Monitoring: Screen 8-K filings for executive turnover, acquisitions, or litigation updates.
- Institutional Equity Research: Build automated historical filing archives for quantitative sentiment analysis.