An automated Python pipeline for bulk extraction of 10-K and 10-Q filings from SEC EDGAR using BeautifulSoup and pandas for structured financial data.
An automated Python pipeline for bulk extraction of 10-K and 10-Q filings from SEC EDGAR. Uses BeautifulSoup and pandas to parse, structure, and store financial disclosure data at scale.
SEC EDGAR contains decades of financial filings, but accessing the data programmatically is cumbersome — EDGAR's interface is built for human browsing, not API consumption. Financial analysts and quant researchers processing large filing volumes across companies and time periods have no clean solution for bulk extraction and structured storage.
Built an automated extraction pipeline targeting SEC EDGAR’s underlying file structure. BeautifulSoup parses HTML/XBRL filing documents, extracting key sections (MD&A, financial statements, risk factors). Pandas structures the extracted data into clean DataFrames ready for analysis. The pipeline handles rate limiting, retry logic, and the varying formats across filing periods and company types.
Tell us about your problem. We'll tell you honestly how we'd approach it — and whether we're the right team.