edgartools

You can extract SEC EDGAR data by creating a Company object in Python to retrieve filings and parse XBRL data across forms like 10-K and 10-Q. This toolkit supports multi-period stitching for trend analysis and predictable exports of financial statements.

Yes, you can automate financial research by integrating the extracted SEC EDGAR data with an MCP server and Claude-based Skills. This enables AI-assisted pipelines to automatically pull filings, extract key metrics, and compare peer performance.

Yes, edgartools supports XBRL data extraction and multi-period stitching across SEC filings. This allows you to stitch financial statements together across different reporting periods for comprehensive trend analysis.

You can retrieve SEC EDGAR data across multiple forms including 10-K, 10-Q, 8-K, DEF 14A, and 13F. The toolkit extracts financials, ownership information, and structured facts for automated analysis and compliance workflows.

To securely access SEC EDGAR filings, the toolkit ensures proper authentication, error handling, and predictable exports of financial statements. This allows reliable integration into automated compliance and research pipelines.

The best way to compare peer metrics is to use this Python toolkit to pull multiple issuers' latest 10-K filings, extract key XBRL data, and stitch statements for trend analysis. You can run peer comparisons in a single automated execution.