Insider and congressional trading

Five years of SEC Form 4 insider transaction filings for the top 918 US-listed companies by reporting history, covering 2021-01-04 through 2026-09-04. 601,965 transactions as a single gzipped CSV — one row per filed transaction. Columns include the filing date, transaction date, reporting insider name and role, transaction type (open-market buy/sell, option exercise, RSU vest, gift, etc.), shares transacted, price per share, total shares owned after the transaction, and a direct link to the EDGAR filing. Sourced from Financial Modeling Prep (Form 4 mirror of EDGAR). Read with pandas.readcsv(path, compression='gzip', parsedates=['filingDate', 'transactionDate']) or duckdb.read_csv('file.csv.gz'). Useful for: tracking insider sentiment, building cluster-buying signals, identifying executives unloading positions ahead of weakness, screen for management vs. board behavior divergence.

Every Senate and House Periodic Transaction Report (PTR) filed under the STOCK Act, as indexed by FMP. 10,100 Senate trades + 10,100 House trades = 20,200 transactions in 2 gzipped CSVs. Each row has the politician's name, party district, transaction date, disclosure date (often weeks/months later — that lag is itself a signal), transaction type (Purchase / Sale / Exchange), asset symbol, asset description, asset type (Stock, Option, Bond, Mutual Fund), amount range (STOCK Act bins like '$1,001 – $15,000'), spouse/dependent owner flag, comment, and a direct URL to the official PDF/HTML disclosure. Read with pandas.readcsv(path, compression='gzip', parsedates=['transactionDate', 'disclosureDate']). Polymarket-trader catnip — track Pelosi, Crapo, Tuberville, et al. in near-real-time. Useful for: lawmaker-replication strategies, sector-rotation signals from committee members trading regulated industries, and detecting unusual transaction clusters around legislation.