Company fundamentals and financial statements

1,152,906 filer-concept-year facts for 16,041 SEC registrants across 15 US-GAAP concepts (revenue, cost of revenue, gross profit, operating income, net income, total assets, liabilities, shareholders' equity, cash, long-term debt, operating cash flow, capital expenditure, diluted EPS and shares outstanding) for calendar years 2009 to 2025, from the SEC's XBRL frames API as of 2026-09-06. Long format: one row per company, concept and calendar-year frame with CIK, entity name, state or country of incorporation, unit, period start and end, value and the accession number of the filing the fact came from, 250 frames fetched. Join to the SEC Company Tickers set on CIK.

Ten years of pre-computed quarterly financial ratios and metrics for the top 3,000 US-listed issuers, covering 103,013 ratio-period rows across 2,973 companies and 102,983 key-metric rows across 2,973 companies. Two complementary files in one listing: (1) keyratiosquarterly — profitability margins, turnover ratios, liquidity, solvency, leverage, valuation multiples (P/E, P/B, P/S, P/FCF, EV/EBITDA), per-share book values, dividend metrics, and tax/interest burdens. (2) keymetricsquarterly — market cap, enterprise value, EV multiples, return-on-capital family (ROA, ROE, ROIC, ROCE), working-capital cycles (DSO, DPO, DIO, cash conversion cycle), and capex/R&D/SBC intensity. Saves buyers weeks of feature engineering on top of raw IS/BS/CFS statements. Read with pandas.readcsv(path, compression='gzip', parsedates=['date']). Useful for: fundamental factor models, multi-factor backtests, screening by financial health, training ML models on pre-computed financial features.

Ten years of quarterly Income Statement, Balance Sheet, and Cash Flow data for every USD-reporting US-listed company FMP indexes (~17,000 issuers including dual-class shares). 1,196,539 statement-period rows packaged as 3 gzipped CSV files, one CSV per statement type (income, balance, cashflow). Columns are the union of all FMP-reported fields per statement type — ~39 IS, ~61 BS, ~47 CFS. Read with pandas.readcsv(path, compression='gzip', parsedates=['reportPeriod', 'filingDate']) or duckdb.readcsv('incomestatement_*.csv.gz'). Coverage spans 2016 through 2025 quarterly. Useful for: fundamental factor research (gross margin, cash conversion, leverage), earnings drift studies, building screeners, fine-tuning LLMs on structured financial reporting, macro/sector-level financial-health rollups.

16,586 account-day rows from Table I of the Daily Treasury Statement (operating cash balance) covering 16 account lines: the Federal Reserve Account and Treasury General Account, the Supplementary Financing Program, Tax and Loan Note accounts and short-term cash investments, with closing balance, opening balance, and the balances at the start of the month and fiscal year in USD millions, from the Treasury's Fiscal Data API as of 2026-09-06.

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.