Alternative data datasets
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SEC EDGAR 10-K & 10-Q financial statements for 2,600+ NASDAQ companies.
View source information and access options.
Revenue, income, assets and cash flow from every SEC filer, latest 4 quarters
Alphabet (GOOGL, GOOG) Quarterly Tables + Prices for Investors & Analysts
Balance-sheet health and structure for every FDIC-insured US bank, reported quarterly: assets, deposits, loans, capital ratios and enforcement actions, exposed through a free API, the bedrock of regional-bank and credit-cycle work.
Financial data parsed from 10-Q, 10-Q/A, 10-K, 10-K/A SEC filings from 2010.
Standardized financial statement records aligned with NACE industry classifications, delivered in an analytics-friendly format with rights cleared for machine learning and artificial intelligence development.
Cross-industry financial statement data normalized to NACE codes, pre-processed for analytical use and permitted for training AI and ML models.
Standardized financial statement data aligned with NACE classifications, formatted for analytics and cleared for use in AI and machine learning model development.
NACE-aligned financial reporting records prepared for downstream analytics and authorized for use in artificial intelligence and machine learning model development.
Financial statement information standardized along the NACE taxonomy, packaged for analytical workflows and licensed to support AI and ML training activities.
Industry-coded financial statements harmonized via NACE, supplied in a form ready for analysis and cleared for artificial intelligence and machine learning purposes.
A consolidated set of NACE-mapped financial statements ready for data analysis and permitted to be used in training AI and machine learning models.
NACE-standardized financial statement data, formatted for analytics pipelines and licensed for building AI systems and machine learning algorithms.
Curated financial reporting records classified under NACE, engineered for analytical workloads and licensed for artificial intelligence and machine learning training.
Standardized financial statement data aligned with NACE classifications, formatted for analytics and cleared for use in AI and machine learning model development.
Financial statements aligned to NACE industry categories, structured for analytic consumption and approved for use in AI and ML model training.
NACE-harmonized financial statement datasets delivered in an analytics-ready state with licensing terms covering AI and machine learning model training.
Standardized financial reporting records mapped to NACE industry classifications, prepared for analytics use and cleared for machine learning applications.
Including vital parameters, stock history, financial and news with sentiments
Raw 10-K financials for 442 US companies extracted from SEC EDGAR
Multi-modal dataset: Bridging GAAP accounting and LLM-powered NLP for the S&P 50
Machine-readable XBRL filings covering 10-K and 10-Q reports, cleaned and standardized so financial figures can be extracted reliably.
Quarterly, annual and TTM financials with XBRL provenance and amendment signals
edgar_xbrl_companyfacts is a Hugging Face dataset published by DenyTranDFW that aggregates U.S. SEC EDGAR XBRL company-facts filings into a single Parquet file. The training split contains roughly 125 million rows of structured financial facts tagged by reporting period and unit.
Historical prices, financials, dividends, and other information
Machine-readable extraction of financial and operating figures drawn from publicly filed corporate documents.
Standardized financial datasets that clean and structure company financial statements from filings for downstream analytics.
Financials data for 3000+ companies parsed directly from the SEC since 2006
Private equity and venture capital dataset with information on deals, funds, and valuations across private-market transactions.
30 airlines × 17 years financials, Boeing vs Airbus order book