Fundamentals Lite API
The Fundamentals Lite API delivers clean, standardized financial statement data—income statement, balance sheet, and cash flow statement—for U.S. public companies, parsed directly from SEC XBRL filings. It is optimized for low-latency queries and lightweight integration into financial dashboards, analytics tools, and backtesting environments.- Normalized Output: Maps complex us-gaap XBRL tags into intuitive, flat field names (e.g., us-gaap:Revenues → revenue) for ease of use.
- Three Statement Coverage: Returns structured values for ~80 commonly used GAAP fields across income, balance, and cash flow.
- JSON Format: Lightweight, developer-friendly payloads with clear period tagging (e.g., fiscal_period, end_date, fiscal_year).
- Fast Query Performance: Sub-200ms response targets via PostgreSQL indexing and optimized fact-table schema.
- Tickers + CIK Support: Query data by ticker symbol or company CIK.
- Building investment research dashboards
- Financial model backtesting
- Time-series visualization for earnings analysis
- Mobile portfolio apps with lightweight statement display
Fundamentals Enhanced API
The Fundamentals Enhanced API extends the Lite dataset with derived financial metrics and textual insights extracted from 10-K/10-Q filings. It blends numeric and NLP-enriched fields into a single response—designed for advanced modeling, risk analysis, and machine learning applications. Key Features- Derived Metrics: Includes calculated ratios and performance metrics like ROIC, free cash flow yield, working capital days, revenue per employee, and more.
- Text Analysis from Filings:
- Risk Factors: Theme extraction, sentiment scoring, and risk count from 10-K risk sections.
- MD&A: Tone, forward-looking language ratio, and keyword themes from management commentary.
- Litigation Mentions: Surface recent litigation headings from legal disclosures.
- Unified JSON Response: Combines Lite fundamentals, derived metrics, and text signals per fiscal period.
- Scalable NLP Pipeline: Text sections parsed and scored using deterministic and ML-based methods.
- Alpha modeling with alternative financial signals
- ESG and litigation risk factor screening
- Earnings tone and sentiment analysis at scale
- Derived metric-based stock screening