StockFit API

StockFit API is a financial data platform engineered specifically for developers, quantitative analysts, and research platforms who require direct, reliable, and auditable access to SEC filing data. Unlike conventional financial data providers that offer a frustrating choice between cheap but inaccurate data and expensive enterprise contracts, StockFit fills this critical gap by sourcing financial information directly from SEC XBRL filings. This means every single number is traceable back to its original filing, eliminating the derived middle layer that often introduces errors and inconsistencies. The platform is built for serious financial analysis, covering fundamentals, ownership data, ETF and mutual fund exposure, insider transactions, and all types of filings. It handles complex scenarios that other APIs ignore, including amended filings, non-December fiscal years, and Q4 reconstructions from 10-K and 10-Q data. Beyond raw numbers, StockFit provides rich economic models per company including offerings, peers, operating levers, competitive advantages, flywheels, strategic initiatives, and failure modes. For ETF and mutual fund exposure, the platform models mandate, portfolio construction, costs, sensitivities, and use cases in an AI-friendly format perfect for LLM workflows. With over 250 million facts, 5 million filings, and daily updates, StockFit delivers standardized financials, sector-aware metrics, and source-cited economic models that are built for valuation, backtesting, and any quantitative research application. The platform offers a playground for testing, comprehensive documentation, and insights for deeper analysis, making it the ideal solution for anyone who needs financial data they can actually model with.

Direct SEC XBRL Data Sourcing

StockFit pulls financial data directly from SEC XBRL filings, ensuring there is no derived middle layer between the original filing and the data you receive. Every single number is traceable back to its original filing, giving you complete confidence that what you are modeling is accurate and auditable. This direct sourcing eliminates the inaccuracies and incompleteness common in cheap API tiers while avoiding the high costs of enterprise contracts.

Standardized and Model-Ready Financials

The platform delivers financial data that is standardized, sector-aware, and model-ready, with no taxonomy drift over time. Income statements, balance sheets, and cash flow statements are normalized across companies and reporting periods, making them immediately usable for financial modeling, backtesting, and quantitative analysis. The data structure includes all key metrics such as revenue, cost of revenue, gross profit, operating expenses, net income, EPS, EBITDA, and more, all presented in a consistent JSON format.

Comprehensive Economic Models

Beyond raw financial numbers, StockFit provides rich economic models per company that include offerings, peers, operating levers, competitive advantages, flywheels, strategic initiatives, and failure modes. These models are designed to give analysts and AI systems a deeper understanding of each company's business dynamics, enabling more sophisticated analysis and decision-making. The models are formatted in an AI-friendly structure perfect for LLM workflows.

Complex Filing Handling and Coverage

StockFit handles the complexities that other APIs ignore, including amended filings, non-December fiscal years, and Q4 reconstructions from 10-K and 10-Q data. The platform covers fundamentals, ownership data, ETF and mutual fund exposure, insider transactions, and all types of filings. With over 250 million facts, 5 million filings, and daily updates, the platform ensures you always have access to the most current and comprehensive financial data available.

Quantitative Backtesting and Strategy Development

Quantitative analysts and hedge funds can use StockFit to backtest trading strategies using accurate, standardized financial data directly from SEC filings. The platform's direct XBRL sourcing ensures that historical financials are reliable and auditable, eliminating the data quality issues that often plague backtesting efforts. With sector-aware metrics and consistent data structures, quants can build and test complex models with confidence in the underlying data.

Fundamental Valuation and Financial Modeling

Investment analysts and valuation professionals can leverage StockFit for building discounted cash flow models, comparable company analyses, and other fundamental valuation frameworks. The platform provides all necessary financial statement data in a model-ready format, including revenue, expenses, net income, EPS, EBITDA, and shares outstanding. The rich economic models add context around competitive advantages, operating levers, and strategic initiatives, enabling more nuanced valuations.

AI-Powered Financial Research and Analysis

AI researchers and developers building LLM-based financial applications can use StockFit's AI-friendly data formats for training and inference. The platform's economic models, ETF and mutual fund exposure data, and standardized financials are structured for easy integration into machine learning pipelines. This enables automated financial analysis, report generation, and investment research at scale, with data that is traceable and auditable.

Portfolio Construction and Risk Management

Asset managers and portfolio analysts can use StockFit's ETF and mutual fund exposure data to understand portfolio construction, mandates, costs, sensitivities, and use cases. The platform models how funds are built and managed, providing insights into portfolio risks and concentrations. With access to ownership data and insider transactions, analysts can monitor changes in institutional positioning and make informed portfolio adjustments.

How does StockFit ensure the accuracy of its financial data?

StockFit pulls financial data directly from SEC XBRL filings, meaning there is no derived middle layer that could introduce errors. Every single number is traceable back to its original filing, providing complete auditability. The platform handles complexities like amended filings and non-December fiscal years, ensuring that the data you receive is as accurate and complete as possible. With daily updates and over 5 million filings indexed, StockFit maintains a high standard of data integrity.

What types of financial data does StockFit cover?

StockFit covers a comprehensive range of financial data including fundamentals (income statements, balance sheets, cash flow statements), ownership data, ETF and mutual fund exposure, insider transactions, and all types of SEC filings. The platform also provides rich economic models per company including offerings, peers, operating levers, competitive advantages, flywheels, strategic initiatives, and failure modes. With over 250 million facts available, the platform offers extensive coverage across all major US publicly traded companies.

Is StockFit suitable for backtesting and quantitative research?

Yes, StockFit is built specifically for valuation and backtesting. The platform delivers standardized financials that are model-ready with no taxonomy drift, meaning data is consistent across companies and time periods. The JSON output format includes all key metrics needed for financial modeling, including revenue, expenses, net income, EPS, EBITDA, and shares outstanding. The direct XBRL sourcing ensures historical data is reliable and auditable, which is critical for accurate backtesting results.

How does StockFit handle complex filing scenarios like amended filings and non-standard fiscal years?

StockFit is designed to handle the complexities that other APIs ignore. For amended filings, the platform tracks and incorporates the latest versions, ensuring you always have the most current data. For companies with non-December fiscal years, StockFit correctly aligns financial periods and provides Q4 reconstructions from 10-K and 10-Q data. This ensures that financial data is consistently comparable across companies regardless of their fiscal year structure.