StockFit API

StockFit API is a financial data platform built specifically for developers, quants, and research platforms who need direct, reliable access to SEC filing data without the usual compromises. If you have ever tried to build a financial model, run a backtest, or analyze company fundamentals using existing APIs, you have likely run into a frustrating choice: pay for cheap tiers that deliver inaccurate or incomplete data, or sign expensive enterprise contracts that drain your startup budget. StockFit fills that gap completely. The platform pulls financial data directly from SEC XBRL filings, meaning there is no derived middle layer and every single number is traceable back to its original filing. This gives you confidence that what you are modeling is accurate and auditable. StockFit covers fundamentals, ownership data, ETF and mutual fund exposure, insider transactions, and all types of filings. It handles complexities that other APIs ignore, such as 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 is built for serious financial analysis.

Standardized Financials with No Taxonomy Drift

StockFit delivers financial statements that are standardized and model-ready, meaning you never have to worry about changing SEC taxonomies breaking your code. Revenue, expenses, net income, and all key line items are presented consistently across companies and time periods. The platform automatically handles GAAP taxonomy changes, so your models keep working without constant maintenance. Every data point is mapped to a clean, predictable schema that makes backtesting and valuation straightforward.

Direct SEC XBRL Sourcing with Full Traceability

Every number in StockFit comes directly from SEC XBRL filings with no derived middle layer. This means you can trace any fact back to its original filing source, including the specific accession number. When you pull an income statement for Microsoft or Apple, the response includes a sources object that maps each fact to its filing ID. This audit trail is invaluable for compliance, research validation, and debugging your models.

Intelligent Fiscal Period Handling

StockFit correctly computes financials for companies with non-December fiscal years, a common pain point with other APIs. It also reconstructs Q4 data by intelligently combining 10-K and 10-Q filings, giving you complete quarterly views. Amended filings are handled gracefully, so you always get the most current and accurate data without manual intervention. This feature alone saves hours of data cleaning and reconciliation work.

AI-Ready Economic and Exposure Models

Beyond raw financials, StockFit provides rich economic models for each company covering 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. These models are designed to be AI-friendly, making them perfect for LLM workflows with tools like Claude and Cursor.

Quantitative Backtesting and Strategy Development

Quants and algorithmic traders can use StockFit to build and backtest financial models with confidence. The standardized financials, correct fiscal period handling, and direct SEC sourcing mean your backtests are based on accurate, auditable data. You can pull 20 years of income statements for thousands of companies, compute valuation metrics, and test trading strategies without worrying about data quality issues. The REST API makes it easy to integrate into Python, R, or any backtesting framework.

AI-Powered Financial Analysis with LLMs

StockFit is built for the age of AI. The economic models and ETF exposure data are structured in AI-friendly formats that work seamlessly with large language models. Developers building financial chatbots, research assistants, or automated analysis tools can feed StockFit data directly into Claude, Cursor, or other AI tools. The MCP server provides native integration, making it trivial to query financial data using natural language.

Startup and Small Team Financial Research

Small research teams and startups no longer need enterprise contracts to get quality financial data. StockFit provides institutional-grade data at accessible pricing, with no hidden fees or minimum commitments. You can analyze insider transactions, ownership changes, and fund exposure for public companies without burning through your budget. The platform scales from a single ticker query to batch processing thousands of companies.

Academic and Independent Research

Researchers, academics, and independent analysts can leverage StockFit for deep fundamental analysis. The ability to trace every number back to its original SEC filing is crucial for academic rigor and reproducibility. The economic models provide structured frameworks for analyzing competitive advantages, strategic initiatives, and failure modes, making it easier to publish research or build investment theses.

How does StockFit ensure data accuracy compared to other financial APIs?

StockFit pulls data directly from SEC XBRL filings with no derived middle layer. Every number in the response includes a sources field that maps to the original filing accession number. This means you can verify any fact against the SEC EDGAR system yourself. The platform also handles amended filings, non-December fiscal years, and Q4 reconstructions automatically, eliminating common sources of error found in other APIs.

Can I use StockFit with AI tools like Claude or Cursor?

Yes, absolutely. StockFit provides a native MCP server that works directly with Claude, Cursor, and other AI tools. This allows you to query financial data using natural language and have the AI tool pull standardized financials, economic models, or ETF exposure data automatically. The data is structured to be AI-friendly, making it easy for LLMs to understand and reason about.

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 SEC filing types. The platform also provides rich economic models per company covering offerings, peers, operating levers, competitive advantages, flywheels, strategic initiatives, and failure modes. In total, there are over 250 million facts from 5 million filings.

How often is the data updated and how do I access it?

StockFit data is updated daily to reflect the latest SEC filings. You can access the data through a REST API for traditional programmatic use, or through the native MCP server for AI tool integration. The platform supports querying by ticker, fiscal period, and specific fact types. Getting started is easy with a free API key and comprehensive documentation available on the website.

StockFit offers a free tier to get started, which includes a generous allocation of API calls and access to core financial data. For higher volume needs, there are paid plans that scale with your usage. Specific pricing details and plan comparisons are available on the StockFit website under the Pricing section. The platform is designed to be affordable for startups and small teams while still providing enterprise-grade data quality and reliability.