StockFit API

StockFit API is a financial data platform that gives developers, quants, and research platforms direct access to SEC filing data without the usual tradeoffs. Most financial APIs force you to choose between cheap tiers with accuracy issues or enterprise contracts that drain a startup's budget. StockFit fills that gap by providing fundamentals, ownership data, ETF and mutual fund exposure, insider transactions, and filings all pulled directly from SEC XBRL with no derived middle layer. Every number is traceable back to the original filing, so you can verify and trust every data point you build your models on. The platform is built for real financial analysis: amended filings are handled correctly, non-December fiscal years are computed properly, and Q4 figures are reconstructed from 10-K plus 10-Q filings. Beyond raw financials, StockFit offers rich economic models per company covering offerings, peers, operating levers, competitive advantages, flywheels, strategic initiatives, and failure modes. ETF and mutual fund exposure models cover mandate, portfolio construction, costs, sensitivities, and use cases all optimized for LLM workflows. With over 250 million facts and 5 million filings updated daily, StockFit provides both a REST API and a native MCP server for Claude, Cursor, and other AI tools. This is financial data you can actually model with, designed for valuation and backtesting workflows that require accuracy, traceability, and continuous improvement.

Direct SEC XBRL Data with Full Traceability

Every financial fact in StockFit is pulled directly from SEC XBRL filings with no derived middle layer. This means no taxonomy drift, no data transformation errors, and no black box calculations. Each fact is linked back to its original filing source document, allowing you to verify, audit, and reconstruct any data point. For developers and quants who need to trust their data for backtesting and valuation models, this traceability is non-negotiable. The platform handles amended filings correctly, computes non-December fiscal years accurately, and reconstructs Q4 figures from 10-K plus 10-Q filings so you always get complete, standardized financials.

Standardized Financials with Sector-Aware Metrics

StockFit delivers standardized financial statements including income statements, balance sheets, and cash flow statements that are consistent across companies and time periods. The data is model-ready with no taxonomy drift, meaning you can compare Apple to Microsoft to Costco without manual normalization. Beyond raw financials, the platform provides sector-aware metrics that understand industry-specific accounting treatments and reporting standards. This standardization is continuously improved as new filings are processed, ensuring your models always work with the most current and consistent data available.

Rich Economic and Exposure Models

Beyond standard financial statements, StockFit provides deep economic models per company that cover offerings, peers, operating levers, competitive advantages, flywheels, strategic initiatives, and failure modes. These models are AI-friendly and designed for LLM workflows, giving you structured insights into what drives each company. For ETF and mutual fund exposure, the models cover mandate, portfolio construction, costs, sensitivities, and use cases. This layered approach means you can move from raw financial data to economic understanding without switching platforms or building custom analysis pipelines.

Native MCP Server for AI Tools

StockFit includes a native Model Context Protocol (MCP) server that works directly with Claude, Cursor, and other AI tools. This means you can query financial data, run analysis, and build models using natural language through your preferred AI assistant. The MCP server handles authentication, query formatting, and data retrieval automatically, so you can focus on analysis rather than API integration. This feature is continuously evolving as new AI tools and protocols emerge, ensuring your workflows stay current and efficient.

Quantitative Backtesting and Strategy Development

Quants and algorithmic traders can use StockFit to build and backtest financial models with accurate, traceable data. The standardized financials and sector-aware metrics eliminate the data cleaning and normalization that typically consumes 80% of development time. You can pull historical fundamentals, ownership data, and insider transactions to test hypotheses about market inefficiencies, factor models, and trading signals. The direct SEC XBRL sourcing means your backtests are based on the actual reported numbers, not derived or interpolated data that could introduce bias. As new filings are processed daily, you can continuously refine and improve your strategies with the most current information.

Fundamental Valuation and Investment Research

Investment analysts and research platforms can leverage StockFit for deep fundamental analysis and company valuation. The platform provides complete financial statements, economic models, and competitive analysis frameworks that support DCF modeling, comparable company analysis, and precedent transactions. You can trace every number back to its original SEC filing, which is critical for audit trails and compliance in regulated investment environments. The sector-aware metrics help you understand industry-specific performance drivers, while the economic models give you structured insights into company strategies, competitive advantages, and potential failure modes. This creates a continuous feedback loop where each analysis improves your understanding of the next company you evaluate.

AI-Powered Financial Analysis and Reporting

With the native MCP server, AI tools and LLM workflows can directly access StockFit data for automated financial analysis and reporting. You can build agents that monitor portfolio companies, generate earnings summaries, analyze insider trading patterns, or screen for investment opportunities based on custom criteria. The economic models are designed to be AI-friendly, providing structured data that LLMs can process and reason about effectively. This use case is particularly powerful for research platforms that need to deliver insights to end users in natural language, or for internal teams that want to automate repetitive analysis tasks. As AI capabilities evolve, StockFit's MCP integration ensures your workflows can adapt and improve continuously.

ETF and Mutual Fund Exposure Analysis

Portfolio managers and risk analysts can use StockFit to understand the exposure of ETFs and mutual funds to individual companies and sectors. The platform provides detailed models covering fund mandate, portfolio construction, costs, sensitivities, and use cases. You can analyze how changes in underlying holdings affect fund performance, identify concentration risks, and evaluate whether a fund is delivering on its stated investment strategy. This is particularly valuable for constructing diversified portfolios, performing due diligence on fund investments, and monitoring ongoing exposure. The data is updated daily, allowing you to track changes in fund holdings and adjust your analysis in real time.

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

StockFit pulls financial data directly from SEC XBRL filings with no derived middle layer, meaning every number is exactly what companies reported to regulators. Most other APIs apply transformations, interpolations, or aggregations that can introduce errors or taxonomy drift. StockFit handles amended filings correctly, computes non-December fiscal years accurately, and reconstructs Q4 figures from 10-K plus 10-Q filings. Every fact is traceable back to its original source document, so you can verify any data point against the actual SEC filing. This direct sourcing approach eliminates the accuracy issues common in cheaper API tiers while avoiding the enterprise lock-in of more expensive solutions.

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

Yes, StockFit includes a native Model Context Protocol (MCP) server that works directly with Claude, Cursor, and other AI tools that support the MCP standard. This allows you to query financial data, run analysis, and build models using natural language through your preferred AI assistant. The MCP server handles authentication, query formatting, and data retrieval automatically. You can also use the standard REST API for direct programmatic access. The MCP integration is continuously updated as new AI tools and protocols emerge, ensuring your workflows stay current.

What types of financial data does StockFit provide?

StockFit provides 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 full SEC filings. The platform covers over 250 million facts from 5 million filings, updated daily. Beyond raw financials, StockFit offers rich economic models per company covering offerings, peers, operating levers, competitive advantages, flywheels, strategic initiatives, and failure modes. ETF and mutual fund exposure models cover mandate, portfolio construction, costs, sensitivities, and use cases. All data is standardized and model-ready with no taxonomy drift.

How does StockFit handle companies with non-December fiscal years?

StockFit is built to handle companies with fiscal years ending in months other than December. The platform computes fiscal periods correctly based on each company's actual fiscal calendar, ensuring that Q1, Q2, Q3, Q4, and full-year data align with the company's reporting structure. For Q4 specifically, StockFit reconstructs the quarter from 10-K and 10-Q filings rather than treating it as a residual. This means you get accurate period-over-period comparisons and consistent time series data regardless of when a company's fiscal year ends. The system is continuously improved as new filing patterns are identified and incorporated.