StockFit API

StockFit API is a specialized financial data platform that provides developers, quantitative analysts, and research platforms with direct, unfiltered access to SEC filing data. The core value proposition of StockFit API lies in its ability to eliminate the common tradeoffs found in the financial data industry. Many financial APIs force users into a difficult choice between cheap, low-tier subscriptions that suffer from accuracy issues and data gaps, or expensive enterprise contracts that drain the budgets of startups and small research firms. StockFit API fills this critical gap by offering a high-quality, reliable, and affordable alternative. The platform delivers a comprehensive suite of financial data points including fundamentals, ownership structures, ETF and mutual fund exposure, insider transactions, and raw filings. All data is pulled directly from SEC XBRL filings, meaning there is no derived middle layer that can introduce errors or bias. Every single number provided by the API is traceable back to its original filing, ensuring complete transparency and verifiability. The platform is built for real-world financial modeling and analysis. It handles complex edge cases such as amended filings, correctly computes financial data for companies with non-December fiscal years, and intelligently reconstructs Q4 data from 10-K and 10-Q filings. Beyond raw financials, StockFit API 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 are equally detailed, covering mandate, portfolio construction, costs, sensitivities, and use cases. With over 250 million facts drawn from more than 5 million filings, updated daily, StockFit API serves as a robust data backbone for valuation, backtesting, and AI-driven financial workflows. It offers both a standard REST API and a native MCP server for seamless integration with AI tools like Claude and Cursor.

Direct SEC XBRL Data with Full Traceability

StockFit API eliminates data intermediaries by pulling financial information directly from SEC XBRL filings. This approach ensures that every data point, from revenue figures to complex ownership structures, is derived from the original source documents. Users can trace any number back to its specific filing, providing an unprecedented level of transparency and auditability. This feature is critical for quantitative models, backtesting frameworks, and any application where data integrity is paramount.

Intelligent Fiscal Period Handling and Reconstruction

The API is engineered to handle the complexities of corporate financial reporting. It correctly processes companies with non-December fiscal years, ensuring that period boundaries align with actual business cycles. A particularly powerful capability is the automatic reconstruction of Q4 financial data. StockFit API intelligently computes Q4 figures by subtracting the cumulative 10-Q data from the annual 10-K filing, providing a complete and accurate quarterly picture that many other APIs fail to deliver.

Comprehensive Economic and Exposure Models

Beyond standard financial statements, StockFit API provides rich, structured economic models for each company. These models cover strategic elements such as competitive advantages, operating levers, flywheels, strategic initiatives, and potential failure modes. For ETF and mutual fund exposure, the models detail mandate, portfolio construction methodology, cost structures, and sensitivities. These models are designed to be AI-friendly, making them immediately usable for LLM workflows and sophisticated analysis without requiring extensive data transformation.

Dual API Access with REST and Native MCP Server

StockFit API offers flexible integration options to suit different technical environments. The standard REST API provides traditional HTTP-based access for most applications and programming languages. Additionally, the platform includes a native MCP (Model Context Protocol) server, enabling direct, high-performance integration with AI tools such as Claude, Cursor, and other LLM platforms. This dual approach ensures that developers can choose the most efficient method for their specific use case, whether building a traditional web application or an AI-powered research tool.

Quantitative Backtesting and Strategy Development

Quantitative analysts and algorithmic traders can leverage StockFit API to build and test financial models with high-fidelity data. The direct SEC XBRL sourcing ensures that backtesting is performed on accurate, traceable data, eliminating the risk of using derived or potentially flawed datasets. The standardized financials and sector-aware metrics are structured specifically for modeling, allowing quants to focus on strategy logic rather than data cleaning and normalization. The ability to handle amended filings and non-standard fiscal periods ensures that backtests reflect real-world conditions.

AI-Powered Financial Research and Analysis

Researchers and analysts using large language models can integrate StockFit API to ground their analysis in verified financial data. The native MCP server provides a direct pipeline for LLMs like Claude and Cursor to access structured financials, economic models, and exposure data. The AI-friendly formatting of economic models, including competitive advantages and failure modes, allows LLMs to generate nuanced, context-aware financial reports and investment theses. This use case significantly reduces the risk of AI hallucination by providing a reliable, traceable data foundation.

Fund and Portfolio Exposure Analysis

Investment professionals and asset managers can utilize StockFit API to conduct deep dives into ETF and mutual fund exposure. The detailed models provide insights into fund mandates, portfolio construction processes, cost structures, and sensitivities to various market factors. This enables comprehensive risk assessment and portfolio optimization. Users can analyze how specific funds are positioned, identify concentration risks, and understand the underlying drivers of fund performance, all supported by directly sourced data from SEC filings.

Startup and Small-Firm Financial Modeling

Early-stage companies and small research firms often face prohibitive costs for enterprise-grade financial data. StockFit API provides a cost-effective alternative that does not compromise on data quality or depth. Startups can access the same level of detailed financials, ownership data, and insider transaction information as larger institutions. This democratization of data allows smaller teams to build sophisticated valuation models, conduct competitive analysis, and make informed strategic decisions without the financial burden of traditional enterprise data contracts.

What types of financial data does StockFit API provide?

StockFit API provides a comprehensive range of financial data directly sourced from SEC XBRL filings. This includes standardized financial statements such as income statements, balance sheets, and cash flow statements. Additionally, the API offers data on ownership structures, ETF and mutual fund exposure, insider transactions, and raw filings. The platform also includes rich economic models per company covering offerings, peers, operating levers, competitive advantages, flywheels, strategic initiatives, and failure modes. In total, the API provides access to over 250 million facts from more than 5 million filings.

How does StockFit API ensure data accuracy and traceability?

Data accuracy is ensured through direct sourcing from SEC XBRL filings, eliminating any derived middle layer that could introduce errors or bias. Every data point provided by the API is traceable back to its original filing document. This means users can verify any number by referencing the specific SEC filing from which it was extracted. The platform also handles complex edge cases such as amended filings and non-December fiscal years correctly, further ensuring data integrity and reliability for modeling and analysis.

Can StockFit API integrate with AI tools like Claude or Cursor?

Yes, StockFit API is specifically designed for AI integration. The platform includes a native MCP (Model Context Protocol) server that enables direct, high-performance integration with AI tools such as Claude and Cursor. This allows large language models to access structured financial data, economic models, and exposure information directly. The data is formatted to be AI-friendly, making it immediately usable for LLM workflows without requiring extensive preprocessing or transformation. Standard REST API access is also available for traditional integration methods.

How often is the data in StockFit API updated?

StockFit API data is updated daily. This ensures that users always have access to the most current financial information available from SEC filings. The daily update cycle covers all 5 million plus filings in the database, including new submissions, amendments, and corrections. This frequent refresh rate is critical for applications that require up-to-date information, such as real-time valuation models, trading strategies, and market analysis. Users can rely on the API to reflect the latest publicly available financial data from the SEC.