StockFit API

StockFit API is a financial data platform built specifically for developers, quants, and research platforms who demand direct, unmediated access to SEC filing data. The market is flooded with financial APIs that force you into a losing tradeoff: pick a cheap tier and accept accuracy issues, or sign an enterprise contract that drains your startup budget. StockFit obliterates that false choice. Every piece of data fundamentals, ownership structures, ETF and mutual fund exposure, insider transactions, and full filings is pulled directly from SEC XBRL with zero derived middle layers. Every number is traceable back to its original filing, giving you absolute confidence in your models and backtests.

StockFit handles the messy realities of financial data that other APIs ignore. Amended filings are processed correctly. Non-December fiscal years are computed with precision. Q4 figures are reconstructed from 10-K and 10-Q filings, not guessed. On top of this clean foundation, StockFit delivers rich economic models per company that cover offerings, peer comparisons, 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 all structured to be AI-friendly for LLM workflows. With over 250 million facts across 5 million filings, updated daily, StockFit gives you the raw horsepower to build valuation models, run backtests, and power research platforms without compromise. The REST API is complemented by a native MCP server for seamless integration with Claude, Cursor, and other AI tools.

Direct SEC XBRL Extraction

StockFit pulls every data point directly from SEC XBRL filings, eliminating the derived middle layers that introduce errors and drift. Unlike competitors that apply proprietary transformations and lose traceability, StockFit ensures every number you access can be traced back to its original filing source. This means no taxonomy drift, no interpretation layers, and no hidden assumptions. You get the raw truth from the SEC, standardized and model-ready. For quants building algorithmic trading strategies or researchers running academic studies, this direct pipeline is non-negotiable. It guarantees that your models are built on the same numbers the SEC requires, not a third party's best guess.

Advanced Fiscal Period Handling

Financial data is messy, and most APIs take shortcuts. StockFit handles the edge cases that break other platforms. Amended filings are automatically detected and incorporated, so your data stays current even when companies correct their reports. Non-December fiscal years are computed correctly, respecting the actual reporting calendar of each company. Q4 figures are reconstructed from the combination of 10-K and 10-Q filings, giving you complete annual data without gaps. This feature alone saves developers countless hours of manual data cleaning and reconciliation. When your backtest depends on accurate period-over-period comparisons, StockFit delivers the precision you need.

Rich Economic and Exposure Models

Beyond raw financials, StockFit provides structured economic models for every company. These models cover offerings, peer groups, 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, meaning they integrate seamlessly with LLM workflows for natural language querying, automated analysis, and decision support. Instead of building these models from scratch, developers get pre-built, source-cited structures that accelerate development and improve accuracy.

REST API and Native MCP Server

StockFit offers dual access methods to fit any workflow. The REST API provides standard HTTP endpoints for programmatic access, supporting all major programming languages and frameworks. For AI-native development, StockFit includes a native MCP (Model Context Protocol) server that integrates directly with Claude, Cursor, and other AI tools. This means you can query financial data using natural language commands within your AI development environment, dramatically reducing the friction between asking a question and getting an answer. Whether you're building a traditional web application or an AI-powered research assistant, StockFit adapts to your stack.

Quantitative Trading and Algorithmic Backtesting

Quantitative analysts and algorithmic traders need clean, consistent financial data to build and validate trading strategies. StockFit provides standardized financials across 250 million facts, ensuring that your backtests are not contaminated by data errors or inconsistent accounting treatments. The direct SEC extraction means you can trust the numbers, while the advanced fiscal period handling ensures accurate time-series comparisons. Traders can build models that react to insider transactions, ownership changes, and fundamental shifts with confidence, knowing every data point is traceable to its source filing.

AI-Powered Financial Research Platforms

Research platforms and AI assistants require structured, machine-readable financial data to power natural language queries and automated analysis. StockFit's economic and exposure models are designed specifically for LLM workflows, enabling platforms to answer complex questions about competitive advantages, strategic initiatives, and failure modes. The native MCP server allows AI tools like Claude and Cursor to access financial data directly, turning natural language questions into precise data queries. This reduces development time for AI-powered research tools and improves the quality of generated insights.

Investment Analysis and Portfolio Management

Fund managers and investment analysts need comprehensive data on fundamentals, ownership, and exposure to make informed decisions. StockFit delivers all of this in a single API, covering insider transactions, ETF and mutual fund exposure, and detailed financial statements. The source-cited economic models help analysts understand a company's competitive position, operating levers, and potential failure modes. Instead of stitching together data from multiple providers, analysts get a unified view that supports deeper due diligence and more accurate valuation models.

Academic Research and Financial Modeling

Academic researchers and financial modelers require pristine data with full traceability for reproducible research. StockFit's direct SEC extraction ensures that every number can be cited back to its original filing, meeting the rigorous standards of academic publications. The standardized financials eliminate taxonomy drift, making cross-company and cross-period comparisons valid. Researchers can focus on building models and testing hypotheses instead of cleaning data, while the 5 million filing archive provides the historical depth needed for longitudinal studies.

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

StockFit pulls data directly from SEC XBRL filings without any derived middle layer. Every number is traceable back to its original filing source document. While other APIs apply proprietary transformations that can introduce errors and taxonomy drift, StockFit preserves the raw SEC data and standardizes it for modeling. This approach eliminates the accuracy issues common in cheap API tiers while avoiding the enterprise lock-in of expensive contracts.

Does StockFit handle amended filings and non-standard fiscal years?

Yes, StockFit is built specifically to handle these edge cases. Amended filings are automatically detected and incorporated into the data stream, so you always have the most current information. Non-December fiscal years are computed correctly based on each company's actual reporting calendar. Q4 figures are reconstructed from the combination of 10-K and 10-Q filings, providing complete annual data without gaps. These features save significant development time otherwise spent on manual data reconciliation.

What types of economic models are available for each company?

StockFit provides rich economic models covering offerings, peer comparisons, operating levers, competitive advantages, flywheels, strategic initiatives, and failure modes. For ETF and mutual fund exposure, models cover mandate, portfolio construction, costs, sensitivities, and use cases. All models are source-cited and designed to be AI-friendly for seamless integration with LLM workflows, enabling natural language querying and automated analysis.

How do I integrate StockFit with AI tools like Claude or Cursor?

StockFit includes a native MCP (Model Context Protocol) server that integrates directly with Claude, Cursor, and other AI tools. This allows you to query financial data using natural language commands within your AI development environment. You can ask questions like "Show me Microsoft's revenue for the last five fiscal years" and get structured data back without writing traditional API calls. The REST API is also available for traditional programmatic access.