StockFit API

StockFit API is a specialized financial data platform engineered specifically for developers, quantitative analysts, and research platforms that require direct, uncompromising access to SEC filing data. The platform addresses a critical gap in the financial data market where users have traditionally faced an unsatisfactory choice between low-cost tiers delivering inaccurate or incomplete information and expensive enterprise contracts that strain startup budgets. StockFit eliminates this compromise entirely by pulling financial data directly from SEC XBRL filings, ensuring no derived middle layer exists and every single number remains traceable back to its original filing source. This direct sourcing provides users with confidence that their modeling is based on accurate, auditable data. The platform covers an extensive range of financial information including fundamentals, ownership data, ETF and mutual fund exposure, insider transactions, and all types of filings. StockFit handles complex data scenarios 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, the platform delivers rich economic models per company including offerings, peers, operating levers, competitive advantages, flywheels, strategic initiatives, and failure modes. For ETF and mutual fund exposure, StockFit models mandate, portfolio construction, costs, sensitivities, and use cases in an AI-friendly format optimized for LLM workflows. With over 250 million facts, 5 million filings, and daily updates, StockFit is built for serious financial analysis, valuation work, and backtesting operations.

Direct SEC XBRL Data Sourcing

StockFit pulls financial data directly from SEC XBRL filings, eliminating any derived middle layer that could introduce errors or inconsistencies. Every single number returned by the API is traceable back to its original filing, giving users complete confidence in the accuracy and auditability of their data. This direct sourcing approach ensures that what you are modeling matches exactly what companies reported to regulators.

Standardized Financials with No Taxonomy Drift

The platform delivers standardized financial statements including income statements, balance sheets, and cash flow statements that are model-ready and free from taxonomy drift. StockFit normalizes financial data across different reporting periods, accounting standards, and company-specific presentations, ensuring that analysts can compare metrics across companies and time periods without manual adjustments or data cleaning.

Comprehensive Coverage and Complex Scenario Handling

StockFit covers over 250 million facts and 5 million filings with daily updates, providing an extensive dataset for serious financial analysis. The platform handles complex data scenarios that other APIs ignore, including amended filings, non-December fiscal years, Q4 reconstructions from 10-K and 10-Q data, and insider transaction details. This comprehensive coverage ensures users have access to the full picture of company financial health.

AI-Ready Economic Models and Fund Analysis

Beyond raw financial 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 specifically optimized for LLM workflows and advanced analytical applications.

Quantitative Backtesting and Financial Modeling

Quants and quantitative analysts can use StockFit to build and backtest financial models with confidence, knowing that every data point is directly sourced from SEC filings and fully auditable. The standardized financials and sector-aware metrics eliminate data cleaning overhead, allowing analysts to focus on model development and strategy testing rather than data validation.

Valuation Analysis for Investment Research

Investment researchers and analysts can perform comprehensive valuation analysis by accessing standardized financials, ownership data, and economic models through a single API. The platform provides the fundamental data needed for discounted cash flow models, comparable company analysis, and precedent transactions, all with traceable source citations that support investment committee presentations.

AI-Powered Financial Analysis and LLM Integration

Developers building AI-powered financial tools can leverage StockFit's AI-friendly data formats to integrate financial analysis into large language model workflows. The platform provides economic models, fund analysis, and structured financial data that LLMs can process effectively, enabling applications like automated earnings analysis, portfolio optimization, and financial question-answering systems.

ETF and Mutual Fund Exposure Analysis

Portfolio managers and risk analysts can use StockFit to model ETF and mutual fund exposures in detail, including mandate analysis, portfolio construction, cost structures, sensitivities, and use case scenarios. This comprehensive fund analysis enables better portfolio construction, risk management, and investment decision-making across diversified investment strategies.

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

StockFit pulls financial data directly from SEC XBRL filings, meaning there is no derived middle layer that could introduce errors or inconsistencies. Every single number returned by the API is traceable back to its original filing, providing complete auditability. This direct sourcing approach ensures that what you are modeling matches exactly what companies reported to regulators, eliminating the accuracy concerns common with aggregated or derived data sources.

What types of financial data does StockFit cover?

StockFit covers an extensive range of financial information including fundamentals such as income statements, balance sheets, and cash flow statements. The platform also provides ownership data, ETF and mutual fund exposure analysis, insider transactions, and all types of SEC filings. With over 250 million facts and 5 million filings, StockFit delivers comprehensive coverage for serious financial analysis across multiple asset classes and reporting requirements.

How does StockFit handle complex filing scenarios like amended filings or non-December fiscal years?

StockFit is specifically designed to handle complex data scenarios that other APIs ignore. The platform processes amended filings correctly, ensuring that restated financials are properly reflected. It also handles non-December fiscal years, providing accurate period alignment for companies with different fiscal year ends. Additionally, StockFit performs Q4 reconstructions from 10-K and 10-Q data, giving users complete quarterly and annual financial statements.

Is StockFit suitable for use with AI and machine learning applications?

Yes, StockFit is specifically built for AI and machine learning workflows. The platform provides rich economic models per company including offerings, peers, operating levers, competitive advantages, flywheels, strategic initiatives, and failure modes in AI-friendly formats. For ETF and mutual fund exposure, StockFit models mandate, portfolio construction, costs, sensitivities, and use cases optimized for LLM workflows, making it ideal for advanced analytical applications.