StockFit API

StockFit API is a specialized financial data platform engineered explicitly for developers, quantitative analysts, and research platforms that require direct, reliable, and auditable access to SEC filing data. The core problem StockFit solves is the longstanding compromise in financial data access: cheap API tiers deliver inaccurate or incomplete information, while enterprise contracts are prohibitively expensive for startups and independent researchers. StockFit completely fills this gap by pulling financial data directly from SEC XBRL filings, eliminating any derived middle layer. This means 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 models. The platform covers an extensive range of data types including fundamentals, ownership data, ETF and mutual fund exposure, insider transactions, and all filing types. StockFit handles complex edge cases 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, valuation, and backtesting. The API delivers standardized financials, sector-aware metrics, and source-cited economic models that are structured specifically for modeling and backtesting workflows.

Direct SEC XBRL Data with Full Audit Trail

Every financial fact returned by StockFit API is sourced directly from SEC XBRL filings with a complete audit trail. The API provides source document identifiers for each fact, allowing users to trace any number back to its original filing. This eliminates the opacity and potential errors introduced by data aggregators that derive or estimate financial figures. Users receive standardized financial statements including income statements, balance sheets, and cash flow statements, with each fact linked to its specific filing source. This feature is critical for compliance, model validation, and research integrity.

Comprehensive Economic Models and Company Analysis

StockFit goes beyond raw financial data by providing rich economic models for each company. These models include detailed analysis of offerings, peer comparisons, operating levers, competitive advantages, business flywheels, strategic initiatives, and failure modes. This structured economic context is designed to be AI-friendly and perfect for LLM workflows, enabling sophisticated analysis without requiring users to derive these insights manually. The models are sector-aware and provide standardized metrics that facilitate cross-company comparisons.

Advanced ETF and Mutual Fund Exposure Modeling

The platform offers sophisticated modeling of ETF and mutual fund exposure that goes far beyond simple holdings data. StockFit models fund mandates, portfolio construction methodologies, cost structures, sensitivities, and use cases. This data is presented in an AI-friendly format that enables users to understand not just what funds hold, but why they hold them and how they might behave under different market conditions. This feature is particularly valuable for portfolio construction, risk management, and investment strategy development.

Complex Filing Handling and Data Standardization

StockFit API expertly handles the complexities of SEC filings that other APIs struggle with. The platform correctly processes amended filings, companies with non-December fiscal years, and performs Q4 reconstructions from 10-K and 10-Q data. All financial data is standardized into a consistent, model-ready format that eliminates taxonomy drift, meaning users get comparable data across companies and time periods without manual normalization. The API returns over 250 million facts from 5 million filings, updated daily.

Quantitative Backtesting and Financial Modeling

Quantitative analysts and researchers can use StockFit API to build robust backtesting frameworks with accurate, auditable financial data. The standardized financials and sector-aware metrics ensure that models are built on consistent data across thousands of companies and multiple fiscal periods. The direct SEC sourcing eliminates the risk of data errors that could invalidate backtest results, while the comprehensive economic models provide additional context for understanding company performance and market dynamics.

AI-Powered Financial Analysis and LLM Integration

Developers building AI-powered financial analysis tools can leverage StockFit's AI-friendly data formats for seamless LLM integration. The economic models, company analysis, and fund exposure data are structured specifically for consumption by large language models, enabling sophisticated natural language queries about company fundamentals, competitive positioning, and investment strategies. The source-cited data allows AI systems to provide verifiable answers with proper attribution.

Portfolio Construction and Risk Management

Investment professionals can use StockFit's ETF and mutual fund exposure modeling to construct and manage portfolios with greater precision. The detailed fund mandate, portfolio construction, and sensitivity data enable users to understand the true drivers of fund performance and risk. Combined with insider transaction data and ownership information, users can build comprehensive risk models that account for multiple layers of market exposure and potential catalysts.

Fundamental Research and Valuation Analysis

Equity researchers and analysts can access standardized financial statements and economic models for thousands of companies through a single API. The platform handles complex filing scenarios automatically, ensuring that data for companies with non-standard fiscal years or amended filings is still comparable and usable. The peer comparison data and competitive advantage analysis provide additional context for valuation work, while the traceable source data supports research integrity and compliance requirements.

How does StockFit API ensure data accuracy and auditability?

StockFit API sources all financial data directly from SEC XBRL filings, meaning there is no derived or estimated middle layer. Every fact returned includes source document identifiers that link back to the original SEC filing. This ensures that every number used in your models is traceable, verifiable, and auditable. The platform handles complex scenarios like amended filings and non-standard fiscal years correctly, further ensuring data integrity.

What types of financial data does StockFit API cover?

StockFit API covers a comprehensive range of financial data including fundamentals (income statements, balance sheets, cash flow statements), ownership data, insider transactions, and all SEC filing types. Additionally, the platform provides rich economic models per company including offerings, peers, operating levers, competitive advantages, flywheels, strategic initiatives, and failure modes. For ETFs and mutual funds, the API models mandate, portfolio construction, costs, sensitivities, and use cases.

How frequently is the data updated?

StockFit API is updated daily with new filings and data points. The platform currently contains over 250 million facts from 5 million filings, and this data is continuously refreshed as new SEC filings become available. This ensures that users always have access to the most current financial information for their analysis, backtesting, and modeling needs.

Is StockFit API suitable for companies with non-standard fiscal years?

Yes, StockFit API is specifically designed to handle non-December fiscal years, which is a common challenge with other financial data providers. The platform also correctly processes amended filings and performs Q4 reconstructions from 10-K and 10-Q data. All financial data is standardized into a consistent format that eliminates taxonomy drift, making it easy to compare companies regardless of their fiscal year structure.