StockFit API
Financial data API for public company filing access
Details
- Follow on
- Target Audience
- DevelopersInvestorsData Scientists
- Pricing
- Freemium from $19
- Alternative To
FinancialModelingPrep
alphavantage.co
About StockFit API
Access company financials, ownership, ETF/MF exposure, insider trades, filings, and much more! All structured directly from SEC XBRL. 250+ million facts, 5+ million filings. No adjusted metrics and everything fully traceable. Everything is updated daily. Free tier, no credit card. StockFit API is built for investors, quant developers, and research platforms that want to go straight to the facts without draining their pockets. For years I kept hitting the same wall: every time I started a side project that needed real financial data, I'd burn 80% of my time just parsing SEC filings - and never get to the actual idea. The weird truth is that most "premium" financial data sitting behind $500–$2,000/month paywalls is actually already public on SEC's EDGAR. Every 10-K, 10-Q, 13F, insider Form 4, and proxy statement is there, for free. The catch is that EDGAR is almost aggressively hostile to practical use. XBRL filings are tagged inconsistently across filers, formats drift between fiscal years, Q4 isn't even reported as a standalone period (you have to derive it from the 10-K and the 9-month Q3 figures), and the same financial concept can map to half a dozen different tags depending on who filed. Every fintech dev I know has written, and thrown away, their own EDGAR parser at least once. StockFit API is what I wished existed instead. It ingests EDGAR daily through a real XBRL pipeline (Arelle under the hood), reconstructs missing periods, normalizes thousands of raw concepts into a curated schema, and hands you clean JSON. Financial statements, ownership, insider transactions, ETF holdings, 13F institutional holdings, executive comp - all from one REST API with proper OpenAPI docs and an MCP server for LLM agents. The free tier is generous enough to test the product. My goal is simple: institutional-grade SEC data, accessible for everyone. No scraping, no XBRL rabbit holes, no $2k/month minimums.
Product Insights
StockFit API provides structured financial data directly from SEC XBRL filings, offering high-fidelity access to fundamentals, insider trades, and institutional holdings. It operates as a REST API and MCP server, ensuring daily updates and audit-traceable facts for data-intensive projects.
- Delivers over 250 million facts processed through a professional XBRL pipeline.
- Provides a Model Context Protocol (MCP) server for direct integration with LLM agents.
- Features a freemium pricing model starting at 19 dollars without requiring a credit card for the free tier.
- Includes full audit trails to trace data back to original SEC EDGAR source filings.
Ideal for: Developers and data scientists can utilize this tool to integrate normalized SEC financial data into trading systems or research platforms without manual XBRL parsing.
StockFit API serves as a structured alternative to financial data providers such as FinancialModelingPrep and AlphaVantage.
Discount Codes
20% off first month(-20% OFF)
Valid until Jun 29, 2026
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Product Updates (1)
Structured Economic Company Model with Audit-Trails
At a time where more and more people use AI as their financial advisor, having data with a real audit trail is paramount. Besides the usual data for fundamentals, I've always wanted to have a more deep lens into what makes a company really perform. These are things like its offerings, flywheels, failure modes (risks), competitive advantages, etc. Now if you look for any of that data in any API, you are on a dead path. Nobody is touching any of that, for good reason. It is really really hard to get. Not anymore! StockFit API now provides structured economic models for every company containing: offerings, value delivery, monetization, cost structure, reinvestment, cash conversion, sensitivities, unit economics, capital allocation, management style, peers, operating levers, strategic initiatives, structural advantages, flywheels, and failure modes. Here is an example of a flywheel for NVDA: { "name": "Developer ecosystem → platform value → adoption loop", "loop": [ "More developers using CUDA and software tools", "More applications optimized for NVIDIA platforms", "Higher platform value and broader adoption across end markets", "More developers using CUDA and software tools" ], "impact": "growth", "sources": [ { "url": "https://www.sec.gov/Archives/edgar/data/1045810/000104581026000021/nvda-20260125.htm", "source": "10-K", "section": "Item 1, Business", "quote": "There are over 7.5 million developers worldwide using CUDA and our other software tools..." } ] } Without an audit trail, it would be a fun toy, but with that, it can become infrastructure! Just think about the possibilities! That data fed into an AI will make it stop hallucinating, and start really talking with a deep understanding of the business coming from auditable sources to defend the claims.
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Clean structured access to public company filings via API is something a lot of fintech builders need. This fills a real gap for financial data pipelines.
@chaudharyarun5797 Thanks - 'fills a real gap' is exactly the framing I was going for. Out of curiosity, are you building a pipeline or app yourself right now, or just seeing this pattern a lot?
Built entirely solo over the past year. Would love honest feedback on the API shape, the docs, and what you'd want to see next. What's the first thing you'd build with it?