Developer Blog
Technical guides for developers building on the StockFit API. XBRL parsing, point-in-time data, backtesting patterns, and SEC EDGAR engineering notes.
Revenue Segmentation API: Revenue by Geography and Product From SEC Filings
The StockFit Revenue Segmentation API returns revenue split by geography (countries, US states, regions, residuals) and by product, period by period, parsed from XBRL dimensional facts in 10-K, 10-Q, and 20-F filings.
Company Peers API: Comparable Companies, Ranked, Done Right
Most financial data APIs have no company peers endpoint, and the one that does returns a flat, often-empty ticker list. The StockFit Company Peers API returns up to 6 same-industry comparables, ranked by revenue, classified by our proprietary SFIC system.
Executive Officers API and Executives API: A Developer Guide
Get a company's current CEO and executive officers, with titles, from an API. How the StockFit Executive Officers API is built from SEC Form 3/4/5 insider filings, with real examples.
StockFit ChatGPT App: Stock Fundamentals and SEC Filings Inside ChatGPT
The StockFit app is live in ChatGPT. Add it, sign up free, and ask about earnings, financial statements, insider trades, and ETF holdings. Every answer comes from SEC EDGAR filings.
Fundamental Stock Analysis Explained: What It's Used For and How Investors Actually Do It
How professional and retail investors use fundamental stock analysis to value companies, screen stocks, and build conviction, plus the financial data inputs that power every step.
Insider Cluster Buys: Do They Beat the S&P 500? 7-Year Test
Three or more officers or directors buying in a 30-day window: does this insider buying signal beat the market? We tested 45 discretionary S&P 500 clusters from 2018 to 2024 against SPY using Form 4 data.
Earnings Calendar API and Earnings API: A Developer Walkthrough
Every endpoint in the StockFit earnings API: the earnings calendar API (upcoming reports, batch lookup, single-ticker date), EPS history, snapshot, trends, growth, quality, and 8-K Item 2.02. Live NVDA JSON and cURL examples.
Form 10-K Financial Statements (and Every Other SEC Filing We Read So You Don’t Have To)
Where the income statement, balance sheet, and cash flow statement actually live — plus 17 more SEC filings that power the StockFit API. What each form is for, what data we extract, and which endpoint serves it back to you.
StockFit API vs. Alpha Vantage for Fundamentals
Both speak JSON. One reads SEC filings. The other reads its own normalized table. A side-by-side look at standardized statements, raw XBRL, filings access, ownership, insider data, executive comp, ETFs, and pricing — with verifiable sources on both sides.
A Deep Lens on Any ETF: From Free-Tier Profile to Forensic Analysis
Walk SPY end-to-end with the StockFit fund endpoints. Start with /profile and /holdings on the entry tiers, then move into the N-PORT and N-CEN power tools (flows, changes, overlap, fund health, fee analysis, service providers) for a forensic view your brokerage page does not show. Live JSON, charts, and a composite playbook.
The Holy Grail API for Stock Backtesting: Point-in-Time Fundamentals With a Full Amendment Audit Trail
Look-ahead bias is the silent killer of quant backtests. StockFit is the only fundamentals API that ships a per-fact, per-amendment audit trail with `before` values, a true point-in-time ledger you can mathematically rewind. Available on every tier, including free.
Sector & Industry-Specific Metrics: StockFit API vs. financialmodelingprep.com
Why a single key-metrics shape silently fails for banks, insurers, and REITs — and how StockFit returns the right metrics for the right business model. With real responses, code examples, and a step-by-step screen.
StockFit API vs. sec-api.io for Financial Data Conversion
How the two products approach XBRL-to-JSON conversion, insider trading data, and developer ergonomics. Verified facts on both sides, no vendor spin.
Converting XBRL Financials to JSON for Backtesting
Why SEC XBRL is hard to use directly, what "as-reported" really means, and how to assemble a clean point-in-time dataset for systematic strategies.
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