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Financial data APIs are the pipes that carry market and company data, quotes, statements, filings, holdings, into the tools investors use. You do not need to be technical to benefit from understanding them, because the quality and breadth of an AI research tool’s data connections determine the quality of its answers. This article explains financial data APIs in plain language and what to look for.

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What a financial data API is

An API is a way for one piece of software to request data from another. A financial data API is a service that delivers financial information on request: ask for a company’s revenue, its filings, or its current price, and the API returns it in a structured form a program can use. When an AI research tool reports a company’s margin, it is, behind the scenes, requesting that figure from a financial data API and relaying it to you. The figure is pulled, not remembered, which is exactly what you want.

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The main categories of financial data

  • Market data — real-time and historical prices, volume, technical indicators
  • Fundamental data — financial statements, ratios, earnings and dividend calendars
  • Filings and disclosures — regulatory filings and company announcements
  • Ownership data — institutional holdings, insider activity, fund flows
  • Specialized data — beyond the basics, sources for signals like congressional trading, 13F institutional holdings, and Form 4 insider transactions in US markets; for A-shares, additional data such as Dragon & Tiger lists, margin trading, and share pledging
Different APIs specialize in different categories. No single source covers everything well.

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Why data quality varies

Not all financial data is equal, and the differences matter:
  • Timeliness — some sources update in real time, others lag. A stale figure can mislead.
  • Accuracy and standards — data quality varies by provider, and for cross-border companies, accounting-standard differences can make figures hard to compare unless the source aligns them.
  • Coverage — a source strong in US equities may be thin on Hong Kong or A-shares.
  • Field definitions — different sources define and label fields differently, which has to be reconciled.
This is why a tool’s choice of sources, and how it handles their differences, affects the answers you get.

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Why orchestration matters more than count

A common claim is “we connect to X data sources.” The count is less important than the orchestration. Connecting many sources is easy; making them work together is hard. The real work is knowing which source to pull which field from, how to cross-check a figure across sources, how to reconcile different formats and definitions, and how to handle missing data gracefully. A tool that simply mounts many APIs and lets the model figure them out is fragile. A tool that orchestrates them, with logic for selection, cross-checking, and gap-handling, is reliable. When evaluating an AI research tool, ask not just how many sources it connects, but how it handles their differences.

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What this means for you

You will not call these APIs yourself, but understanding them helps you evaluate tools and read answers critically:
  • Prefer tools that pull live data over those that recall from training
  • Ask any tool to cite its sources so you can see what it used
  • Be aware that coverage varies by market, confirm the markets you care about are well covered
  • Value orchestration (cross-checking, gap-handling) over raw source count

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Where Driven fits

Driven connects to 245 endpoints across 28 categories and orchestrates them: each Skill knows which source to pull which field from, how to cross-check, and how to handle gaps. For Hong Kong, A-share, and Chinese ADR stocks, statement data is sourced to match local accounting standards. See Data sources and Data and coverage.