EOD Historical Data APIs

The market's own diary, kept properly. EOD Historical Data APIs - filed under Diversified Capital Markets, compiled by EODHD (operated by Unicorn Data Services) - is not one feed but 45-plus endpoint families organized into seven groups, and this record catalogs the whole suite. Market data covers end-of-day history, intraday bars, live OHLCV quotes, real-time streams, US tick data, delisted companies, splits and dividends, a stock screener and technical indicators. Fundamental & economic data adds SEC filings (10-K, 10-Q, 8-K), fundamentals for stocks, ETFs, funds, indices and crypto, ESG scores, historical market cap, a dividends-earnings-IPO-splits calendar, Form 4 insider transactions, and rates from SOFR and Fed Funds to ECB and BoE.

The remaining groups widen the frame: exchange & instruments listings that map CUSIP, ISIN, FIGI, LEI and CIK identifiers onto symbols, alternative & derived data with US options chains carrying Greeks at end of day, economic events, a sentiment-tagged news feed, commodities, congressional trades, OFAC sanctions lists, real estate series and US Treasury rates. The scale claim is concrete: more than 150,000 tickers worldwide, US history back to earliest availability (Ford opens in June 1972), most non-US exchanges starting January 3, 2000.

In Datadory's catalog of 1,744 datasets across 159 viable industries, this record scores 8/10 for quality - a band shared by 427 records against a 7.81 catalog-wide average - and anchors the diversified capital markets shelf as its commercial per-ticker suite: the one record where a ticker, not an economy or a venue, is the unit of account.

Get a sample of this dataset

Captured live during the August 2026 research pass. One row tells you most of what you need: the seven-column spine, and the split discipline between what traded and what compounding requires.

date           2026-08-10
open            306.83
high            308.26
low             304.61
close           308.26
adjusted_close  308.26
volume       44812500

date           2026-08-11
open            307.75
high            309.97
low             302.79
close           304.91
adjusted_close  304.91
volume       37476700

date           2026-08-12
open            305.10
high            305.66
low             300.57
close           302.25
adjusted_close  302.25
volume       41657800

Reading the rows: close stays raw as-traded while adjusted_close corrects for both splits and dividends - on these three sessions they coincide because no corporate action intervened, and the day one does is precisely the day careless return math invents a phantom crash. volume adjusts for splits only, never dividends, so share-count histories stay honest without pretending cash distributions re-scale shares. Every ticker repeats this identical seven-field shape, so a parser written once runs unchanged from mega-caps to delisted micro-caps.

Seven fields define each end-of-day bar, all verified against captured rows during the August 2026 review:

• date - the trading session in YYYY-MM-DD (2026-08-14), the join key everything else hangs off.
• open / high / low / close - raw, unadjusted session prices (306.83 open). What actually printed, kept separate from what total-return math needs.
• adjusted_close - close corrected for both splits and dividends (305.93), the column signal research reads.
• volume - session volume, split-adjusted but deliberately not dividend-adjusted (28229400).

That spine stays constant across the whole universe. The wider suite's depth rides above it rather than inside it: intraday intervals and US tick data at finer resolution, fundamentals payloads per ticker, SEC filing text, options chains with Greeks, insider transactions and economic calendars. Those extensions fold under additional fields on request - name the families you want when you ask and they ship alongside the price spine in the same table.

Three chips, honestly drawn:

• Geography - global: more than 150,000 tickers across major US, European and Asian exchanges, extending into crypto pairs and FX. One honest caveat travels with those last two: CFD and forex series aggregate market-maker quotes rather than exchange feeds and are flagged indicative - fine for research on levels, wrong for execution modeling.
• Temporal - thirty-plus years for many instruments. US stocks, ETFs and funds reach earliest availability (Ford from June 1972); most non-US exchanges begin January 3, 2000. Delisted companies stay retrievable, so survivors-only bias stays a choice rather than a side effect.
• Granularity - one row per instrument per session at end-of-day resolution, with intraday intervals, real-time streams and US tick-level depth available across the wider suite depending on family.

API, files, or your warehouse. Daily, weekly, or hourly.

The bars land however your stack wants them - pushed to storage, served over an endpoint, or synced straight into your database, as JSON or CSV. You pick the channel and the cadence; holding the adjustment conventions steady across forty-five families is our problem, not your integration backlog. Name the tickers, the families and the span when you request the sample; the sample ships first either way.

Per-ticker price-and-fundamentals plumbing earns its keep in specific jobs:

• Investors & quants - backtest on thirty-plus years of adjusted history across 150,000-plus tickers; enough depth to trust a factor through several regimes, with delisted names included so the survivorship trap is optional.
• Data scientists & ML engineers - engineer features from a stable seven-column price spine plus fundamentals and options Greeks, with schemas documented tightly enough to survive ticker-by-ticker expansion.
• Developers & data-product builders - build screens, dashboards and watchlist products where corporate actions, calendars and identifier mapping arrive pre-resolved instead of hand-stitched.
• Competitive intel & product teams - track listed-company performance, filings cadence and insider behavior around competitors and prospects through the same tables analysts use.
• Journalists, academics & students - pull citable price histories and fundamentals for charts and teaching examples without splicing incompatible vendor formats.

Two persona groups get outsized value here. Investors and quant researchers get the raw material of the entire backtesting discipline - adjusted prices deep enough to trust, joined to the filings and corporate actions that explain the jumps. See investors and quants use cases.

Developers and data-product builders get one consistent contract across forty-five-plus families, so adding options Greeks beside a price panel is a query change rather than a second vendor integration. See developers and builders use cases.

Data scientists and ML engineers (2/3 relevance) get typed, stable columns that drop straight into feature pipelines. See data scientists use cases. Competitive-intel and product teams (2/3) read competitor equity behavior off the same rows. See competitive intel use cases. Journalists, academics and students (2/3) cite one source for every chart. Market researchers and consultants (1/3) screen listed companies during diligence. See market researchers use cases.

Three things worth knowing upfront. First, mind which close you mean: raw close answers what printed, adjusted_close answers what holding through splits and dividends earned, and mixing them mid-series is the classic way a backtest quietly breaks - signals on adjusted, fills on raw. Second, FX and CFD series are aggregated from market makers rather than exchange feeds and are labeled indicative, so treat those rows as level context, never as execution evidence. Third, breadth claims have edges: the supported-exchanges inventory was unreachable during our verification pass, so the 150,000-plus ticker figure comes from the publisher's own coverage documentation, and per-family depth varies - name the tickers, families and window you want when you ask, and the sample arrives cut to match, gaps flagged rather than papered over.

Notes worth reading next:

• Diversified capital markets data hub - the pooled view of the industry slice, nine primary datasets deep, from regulator transparency to this per-ticker suite.
• Tiingo Financial Data API - the nearest rival, likewise scored 8 out of 10: thirteen-field CRSP-adjusted rows and tagged news across roughly 109,000 symbols against this suite's 150,000-plus; the trade-offs are set out honestly.
• World Bank Capital Markets Indicators vs EOD Historical Data APIs - country-level ratios against security-level bars, scored so you can tell which job each actually does.
• CFTC Commitments of Traders (COT) Reports - weekly positioning by trader category since 1986; positioning gives the who, end-of-day bars give the where-it-went.
• World Federation of Exchanges Statistics - venue-level volume and capitalization benchmarks for normalizing any single ticker against its whole market.
• EOD Historical Data APIs source profile - who publishes the suite, how the record is cataloged, and what else ships from the same source.
• Best diversified capital markets datasets - where this record ranks within the slice and what beats it for other jobs.
• Daily OHLCV history and corporate actions - the two terms this page leans on hardest, unpacked.