Nasdaq Data Link (formerly Quandl)

Quandl built the template for selling alternative data by short code; Nasdaq bought the template in 2018 and made it an exchange product. What survives today is a consolidated catalogue where Nasdaq's own exchange data sits beside vetted third-party vendors - Sharadar, Mergent, Zacks, CFTC, QuoteMedia among them - all addressed the same way: a short database code, a documented field list, a dated row.

The shelves worth knowing by name. EOD carries end-of-day US equity prices from QuoteMedia. SF1 is Sharadar's core US fundamentals - income statement, balance sheet, cash flow and ratios per ticker per fiscal period. MF1 extends fundamentals globally through Mergent. ZEA, ZEE and ZAR carry Zacks consensus estimates, year-end targets and ratings. OWF, OWIS, VOL, OPT and CLSH cover option flow, institutional holdings and volatility surfaces. CFTC republishes commitment-of-traders positioning and CUR carries foreign-exchange rates.

Two shapes run through the whole catalogue. Time-series products return dated rows - a bar, a print, a rate - while tables products return entity-level records keyed by ticker and reporting period, which is the right grain for screens and panels rather than charts. In Datadory's catalog of 1,744 datasets across 159 viable industries, this record scores 8/10 for quality - above the catalog-wide average of 7.81 - and anchors the financial exchanges shelf as the fundamentals-and-breadth play beside the indicator-ready price suites.

One honest note first: our August 2026 research pass verified the dictionary against the publisher's own documentation because the public site gates automated capture, so the rows below illustrate the documented table contracts rather than quote a live print. Values are rounded placeholders; the shapes are exactly what ships.

Time-series products return one row per date, columns fixed by the product:

date         open     high     low      close    volume
2026-08-19   100.00   102.50    99.60   102.10   48210000
2026-08-20   102.20   103.80   101.55   103.42   51740000
2026-08-21   103.50   104.90   102.88   104.61   46980000

Tables products return one row per entity per period, long-format:

ticker   dimension      period      amount          currency
AAA      revenue        FY2025      1204500000      USD
AAA      netinc         FY2025      187300000       USD
AAA      assets         FY2025      8642000000      USD
BBB      revenue        FY2025      312900000       USD

Read together they show why the catalogue has outlived its imitators. The first block is chart food - five values per date, ready to plot as-is. The second is screen food - a dimension column you filter on, so "net margin by year" is one predicate instead of a column hunt across wide statements. Same short-code addressing, two grains, and every product in the catalogue picks one.

Fourteen structures were documented down to the enum level during our August 2026 research pass. The dictionary below shows the spine shared by every time-series response plus the job-status fields that ride along with large-table extracts; per-product field lists - Sharadar's dimension vocabulary, Zacks' estimate periods, Mergent's statement lines - fold under additional fields on request. Name the products with your sample and their dictionaries ship in the same table.

Three chips, honestly drawn:

• Geography - US exchange data (Nasdaq, PSX), US and international equities fundamentals through Mergent, global FX, futures and options across major exchanges, worldwide economic indicators.
• Temporal - research staples such as Sharadar fundamentals and EOD prices carry multi-decade histories; realtime Nasdaq exchange products cover the current trading session; entity tables span every reported fiscal period in each vendor's archive.
• Granularity - realtime and delayed quote values on the exchange side, one dated row per bar on time-series products, one row per ticker per reporting period on tables products, with collapse and transform settings able to resample a series to monthly or hand back percent-change views instead of raw levels.

Scale check: individual tables run from thousands of rows to billions - full equity-fundamentals histories are the heavy end - spread across hundreds of products from Nasdaq and its third-party providers. That range is the point: one catalogue holds both a single-country rate series and a multi-decade global statement archive, under the same row grammar.

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

Tables land however your stack wants them - pushed to storage, served on request, or synced straight into your database, as JSON, CSV or Parquet for the heavy extracts. Name the products, the field cut and the cadence when you request the sample; the sample ships first either way. Get a sample of this dataset and both row shapes above arrive alongside the full dictionary for whichever catalogue codes you pick.

A catalogue this deep earns its keep in specific jobs:

• Run factor screens off clean fundamentals - Sharadar's SF1 gives income statement, balance sheet, cash flow and ratios in long format per ticker per period, so value, quality and accrual screens are filters rather than spreadsheet surgery. See investors and quants use cases.
• Backfill price history behind a model - EOD bars plus collapse/transform settings deliver resampled or percent-change series without rebuilding aggregation logic in pandas. See data scientists use cases.
• Track positioning and sentiment beside price - CFTC commitment-of-traders rows and Zacks estimates/revisions give the why-beside-the-what that pure price feeds lack.
• Take a fundamentals screen global - Mergent's MF1 applies the statement-archive treatment outside the US, useful when a domestic-only universe quietly caps the strategy.
• Wire market data into a product - stable codes and documented dictionaries make this the catalogue to prototype against when the roadmap says "prices and fundamentals, one integration." See developers and builders use cases.

Four of Datadory's eight personas tag this record, ranked by relevance:

• Investors & quant researchers (relevance 3/3) - multi-decade fundamentals plus EOD prices is the canonical screening stack; confirm redistribution scope before anything commercial ships downstream.
• Developers & data-product builders (2/3) - short codes and documented dictionaries make integration legible; the planning cost sits in entitlement tiers, not schema.
• Data scientists & ML engineers (2/3) - long histories at sane grain for feature engineering; the heavy tables want the extract route rather than row-at-a-time pulls.
• Journalists, academics & students (1/3) - positioning, rates and fundamentals series make defensible charts; cite the producing vendor, which the catalogue makes explicit.

Three things worth deciding upfront. First, which grain your job actually needs: time-series products and tables products answer different questions, and asking a tables product for chart rows (or the reverse) is the classic mismatch. Second, breadth versus depth: a broad EOD-plus-ratios cut ships fast, while full multi-decade statement archives are heavy enough that we stage them as extracts - say which you want and the sample gets cut to match. Third, tiering: several shelves have both delayed and realtime variants of the same instrument coverage, so tell us whether your job watches the tape or studies it.

Two honest notes from our August 2026 research pass. Live row capture was gated during research, so samples are produced from the documented dictionaries rather than quoted captures. And the dedicated documentation property carries a retirement notice dated August 31, 2026, with docs migrating to the main platform - we re-verify the dictionary against the successor location before any production delivery.

Notes worth reading next:

• Financial exchanges data hub - the pooled view of the industry slice, from commercial market-data suites to official statistics.
• Tiingo Financial Markets API - corporate-action-adjusted end-of-day prices for 80,000+ assets back to 1962; the scored trade-offs sit in the head-to-head comparison.
• Alpha Vantage Stock and Financial Market Data API - the indicator-ready rival: nine asset families with 50+ precomputed technical indicators against this catalogue's fundamentals-first depth.
• Nasdaq Data Link (Quandl) - REIT Datasets - the sector-cut version of this shelf, REIT fundamentals and estimates, scored for the office-reits slice.
• Nasdaq source profile - who publishes the catalogue, how the record is cataloged, and what else ships from the same source.
• Best financial exchanges datasets - where this record ranks within the slice and what beats it for other jobs.