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The labour-market prints markets actually trade: US non-farm payrolls, the unemployment rate, participation, average hourly earnings and job openings from the Bureau of Labor Statistics; weekly initial and continued jobless claims from the Department of Labor; the UK rate, employment rate, vacancies, claimant count and earnings growth from the ONS; Canadian employment and labour force from Statistics Canada; and unemployment for the euro area, the EU and seven member states from Eurostat.

One endpoint, one row shape, twelve markets. The alternative is a different portal, a different vintage convention and a different release calendar for each statistical office — which is why most people end up paying for this and then re-deriving it anyway.

Weekly claims are here alongside the monthly prints because they are the highest-frequency read on US labour there is: a 5-day lag against roughly 7 days for payrolls and 36 for job openings.

Available Data Points:

• Period Date: The period the value describes — what the number is about (the valid clock).
• Published At: When the value first became knowable — when the publisher released it (the knowledge clock). Filter on this for anything where look-ahead bias would matter.
• Value: The numeric value, where the series has one.
• Value Text: The textual value, for categorical series such as a trial's status or phase.
• Unit: Unit of the value, e.g. vehicles, percent, USD.
• Value Type: number, percent, currency, count or text.
• Frequency: How often the publisher releases the series.
• Market: The market the reading describes — an ISO-3166 alpha-2 code (DE), a sub-national one (US-CA), a supranational aggregate (EA, EU) or GLOBAL.
• Series Slug: Which series the reading belongs to. Unique within a publisher, not globally.
• Series Name: Human-readable name of the series.
• Source Slug: Publisher/feed identifier, e.g. kba_registrations.
• Source Name: Human-readable name of the publisher or feed.
• Publisher: Who publishes the underlying data.
• Entity Ref: The publisher's own identifier for what the reading is about — a manufacturer name, a securitisation trust, a reactor unit, a country code. Kept so any value can be traced back to the row it was parsed from.

This dataset describes a market, not a company

Every reading here is about a country or a whole market, so there is no ticker in the path and no instrument in the response envelope. That is deliberate: attaching a national print to a listing would be a claim the data does not support. Read it as the context the company-level endpoints are measured against.

Coverage

Four publishers, twelve markets. Seasonally adjusted where the office publishes it that way; US claims are the not-seasonally-adjusted series. Levels are in thousands where the publisher states them so, and rates are percent of the labour force.

Release lags run from about 5 days (US weekly claims) to about 76 (the UK labour market release). Preliminary US payroll months are restated twice, so a recent month's value will move.

What to look for

Do not compare rates across offices as though they were one series. Each office defines unemployment, adjusts for seasonality and revises on its own terms; the euro-area and member-state Eurostat series are mutually consistent, and nothing else here is.

Revisions are real data, not corrections to route around. Because every reading carries publishedAt, you can reconstruct what was knowable on any past date — which is the only honest way to backtest against a series that restates itself twice.

initial_claims_nsa is weekly and unadjusted, so it has a strong seasonal shape. Compare it with the same week of prior years, not with last week.

Two dates on every reading, and the difference is the edge

periodDate is what the number is about. publishedAt is when it first became knowable. The gap between them is the whole point of alternative data — and it is also the trap. A Form 10-D describing July is filed in mid-August, so a backtest that treats the period as the knowledge date reads a document six weeks before it existed.

from_date and to_date filter on periodDate, because that is the axis you plot against. Join on publishedAt when you are joining against returns.

Filtering: one market or all of them

Called with no filters, the endpoint returns every series in the dataset, for every market ORTEX covers it in. Each row names its own market, seriesSlug and sourceSlug, so a single request is enough to chart the whole dataset.

Narrow it with any combination of market (one market), series (one measure) and source (one publisher). Filter values that are each valid but select nothing together return 400 invalidSelection rather than a misleading empty result.

A series slug identifies a series within a publisher, not globally

unemployment_rate is published under that exact slug by the US Bureau of Labor Statistics, the UK Office for National Statistics and Statistics Canada. Where a slug names more than one series in a dataset, passing it alone returns 400 invalidSeries listing the alternatives — pair it with market or source. ORTEX would rather refuse than hand you one country's print labelled as another's.

How much history there is

Several of these publishers overwrite their own pages and keep no archive. Where that is so, the series begins on the day ORTEX started capturing it and lengthens from there — nobody, including the publisher, can supply the days before that. Everything ORTEX has ever fetched is archived on capture, so the series only ever grows.

Try it out! You can test the API with the trial key TEST. Simply include it in your request headers; either here, or add the header "Ortex-Api-Key": "TEST" to your http request.

Trial key restrictions: Trial keys may be limited to a specific date range and set of regions. Requests outside the allowed regions or entirely outside the allowed date range are rejected; a date window that partially overlaps the allowed range is trimmed to it, reported in meta.restriction.

Get full access! Get a key at app.ortex.com/apis