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The national and state totals that manufacturer volumes are a share of: new-vehicle sales for the markets whose statistical office publishes a headline figure with no manufacturer breakdown, and the US registered fleet by state, by vehicle class and by fuel.

A manufacturer's registrations rising 4% means one thing in a market up 10% and the opposite in a market down 6%. This endpoint is what turns a volume series into a share series. For the US it also answers the electric-vehicle question the per-make data cannot: how many battery-electric and plug-in hybrid vehicles are actually on the road in a state, and how fast first-time electric registrations are growing against all fuels.

Statistics Canada splits its sales total by vehicle type, so an EV mix can be read off directly. The Federal Highway Administration's table MV-1 is the long view: every state's registered automobiles, trucks, buses and motorcycles, annually since 1900.

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

Three publishers. Statistics Canada's national new-vehicle sales table, monthly, about 30 days after the month closes. The US state motor-vehicle departments that publish registrations by fuel in a machine-readable form: Washington and New York as a monthly flow of first-time registrations; Maryland, Delaware, North Carolina, Hawaii and Texas as the fleet registered on a date; California as an annual snapshot. And FHWA table MV-1, annually, for all fifty states and the District of Columbia, with the national fleet by energy source alongside.

Each state is its own entity, named in entityRef. Markets whose register breaks out manufacturers are served by the per-instrument vehicle_registrations endpoint instead, where each reading belongs to the company that made the car.

What to look for

Never mix a stock with a flow. How many vehicles are registered on a date (fleet_) and how many were registered for the first time in a month (first_registrations_) are different measures, and only the flow is comparable with a manufacturer's monthly volumes.

Do not add states together. Most of the state files count electric vehicles only, on different dates and definitions, so a sum is neither total registrations nor total electric vehicles. Read one state against its own history, and use MV-1 when a comparable cross-state figure is what you need. MV-1 counts a vehicle registered at any point in the year, not the fleet on a given day.

The type series overlap: vehicle_sales_ca_total contains cars and trucks, and vehicle_sales_ca_zev cuts across both, as fleet_plugin_all contains fleet_electric. Summing them double-counts.

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