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Airline and airport traffic from the aviation authorities that collect it, in thirteen markets: passengers, capacity, load-factor inputs, cargo, fuel and punctuality per airline, and passengers, movements and freight per airport. The UK Civil Aviation Authority, Spain's Aena, France's DGAC, Turkey's DHMI, Eurostat, the US Bureau of Transportation Statistics, Statistics Canada, Mexico's AFAC, Brazil's ANAC, India's DGCA, Australia's BITRE, Taiwan's Civil Aeronautics Administration and Peru's Ministry of Transport and Communications.

Every airline files these returns with its regulator, and most regulators publish them long before the airline's quarter is reported. Jet2 publishes no monthly traffic at all, and the CAA's table is the only monthly read on it. IndiGo's domestic passengers and seat-kilometres are out about two weeks after the month; Azul's and GOL's fuel burn is in ANAC's file, which almost no other regulator publishes. For an airport operator (Aena, TAV Havalimanlari) the traffic tables are close to the revenue line itself.

Request it for one listing and you get every regulator that counts that company, each row naming its own source and market, so an airline flying into several of these markets is covered in one call.

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.

Coverage

Monthly throughout. Release lags run from about twelve days (Aena) and two weeks (DGCA, DHMI) to roughly eight weeks (UK CAA, Statistics Canada) and three and a half months (US T-100). France's DGAC posts a whole year the following summer, so its value is depth rather than timeliness: airport history from 1990 and airline history from 2010.

Coverage differs by regulator and each description matters. The UK airline tables cover UK-licensed operators only; DGCA covers Indian domestic flying only; Taiwan covers international and cross-strait scheduled services only; BITRE covers Australian domestic punctuality; Statistics Canada named individual airlines only until January 2016 and publishes an industry aggregate since; Peru's MTC names airlines for domestic flying only, publishes once a year, and states international passengers only as a market total. Only scheduled service is imported wherever a regulator separates it from charter.

Korea's Airportal per-carrier statistics are listed here under KR, but the portal reserves all rights, so their raw values are not served: the KR market returns no rows and naming the source returns 403 seriesNotAvailable. Korean Air's, Asiana's and the Korean low-cost carriers' ORTEX signals from it are available from the signals endpoint.

What to look for

Load factor is never stored. It is a ratio, so it cannot be added across carriers or averaged across months without weighting; divide the traffic measure by the capacity one yourself (RPK by ASK, passengers by seats). Capacity usually moves first, because it was committed months earlier.

Airline and airport rows answer different questions and never sum to each other: a flight counts once for the carrier and at both ends for the airports. Eurostat's country total is likewise not the sum of its airports, because a domestic flight is counted twice there.

Read a carrier or an airport as a share of its market's total rather than on its own. Air travel grows in most years, and the question worth answering is who is taking share. Compare a month with the same month a year earlier; holiday calendars move traffic more than anything else.

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.

Raw readings here; comparable ones next door

This endpoint serves the publisher's numbers. Raw values are frequently not comparable — receivables of $10.9bn against $4.9bn say nothing without each book's size, and a 5.3% charge-off rate means something different on a subprime book than a prime one. The Alt Data signals endpoint serves the same series as percentiles and z-scores against a stated comparison set, which is the form a screen or a backtest wants.

Why a company you expect may be missing

Three reasons, and none of them is a bug:

• The dataset is a window, not a census: Several of these publishers release a rolling window rather than a complete history.
• The filer is not the listed company: Registers name a marque, a charter or a trust. ORTEX maps those to the listed parent where the link is defensible and leaves them unmapped where it is not — a wrong mapping silently attributes another company's data to your ticker.
• The population is genuinely narrow: Not every listed company appears in every register, and no amount of mapping changes that.

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.

What ORTEX may republish, and what it may not

Public-sector publications are served as the publisher wrote them. A handful of sources are derived from a company's or a trade body's own website, and their raw values are not ours to redistribute — those are excluded from this endpoint, and naming one explicitly returns 403 seriesNotAvailable. Their derived ORTEX signals (percentiles, z-scores) are available from the Alt Data signals endpoint, because a computed comparison is ORTEX's own work rather than the publisher's data.

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