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What whole gaming markets took from players, as their regulators print it, with no operator dimension: Macau's gross gaming revenue from the DICJ, and the statewide totals four US regulators publish for New Jersey, Pennsylvania, New York and Nevada.

Macau: the monthly total from games of fortune with its year-on-year change, and the quarterly split by game type, VIP baccarat, mass-market baccarat, slot machines and the rest. It is one of very few numbers a regulator releases before any operator does, on the first working day after the month ends, and six listed concessionaires report against it: Sands China, Galaxy Entertainment, Wynn Macau, MGM China, Melco and SJM. The print itself is the event.

United States: New Jersey's casino, online casino and sportsbook revenue, Pennsylvania's sports wagering and online casino totals, New York's weekly mobile sports handle and revenue, and Nevada's monthly gaming win statewide and for the Las Vegas Strip, downtown, the Boulder Strip and the two largest counties. Each series names its own market (MO, US-NJ, US-PA, US-NY, US-NV), so select one; they are never added together.

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

Macau: monthly from 2010, in millions of patacas, published the first working day after the month. The by-game quarterly split arrives about sixteen days after the quarter. Each month is rounded independently, so three months rarely sum exactly to the quarter.

United States: every total is the regulator's own printed figure, never a sum computed here. New York is weekly from January 2022, Pennsylvania monthly from July 2019, New Jersey monthly from January 2025, and Nevada monthly from about eleven months back, as far as the release page links. Each state measures differently, so every state is its own series under its own market.

What to look for

This is the market, not any one operator. Share between the six Macau concessionaires moves independently, so a strong month for Macau can still be a weak one for a house losing share; the same holds for the US operators whose state shares the per-listing gaming_revenue endpoint serves.

Mass-market baccarat is far more profitable per pataca than VIP play, so a Macau quarter that grows on VIP alone is worth less to operators than the headline suggests. In the US, handle is the steadier measure of activity where a state publishes it; revenue swings with game results.

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