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How much heating and cooling the weather called for across the United States, every week and every month, from NOAA's Climate Prediction Center: heating and cooling degree days for each state, each of the nine census regions and the nation, weighted by where people live, with how far each was from normal and from a year earlier.

Weather is the largest swing factor in a utility's or a gas distributor's quarter, and a week 40 heating degree days colder than normal is more gas burned and more power sold than the same week in an average year. The weekly summary appears the Monday after the week ends, so the quarter's weather is known before the quarter is.

Weather rather than an activity index, which is why it is its own dataset rather than part of economic_output: a customer modelling gas demand asks for degree days by name.

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

Weekly summaries covering Sunday to Saturday, published the following Monday, and monthly summaries on the second or third of the next month, from 2022. Each state, census region and the national figure is its own entityRef; all are population-weighted, so a hot week in Texas counts for more than one in Wyoming. The heating year runs from July and the cooling year from January, so a summer week shows near-zero heating demand by design.

What to look for

Read the departures, not the totals: hdd_week_vs_normal is what moves volumes against an average year, and hdd_week_vs_last_year is what a company's own year-on-year comparison will blame or credit. The same two measures exist for cooling (cdd_) and for the month.

Unusually cold or hot months also explain retail and restaurant misses before the companies do, and a run of extreme weeks feeds insurers' catastrophe and claims expectations.

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.

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