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Credit-card and auto-loan performance for the large US lenders, reported monthly by the securitisation trusts they fund those loans through. Delinquencies, charge-offs, payment rates, portfolio yield and excess spread — measured on the actual loan book, and filed with the SEC roughly six weeks before the lender's own earnings.

A bank does not pre-announce a bad quarter. Its card trust files a Form 10-D every month regardless, because the bondholders require it, and that filing states what the receivables actually did. Consumer credit deteriorates in the trust data first, in public, on a monthly clock — while the issuer is still a quarter away from having to talk about it.

Trusts map to the sponsoring lender, so a request for one ticker returns every trust ORTEX attributes to it. This is the dataset behind most credit-normalisation calls, and it is free, structured and public — it has simply never been collected, mapped to tickers and served.

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

Trusts filing Form 10-D with the SEC, mapped to their sponsoring lender. Sponsors open a new trust per deal rather than reusing one, so coverage follows sponsor name patterns rather than a fixed list of entities — a new trust is picked up when it files, not when someone adds it.

Monthly, published roughly two to three weeks after the month ends.

What to look for

Ratios travel; levels do not. charge_off_rate_net, delinquency_30_plus, payment_rate and excess_spread are comparable across lenders. principal_receivables is a dollar balance and only means something against the same book's own history — $10.9bn next to $4.9bn says nothing about which book is deteriorating.

Direction is not uniformly good or bad. A rising charge-off rate is bad; a rising excess spread is good; a falling payment rate means borrowers are revolving more, which raises yield and risk at the same time. The signals endpoint states which direction is the bullish one for each series so a screen does not have to encode it.

A trust reports the loans it holds, not the lender's whole book. It is a large, representative sample of the portfolio, not the portfolio.

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.

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