How to Get a History of Public Company Defaults via API

Pull a clean, dated log of public-company default events from Alphanume and use it as labels or as a portfolio filter.

Whether you are building a distress short book or simply trying to keep defaults out of a long portfolio, you need the events as structured data, not as headlines. The Alphanume Corporate Default Events endpoint provides a dated, sourced log you can pull on demand.

The endpoint

GET https://api.alphanume.com/v1/corporate-default-events

Authenticate with ?api_key=alp_your_key or an X-API-Key header. Each row carries an event_date, a ticker, and a filing_url, ordered newest first. Use date range filters to pull a specific window.

Pull the recent log

import requests

r = requests.get(
    "https://api.alphanume.com/v1/corporate-default-events",
    params={"date_gte": "2026-01-01", "api_key": "alp_your_key"},
)
for row in r.json()["data"]:
    print(row["event_date"], row["ticker"])

Use it as ML labels

Because every event is dated and point-in-time, the log makes a natural label set. Join the event_date and ticker to your feature history to train models that flag distress before it shows up in price, or to study how equities behave in the sessions around a default disclosure.

Use it as a portfolio filter

On the long side, the simplest application is exclusion. Maintain a rolling set of recent default tickers and screen them out of any new positions. The filing_url lets you drop straight into the source filing when you want to confirm the event detail.

Access and limits

Corporate Default Events is on the free tier as a rolling 30-day delayed window, Pro at 600 requests per minute. Dates use the YYYY-MM-DD format. Start with a free Alphanume API key and the API documentation, or explore it on Alphanume.

Labels and filters from one feed

Because every default event is dated and point-in-time, the log serves two distinct purposes from a single feed. As labels, you can join event_date and ticker to your feature history to train models that flag distress before it shows up in price, without leaking the future into training.

As a filter, the simplest application is exclusion: maintain a rolling set of recent default tickers and screen them out of new long positions. When an event needs confirmation, the filing_url on each row drops you straight into the source SEC filing the label was drawn from, so you are never trading on an unexplained flag.

Frequently asked questions

What is the defaults endpoint?

GET https://api.alphanume.com/v1/corporate-default-events. Authenticate with ?api_key=alp_your_key or an X-API-Key header. Rows are ordered newest first.

How do I pull a specific period?

Use the date range filters date_gte, date_lte, date_gt, and date_lt with YYYY-MM-DD dates.

Can I use the events as machine-learning labels?

Yes. Every event is dated and point-in-time, so you can join event_date and ticker to your feature history to train distress models without leaking the future.

How do I confirm an event?

Open the filing_url on the row to read the source SEC filing the default label was drawn from.

What is the rate limit?

Pro allows 600 requests per minute, with HTTP 429 on overage. The dataset is free on a rolling 30-day delayed window.