Historical Optionable Tickers

Your agent pulls the snapshot that was current on each of your test dates and drops any position in a name that had no listed chain, or no weekly expirations, at that point.

The options universe, frozen one month at a time.

A stock is optionable when an exchange has listed standardized options contracts on it. Not every US equity is. Listing is a decision an exchange makes against criteria like price and share count, and it is unwound when a company is acquired or delisted. This dataset records who was on that list, month by month.

Each row is one ticker on one snapshot date, taken on the first trading day of the month. Alongside the ticker, the row carries the shape of the expiration ladder listed at that moment: how far apart the next six expirations sat, and whether weekly expirations ran consecutively.

Stop backtesting options on stocks that had none.

Survivorship bias in options research usually enters through the universe rather than the prices. Screen for optionable names today and you get a list shaped by everything that happened since: the small cap that got a chain in 2023 looks like it had one in 2019, and the names that were bought out or delisted are missing entirely. A strategy tested against that list is trading contracts nobody could have bought.

Expiration density is the other half of the problem. A weekly strategy is only implementable in names where weeklies actually ran. When avg_days_between sits closer to 30 than to 7, the next expirations were a month apart, and a test that assumes a seven-day hold in those names is measuring a trade that did not exist.

• Rebuild the optionable universe for each rebalance date instead of screening once and applying the result backward.
• Restrict a weekly-expiration study to rows where has_weeklies is 1 on the date being traded.
• Rank candidates by avg_days_between when the strategy needs a tight expiration ladder.
• Join a snapshot against your signal dataset to drop signals in names with no chain to trade.
• Measure how much of a published options result survives once the universe is dated.

Snapshotted on the first trading day, never rewritten.

Every snapshot is taken on the first trading day of the month by reading the option expirations listed for each ticker at that time. avg_days_between is the average gap across the next six consecutive expirations after the first week window, so a value near 7 describes a dense weekly ladder and larger values describe biweekly or monthly listings. has_weeklies is set to 1 when multiple consecutive weekly expirations were present.

Records are written once and never retroactively altered. A row dated March 2024 reflects only what was listed in March 2024, which is what makes it safe to reuse as a historical universe. Dates come back as YYYY-MM-DD strings, and large pulls page deterministically with cursor_date and cursor_ticker.

The four columns that decide what your universe contains.

The headline fields below are a subset. Every field, with exact types and semantics, is documented in the API reference.

One call returns the tradeable list for a date.

One key works across the REST API, the hosted MCP server, and this dashboard. Every response is JSON with a { count, data } envelope.

import requests

url = "https://api.alphanume.com/v1/optionable-tickers"
params = {
    "date": "2026-02-02",
    "api_key": "alp_abc123"
}

r = requests.get(url, params=params)
print(r.json())

curl "https://api.alphanume.com/v1/optionable-tickers?date=2026-02-02&api_key=alp_abc123"

{
  "count": 1,
  "has_more": false,
  "next_cursor": null,
  "data": [
    {
      "date": "2026-02-02",
      "ticker": "PFE",
      "avg_days_between": 7.0,
      "has_weeklies": 1
    }
  ]
}

What this data does not claim.

• The snapshot is monthly. A chain listed on the tenth of a month does not show up until the next snapshot, so listings and removals inside a month are invisible.
• The presence of a chain is not liquidity. A row says options were listed, not that they had open interest, volume, or a spread worth crossing.
• avg_days_between describes only the next six expirations from the snapshot date. It is a reading of the ladder at that moment, not a full expiration calendar.
• There are no strikes, prices, or greeks here. This is a universe file, meant to be joined against your own options data.
• The dataset publishes monthly snapshots. Pro provides the full point-in-time history and the latest published snapshot.