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Data availability is not a yes/no claim until the venue family, symbol, data family, time window, freshness tolerance, and plan access are named. A venue can have current order-book data and a shorter historical window for another data family. Treat availability as a preflight step, not a marketing claim. If a window is missing, stale, or degraded once you pull it, Data gaps covers how to handle it.

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Availability Dimensions

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Preflight Pattern

1

Name the route family

Write the family in the job config before writing the endpoint. For example, hyperliquid_spot and hyperliquid_core should not collapse into one label.
2

Probe one exact market

Call one instrument, /v1/symbols, coverage, freshness, order-book, or trade route for the exact symbol before looping over symbols.
3

Check the data family

Confirm that the requested data family exists for the route. L4, L3, replay, funding, and open-interest coverage can have different constraints.
4

Attach the decision to output

Store the preflight result, route, symbol, window, and request IDs beside backtests, exports, dashboards, and model inputs.

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Common Mistakes

Do not infer availability from a category name. A phrase such as “RWA market”, “Spot pair”, or “outcome market” still needs a route-family and symbol check. Do not treat a successful latest snapshot as evidence that every historical window exists. Do not hide a freshness or incident warning from downstream systems just because the API returned a payload. If a route returns a valid response but the freshness or coverage state is outside tolerance, mark the output incomplete or stop the job. That is especially important for backtests, alerts, exports, and model features, where bad data can look like a real market signal.

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Availability Checklist

For every availability answer, capture the exact venue family, symbol, data family, UTC window, freshness tolerance, access tier, and first probe result. If the answer cannot name those fields, it is only a category-level guess.

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Next Step

Open Data quality for the practical gate, then use Historical market data or Point-in-time backtesting if the job depends on historical windows.
Last modified on August 13, 2026