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Documentation Index

Fetch the complete documentation index at: https://docs.entityml.com/llms.txt

Use this file to discover all available pages before exploring further.

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Coverage model

Coverage is market-specific. A market can have stored objects for a UTC date while still having intra-day gaps, null fields, or source-specific quality issues.

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Tick-level L2 terminology

EntityML raw market data endpoints expose historical event-level L2 orderbook records: full book snapshots plus update events or deltas with price levels and sizes. These are the records to use when you need to replay an orderbook. Orderbook summary endpoints are different. They reconstruct bucketed top-of-book values and OHLC quote candles from raw events, so they are useful for analytics but should not be described as tick-by-tick L2. EntityML does not currently claim a live low-latency trading feed with exchange-grade sequence guarantees across every historical window. Use the validation workflow below before depending on a specific date range for backtests.

Check stored dates

Use date-range endpoints to find which UTC dates have S3 objects for a condition ID or ticker.

Inspect raw events

Raw endpoints expose snapshots, deltas, and Polymarket gap markers when they are present.

Use summaries for quotes

Summary endpoints reconstruct top-of-book quotes and OHLC candles from raw orderbook events.

Prefer ranges for audits

Range endpoints let you audit a partial day or a multi-day window with cursor pagination.

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Known windows

Affected Kalshi orderbook records from 2026-03-31 through 2026-04-24 can have price: null, delta: null, and empty snapshot sides even when the event count and order are present.The visible symptom in GET /api/v1/kalshi/market/orderbook-summary is quote_count > 0 while best_bid, best_ask, mid_price, spread, and size fields remain null.The confirmed customer reproducer was a KXBTC15M ticker on 2026-04-04, for example KXBTC15M-26APR040030-30.A forward fix was deployed on 2026-05-03 so new Kalshi raw records preserve the original websocket payload under msg. The March 31-April 24 affected payloads were not recoverable from the available Entity storage because the old worker discarded the original payload before durable storage.
Some Polymarket markets have gap-marked or missing data during an April 2026 collector instability window. The most documented production incident was on 2026-04-08 and 2026-04-09; market-level impact can span adjacent UTC dates depending on shard assignment, collector state, and reconciliation status.For raw Polymarket data, look for event_type values such as gap_start and gap_end. These markers indicate that a collector opened or closed a recovery gap for one or more asset books.The date-range endpoint reports stored dates, not second-by-second completeness. For April 2026 work, confirm the exact market and timestamp window with raw data or range queries.
Polymarket has selected historical data from 2025-09-01. Broad Polymarket crypto-related coverage begins on 2026-02-04, and the API rejects Polymarket data requests before 2026-02-08.Kalshi has selected historical data from 2026-02-17. Broad Kalshi crypto-related coverage begins on 2026-02-25.Broad all-market capture begins on 2026-04-02 for Polymarket and 2026-03-31 for Kalshi, subject to the April quality notes above.

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Validation workflow

1

Resolve the market identifier

For Polymarket, use Look Up Polymarket Slug if you only have a slug or URL. For Kalshi, use the exact ticker from Kalshi.
2

Check stored date range

Call the matching date-range endpoint to confirm whether the market has stored objects for the UTC dates you need.
3

Sample raw records

Pull a small raw page around the target time. For Polymarket, inspect event_type and gap markers. For Kalshi, inspect whether snapshots have yes or no levels and whether deltas have non-null price fields.
4

Use summaries for top-of-book analytics

Use orderbook summaries for best bid, best ask, midpoint, spread, and OHLC. Confirm quote_count, is_forward_filled, and null fields before treating a bucket as tradable quote data.

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What nulls mean

Field patternInterpretation
data_count = 0No stored records matched the requested market and date or range.
quote_count = 0 and is_forward_filled = trueNo quote was observed in that bucket; values were carried forward from the previous bucket.
quote_count > 0 and quote fields are nullEvents were observed, but a complete top-of-book could not be reconstructed. This can happen before both sides are seen or during the Kalshi parser issue window.
Polymarket gap_start / gap_end recordsThe collector marked a recovery gap. Treat the covered interval as lower confidence unless reconciled data confirms coverage.
For high-stakes backtests, store the raw response payloads and the date you pulled them. Historical availability can improve after backfills, but unrecoverable parser-loss windows remain documented here.