Kalshi API Guide: Market Data, Historical Endpoints and Order Books
Kalshi's official API is a clean source for current exchange data. Historical execution research still depends on whether you retained the book before it changed.
A Kalshi API integration should separate current exchange state from historical replay. Official endpoints expose markets, trades, candlesticks and order-book state; their documentation remains the authority for supported fields and access. When a strategy needs to know what size was displayed at a past price, it needs a time series of recorded yes/no ladders rather than only trades or candles.
Choose the correct historical object
| Object | Answers | Cannot answer alone |
|---|---|---|
| Trade history | Where matched trades printed | What size was waiting away from the trade |
| Candlesticks | Aggregated price movement over an interval | Spread and execution for a sized order |
| Current order book | Liquidity displayed now | Liquidity displayed before the latest changes |
| Recorded book history | Past levels, sizes and spread | Hidden liquidity or queue priority |
Normalize yes and no without losing the source
Kalshi represents binary outcomes as yes/no prices and sizes. A cross-venue loader may normalize those fields into a common bid/ask structure, but it should retain the native ticker, side and source values so every transformation can be reversed and audited.
Use integer or exact decimal handling for price units instead of accumulating floating-point drift. Record the observation time independently from market settlement fields; a resolved outcome must not leak into a row used by an earlier decision.
How DepthFeed records Kalshi
DepthFeed continuously polls Kalshi full-depth public books with adaptive pacing under the upstream quota and stores normalized observations with up to 100 levels per side. The realized interval varies with active-market load, so the product documents capture method rather than promising an artificial fixed tick.
The same REST schema used for Polymarket carries Kalshi ladders, timestamps and market metadata. Researchers can replay a rule in the browser, pull a historical window over the API or compare the result with live paper trading without writing a second venue-specific fill model.
A safe implementation sequence
- Read Kalshi's official documentation for current authentication, limits and endpoint contracts.
- Store series, event and market tickers with the raw yes/no book.
- Normalize prices only after preserving the native representation.
- Separate trades, candles, current books and recorded historical books in the data model.
- Use observation-time information only when evaluating an entry rule.
- Stress-test adaptive polling gaps and rate-limit responses before production.
Key takeaways
- 01Kalshi trades, candles and order books answer different research questions.
- 02A current book does not recreate the full book at an earlier time.
- 03Cross-venue normalization should preserve native Kalshi tickers and yes/no values.
- 04DepthFeed records full-depth Kalshi observations with documented adaptive pacing.
- 05A backtest must keep future settlement information out of earlier decisions.
Inspect normalized Kalshi depth with the native ticker and source fields still available. Free Explorer tier, no card.
Query Kalshi dataView pricing