Kalshi order book data
Kalshi Order Book Data
The mid-price hides the spread and the size resting at each level. DepthFeed captures Kalshi's full order book — the complete yes/no book — up to 100 levels per side — so you can measure the slippage a real order would have paid and the liquidity that was genuinely there.
Kalshi order book data is the Level-2 view of the market: resting bid and ask prices with their displayed sizes. DepthFeed records continuous full-depth polling of Kalshi's public REST orderbook and serves the full ladder at each stored observation, allowing a backtest to walk recorded depth instead of assuming unlimited midpoint liquidity.
Kalshi order book data at a glance
- Capture
- Paced full-depth REST polling
- Depth
- Up to 100 levels per side (yes/no)
- Series
- KX{ASSET}15M · KX{ASSET} · KX{ASSET}D
- Market windows
- 15-min · hourly · daily · weekly
- Assets
- 7 — BTC · ETH · SOL · XRP · DOGE · BNB · HYPE
- Timestamps
- Millisecond observation time; venue-specific source stamps
- Underlying price
- Nearest preceding Binance reference when available
- History
- 7/30/90-day windows + full archive (Desk)
- Delivery
- REST history + normalized live WebSocket frames
- Resolution
- Raw stored observations or ?interval= 1s–1d
Request a Kalshi order book by exact ticker
Kalshi series names and market tickers are different identifiers. Discover venue-native tickers for the requested time window, then pass one exact ticker to the book or snapshot endpoint.
curl -s "https://api.depthfeed.com/v3/kalshi/markets?series=KXBTC15M&limit=5" \
-H "Authorization: Bearer df_your_key"
curl -s "https://api.depthfeed.com/v3/kalshi/<exact_market_ticker>/orderbook/latest" \
-H "Authorization: Bearer df_your_key"- Do not synthesize historical market tickers from UTC timestamps; use the identifiers returned by discovery.
- DepthFeed normalizes prices to 0–1 dollars while retaining the venue-native ticker and Yes/No book semantics.
Kalshi order book data
Why full depth matters
Both sides, every level
Top-of-book or a single mid tells you almost nothing about execution. DepthFeed serves the complete yes/no book — up to 100 levels per side for kalshi, with bid/ask price and size arrays on each snapshot — the columns you actually reconstruct a book from. That is what lets you compute spread, queue position, and the slippage of a real-sized order.
Cadence is stated, not implied
A stored order book cannot describe activity between its observations. DepthFeed records paced REST polling; realized cadence varies with active-market load and Kalshi's upstream quota; the API returns the timestamps actually present and never interpolates a missing book state.
Align the recorded book and reference series
API responses include a millisecond observation time and ASOF-align the nearest preceding Binance reference price when available. That supports timestamped research without claiming an exchange timestamp or reference value that the source did not provide.
Interpret Kalshi Yes/No ladders before normalizing
Kalshi's current fixed-point order-book response exposes Yes and No bid ladders as dollar-price and size pairs. The opposite ask is implied by the binary complement: a Yes ask corresponds to 1 minus the best No bid, and a No ask corresponds to 1 minus the best Yes bid. DepthFeed normalizes this into a consistent 0–1 book while retaining the exact ticker and native Yes/No meaning for auditability.
Test the ladder at your intended order size
Use a recorded bid/ask ladder to calculate spread, VWAP, filled size and unfilled remainder before assuming midpoint execution.