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.

Calculate slippage Get API access

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.

Copyable requestshell

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.

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.