Kalshi Weather Markets Data — KXHIGH & KXLOW

Pull Kalshi weather markets data — every KXHIGH (daily high temperature) and KXLOW (daily low temperature) bracket market — as clean JSON: live bid/ask, volume, open interest, plus full settlement history with the official settled temperature per city per day. Covers all 20 Kalshi temperature cities, each mapped to the exact weather station the market settles on (including the gotchas: Chicago = Midway, Dallas = DFW, Houston = Hobby).

Uses Kalshi's public market-data API directly — no login, no API key, no scraping.

What you get

One record per market (bracket):

Field	Example	Notes
ticker	KXHIGHNY-26JUN10-T82	Kalshi market ticker
event_ticker	KXHIGHNY-26JUN10	groups all brackets of one city-day
city	NYC	canonical city name
station_id	KNYC	ICAO station the market settles on
series_ticker	KXHIGHNY
series_type	KXHIGH	KXHIGH or KXLOW
bracket_label	83° or above	human-readable bracket
strike_type	greater	greater, less, or between
floor_strike	82	bracket lower bound in °F (null for "or below" brackets)
cap_strike	null	bracket upper bound in °F (null for "or above" brackets)
status	finalized	active (live) or finalized (settled)
result	no	yes / no / null while live
settlement_temp_f	82.0	official settled temperature (settled markets only)
settlement_time	2026-06-11T12:01:51Z
open_time	2026-06-09T14:00:00Z	ISO timestamp
close_time	2026-06-11T04:59:00Z	ISO timestamp
yes_bid	0.01	USD per contract, 0.00–1.00
yes_ask	0.02	USD per contract, 0.00–1.00
last_price	0.02	USD per contract, 0.00–1.00
volume	64488.09	contracts (fractional trading enabled)
open_interest	31144.75	contracts (fractional trading enabled)
liquidity	0.0	USD value of resting orders
trades	array	only with includeTrades — up to 1,000 recent fills
orderbook	object	only with includeOrderbook — bid levels, see below

Optional keys: the trades and orderbook keys exist in a record only when the corresponding flag is enabled — with the flag off the key is omitted entirely (not null), so read them with .get() rather than direct indexing. With the flag on, a per-market fetch failure leaves the key present with value null.

Orderbook structure: orderbook is {"yes_dollars": [[price, size], ...], "no_dollars": [[price, size], ...]} — despite the names (Kalshi's own field names, passed through), each value is a list of bid levels, not a single dollar amount: price in USD per contract, size in (fractional) contracts.

Example settled record:

{
  "ticker": "KXHIGHNY-26JUN10-B81.5",
  "event_ticker": "KXHIGHNY-26JUN10",
  "city": "NYC",
  "station_id": "KNYC",
  "series_ticker": "KXHIGHNY",
  "series_type": "KXHIGH",
  "bracket_label": "81° to 82°",
  "strike_type": "between",
  "floor_strike": 81,
  "cap_strike": 82,
  "status": "finalized",
  "result": "yes",
  "settlement_temp_f": 82.0,
  "settlement_time": "2026-06-11T12:01:51.253194Z",
  "open_time": "2026-06-09T14:00:00Z",
  "close_time": "2026-06-11T04:59:00Z",
  "yes_bid": 0.0,
  "yes_ask": 1.0,
  "last_price": 0.99,
  "volume": 48549.61,
  "open_interest": 22770.89,
  "liquidity": 0.0
}

Export as JSON, CSV, or Excel directly from the dataset.

Input

Default (live markets, one city — fast, a handful of records):

{
  "cities": ["NYC"],
  "seriesType": "both",
  "mode": "live"
}

Settled-history example (intentionally heavier — ~1,000–2,400 records for two cities at 90 days):

{
  "cities": ["NYC", "Chicago"],
  "seriesType": "both",
  "mode": "settled",
  "lookbackDays": 90
}

• cities — city names or aliases (NYC, Philly, SF, NOLA, DFW…), or "all" for every supported city.
• seriesType — KXHIGH (daily highs), KXLOW (daily lows), or both.
• mode — live (currently tradeable brackets with current prices), settled (finalized markets with results and settled temperatures), or both.
• lookbackDays — 1–365, settled-history window (only used in settled/both mode).
• includeTrades — attach up to 1,000 recent fills per market (one extra API call per market).
• includeOrderbook — attach the current order book per market (one extra API call per market).

All prices are USD per contract (each contract pays $1.00). Quantities (volume, open_interest, trade count_fp, book sizes) are in contracts; Kalshi allows fractional contracts, so they can be non-integer.

Supported cities

City	Aliases	KXHIGH series	KXLOW series	Settlement station
NYC	New York, NY	KXHIGHNY	KXLOWTNYC	KNYC (Central Park)
Chicago	—	KXHIGHCHI	KXLOWTCHI	KMDW (Midway, not O'Hare)
Miami	—	KXHIGHMIA	KXLOWTMIA	KMIA
Austin	—	KXHIGHAUS	KXLOWTAUS	KAUS
Los Angeles	LA	KXHIGHLAX	KXLOWTLAX	KLAX
Denver	—	KXHIGHDEN	KXLOWTDEN	KDEN
Phoenix	—	KXHIGHTPHX	KXLOWTPHX	KPHX
Philadelphia	Philly	KXHIGHPHIL	KXLOWTPHIL	KPHL
Houston	—	KXHIGHTHOU	KXLOWTHOU	KHOU (Hobby, not Bush/IAH)
Minneapolis	Min	KXHIGHTMIN	KXLOWTMIN	KMSP
Oklahoma City	OKC	KXHIGHTOKC	KXLOWTOKC	KOKC
San Francisco	SF	KXHIGHTSFO	KXLOWTSFO	KSFO
Washington DC	DC	KXHIGHTDC	KXLOWTDC	KDCA
Boston	—	KXHIGHTBOS	KXLOWTBOS	KBOS
Dallas	DFW	KXHIGHTDAL	KXLOWTDAL	KDFW (not Love Field)
Seattle	—	KXHIGHTSEA	KXLOWTSEA	KSEA
Las Vegas	LV, Vegas	KXHIGHTLV	KXLOWTLV	KLAS
Atlanta	—	KXHIGHTATL	KXLOWTATL	KATL
San Antonio	SATX	KXHIGHTSATX	KXLOWTSATX	KSAT
New Orleans	NOLA	KXHIGHTNOLA	KXLOWTNOLA	KMSY

Pricing

Pay per market record — no subscription. A live pull for one city is typically 6–14 records; 90 days of settled history for one city is ~500–1,200 records.

Why this instead of scraping the Kalshi website?

• Structured market data, not HTML. Direct from Kalshi's public market-data API — stable JSON, no scrape fragility, no login wall.
• Settlement-grade joins. Every market is mapped to the exact ICAO station its NWS Climatological Report settles on — the mapping mistakes (O'Hare vs Midway, Love Field vs DFW, Bush vs Hobby) that cost real traders real money are already handled.
• Backtest-ready history. Settled mode gives you bracket, result, and the official settled temperature per city-day in one flat table — join it against any weather dataset by station_id and date.

FAQ

Which Kalshi weather markets are covered?

All daily high (KXHIGH*) and daily low (KXLOW*) temperature bracket markets across Kalshi's 20 cities. Series are discovered live from the API, so newly listed cities in these families appear automatically once mapped.

What units are the prices in?

USD per contract, 0.00–1.00 (each contract settles at $1.00). yes_bid 0.04 means 4 cents. Volume and open interest are in contracts and can be fractional, since Kalshi supports fractional trading.

How do I get settled temperature history for a city?

Set mode to settled, pick your cities and lookbackDays. Each settled record carries settlement_temp_f — the official high (or low) Kalshi settled on — plus the winning/losing bracket via result.

Which weather station does each market settle on?

See the table above. Kalshi settles on NWS Climatological Reports for one specific station per city — Chicago is Midway (KMDW), Dallas is DFW airport (KDFW), Houston is Hobby (KHOU). Every record carries station_id so you can join against observation data.

Can I get the order book and trade history?

Yes — includeOrderbook attaches the book as {"yes_dollars": [[price, size], ...], "no_dollars": [[price, size], ...]} (lists of bid levels), includeTrades attaches up to 1,000 recent fills per market (price, size, taker side, timestamp). With either flag off, the corresponding key is omitted from the record entirely — use .get().

Is this affiliated with Kalshi? Does it trade?

No and no. It reads Kalshi's public, unauthenticated market-data API. No account, no credentials, no orders — data only.

Use with AI agents (MCP)

Connect your agent to the Apify MCP server (mcp.apify.com) and it can call this actor as a tool: ask for "current KXHIGH brackets in Chicago" or "settled NYC daily-high markets from the last 30 days" and the agent fills the input schema (cities, seriesType, mode, lookbackDays) itself. A clean market-data source for trading agents and prediction-market research.

More prediction-market data actors

• METAR & NWS Weather Station Observations API - the raw station observations these markets settle on.
• Prediction Market Resolution Evidence Checker - paste a market URL, get the live observation that resolves it, graded.