Kalshi Weather Index — minute-by-minute city temperature, the series that settles Kalshi's temperature markets

Kalshi publishes its own temperature index for ten US metro areas: one value per minute, computed from a quorum of weather stations, versioned and calibrated. It is the number that settles Kalshi's hourly and daily temperature markets (KXTEMP…, KXHIGH…, KXLOW…). This Actor pulls that series into a dataset — one row per minute per city — so you can backtest, build forecasts against the exact settlement source, or keep a history Kalshi itself only serves as a rolling window.

Source: Kalshi's public live-data endpoint. No Kalshi account, no API key, no scraping. Runs cost cents and do not break.

Cities

miami, dfw (Dallas–Fort Worth), houston, phl-delaware-valley (Philadelphia), puget-sound (Seattle), sf-bay, greater-boston, southeast-michigan (Detroit), kansas-city, minneapolis-st-paul. Leave the list empty for all ten.

Input

{ "cities": ["miami", "greater-boston"], "lastHours": 24, "detailed": false, "archiveDatasetName": "kalshi-weather-index" }

Field	What it does
cities	Index city IDs (above). Empty = all ten.
lastHours	Trailing window, 1–168 hours. 60 points per hour per city.
from / to	Explicit window instead (ISO time or unix milliseconds).
detailed	Attach every member station's reading and quality-control disposition to each point.
maxItems	Cost cap per run.
archiveDatasetName	Also append to a named dataset in your account. Schedule hourly and you keep the full history.

Output

{
  "city": "miami",
  "time": "2026-09-03T10:56:00.000Z",
  "timestampMs": 1788432960000,
  "temperatureF": 79.52,
  "temperatureC": 26.4,
  "status": "normal",
  "contributors": 5,
  "configVersion": "miami-temperature-v1.0-cal-20260831",
  "units": "fahrenheit",
  "fetchedAt": "2026-09-03T11:56:26.995Z"
}

status is normal, degraded or incomplete. Minutes where the station quorum failed are absent from Kalshi's series and therefore absent here — a gap is a real gap, never interpolated. configVersion changes when Kalshi recalibrates the index; keep it if you compare across weeks.

What people use it for

• Backtesting temperature-market strategies against the exact series that settles them, not a nearby airport's METAR.
• Forecast calibration: compare your model or a public ensemble to the index minute by minute.
• Live dashboards and alerts for hourly temperature markets: schedule every 15 minutes with lastHours: 1.
• History: Kalshi serves a window; with archiveDatasetName and a daily schedule you own the archive.

Pairs with Prediction Markets Scraper for the market prices themselves (kalshiSeriesTickers: ["KXTEMP*", "KXHIGH*", "KXLOW*"]).

Pricing

Pay per 1,000 index points delivered. Platform usage included. A run that returns nothing costs nothing.

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