Kalshi Weather Markets — Odds, Brackets & Forecast

Kalshi Weather Markets turns every daily temperature market on Kalshi into a table — 24 US cities, highs and lows — and puts a weather forecast next to each price. Every bracket comes back with its temperature band, the price, the implied probability, the fair probability with the exchange's overround stripped out, volume, open interest and the close time. No API key, no login, no browser.

Four modes: brackets (one row per tradable band), days (one row per city and day with the whole distribution), forecast vs market (where the models and the crowd disagree), and series (what Kalshi lists right now).

What is Kalshi Weather Markets?

Kalshi runs a temperature market for each major US city, every day: a ladder of brackets — "83° or below", "84° to 85°", "86° to 87°" — where each contract pays $1 if the day's high or low lands in that band. The prices are the crowd's probability distribution over tomorrow's weather.

This Actor reads that distribution and hands it back as data, alongside the two things you need to judge it: the forecast for the exact station the market settles on, and what that station has already recorded today.

What makes it different

The forecast is matched to the settlement station, not to the city. A Kalshi market settles against one specific NWS climate report, and that station is often not where you would guess. Chicago settles at Midway, not O'Hare. Los Angeles settles at the airport by the ocean, ten degrees cooler than downtown on a hot day. One series titled "HIGHEST Temperature SATX" settles in Louisville. This Actor reads the station out of each market's own resolution rules and fetches the forecast for that point — the difference against a naive city-centre lookup ran to seven degrees on the day this was written.

Probabilities that add up. The prices of a bracket ladder sum to more than 1; the surplus is the exchange's overround. Every row carries both the raw probability and the fairProbability with that surplus removed, plus the overround itself.

Dead days are labelled, not dressed up. Kalshi lists city-days nobody has traded, where all six brackets sit at a fraction of a cent. Normalising those produces confident-looking probabilities out of two cents of volume. Those days come back flagged illiquid, with fair probabilities withheld, and are left out of the forecast mode entirely.

What data does it return?

Brackets

Field	Example
city, station, date	New York City · CLINYC · 2026-09-09
metric	high · low
bracket, floor, cap, strikeType	84° to 85° · 84 · 85 · between
probability, fairProbability	34.0 · 32.4 (per cent)
yesPrice, noPrice, bestBid, bestAsk, spread	0.34 · 0.66 · 0.33 · 0.35 · 0.02
volume, volume24h, openInterest	6,802 · … · 4,120
marketExpected, overround, illiquid	84.1° · 0.05 · false
forecastValue, forecastPicksThisBracket	86.3 · false
status, result, closeTime, rules, url	active · empty until it settles · …

Days

One row per city and day: every bracket with its probability, the favourite band, the market's own marketExpected temperature, the forecast, the gap between them, and which bracket the forecast lands in together with what the market charges for it. That last number is the useful one — a forecast pointing at a bracket the market prices at 2% is a disagreement worth reading.

Forecast vs market

The same rows, filtered to days where a forecast exists and the market is actually priced. For a day already under way it also carries observedHigh and observedLow: what the station has recorded so far. A model that still disagrees with a market at 2pm is usually a stale model, not an edge, and the observation is what tells you which.

How much does it cost?

Pay per row, and only for rows you actually get:

Event	Price
Bracket	$0.001
Day	$0.003
Forecast row	$0.004
Series row	$0.0005

Error rows are never charged, and there is no charge for starting a run. Two public JSON APIs, no browser and no proxy, so platform usage is negligible.

Job	Cost
Every city, highs and lows, today and tomorrow (≈290 brackets)	$0.29
A daily snapshot of all cities for a month	$8.70
Forecast vs market for every city, once a day for a month	$4.90

The other Kalshi weather Actor charges $0.001 per row plus $0.005 for every run and returns market records only. This one matches the row price, drops the start fee, and adds the forecast and the observations.

How to use it

1. Choose 📋 What to get: brackets, days, forecast vs market, or the series list.
2. Pick 🌡️ What kind of weather — daily high and low are the ones traded every day.
3. Narrow to 🏙️ Cities if you want (partial names work), and set 📅 Days ahead.
4. Leave 🔭 Add the weather forecast on unless you want a pure market snapshot.
5. Click Start, then export as JSON, CSV or Excel, or read the dataset through the API.

⬇️ Input

{
  "mode": "markets",
  "metrics": ["high", "low"],
  "days": 2,
  "withForecast": true,
  "maxRows": 500
}

Where the forecast and the market disagree

{ "mode": "forecast", "metrics": ["high", "low"], "days": 3 }

One city, several days

{ "mode": "events", "cities": ["Chicago"], "days": 7 }

Named series only

{ "mode": "markets", "seriesTickers": ["KXHIGHNY", "KXLOWTNYC"], "days": 5 }

⬆️ Output

{
  "type": "bracket",
  "seriesTicker": "KXHIGHNY",
  "eventTicker": "KXHIGHNY-26SEP09",
  "marketId": "KXHIGHNY-26SEP09-B84",
  "metric": "high",
  "station": "CLINYC",
  "city": "New York City",
  "country": "US",
  "date": "2026-09-09",
  "question": "Highest temperature in New York City on Sep 9, 2026?",
  "bracket": "84° to 85°",
  "floor": 84,
  "cap": 85,
  "midpoint": 84.5,
  "probability": 34,
  "fairProbability": 32.4,
  "yesPrice": 0.34,
  "bestBid": 0.33,
  "bestAsk": 0.35,
  "volume": 6802.27,
  "openInterest": 4120.5,
  "overround": 0.05,
  "illiquid": false,
  "marketExpected": 84.1,
  "forecastValue": 86.3,
  "forecastPicksThisBracket": false,
  "closeTime": "2026-09-10T05:00:00Z",
  "url": "https://kalshi.com/markets/kxhighny",
  "scrapedAt": "2026-09-09T11:00:00.000Z"
}

A source that fails arrives as a type: "error" row with the reason, never as a silently short list.

Use cases

Trading the temperature ladder

Pull every bracket with its fair probability and compare it against the forecast for the station that actually settles the market.

Weather desks and newsletters

One row per city and day with the crowd's full distribution — a cleaner "what does the market think it will be tomorrow" than any single number.

Research

Schedule the run and keep a time series of how the distribution moved as the day approached, then compare it against what the station recorded.

Energy and logistics

Temperature markets are a live, priced consensus for the days ahead, in the cities where load and delivery costs actually move.

AI agents

An agent asked "what does the market think tomorrow's high in Chicago will be" runs the days mode for one city and reads favourite and marketExpected off the row.

🤖 For AI Agents & LLM Apps

Compact reference for agents calling this Actor through the Apify MCP server or the Apify API (lergassy/kalshi-weather-scraper).

Purpose: returns Kalshi's daily temperature markets as a probability distribution per city and day, with a forecast and today's observations beside them.

Minimal input:

{ "mode": "events", "cities": ["Chicago"], "days": 3 }

Behaviors an agent should know:

• Quote fairProbability, not probability: the raw prices of a ladder sum to more than 100%, and the fair figure is the one that adds up.
• marketExpected is a fair-weighted average of bracket midpoints. When marketExpectedIsBounded is true, most of the money sits in an open-ended end bracket ("93° or above"), the average is capped by construction, and a gap against the forecast there is an artefact — read forecastBracketFairProbability instead.
• Rows flagged illiquid have no meaningful prices; the forecast mode drops them already.
• station is the NWS climate report the market settles against, and it is authoritative — the city in a series title can be wrong.
• For today's date, observedHigh and observedLow are what the station has already recorded. A forecast that disagrees with the market after those are in is usually stale.
• Temperatures are in Fahrenheit, because that is the unit Kalshi settles in.
• Both APIs are public: no key, no account, no proxy.

Kalshi weather API without a key

Kalshi publishes open endpoints for series, events and markets, and this Actor handles the nesting, the bracket strike types and the two flavours of open-ended band. The forecast comes from Open-Meteo and the observations from the US National Weather Service, both public and both keyless.

❓ FAQ

Is this financial advice or a trading tool?

No. It reads public market data and public weather data and returns them as a table. It places no orders and holds no positions.

Which cities are covered?

The 24 US cities Kalshi runs daily temperature markets for, from New York, Chicago and Los Angeles to Phoenix, Seattle and New Orleans. Run the series mode to see the current list — Kalshi adds and retires series.

Why is the forecast sometimes far from the market?

Two honest reasons and one artefact. The models can genuinely disagree with the crowd; a model run for a day already under way can be stale while the market has seen the morning readings; and when the money sits in an open-ended end bracket, marketExpected understates by construction — that case is flagged.

Does it cover rain, snow and hurricanes?

The filters accept them and the series mode lists them, but Kalshi frequently has no open event on those series. Daily highs and lows are what trades every day.

How fresh are the numbers?

Live at the moment of the run. Schedule the Actor to build your own time series.

Can I use it with the Apify API or an MCP server?

Yes. It runs from the API and the official clients, and AI agents reach it through the Apify MCP server without extra setup.

Your feedback

Missing a city, or a field you need? Open an issue on the Issues tab — every one gets answered.

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Also known as

People look for this Actor as a Kalshi weather API, KXHIGH and KXLOW data, temperature market odds, weather prediction market data, Kalshi temperature brackets and daily high temperature betting odds.