Kalshi Weather Markets + Station Nowcast

Get live Kalshi weather market data — temperature markets (KXHIGH, KXLOWT), rain, snow and hurricane series — as clean JSON, joined with the latest NWS/METAR station observation and a computed distance to strike. Built for weather-market traders, quant researchers and AI agents.

Rebuilt in August 2026 for Kalshi's new API. Kalshi removed the legacy integer price fields (last_price, yes_bid …) and replaced them with *_dollars / *_fp strings, renamed ticker families (KXLOW → KXLOWT) and moved settlement of daily temperature markets to The Weather Company (effective 2026-08-14). Actors that were not migrated fail on most runs. This actor parses both field generations, discovers series at runtime instead of hardcoding city tickers, and runs a public daily canary self-test against every endpoint it uses.

What is Kalshi Weather Markets + Station Nowcast?

Kalshi runs 350+ Climate & Weather series — daily high/low temperature markets for NYC, Chicago, Denver, Phoenix, Philadelphia, Miami, Austin, plus international stations (London EGLL, Berlin, Hong Kong VHHH, Shanghai, Mexico City), rain, snow, and hurricane markets. This actor gives you, without any API key:

• Market snapshots — normalized prices (yes_bid, yes_ask, last_price in dollars), volume, open interest, strike type and strike, close/expiration times
• Station nowcast join — the current METAR/NWS temperature at the settlement station, 3-hour trend, and distance_to_strike_f (current °F minus strike) per market
• Orderbooks — full bid/ask depth per market
• Candlestick history — 1-minute, hourly or daily OHLC for any market
• Settled markets — results for backtesting weather trading models
• Series discovery (free) — every active weather series incl. settlement sources
• Canary self-test (free) — verify all endpoints are healthy before you rely on them

Live example (2026-08-19, NYC + Chicago daily highs)

One run, mode: "markets", 12 open markets — each record joins the market with the current observation at its settlement station:

Market	Last	Strike	Station	Station °F	3h trend	Distance to strike
KXHIGHNY-26AUG19-T92	$0.01	>92	KNYC	73.9	—	−18.1 °F
KXHIGHNY-26AUG19-T85	$0.11	≤85	KNYC	73.9	—	−11.1 °F
KXHIGHCHI-26AUG19-B80.5	$0.35	80–81	KMDW	68.0	−1.1	−12.0 °F

distance_to_strike_f is the field no other Kalshi actor produces: current station temperature minus the market's strike, computed per market. Sample CSV and a Python example: github.com/nanare-sudo/kalshi-polymarket-spreads

Input

{
    "mode": "markets",
    "seriesTickers": ["KXHIGHNY", "KXHIGHCHI"],
    "includeNowcast": true,
    "includeOrderbooks": false,
    "maxMarkets": 200
}

Leave seriesTickers empty to auto-discover all active weather series. Filter with cityFilter (e.g. "NYC", "rain", "London").

Output

{
    "type": "market",
    "series_ticker": "KXHIGHNY",
    "ticker": "KXHIGHNY-26AUG19-T92",
    "title": "Will the maximum temperature be >92° on Aug 19, 2026?",
    "status": "active",
    "strike_type": "greater",
    "floor_strike": 92,
    "yes_bid": 0.03,
    "yes_ask": 0.05,
    "last_price": 0.03,
    "volume_24h": 10342,
    "open_interest": 874,
    "close_time": "2026-08-20T05:00:00Z",
    "station": "KNYC",
    "nowcast": {
        "station": "KNYC",
        "temp_f": 88.9,
        "temp_trend_3h_f": 2.1,
        "observed_at": "2026-08-19T14:51:00Z",
        "raw_source": "nws-observation"
    },
    "distance_to_strike_f": -3.1,
    "settlement_note": "Kalshi daily temperature markets settle on The Weather Company data since 2026-08-14 …"
}

Pricing

Pay per event — you only pay for data you actually receive:

Event	Price
Actor start	$0.005
Market snapshot (incl. nowcast join)	$0.004
Orderbook snapshot	$0.002
Settled market record	$0.002
Station observation	$0.0008
Candlestick batch (100 candles)	$0.01

discover and canary modes are free (only the start fee). A typical snapshot of all open NYC + Chicago temperature markets costs a few cents.

Use with AI agents (MCP)

Every mode works through the Apify MCP server — point your agent (Claude, ChatGPT, LangGraph, CrewAI …) at Apify and it can call this actor directly, e.g. "fetch today's Kalshi high-temperature markets for NYC with the current Central Park observation". The input schema is designed to be agent-friendly: one mode switch, sensible defaults, no API keys.

FAQ

Is this legal? The actor only reads Kalshi's public, unauthenticated market-data API (which Kalshi documents for exactly this purpose) and U.S. government weather data (public domain). No login, no paywall, no personal data.

Does it use the new or the old Kalshi API fields? Both. The parser accepts the new *_dollars/*_fp string fields and falls back to the legacy integer-cent fields, so records stay stable even if Kalshi migrates again.

Which station settles my market? Since 2026-08-14 Kalshi settles daily temperature markets on The Weather Company data (see weather.com/kalshi). The nowcast in this actor uses the corresponding NWS/METAR station as a real-time indicator — the record carries the official settlement_sources from the Kalshi API plus a settlement_note.

Can I backtest? Yes — mode: "settled" returns markets with results (yes/no) for the last N days, and mode: "candlesticks" returns full price history.

What about rate limits? The actor batches requests, respects Kalshi's public rate limits and retries with backoff across both Kalshi API hosts.

Keywords

kalshi weather markets, kalshi api without key, kalshi temperature market data, KXHIGH, KXHIGHNY, KXHIGHCHI, KXLOWT, kalshi orderbook api, kalshi candlesticks, kalshi settlement weather company, NWS station observations, METAR temperature api, prediction market weather data, weather trading bot data, kalshi backtesting settled markets, distance to strike