kalshi-weather-markets

Kalshi Weather Markets — Daily Temperature High/Low

Kalshi lists daily high and low temperature options for ~20 US cities as binary contracts that settle YES ($1.00) or NO ($0.00). This skill covers the market structure, the forecast-to-probability map, exact settlement mechanics, and hard-won pitfalls. It builds on the exchange layer — for Kalshi API mechanics (host, auth, orders, order book, candlesticks) see the kalshi-api skill; for strategy, sizing, and backtesting see prediction-market-strategy.

Contract Types

Brackets — B<center>

A bracket ticker B<center> is a 2°F-wide, both-ends-inclusive window.

• B74.5 covers the two integers {74, 75}°F.
• YES iff the settled temperature is exactly 74 or 75.
• Brackets in one event are mutually exclusive and (with two open tail markets) collectively exhaustive.
• Their YES prices sum to the overround (fair = 1.0; > 1.0 = aggregate overpricing).

Thresholds — T<strike>

A threshold ticker T<strike> is a one-sided binary.

• greater → YES iff cli >= strike + 1
• less → YES iff cli <= strike - 1
• Critical: strike_type ("greater" / "less") is not inferable from the ticker. Read it from the API strike_type field every time.

Ticker Format

KXHIGH<CITY>-<YYMONDD>-B<center>     # bracket high
KXLOW<CITY>-<YYMONDD>-T<strike>      # threshold low

The date is encoded in the ticker, not derivable from close_time.
KXHIGHNY-26JUN21 settles 2026-06-21 LST. close_time is next-day UTC (~00:59 ET). Joining on close_time off-by-ones every label — use the ticker date.

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Forecast → P(YES)

Given a forecast distribution N(μ, σ) for the day's extreme, apply the half-integer continuity correction (mandatory — settlement is on integers, not a continuous scale):

# Bracket B<center>, covering integers {floor, cap}
P(YES) = Φ((cap + 0.5 − μ) / σ) − Φ((floor − 0.5 − μ) / σ)

# Threshold "greater":
P(YES) = 1 − Φ((T + 0.5 − μ) / σ)

# Threshold "less":
P(YES) =     Φ((T − 0.5 − μ) / σ)

Φ(x) = 0.5 · (1 + erf(x / √2))   # stdlib only, no scipy needed