kalshi-weather-markets Checked
Installation
Copy the prompt below and send it to your AI assistant (e.g. Claude Code) — it will follow the instructions and install automatically.
Install the "kalshi-weather-markets" skill by following the instructions at https://skill123.me/install/kalshi-weather-markets.
Run in your terminal — downloads and installs to ~/.claude/skills/.
curl -fsSL https://skill123.me/install/kalshi-weather-markets.sh | bash
Download the zip and extract it into your skills directory (e.g. ~/.claude/skills/), then restart your session.
⬇ Download v1.0.0 · 12 KBNot live-tested yet — this skill is rated on static analysis only.
About this skill
Daily temperature high/low bracket and threshold contracts on Kalshi — contract structure, forecast→P(YES) map, settlement rules, cross-venue divergences, and weather-specific pitfalls
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.
Documentation
Daily temperature high/low bracket and threshold contracts on Kalshi — contract structure, forecast→P(YES) map, settlement rules, cross-venue divergences, and weather-specific pitfalls
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.
B74.5covers 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 iffcli >= strike + 1less→ YES iffcli <= strike - 1- **Critical:**
strike_type("greater"/"less") is **not inferable from the ticker**. Read it from the APIstrike_typefield 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.
---
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
The ±0.5 shift is **not optional**. Dropping it biases every bracket. Treating 2°F brackets as 1°F half-open windows produced a **+1640% phantom backtest** in one project.
See scripts/weather_brackets.py for runnable implementations of all four functions.
---
Deriving (μ, σ) from Ensemble Quantiles
sigma_raw = max((p90 − p10) / 2.56, 0.5) · sigma_scale · sigma_mult
mu = p50 # or nowcast-blended (see forecasting.md)
sigma = max(sigma_raw, 0.1) # hard floor against degeneracy
The **2.56 divisor** is the 10th–90th percentile span of a standard normal (2 × 1.28σ).
---
CLI-Space Bias Correction
View full documentation
The settlement value (NWS CLI integer °F, LST day) is **not** the same as raw ASOS/METAR hourly max/min — CLI applies QC, backup-station fallback, and LST aggregation. Shift μ before computing P(YES):
mu_cli = mu_metar + bias_city_season # bias = oracle_extreme − asos_extreme, fit per city + season
Fit bias_max / bias_min as seasonal (circular) curves per city. Skipping this systematically misprices every bracket for cities with a structural CLI/METAR gap.
---
Settlement Rules
### Kalshi
- **Source:** NWS Climatological Report (CLI) — the official daily climate summary issued by each WFO.
- **Fallback:** IEM ASOS daily download matches CLI 100% and is available programmatically.
- **Window:** **LST (Local Standard Time), no DST adjustment.** The day runs midnight-to-midnight LST year-round.
- **Value:** Integer °F maximum (HIGH) or minimum (LOW) temperature for that LST day.
- **Bracket:** YES iff
cli ∈ {floor, cap}(both ends inclusive). - **Threshold greater:** YES iff
cli >= strike + 1. - **Threshold less:** YES iff
cli <= strike - 1.
### Settlement-Source References
> **Read each market's own rulebook before scoring or trading.** Settlement source, station, and day-window are per-market contract terms that can change.
| Resource | URL |
|----------|-----|
| Kalshi market rules / Rulebook | <https://docs.kalshi.com> (per-market "Rulebook") |
| NWS Climatological Report (CLI) | <https://www.weather.gov/wrh/Climate> |
| IEM ASOS daily download | <https://mesonet.agron.iastate.edu/request/daily.phtml> |
| Polymarket resolution (WU) | <https://www.wunderground.com> |
| Polymarket disputes (UMA) | <https://docs.uma.xyz> |
---
Cross-Venue Divergence
The same metro on the same date can settle to **different values** across venues — both because of the **station** and the **DST window** in spring/fall.
| Axis | Kalshi | Polymarket |
|------|--------|------------|
| Source | NWS CLI / IEM ASOS | Weather Underground |
| Day window | LST (no DST) | Local clock (with DST) |
| NYC station | **KNYC** (Central Park) | **KLGA** (LaGuardia) |
| Rounding | Integer °F, t ∈ {floor, cap} | Per WU history |
Any cross-venue analysis must settle each leg on its own source.
---
Nowcast Blending (Same-Day Path)
Once an intraday observation is available, pull μ toward reality and shrink σ:
- **HIGH:** clamp μ to
[obs, obs + drift · hours_remaining] - **LOW:** clamp μ to
[obs − drift · hours_remaining, obs] - σ shrinks as
sigma_raw · sqrt(hours_remaining / 24), floored atsigma_floor(≈ 0.5) drift≈ 3.0°F/hr default
Optional NWP prior blend: new_p50 = w · hrrr + (1−w) · p50 (w ≈ 0.5), then rebuild symmetric quantiles using a calibrated σ.
---
Calibrated Model Performance (Reference Numbers)
Per-city OOS Brier scores across 22 highs + 22 lows (v1.5, 2026-06-17 baseline):
| Metric | Range |
|--------|-------|
| Per-city OOS Brier | 0.07 – 0.14 (lower = better; 0.25 = climatology) |
| Per-city accuracy | 65–85% (bracket classification) |
**Forecast skill ≠ trading edge.** A calibrated model that beats climatology by 0.05 Brier does not guarantee positive EV at market prices — the market already incorporates NWP. The practical edge is maker-side fading of mispriced longshot brackets (favorite–longshot bias), not raw directional forecasting.
---
Weather Pitfalls
1. **Wrong settlement source.** Scoring against a derived truth that correlates with but differs from the venue's resolution flips ~10% of outcomes. Settle on the venue's own result.
2. **Bracket off-by-one (phantom +1640%).** Treating 2°F inclusive brackets {floor, cap} as 1°F half-open [floor, cap) manufactures a large phantom backtest edge. The bracket is **both-ends-inclusive**.
3. **strike_type not inferable from ticker.** T74 on a low market might be greater or less. Always read strike_type from the API. Never guess.
4. **Date-in-ticker, not close_time.** Use the date embedded in the ticker string for settlement-date joins, not close_time (which is next-day UTC).
5. **LST ≠ local clock.** Kalshi settles on LST (no DST). In spring/fall, the LST window shifts relative to local time. Cross-referencing WU (which uses local clock) against CLI on DST-transition days will produce mismatches.
6. **CLI ≠ METAR.** Raw ASOS hourly max/min is not the settlement value. CLI applies QC, backup-station fallback, and LST aggregation. Fit per-city seasonal bias corrections before computing P(YES).
7. **UTC vs local-day feature aggregation.** Aggregating forecast features over UTC days instead of LST days misaligns labels — cost ~14 percentage points of accuracy in one study.
8. **Clock-mismatch look-ahead.** Filling at an 18:00Z book snapshot while features are cut at 14:00 LST trades non-Eastern cities on future information. Use each city's own local decision time.
9. **Phantom penny asks.** 1¢ ask levels are frequently spoofed; assuming you fill them over-credits PnL ~23×. Count only depth that persists across snapshots and is corroborated by trade prints.
10. **Overround as a diagnostic.** Sum the YES prices across an event's full bracket set. overround > 1.0 is normal (house edge); `o
Score breakdown
Assessment methodology
Version history
Imported from agiprolabs/claude-trading-skills@main