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Prediction-market research, joined to reality.

Connect Kalshi and Polymarket markets to the public sources, reference stations, and observed outcomes behind them. Study venue history, reproduce contract resolution, and move from historical research to live market analysis.

settle.py
1from datetime import date
2from mostlyright.markets.catalog import kalshi_nhigh
3
4settlement = kalshi_nhigh.resolve("KHIGHNYC", date(2025, 1, 6))
Kalshi

NHIGH / NLOW weather-contract resolvers map a ticker to its official reference source and station. Pair that reference mapping with venue history for reproducible research.

NHIGH · NLOW CLI outcomes

Discover active weather events, identify the reference station in each resolution rule, and compare contract outcomes with international daily extremes and quality alerts.

discover · resolve 49+ intl cities

From market question to research-ready data

See all resolvers →

01
Parse contract

Start with the URL slug or ticker for a covered daily-extreme weather market.

02
Map reference

Connect the contract to its reference station and public source.

03
Pull data

Fetch that station's observed extreme for the contract window, in its native unit.

04
Analyze

Apply the contract threshold, record the outcome, and include any quality alert.

settle({ event, loader }) →
{
  eventId: "highest-temperature-in-london-on-…",
  icao: "EGLC", temperature_extreme: "high",
  resolvedValueC: 30, resolvedValueF: 86,
  unit: "celsius", resolutionSourceType: "noaa_wrh",
  dataQualityAlert: null
}

Reference stations across 49+ international cities

See the full registry →

London EGLC
New York KLGA
Paris LFPG
Tokyo RJTT
Seoul RKSI
Singapore WSSS
Moscow UUWW
Sydney-area +42
Hong Kong HKO

Connect market questions to data you can audit.

Read the docs
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