Kalshi Historical Weather Data (2021–2025)
Explore historical Kalshi weather market data from 2021–2025, including trading volume, market categories, price history, volatility, and market outcomes across temperature, rainfall, snowfall, hurricane, tornado, and other weather prediction markets.
Part of kalshi historical data · Browse all dashboards
Created by @misterrpink using LycheeHistorical Kalshi Weather Market Dataset Overview
This section provides an overview of historical Kalshi weather market activity from 2021–2025. Explore trading volume growth, market category distribution, and how participation has evolved across temperature, rainfall, snowfall, hurricane, tornado, Arctic ice, and other weather prediction markets.
Historical Weather Market Volume
Monthly Kalshi historical weather market volume from 2021–2025.
Weather market activity increased substantially throughout the dataset, culminating in a major volume surge during late 2024. The spike in November 2024 highlights a period of accelerated participation and liquidity growth across Kalshi weather prediction markets.
Weather Market Volume by Category
Historical Kalshi weather market volume grouped by weather market category.
Temperature prediction markets account for the majority of historical weather market trading volume on Kalshi. Rainfall, snowfall, hurricane, tornado, Arctic ice, and other weather contracts contribute meaningful activity but represent a smaller share of overall participation.
Annual Weather Market Volume by Category
Historical Kalshi weather market volume by year, segmented by weather category.
This chart shows how weather market activity evolved over time across individual weather categories. Temperature markets consistently represent the largest share of trading volume and have expanded significantly as overall participation in Kalshi weather markets has grown.
Largest Historical Kalshi Weather Markets
Weather prediction markets vary significantly in trading activity and liquidity. The markets below represent the highest-volume weather contracts in the historical Kalshi weather dataset and provide useful examples for studying market behavior, price formation, and prediction market dynamics.
Top Weather Markets by Volume
Historical Kalshi weather markets ranked by total trading volume. A small number of weather markets account for a disproportionate share of historical trading activity. These high-volume contracts attracted the greatest participation and serve as useful reference points for deeper analysis throughout the remainder of this dashboard.
882092
Will Jan 2025 be the hottest Jan ever?
yes
517350
Will the high temp in Chicago be <38° on Nov 9, 2025?
no
517209
Will the **high temp in NYC** be 48-49° on Nov 13, 2024?
no
495353
Will the **high temp in NYC** be <58° on Oct 13, 2025?
no
463898
Will the high temp in Chicago be <75° on Jun 4, 2025?
no
426563
Will the **high temp in Austin** be 91-92° on Oct 4, 2025?
no
407179
Will the **high temp in NYC** be 68-69° on Oct 20, 2025?
yes
404913
Will the **high temp in NYC** be >56° on Nov 12, 2024?
yes
Historical Weather Market Price History
Prediction market prices can be interpreted as implied probabilities. The charts below track how market expectations evolved over time for the highest-volume weather markets in the historical dataset using volume-weighted average price (VWAP) aggregation.
Price History: Will Jan 2025 Be the Hottest January Ever?
Historical implied probability for KXHMONTH-25JAN using volume-weighted average price (VWAP) aggregation.
KXHMONTH-25JAN ($882,092) As the highest-volume weather market in the historical dataset, KXHMONTH-25JAN provides a useful example of long-duration prediction market behavior. Trading remained relatively stable throughout most of the market's lifecycle before ultimately converging toward a 100% probability as the event approached resolution and ultimately settled YES.
Price History: Will the High Temperature in Chicago Be Below 38°F on November 9, 2025?
Historical implied probability for KXHIGHCHI-25NOV09-T38 using volume-weighted average price (VWAP) aggregation.
KXHIGHCHI-25NOV09-T38 ($517,000) This market accumulated more than $517,000 in trading volume and ultimately resolved NO. Early trading exhibited noticeably higher uncertainty, with prices briefly rising above 10% implied probability before rapidly declining. After the initial repricing period, the market settled into a relatively narrow range between 1% and 3%, suggesting that traders quickly reached broad consensus on the likely outcome well before resolution.
Price History: Will the High Temperature in NYC Be 48–49°F on November 13, 2024?
Historical implied probability for KXHIGHNY-24NOV13-B4849 using volume-weighted average price (VWAP) aggregation.
KXHIGHNY-24NOV13-B48.5 ($517,209) Unlike the more gradual price discovery observed in some larger weather markets, this contract experienced significant repricing near settlement. Implied probability fluctuated throughout the final trading window, briefly reaching nearly 80% before rapidly collapsing as new information entered the market. The sharp decline highlights how narrowly defined temperature ranges can remain highly sensitive to forecast updates until shortly before resolution.
Price History: Will the High Temperature in NYC Be Below 58°F on October 13, 2025?
Historical implied probability for KXHIGHNY-25OCT13-T58 using volume-weighted average price (VWAP) aggregation.
KXHIGHNY-25OCT13-T58 ($495,353) This market exhibited a similar late-stage repricing pattern, with implied probability remaining relatively stable before increasing as the event approached and then rapidly declining toward zero. The behavior illustrates how weather prediction markets can quickly incorporate new forecast information when outcomes remain uncertain close to settlement.
Weather Market Volatility and Returns Analysis
Price movements alone do not fully capture market behavior. This section compares normalized price, volatility, and return series for the largest historical weather markets (using min-max normalization), highlighting periods of rapid repricing and changing market expectations.
#1 result: YES
Will Jan 2025 be the hottest Jan ever
#2 result: NO
Will the high temp in Chicago be <38° on Nov 9, 2025?
#3 result: NO
Will the **high temp in NYC** be 48-49° on Nov 13, 2024?
Key Insights from Historical Weather Markets
Historical Kalshi weather markets exhibit a range of behavioral patterns depending on contract structure, duration, and proximity to settlement. Across the largest markets in this dataset (2021–2025), price discovery tends to follow a few recurring dynamics: gradual convergence in longer-duration markets, early uncertainty followed by rapid consensus formation, and late-stage repricing in response to updated weather forecasts. While individual markets vary significantly, overall trading activity is dominated by temperature-based contracts, with liquidity concentrated in a small number of high-volume markets. These patterns highlight how weather prediction markets continuously incorporate new information and adjust implied probabilities as events approach resolution. This dashboard provides a structured view of historical Kalshi weather market data for exploration, research, and backtesting purposes. Explore individual markets, build custom analyses, or fork this dashboard to create your own view of historical prediction market behavior using Lychee