Real-time expected value analysis for Kalshi temperature markets. Compare forecasts, spot mispricings, and trade with confidence.
Professional-grade tools to give you an edge in weather prediction markets.
Automatically calculate expected value for every market. See at a glance which trades offer positive edge.
Compare leading forecast models alongside our proprietary blend built specifically to exploit weather betting markets.
Live temperature tracking from ASOS weather stations. 5-minute updates with pace-adjusted forecasts.
Intelligent position sizing recommends optimal bet amounts based on your edge and bankroll.
Full coverage of every Kalshi weather market: NYC, Chicago, Miami, LA, Phoenix, Denver, and more.
Your API keys stay in your browser. Direct connection to Kalshi with no middleman.
Cleaner market depth display and smoother trade execution than the native Kalshi interface.
Track win rates, ROI by edge bucket, calibration accuracy, and city-level P&L breakdowns.
Know before you trade — we track historical forecast accuracy for each city so you can see which markets deliver the most predictable conditions and size your trades accordingly.
Get started in three simple steps.
Enter your Kalshi API credentials. Your keys are stored locally and never sent to our servers.
Our dashboard analyzes every weather market in real-time using our backtested proprietary model.
Execute trades directly through Kalshi. Track your positions and performance all in one place.
Watch a full walkthrough of the dashboard and how to find your first edge in Kalshi temperature markets. The tutorial covers connecting your Kalshi API credentials, reading the EV-ranked market table, understanding pace-adjusted forecasts, and executing your first trade.
Professional tools for serious traders. One plan, everything included.
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Guides on how to trade Kalshi weather markets smarter.
The data, edge, and market dynamics that separate same-day from next-day trading — and why treating them the same is a costly mistake. Same-day markets move with real-time temperature pace; next-day markets are driven by forecast model divergence and NWS bias patterns.
GFS, HRRR, ECMWF, and NWS: what each model does, how they differ, and which ones actually matter for temperature markets. HRRR dominates same-day precision; GFS and ECMWF matter for next-day directional bias; NWS is the settlement authority.
NWS bias varies by city, season, and temperature type. Understanding it is one of the most measurable and persistent edges in Kalshi temperature markets. Some cities systematically undershoot on summer highs; others run warm on overnight lows.
View all trading guides and resources
Kalshi weather markets are CFTC-regulated binary contracts tied to daily temperature outcomes in major U.S. cities. Each market is divided into 2°F brackets. You buy YES if you think the temperature will land in that range, or NO if you think it won't. YES and NO prices add up to roughly $1. If you buy YES at 30¢ and the temperature lands in your bracket, you collect $1. Markets settle based on the official NWS Daily Climate Report, not weather app readings.
Kalshi weather contracts settle on the official National Weather Service Daily Climate Report, which is issued the following morning. The report pulls temperature data from ASOS (Automated Surface Observing Systems) stations at major airports. These readings can differ from consumer weather apps due to Celsius-to-Fahrenheit conversion rounding and different time windows.
Edge in weather markets comes from identifying when the market's implied probability for a temperature bracket differs from your own probability estimate. This requires building a forecast baseline using NWS data, understanding forecast accuracy and biases by city, tracking real-time temperature pace against expectations, and sizing positions based on the magnitude of the edge rather than gut conviction.
Weather markets on Kalshi are beatable for traders who approach them systematically. The core data is publicly available through the National Weather Service and ASOS stations. The edge comes from processing that data more rigorously than other market participants, tracking forecast biases by city, and applying disciplined position sizing. Most participants trade on gut feel or a quick weather app check, which creates persistent mispricings for systematic traders to exploit.
Weather Edge Finder is a market scanner and expected value analysis tool for Kalshi temperature prediction markets. It blends NWS forecast data with live market pricing to build probability distributions for each city's temperature outcome, then identifies brackets where the model's probability diverges from the market price. It surfaces trades with positive expected value, ranked by EV, with position sizing guidance based on your bankroll.
Expected value (EV) is the average profit or loss you'd expect per trade over many repetitions. In weather markets, EV is calculated by comparing your model's probability for a bracket against the market price. If your model says a bracket has a 40% chance of hitting and the market is selling it at 25¢, the expected value is positive. Profitable weather traders focus on EV rather than trying to predict exact temperatures.
Join traders who are using data-driven analysis to trade weather markets smarter. Start your 3-day free trial today — no credit card required until the trial ends.