Weather Edge Finder was built by traders with backgrounds spanning 20+ years in trading, poker, daily fantasy sports, and prediction markets. We built this tool because we wanted it for ourselves — and found that nothing on the market did what we needed.
Kalshi weather markets are one of the few prediction market categories where the underlying data is truly public, the settlement process is transparent, and systematic analysis can produce a measurable edge. We saw an opportunity to build a tool that processes that public information more rigorously than any individual trader doing it by hand.
We track NWS forecast accuracy for all 20 Kalshi temperature cities continuously, correlating every forecast against actual settlement values. Over time, patterns emerge — city-specific biases, seasonal tendencies, high vs. low forecast divergence — that give systematic traders an advantage over those anchored only to the headline NWS number.
We blend multiple data sources: NWS official forecasts, HRRR hourly model runs, real-time ASOS station observations, and Kalshi market prices. Our model computes a probability distribution for each city's temperature outcome, compares it against the market's implied probabilities, and surfaces the contracts where the gap is widest — ranked by expected value.
Questions, feedback, or partnership inquiries: reach us through the contact page or on X at @WeatherEdgeFind.