We compare weather forecasts with market prices.
We are not trying to guess one winning bracket. We are trying to find where the market price is wrong.
Four steps from forecast to signal.
We start with a 51-member ECMWF ensemble, then add regional models, AIFS, and live weather features like cloud, radiation, and pressure.
Each city has its own post-processor. It uses 10+ of propreitary inputs, seasonal history, lead time, and station behavior to calibrate the raw forecast.
We turn the forecast into bracket probabilities, then compare those numbers with live prediction market prices to find the mispriced side.
We skip weak, noisy, or illiquid setups. High spread, bad calibration, tiny edge, and thin markets all get filtered out.
The best trade is often the one the market overpriced.
Example: the model gives one bracket only a 28% chance, but the market prices that same YES token at 55c. That means the market is much more confident than the model is.
A cleaner signal, not the full model dump.
- Direction: YES or NO
- Bracket: the market being traded
- Edge and EV: why the trade exists
- Price context: what the market was charging at entry