Weather Markets bots

How weather bots trade prediction markets

Weather is the rare corner of prediction markets where the edge is openly published every few hours. Kalshi lists daily high-temperature contracts across US cities and Polymarket carries weather and climate events — and both are priced by traders who, mostly, checked one forecast app. A weather bot instead pulls entire model ensembles: NOAA's GFS runs 31 physics simulations with perturbed starting conditions, ECMWF runs 51, and the new AI families built on models like GraphCast add dozens more. When 120 of 160 ensemble members clear a temperature threshold that the market prices at 40 cents, the gap between model consensus and market price is the trade.

The discipline is in the filter, not the forecast. Good weather bots trade only when independent model families agree, size positions to the edge, and sit out when the ensembles disagree — which is most of the time. They also respect resolution mechanics: contracts settle on specific weather-station readings, so the bot must model the same station the market settles on, not the city average.

What to look for in a weather trading bot

Ensemble breadth first: a bot reading one model is a coin flip with extra steps, so look for multiple independent model families and an explicit agreement threshold before it trades. Then check the boring safety math — weather books are thin, so fee and slippage assumptions, per-city exposure caps and daily loss limits decide whether a real forecasting edge survives execution. Finally, prefer authors who publish a live, settled-trade track record; weather resolves daily, so a seller with no verifiable results has chosen not to show them.

Frequently asked questions

Why would weather markets be beatable at all?

Because most participants price them from a single consumer forecast, while ensemble systems publishing 30–50 member runs are freely available. The bot's edge is aggregating more independent forecasts than the marginal trader — publicly available data, applied more thoroughly.

What data do weather bots actually use?

Mostly free government ensembles: NOAA's GFS and its AI-enhanced variant, ECMWF's IFS and AIFS, plus station observations for nowcasting. No paid API is required — which is why several bots in this category ship as source code with no subscription.

Doesn't everyone running the same models erase the edge?

Slower than you'd think. The edge comes from the gap between ensemble consensus and the price set by casual traders, and more model-driven volume narrows but rarely closes it — weather books are small enough that sophisticated capital stays limited.