Kalshi Weather Trading Bot

Kalshi is a US prediction market exchange regulated by the CFTC — the same federal body that oversees futures markets. It is not offshore, not a grey-market platform, and not sports betting. It is a licensed exchange where you trade binary contracts on real-world events.

Weather contracts are among the most active markets on Kalshi. The structure is always the same: a city, a weather metric (daily high or low temperature), a threshold, and a date. Will Chicago's high temperature exceed 65°F on April 12? Yes or No. Buy YES at the current market price. If the official NOAA weather station data confirms the outcome at settlement, your YES contract pays $1. If not, it expires worthless.

Settlement is objective and automatic. NOAA's Automated Surface Observing System (ASOS) measures temperature at defined stations for each Kalshi city. There is no ambiguity, no judgment call, and no possibility of manipulation. Either the thermometer hit the number or it did not.

Markets are open 24/7. New contracts appear several days ahead of the settlement date, giving the bot time to identify mispriced contracts before the market efficiently prices in updated model guidance. Most liquidity concentrates in the 1-3 day forward window.

The average Kalshi weather trader checks weather.com or the NWS app, forms a rough guess about tomorrow's high temperature, and places a trade based on intuition. They may be right more often than random chance, but they have no systematic edge over the market price.

A bot changes this. Instead of one forecast, the bot consults 62 independent model simulations and computes the mathematical probability of each weather outcome. Instead of a gut feeling, it has a calibrated probability estimate. Instead of checking the market once a day, it scans every five minutes and catches mispricings as they appear.

Weather markets are also uniquely well-suited to automation because settlement is objective. The bot never has to predict how a judge will rule or what a company will announce. It only has to estimate whether a number will exceed a threshold. That is a well-defined statistical problem with publicly available model data.

The catch: most weather contracts are priced efficiently. Real forecasters, commercial weather firms, and other algorithmic traders keep prices close to true probability. The bot cannot profitably trade most contracts, most of the time. In practice, Predict & Profit rejects over 95% of scanned contracts. The edge is in being disciplined enough to wait.

Every scan cycle runs the same pipeline. First, the bot pulls 62 independent weather forecasts — 31 GFS physical model members via Open-Meteo and 31 AIGEFS AI model members from NOAA's AWS S3 — and computes a probability distribution for each Kalshi city's temperature. For each active Kalshi contract, it compares the ensemble probability to the current market price.

If the probability gap exceeds a minimum threshold, the contract advances to the edge scoring stage. Four signals are evaluated:

Only contracts with a composite edge score above the execution threshold get an order. The bot then places the order via the Kalshi API with RSA-PSS authentication, logs the trade to PostgreSQL, and continues scanning. See the full methodology page for a deeper technical breakdown.

Risk management runs in parallel: per-city exposure limits, per-series concentration limits, a maximum cost per single trade, and a daily loss kill switch. If losses for the day hit the configured threshold, the bot stops placing new orders for the remainder of the session.

The full Python source code is available as a one-time purchase. It includes everything needed to deploy and run the bot:

• ›Complete Python source code (trading bot, data pipeline, edge scoring)
• ›Kalshi API client with RSA-PSS authentication
• ›GFS + AIGEFS ensemble data fetchers
• ›PostgreSQL schema for trade logging and P&L tracking
• ›7-day deployment guide for Linux VPS
• ›Risk management configuration (budget caps, loss limits, exposure limits)

You can start with as little as $10 on Kalshi. The bot is configured for small capital — it is designed to be profitable at the individual trader scale, not a hedge fund. We run our own live instance with approximately $90 deployed.