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 reading a single number off an app, it reads NOAA's own probabilistic guidance for the exact station the contract settles on, and converts the published percentiles into a probability. Instead of a gut feeling, it has a calibrated estimate. Instead of checking the market once a day, it scans continuously 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 resolves each market to its settlement station using Kalshi's own series metadata, rather than guessing from the city name. Chicago settles on Midway, not O'Hare. Houston settles on Hobby, not Bush. Forecasting the wrong airport is forecasting the wrong contract.

It then pulls NOAA's National Blend of Models bulletin for that station. NBM is the operational product NOAA calibrates and bias-corrects against the observations the station actually reports, and it publishes mean, standard deviation and percentiles rather than a single number. Probability comes from interpolating those published percentiles, with normal tails beyond P10 and P90 and a 1.15 multiplier on sigma, because real temperature outcomes have fatter tails than a Gaussian.

Five raw forecast systems, GFS, AIGEFS, ECMWF IFS, AIFS and HRRR, are still fetched and still carry a combined weight of 0.25, but they act as a disagreement check rather than as the basis of the decision. If NBM has no guidance for a market, the bot does not trade it. That includes every same-day market, because NBM coverage begins at forecast hour 24.

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 SQLite, 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
• ›NBM bulletin parser plus the five raw ensemble data fetchers
• ›SQLite 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, with budget caps, per-trade cost limits and a daily loss kill switch, and it is built for the individual trader scale rather than a fund. We run our own live instance with approximately $240 deployed across both bots. Run it in dry-run mode for a week or two first. The shipped changelog documents what is still open, and it is worth reading before you go live.