How Kalshi Weather Markets Work: A Complete Guide for Algo Traders
TL;DR / Key Takeaways
- Kalshi weather contracts are binary event markets that settle against official weather-station observations.
- Fees, bid-ask spread, and liquidity matter as much as forecast accuracy for automated trading profitability.
- Weather is a strong bot target because data is public, frequent, and measurable against objective settlement rules.
- The Predict & Profit approach focuses on temperature markets where ensemble forecasts can be converted into probabilities.
When I first started looking at Kalshi, I spent a week just trying to understand how the contracts actually worked. Most resources either oversimplified it or assumed you already knew futures market mechanics. Neither is useful when you are trying to build a bot.
This is the guide I wish I had. Here is how Kalshi weather markets work, why they are well-suited to algorithmic trading, and what your bot actually needs to know to trade them correctly.
What Kalshi is
Kalshi is a US-regulated prediction market exchange licensed by the Commodity Futures Trading Commission (CFTC). That regulatory status matters for a few practical reasons. Kalshi is not an offshore gambling operation. It operates under the same legal framework as commodity futures markets, which means traders in all 50 states can participate legally and deposits are held with regulated custodians.
On Kalshi, you trade binary event contracts. A binary contract pays $1.00 if a specific event occurs and $0.00 if it does not. The market price of a contract — say, $0.72 — represents the market's implied probability that the event will happen. Buy YES at $0.72, and you win $0.28 per contract if it settles YES. You lose $0.72 if it settles NO. There is no leverage, no margin, no liquidation. You know your max loss when you enter.
How weather contracts are structured
Every Kalshi weather contract follows the same format: a city, a weather metric, a threshold, and a date.
Will the high temperature in Washington DC reach or exceed 55°F on April 9, 2026? YES / NO binary contract
Kalshi currently covers temperature contracts — daily highs and lows — for approximately 14 major US cities including Washington DC, New York, Chicago, Houston, Los Angeles, Miami, and others. Contracts open several days in advance of the measurement date. For a bot scanning all 14 cities across multiple upcoming dates with both high and low contracts, that is a meaningful universe of opportunities to evaluate every time the ensemble data updates.
How settlement works — and why it matters
Settlement is based on official temperature data from NOAA's Automated Surface Observing System (ASOS) network. The specific station for each city is defined in the contract specifications. When a contract expires, Kalshi pulls the recorded high or low temperature from the designated ICAO station code and settles accordingly.
This is important for two reasons. First, there is no judgment call. Either the temperature hit the threshold or it did not. No committee, no interpretation, no ambiguity. Second, it means your bot's probability model needs to be calibrated to the specific measurement station, not to "the weather in Chicago" in general. The sensor at O'Hare International Airport (KORD) has specific characteristics — including urban heat island effects and measurement timing conventions — that differ from a residential weather station three miles away. If your GFS ensemble interpolation is not accounting for station-level bias, your probability estimates are wrong at the margin.
I have a detailed post on ASOS sensor precision covering exactly this — including documented station-specific biases that affect settlement outcomes.
Bid-ask spread, fees, and liquidity
Like any exchange, Kalshi weather markets have a bid-ask spread. Active contracts for major cities on near-term dates have tight spreads — sometimes just a penny or two. Less active contracts, particularly far-dated or unusual threshold contracts, can spread $0.05 or wider. Wide spreads directly destroy edge before a trade even settles.
Kalshi charges fees on winning trades only. The fee formula is: fee = 0.07 × C × P × (1 - P) where C is the number of contracts and P is the contract price. This formula peaks at $0.50 contracts — exactly where most retail volume occurs. A trade at 50 cents costs roughly four times more in fees than a trade at 10 cents or 90 cents. See the full fee trap breakdown for why this matters so much.
Volume is the third variable your bot needs to check. A contract with almost no trading volume may show a favorable price, but filling a meaningful position without moving the market against yourself is impossible. I require minimum volume thresholds and bid-ask spread checks before the edge score is even calculated.
Why weather is the right asset class for a trading bot
Three properties make weather prediction markets the best fit for systematic, automated trading that I have found.
Objective settlement. Temperature is measured by a specific government sensor at a specific location. There is no ambiguity about whether an event occurred. The model either predicted the right side or it did not. This is completely different from event contracts that depend on interpretation, legal rulings, or subjective judgment.
No insider information. The atmosphere does not have insiders. NOAA's weather model data is public and free. Every trader has access to the same National Weather Service forecast. The edge comes from using that data more rigorously — computing ensemble probabilities from 62 model runs instead of reading a single point forecast — not from having secret data. That is a structural advantage for technically sophisticated retail traders.
Repeatable structure. Every weather contract has the same format: city, metric, threshold, date. A bot can evaluate the entire market — all 14 cities, all available dates, both high and low contracts — using one well-calibrated probability model. One system covers everything. You build it once and it keeps running.
These three properties are why I chose weather markets as the focus for Predict & Profit rather than political, economic, or sports contracts. The signal is objective, the data is public, and the structure is consistent.
Ready to run an automated weather trading bot on Kalshi? The full Python source code — including the HGEFS ensemble pipeline, Kalshi API integration, edge scoring, and fee filter — is available now.
Also available: the companion ebook that walks through the strategy, architecture, and implementation from scratch.
Predict & Profit Ebook — $9.99 on Amazon
Frequently Asked Questions
Q: How do Kalshi weather contracts settle?
A: They settle as binary YES/NO outcomes against official weather-station observations defined by the contract rules. The relevant data source is the settlement station, not a generic weather-app forecast.
Q: Why do fees and spreads matter for a weather bot?
A: A forecast edge can disappear if the entry price, bid-ask spread, and Kalshi fee consume the expected value. The bot must calculate trade quality after costs, not before them.
Q: Why are weather markets suitable for automation?
A: Weather markets have public data, objective settlement, frequent forecast updates, and repeatable contract structures. Those features make them easier to model systematically than narrative-driven markets.