Trading the Weather: How We Turn Forecasts into Profits on Kalshi

Weather markets are one of the most underappreciated corners of Kalshi. Unlike political or crypto markets, where sentiment and narrative drive prices, weather contracts are grounded in hard physical data. Temperatures get recorded. Forecasts converge. Outcomes resolve with minimal ambiguity. That makes weather one of the cleanest prediction market categories for traders who want to build a systematic, data-driven edge.

I trade these markets daily with an automated bot, and in this guide I will walk through exactly how weather contracts work on Kalshi, how to find mispriced markets, and the practical mistakes to avoid.

What Are Weather Markets on Kalshi?

Kalshi offers daily weather contracts tied to the observed high temperature in specific U.S. cities. The most common structure is a binary contract asking whether the daily high temperature in a given city will be above or below a specific strike temperature on a particular day.

For example, you might see a contract like:

Will the daily high temperature in Chicago be above 35 degrees F on February 25?

You can buy Yes if you think the high will exceed 35 degrees, or buy No if you think it will stay at or below 35. Each contract settles at either $1.00 (if the outcome occurs) or $0.00 (if it does not).

Cities and Coverage

Kalshi typically lists weather markets for major U.S. cities, including New York, Chicago, Miami, Los Angeles, Austin, and others. The available cities and strike temperatures rotate based on season and interest, but you can generally find markets for at least a handful of metro areas on any given day.

How Settlement Works

This is a critical detail that many new traders overlook. Weather contracts on Kalshi settle based on official weather station data — specifically, the recorded daily high temperature as reported by NOAA-affiliated stations. Settlement does not happen at midnight on the observation day. Instead, contracts typically settle the next morning after the observation day, once official data has been published and verified.

That delay matters. It means the platform waits for the authoritative record rather than relying on real-time readings, which reduces disputes but also means your capital is tied up slightly longer than you might expect.

How Pricing Works: Reading the Implied Probability

If a weather contract is trading at $0.65, that price implies the market collectively assigns a 65% probability that the temperature will exceed the strike. A contract at $0.15 implies only a 15% chance.

This is where the opportunity lives. If your independent analysis says the probability of exceeding the strike is 80%, but the market is pricing it at 65 cents, you have an estimated 15-cent edge on that contract. Buy Yes at $0.65, and if you are right about the true probability, you will profit over a large sample of trades.

The inverse works too. If the market prices a contract at $0.85 but your model says the true probability is only 70%, you can buy the No side at $0.15 and capture the difference.

Understanding this probability-price equivalence is the foundation of every weather trading strategy. For a deeper dive into the math, see our guide on event contract pricing and probability.

Building an Edge with Forecast Data

The single biggest advantage in weather markets is that professional-grade forecast data is freely available. Unlike sports betting or political markets, you do not need insider information or proprietary models. The National Weather Service publishes detailed forecasts, and multiple weather APIs provide granular temperature predictions.

Data Sources Worth Using

National Weather Service (NWS) API: Free, reliable, and updated frequently. Provides point forecasts, hourly breakdowns, and probabilistic temperature ranges. This should be your primary source.
Open-Meteo: Free weather API with historical data and ensemble model outputs. Useful for backtesting your pricing approach.
Weather company APIs (Tomorrow.io, Visual Crossing, etc.): Paid tiers offer ensemble spreads and confidence intervals, which translate directly into probability estimates.

From Forecast to Probability

A simple but effective approach: take the forecast high temperature and the expected forecast error (standard deviation) for that lead time, then calculate the probability that the actual high will exceed the contract’s strike temperature.

For example, if the NWS forecasts a high of 72 degrees F for Austin on Thursday, and you estimate the forecast error at that lead time to be roughly 3 degrees F (one standard deviation), you can model the actual high as a normal distribution centered on 72 with a standard deviation of 3. The probability of exceeding a strike of 70 degrees would be approximately 75%.

If the contract is trading at $0.60, you have a meaningful edge.

The key variable is forecast error magnitude, which depends heavily on:

Lead time: A 3-day-out forecast has significantly more uncertainty than a 1-day-out forecast.
Season: Winter forecasts in the Midwest are less accurate than summer forecasts in the Southwest.
Weather regime: Stable high-pressure systems are easier to forecast than transitional patterns with fronts moving through.

You can calibrate your error estimates by backtesting NWS forecasts against actual observed highs for each city. This historical calibration is what separates a guessing trader from one with a genuine statistical edge.

Timing: When to Trade and When to Stay Away

Not all weather contracts are created equal. The timing of your entry matters enormously.

The Sweet Spot: 1 to 3 Days Out

Markets for events 1 to 3 days in the future offer the best risk-reward balance. Forecasts at this range are good enough to give you a directional edge, but uncertain enough that the market has not fully converged to the correct price. You will find the widest mispricings here, especially when a forecast model update shifts the expected high by a degree or two and the market has not caught up yet.

Same-Day Markets: A Trap for the Unwary

This is the single most important practical warning I can give: do not trade same-day weather markets in the afternoon. By mid-afternoon, the daily high temperature in most cities has already been observed. The outcome is effectively known, even though the contract has not settled yet. You might look at a contract and see what appears to be an edge, but the high for the day has already been recorded at the weather station. You are not predicting anything — you are just racing against settlement with stale data.

I learned this the hard way. My bot was trading same-day markets where the high had already been observed hours earlier. The model was calculating a sigma based on hours remaining until next-morning settlement, which made the forecast uncertainty look nonzero. But the actual temperature outcome was already decided. The result was a string of losses on markets where the “edge” was a phantom.

The fix is simple: require a minimum time buffer before settlement. I now skip any market that settles within the next 18 hours. That ensures you are always trading into genuine uncertainty, not into an outcome that has already occurred.

Watch for Forecast Model Updates

Weather forecast models (GFS, ECMWF, NAM) run on regular cycles — typically every 6 or 12 hours. When a new model run comes in and shifts the forecast high by even 1-2 degrees, it can create a temporary mispricing before the Kalshi market adjusts. If you are monitoring forecast updates programmatically, you can be among the first to act on these shifts.

Why Automation Works Exceptionally Well Here

Weather markets are one of the best candidates for automated trading on Kalshi, for several reasons:

Quantifiable inputs: Temperature forecasts are numerical. You do not need to interpret qualitative information or sentiment.
Systematic pricing: You can build a pricing model with a normal distribution, a forecast center, and a calibrated sigma. The math is straightforward.
High market volume: Kalshi lists dozens of weather contracts across multiple cities every day, giving an automated system many opportunities to find edges.
Fast convergence: As settlement approaches and forecast uncertainty shrinks, prices converge toward 0 or 100 cents. A bot can take profit on winning positions before settlement, capturing gains without waiting for the contract to expire.

An automated approach also removes the emotional element. When you are pricing 30 weather contracts per day, you do not want to be manually checking each one against a forecast. A bot can pull the latest NWS data, calculate fair value, compare it to the market price, and execute in seconds.

If you are technically inclined, weather markets are an ideal place to start building a prediction market trading system. Our guide to prediction market APIs walks through the code for building an automated weather trading bot. The data pipeline is clean, the contracts are simple, and the feedback loop is fast — you know within a day whether your model was right.

Seasonal Patterns and Market Selection

Not every city or season offers equally good opportunities. Some practical observations:

Winter markets in northern cities (Chicago, New York) tend to have wider forecast errors due to arctic air intrusions and lake-effect dynamics. More uncertainty means more potential mispricing.
Summer markets in desert cities (Phoenix, Las Vegas) are often too easy to forecast. The high is 110 degrees F for weeks at a time, and the contracts are priced near $0.95 or $0.05. There is no edge when the outcome is obvious.
Transition seasons (spring and fall) offer the most opportunity. Forecast models struggle with frontal boundaries, and temperature swings of 15-20 degrees between days create wide probability ranges.

Focus your attention where the forecast models disagree with each other. When GFS and ECMWF are showing different temperature solutions for a city, the true probability is harder to pin down — and the market is more likely to be mispriced.

Risk Management

Weather markets are low-stakes per contract (max loss is whatever you paid), but losses compound quickly if your model is miscalibrated. A few principles:

Size positions based on edge magnitude. A 5-cent edge deserves a smaller position than a 20-cent edge. Kelly criterion or a fractional Kelly approach works well here.
Diversify across cities and days. Do not put all your capital into one city’s weather outcome. Spread across multiple uncorrelated markets.
Track your calibration. If your model says “70% probability” but you are winning only 55% of those trades, your sigma estimate is wrong. Adjust it.

Key Takeaways

Weather contracts on Kalshi are binary markets on whether a city’s daily high temperature will be above or below a strike, settled using official NOAA weather station data the next morning.
Market price equals implied probability: a contract at $0.65 implies 65% odds. Your edge is the gap between market price and your model’s probability.
Use free forecast data (NWS, Open-Meteo) to build a probability estimate based on forecast center and calibrated error.
Trade 1-3 days out for the best balance of forecast accuracy and market mispricing. Avoid same-day markets where the outcome may already be determined.
Watch for model update cycles (every 6-12 hours) that shift forecasts and create temporary mispricings.
Automation is a natural fit — weather data is numerical, pricing math is simple, and Kalshi lists enough contracts to keep a systematic approach busy.
Calibrate and track performance relentlessly. Your sigma estimate is your edge. If it is wrong, your profitability disappears.

Weather markets will not make you rich overnight, but they are one of the most intellectually honest trading opportunities on any prediction market platform. Weather is just one of several strategies that work for retail traders. The data is public, the math is tractable, and the outcomes are unambiguous. If you are willing to put in the work to calibrate a model, the edge is real.