The Tool Desk
Outbyte PC Repair FREEClear out junk files and repair common Windows errorsFree Scan →Outbyte Driver Updater FREEFix the driver behind crashes, sound loss and screen glitchesFind Drivers →The title’s conclusion is that a forecast-based model lost to Kalshi’s temperature markets. But the accessible records for the post do not show its data, dates, model, or results, so that conclusion cannot be independently evaluated from the available evidence. What can be said with confidence is that a weather forecast can be accurate without creating a profitable trade: price, contract settlement rules, fees, and execution all matter.
What the backtest’s title tells us—and what it does not
The DEV Community post is credited to myfirstcodeo, and its indexed description says it compares a city’s National Weather Service (NWS) forecast high with Kalshi temperature markets. The author’s title says “The market won.” The accessible records do not establish the model specification, sample dates, number of markets or trades, market series, baseline, fee treatment, or numerical results. Those details should not be inferred or borrowed from other backtests. DEV Community index and an indexed snippet identify the post and its general approach, but do not provide the missing methodology.
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That makes the title a reported conclusion, not enough information to quantify how large or reliable any gap was. In particular, there is no basis here to say the strategy lost a specific amount, that Kalshi markets always beat weather forecasts, or that a bot could never profit.
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A forecast’s accuracy and a trade’s profitability are different tests. A trader needs a probability estimate that is better than the probability implied by the available contract price by enough to cover transaction costs and execution losses. A correct forecast can still lose if the contract was priced too favorably for the other side; a forecast error can still make money if the price was sufficiently mispriced.
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- Price: A forecast must be compared with the market price at the time an order could actually be placed, not with a later or more convenient price.
- Settlement: The contract’s exact definition of the event—including location, observation source, and timing—must match the weather data being used.
- Fees and execution: Fees, bid-ask spreads, liquidity, slippage, and whether an order would have filled can erase a modeled edge.
- Forecast timing: The model must use the forecast version available at the time of the hypothetical trade, rather than a later update that contains information unavailable then.
Location and settlement matching can change the result
Temperature contracts are tied to defined settlement terms. A model using a forecast for one station or city may not be forecasting the same quantity that determines the contract outcome. A separate, self-reported New York strategy backtest used NWS data from LaGuardia; its discussion raised concern about whether that station matched the market’s settlement station. That is a warning about how to design a test, not evidence about the myfirstcodeo post. The comparable Reddit backtest also cautions that it covered one city, station, series, and 30-day window and used modeled fills.
For a defensible comparison, identify the settlement station and source in the contract rules, then align that definition with the forecast location and observation period. A city label alone is not enough to establish a match.
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What other weather-market backtests can—and cannot—show
Other authors’ results provide context, but they are not substitutes for the missing data behind this title.
| Study | Reported result | Important scope limit |
|---|---|---|
| Reddit author’s New York backtest, 2026 | The author reports that 70 of 500 strategies ended positive and that median ROI was -41.61%. | Self-reported 30-day test using LaGuardia NWS data; one city, station, series, and window, with modeled fills. The figures are not results from the myfirstcodeo post. |
| Rodrigo’s Medium study | The author reports that markets tracked an already-observed daily maximum closely in a 67-day sample, with remaining candidate signals needing further validation. | A single spring sample; live fill rates and performance in other weather regimes were not established. The publication date is not stated in the accessible page. These are the author’s findings, not independent verification. |
Sources: the Reddit backtest and Rodrigo’s Medium study.
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Rodrigo’s result concerns markets tied to an already-observed daily maximum in that study’s sample; it does not establish that every market is efficient or that a forecast model cannot find an edge. Likewise, a historical result from one strategy set or weather regime cannot establish that a bot will keep profiting. The Reddit author’s own warning is apt: “Past performance, including backtested performance, does not predict future results.”
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to judge whether a claimed edge is credible
A useful backtest should make the following information available so another reader can reproduce the comparison and see where assumptions enter:
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- Market and weather data: The specific city, station, contract series, source, and date range.
- Forecast vintage: The forecast values and issue times actually available when each trade would have been made.
- Settlement definition: The contract’s precise outcome rules and the matching observation source and time.
- Trading assumptions: The price available at decision time, order type, fill logic, liquidity, slippage, and historical fees.
- Baselines: A market-price benchmark, a simple forecast or climatology benchmark, and a no-trade baseline.
- Validation: A held-out period or prospective forward test, with strategy choices fixed before evaluation rather than tuned on the same data used to report performance.
Without those details, a backtest can be an interesting experiment, but its result is difficult to interpret as evidence of a repeatable trading advantage. For the titled post specifically, the accessible records do not establish its reproducibility or whether it was tested out of sample.
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