SUPPORT
Everything you need to know about automated Kalshi weather and inflation trading, the NOAA forecast guidance behind the weather bot, and how the bots decide when to trade.
Predict & Profit is source code for traders and builders who want to inspect the mechanics themselves. The weather bot and inflation bot automate data collection, probability estimation, order submission, trade logging, and settlement tracking, but they do not remove market risk.
Use this page to understand what the bot does, what it does not do, how Kalshi fees and account funding work, and why dry-run testing matters before live trading. The answers below are also rendered as structured FAQ data for search engines, but the core summaries are visible here on the page.
Kalshi is a regulated US prediction market where you trade binary contracts on real-world events like weather and macro releases. A trading bot automates scanning contracts, estimating probabilities from data, and placing orders when pricing diverges from those estimates. Predict & Profit ships two Python bots: one for weather and one for inflation.
From NOAA's National Blend of Models. NBM publishes calibrated, bias-corrected temperature guidance at individual stations, including mean, standard deviation and percentiles, for exactly the stations Kalshi settles against. The bot interpolates NOAA's published percentiles to get the probability of a threshold being crossed, uses normal tails beyond P10 and P90, and widens sigma by 1.15 because real temperature outcomes have fatter tails than a Gaussian. If that probability differs enough from the market price, the contract becomes a candidate.
We tried, and we measured it. Earlier versions built probability by counting agreement across four raw ensemble systems. Across 112 settled trades that model scored a Brier of 0.2858 against 0.2439 for simply predicting the base rate, which is worse than useless. Raw ensemble spread on overnight minima was between 2.1 and 4.0 times too narrow, and gridpoint bias reached 7F. v2.5 replaced it with NBM. The five raw systems (GFS, AIGEFS, ECMWF IFS, AIFS, HRRR) remain at a combined 0.25 weight as a disagreement check, not as the basis of the decision.
NBM coverage begins at forecast hour 24. Rather than fall back to the model it replaced, the bot declines to price what it cannot price. You will see fewer weather candidates than in earlier versions. That is the intended behaviour.
Kalshi charges a trading fee when your order executes, and whether you pay the maker or the taker rate depends on how the order fills rather than how it was submitted. As of v2.5 the bot records the fee Kalshi actually reports at order time and subtracts it at settlement, in paper trading as well as live, so a voided market shows the fee loss instead of $0.00.
Python 3.11, 3.12, or 3.13. The source code includes the trading bots, Kalshi API integration with RSA-PSS authentication, SQLite database for trade logging, settlement tracking, and P&L reporting. Everything runs on a standard Linux server or VM.
Yes. The bot trades weather, not stocks. There is no insider information and no alpha decay from sharing. The edge comes from mathematical modeling that most traders cannot build themselves. More users running the same model on Kalshi binary contracts does not move the underlying weather.
You can start with as little as $10 on Kalshi. The bot is configured with budget limits, maximum cost per trade, and daily loss limits. Our live deployment currently runs with roughly $240 across the weather and inflation bots.
No. Predict & Profit is educational software for data science and research purposes. Trading prediction markets involves significant risk of loss. You are responsible for your own trading decisions.
Kalshi is a regulated US prediction market where you trade binary contracts on real-world events like weather and macro releases. A trading bot automates scanning contracts, estimating probabilities from data, and placing orders when pricing diverges from those estimates. Predict & Profit ships two Python bots: one for weather and one for inflation.
From NOAA's National Blend of Models. NBM publishes calibrated, bias-corrected temperature guidance at individual stations, including mean, standard deviation and percentiles, for exactly the stations Kalshi settles against. The bot interpolates NOAA's published percentiles to get the probability of a threshold being crossed, uses normal tails beyond P10 and P90, and widens sigma by 1.15 because real temperature outcomes have fatter tails than a Gaussian. If that probability differs enough from the market price, the contract becomes a candidate.
We tried, and we measured it. Earlier versions built probability by counting agreement across four raw ensemble systems. Across 112 settled trades that model scored a Brier of 0.2858 against 0.2439 for simply predicting the base rate, which is worse than useless. Raw ensemble spread on overnight minima was between 2.1 and 4.0 times too narrow, and gridpoint bias reached 7F. v2.5 replaced it with NBM. The five raw systems (GFS, AIGEFS, ECMWF IFS, AIFS, HRRR) remain at a combined 0.25 weight as a disagreement check, not as the basis of the decision.
NBM coverage begins at forecast hour 24. Rather than fall back to the model it replaced, the bot declines to price what it cannot price. You will see fewer weather candidates than in earlier versions. That is the intended behaviour.
Kalshi charges a trading fee when your order executes, and whether you pay the maker or the taker rate depends on how the order fills rather than how it was submitted. As of v2.5 the bot records the fee Kalshi actually reports at order time and subtracts it at settlement, in paper trading as well as live, so a voided market shows the fee loss instead of $0.00.
Python 3.11, 3.12, or 3.13. The source code includes the trading bots, Kalshi API integration with RSA-PSS authentication, SQLite database for trade logging, settlement tracking, and P&L reporting. Everything runs on a standard Linux server or VM.
Yes. The bot trades weather, not stocks. There is no insider information and no alpha decay from sharing. The edge comes from mathematical modeling that most traders cannot build themselves. More users running the same model on Kalshi binary contracts does not move the underlying weather.
You can start with as little as $10 on Kalshi. The bot is configured with budget limits, maximum cost per trade, and daily loss limits. Our live deployment currently runs with roughly $240 across the weather and inflation bots.
No. Predict & Profit is educational software for data science and research purposes. Trading prediction markets involves significant risk of loss. You are responsible for your own trading decisions.
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