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Everything you need to know about automated Kalshi weather and inflation trading, the 164-member grand ensemble, 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.
The Global Forecast System (GFS) runs 31 slightly different weather simulations to capture uncertainty. Instead of one forecast, you get 31. We compute the probability of each weather outcome (like "will DC hit 55F tomorrow?") by counting how many of the 31 members predict that outcome. If 28 out of 31 members say yes, that is a 90% probability. If the Kalshi market prices it at 75%, we have a 15% edge.
Predict & Profit v2.2 stacks four independent forecast families — NOAA GFS (31 runs), NOAA AIGEFS (31 AI runs), ECMWF IFS (51 runs), and AIFS-ENS (51 runs) — for 164 total members before any trade is considered. The weather bot requires at least three of the four systems to agree before it acts, which filters out marginal setups.
Kalshi charges fees on winning trades only. The fee is typically a few cents per contract. When calculating edge, the bot accounts for fees so you only trade when the expected profit after fees is positive. Losing trades cost you the contract price but no additional fee.
Python 3.11 or 3.12. 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.
The Global Forecast System (GFS) runs 31 slightly different weather simulations to capture uncertainty. Instead of one forecast, you get 31. We compute the probability of each weather outcome (like "will DC hit 55F tomorrow?") by counting how many of the 31 members predict that outcome. If 28 out of 31 members say yes, that is a 90% probability. If the Kalshi market prices it at 75%, we have a 15% edge.
Predict & Profit v2.2 stacks four independent forecast families — NOAA GFS (31 runs), NOAA AIGEFS (31 AI runs), ECMWF IFS (51 runs), and AIFS-ENS (51 runs) — for 164 total members before any trade is considered. The weather bot requires at least three of the four systems to agree before it acts, which filters out marginal setups.
Kalshi charges fees on winning trades only. The fee is typically a few cents per contract. When calculating edge, the bot accounts for fees so you only trade when the expected profit after fees is positive. Losing trades cost you the contract price but no additional fee.
Python 3.11 or 3.12. 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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