Write-up of a Python engine that priced Kalshi daily-high temperature markets from day-ahead forecasts with a fitted error model and fee-aware expected value. The author documents three bugs found by backtesting (strike direction, bucket edges, settlement source) and concludes the model was less calibrated than the market, so it stays in paper mode. The code itself is private; the repo is the write-up. Results are self-reported.
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Kalshi weather engine: an honest backtest write-up is an intermediate-level forecasting agents project for Kalshi, published by tarang-tj and built with Python, Kalshi public API, weather forecasts and Brier scoring. Its source dates from Oct 24, 2026 and was last updated Oct 24, 2026. Forecasting agents are programs, often built around large language models, that read information about an event and output a probability, then compare it with the market price.
Write-up of a Python engine that priced Kalshi daily-high temperature markets from day-ahead forecasts with a fitted error model and fee-aware expected value. The author documents three bugs found by backtesting (strike direction, bucket edges, settlement source) and concludes the model was less calibrated than the market, so it stays in paper mode. The code itself is private; the repo is the write-up. Results are self-reported. See the source for setup, usage and current status.
Tools: PythonKalshi public APIweather forecasts+1
Difficulty: intermediate
Platforms: Kalshi
Write-up of a Python engine that priced Kalshi daily-high temperature markets from day-ahead forecasts with a fitted error model and fee-aware expected value. The author documents three bugs found by backtesting (strike direction, bucket edges, settlement source) and concludes the model was less calibrated than the market, so it stays in paper mode. The code itself is private; the repo is the write-up. Results are self-reported. It is listed under Forecasting agents in this directory.
According to its source, it works with Kalshi.
The source lists: Python, Kalshi public API, weather forecasts, Brier scoring.
It was published by tarang-tj. The public source is at github.com/tarang-tj/kalshi-weather-engine-report.
This directory rates it intermediate (beginner, intermediate or advanced).
It had 0 GitHub stars as of Oct 24, 2026.
The repository was last updated on Oct 24, 2026, as checked on Oct 24, 2026. Check the source for current status.
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