Open Python research project that checks whether Kalshi and Polymarket prices match how often outcomes actually happen. It measures prices at fixed horizons before close, tests for the favourite-longshot bias, and asks whether any mispricing survives trading costs. Includes CI and an interactive results page. Findings are the author's own and not independently verified.
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Open Python research project that checks whether Kalshi and Polymarket prices match how often outcomes actually happen. It measures prices at fixed horizons before close, tests for the favourite-longshot bias, and asks whether any mispricing survives trading costs. Includes CI and an interactive results page. Findings are the author's own and not independently verified. See the source for setup, usage and current status.
Tools: PythonpandasKalshi API+1
Difficulty: intermediate
Platforms: Kalshi · Polymarket
Open Python research project that checks whether Kalshi and Polymarket prices match how often outcomes actually happen. It measures prices at fixed horizons before close, tests for the favourite-longshot bias, and asks whether any mispricing survives trading costs. Includes CI and an interactive results page. Findings are the author's own and not independently verified. It is listed under Data & analytics in this directory.
According to its source, it works with Kalshi and Polymarket.
The source lists: Python, pandas, Kalshi API, Polymarket API.
It was published by hrishi-tailor. The public source is at github.com/hrishi-tailor/prediction-market-calibration.
This directory rates it intermediate (beginner, intermediate or advanced).
It had 0 GitHub stars as of Oct 22, 2026.
The repository was last updated on Oct 22, 2026, as checked on Oct 23, 2026. Check the source for current status.
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