kalshi-polymarket-spreads

This repository is a highly relevant and fresh addition to the cross-venue arbitrage space. It consists of a Jupyter Notebook that scrapes or ingests data from both Polymarket and Kalshi, matches mutually exclusive contracts (e.g., 'Will Candidate X win?'), calculates the implied probabilities, and computes the executable spread after accounting for order book depth and transaction fees. The description's key insight asserts that '47% of observed spreads are negative once you account for both [fees and depth]', which is a crucial reality check for any naive arbitrage strategy. It emphasizes that finding a nominal price difference is insufficient; book depth and fees often erase the edge. The repo's strategy is primarily analytical: it provides the data and code to assess when a true cross-exchange arb exists. For so many bots in the database claiming to do 'Polymarket-Kalshi arbitrage', this repo offers the actual data science to verify those claims. Written in Python (Jupyter Notebook), it is a well-documented analysis with a clear methodology, providing a Python library for fetching data via APIs (like Apify) and computing the spreads. It's small (203 KB) and safe to run. Although it has zero stars and was created on 2026-08-19, its timing is excellent for the current cycle. This is a must-have for any trader active on both venues. While it doesn't have auto-execution, it provides the scanner logic and data pipeline needed to build a true arbitrage bot. I recommend cloning this to understand the real cross-venue dynamics. Verdict: Worth Cloning.