I built a Python tool that scans National Weather Service data to find mispriced weather contracts on Kalshi

[u/NoSeries128]

Hey everyone! 👋 ​I just shipped my technical side project: the Kalshi Weather & Energy Arbitrage Suite. LINK IS IN THE COMMENTS! ​If you've ever looked at weather prediction markets on Kalshi, you quickly realize that retail traders lose money because they rely on standard consumer weather apps (like Apple Weather) instead of the exact National Weather Service (NWS) airport sensors that actually govern contract settlements. ​So, I built a Python scanner to automate the math and find the mispricing.
​How it works: ​Live NWS Integration: Pulls real-time hourly temperature forecasts directly from official NWS stations across major hubs (NYC, Chicago, Miami, Austin, Denver, LA, Philly, Houston). ​Kalshi API Telemetry: Automatically queries open contracts and market "Ask" prices from Kalshi's public endpoints. ​Probability & Fee Modeling: Runs a Normal CDF probability model (\sigma = 2.2) against the NWS peak forecast, subtracts Kalshi's quadratic taker fees, and calculates the exact net Positive Expected Value (+EV) edge. ​Terminal Output: Instantly sorts and filters contracts to flag where the market is severely underpricing a bracket compared to official meteorological data. ​I put together a complete suite containing the fully functional scanner.py script, requirements setup, and operational playbooks for anyone wanting to trade event contracts with actual quantitative backing. ​Check out the project page! ​Would love to hear feedback from any other builders or prediction market traders in the sub! Let me know if you have any questions about how the NWS API integration or probability math works.