Comprehensive analysis of the “reading the thermometer” strategy for automated weather contract trading on Kalshi. 171 days of data, 7 NWS stations, reviewed by Grok-4 and Gemini-2.5-Flash. February 2026.
Kalshi offers daily high/low temperature contracts for 7 US cities. These contracts settle off the NWS Daily Climate Report (CLI), which uses integer °F values from ASOS weather stations. Instead of predicting the weather, we wait until the temperature is observable via real-time METAR data, then buy contracts where the current reading confirms the outcome. We call this “reading the thermometer.”
By the time the daily low occurs (~4–6 AM) or the daily high peaks (~2 PM), we can read the actual temperature and know with 80–99% confidence what the CLI will report the next morning.
How Kalshi actually settles temperature contracts. ASOS sensor pipeline, F→C→F rounding analysis, CLI report format, station-by-station comparison, DST timing implications.
171 days of METAR vs CLI settlement data. Per-station accuracy at ±1/2/3°F thresholds. Overall: 81% combined, 92.9% highs, 69.1% lows. Worst misses analysis.
Hour-by-hour accuracy heatmaps for all 7 stations. When does “reading the thermometer” cross the 80% threshold? Interactive bar charts showing accuracy progression.
Full unedited feedback from Grok-4 and Gemini-2.5-Flash. 5 consensus points, points of disagreement, and our response. The most critical page for understanding risks.
The final revised bot design. Station-specific configs, deterministic rule engine, data pipeline, reliability model, risk management, and implementation roadmap.
Architecture reviewed by two independent AI models. Awaiting Kalshi historical price data to validate whether the accuracy edge survives market pricing.
Last updated: February 19, 2026