If you ship a probabilistic forecast, the single highest-value habit you can build is logging your forecasts so you can grade them later. Sabermetrics figured this out forty years ago. Weather forecasting has done it for a century. Most ML model owners still do not do it.
This post walks through a 40-line Python recipe that logs an ML option-pricing model's per-contract probability-ITM forecast to a CSV, so you can compute the Brier loss after the option expires. The recipe is part of a small open-source cookbook for the Helium MCP REST surface — an MCP server that also exposes its tools as plain HTTPS GETs, which makes it convenient as a teaching substrate even if you do not use MCP.
You will not need an API key, a signup, or a Python SDK.
What we are doing
For every option contract we care about, we want one row that records:
