A forecast record only means something if its history stays intact.
Graham Says records model versions, observations, forecasts and scored outcomes separately. The purpose is to preserve what was known, what was predicted and what happened afterwards.
Each part of the experiment is stored separately.
The order of the record matters.
The forecasting rule is identified before the target result.
The permitted market information is written to the observation log.
The target-date prediction becomes a dated record.
The market closes and the realised index level becomes known.
The outcome is joined to the existing forecast rather than replacing it.
Later runs should not silently change earlier evidence.
Write-once and immutability checks are used so an existing dated forecast or scored result cannot simply be replaced by a different value on a later execution. If the research specification changes, the clean response is a new model version or a new prospective record—not revision of history.
Enough information to inspect the experiment without publishing the private data warehouse.
The public site shows forecasts, actual closes, errors, model-race summaries and selected observation fields. Raw market-data operations and credentials remain private.
The record should be inspectable without relying on a screenshot.
The design goal is for a published number to trace back through a target date, recorded input, model version and resulting score. Reproducibility is therefore an engineering property of the workflow rather than a paragraph added to the write-up afterwards.