**tabSutra_39M_v1.0_rules**
This pull request adds the folder eval/tabSutra_39M_v1.0_rules/ with tabSutra_39M_v1.0_rules.parquet and config.json.
The method is our history-only model, tabSutra_39M_v1.0, which is already submitted for the leaderboard, with its forecasts edited
by three deterministic rules that read the structured effects. There is no LLM and no agent in the pipeline, and the
model itself is unchanged. The category is "TSFM + rules" and the input is "History + Macro + Micro".
**How the rule set was chosen**
We split the 10,000 instances into two fixed halves, stratified by dataset and term, so that both halves contain every
group. We scored twelve candidate rule sets on half A and chose the best one. We then checked that the same rule set was
also the best on half B. It was: clip + mean_shift + excursion scores 0.639 on half A, 0.614 on half B, and 0.627 on
all instances. The candidates that invent a magnitude the schema does not give, namely a ramp for the trend, a re…
