geometrybase
58ad869f84
Request: - Re-analyze the IPO model using the updated historical archive after T1 demand backfill. Changes: - Regenerate the v0 analysis dataset from the current SQLite archive. - Refresh the v0 calibration report with expanded T1 coverage and new empirical bucket rates. - Update the report template to show pending T1 rows and field-level blanks. - Clarify v0 limitations and record why the score formula stays unchanged for this refresh. Verification: - Ran scripts/build_analysis_dataset.py against data/hk_ipo.sqlite. - Ran py_compile for scripts/build_analysis_dataset.py. - Checked dataset row count, T1 demand coverage, source-only T1 gaps, and repo-relative paths. - Ran git diff --check. Next useful context: - T1 structured coverage is now 291 rows, with 06106 and 06675 still pending_not_due. - The high-conviction T1 bucket remains differentiated, but middle and low buckets are still not monotonic enough for a v1 rule change.
2.4 KiB
2.4 KiB
Rule Change Log
2026-06-15 - Refresh ipo_score_v0 after T1 demand backfill
Request:
- Re-analyze the model using the known historical archive after T1 demand text backfill.
Change:
- Regenerated
data/snapshots/analysis_model_v0_dataset.csvfrom the current SQLite archive. - Refreshed
reports/2026-06-15_analysis_model_v0.mdwith the expanded T1 demand coverage and new empirical calibration. - Kept the
ipo_score_v0score formula unchanged because the expanded sample still shows non-monotonic middle and low score buckets. - Updated model limitations to reflect that T1 is structurally complete for listed rows, while field-level NULLs remain when source documents do not explicitly state a field.
Rationale:
- T1 structured coverage increased from 154 to 291 rows after archivist backfilled demand facts from extracted PDF text.
- The high-conviction bucket remains clearly differentiated, but the rest of the calibration is not strong enough to justify a v1 rule change.
- Avoiding a threshold rewrite here preserves the feedback loop: future rule changes should be tied to reviewed predictions and named error cases.
Verification:
- Rebuilt the analysis dataset and model report from
data/hk_ipo.sqlite. - Confirmed post-listing returns remain labels only and are not score inputs.
- Confirmed durable source paths remain repo-relative.
2026-06-15 - Introduce ipo_score_v0
Request:
- Start digesting the downloaded IPO archive and build the first analyst model.
Change:
- Added
rules/ipo_score_v0.yamlas the initial transparent scoring baseline. - Added
scripts/build_analysis_dataset.pyto generate a feature dataset and calibration report fromdata/hk_ipo.sqlite. - Added
data/snapshots/analysis_model_v0_dataset.csvas the first model-ready snapshot. - Added
reports/2026-06-15_analysis_model_v0.mdto document coverage, calibration, and known gaps.
Rationale:
- The archive now has enough T0 facts and D1/D5/D20/D60 labels to support a repeatable baseline.
- T1 demand data is partially structured and highly informative where available.
- T2 grey-market data remains blocked until a reliable source exists, so it is excluded from v0.
Verification:
- Generated the dataset from the current SQLite archive.
- Confirmed the model keeps post-listing returns as labels only.
- Recorded non-monotonic middle buckets as a limitation rather than overfitting them away.