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.
52 lines
2.4 KiB
Markdown
52 lines
2.4 KiB
Markdown
# Rule Change Log
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## 2026-06-15 - Refresh `ipo_score_v0` after T1 demand backfill
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Request:
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- Re-analyze the model using the known historical archive after T1 demand text backfill.
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Change:
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- Regenerated `data/snapshots/analysis_model_v0_dataset.csv` from the current SQLite archive.
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- Refreshed `reports/2026-06-15_analysis_model_v0.md` with the expanded T1 demand coverage and new empirical calibration.
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- Kept the `ipo_score_v0` score formula unchanged because the expanded sample still shows non-monotonic middle and low score buckets.
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- 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.
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Rationale:
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- T1 structured coverage increased from 154 to 291 rows after archivist backfilled demand facts from extracted PDF text.
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- The high-conviction bucket remains clearly differentiated, but the rest of the calibration is not strong enough to justify a v1 rule change.
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- Avoiding a threshold rewrite here preserves the feedback loop: future rule changes should be tied to reviewed predictions and named error cases.
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Verification:
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- Rebuilt the analysis dataset and model report from `data/hk_ipo.sqlite`.
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- Confirmed post-listing returns remain labels only and are not score inputs.
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- Confirmed durable source paths remain repo-relative.
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## 2026-06-15 - Introduce `ipo_score_v0`
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Request:
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- Start digesting the downloaded IPO archive and build the first analyst model.
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Change:
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- Added `rules/ipo_score_v0.yaml` as the initial transparent scoring baseline.
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- Added `scripts/build_analysis_dataset.py` to generate a feature dataset and calibration report from `data/hk_ipo.sqlite`.
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- Added `data/snapshots/analysis_model_v0_dataset.csv` as the first model-ready snapshot.
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- Added `reports/2026-06-15_analysis_model_v0.md` to document coverage, calibration, and known gaps.
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Rationale:
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- The archive now has enough T0 facts and D1/D5/D20/D60 labels to support a repeatable baseline.
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- T1 demand data is partially structured and highly informative where available.
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- T2 grey-market data remains blocked until a reliable source exists, so it is excluded from v0.
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Verification:
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- Generated the dataset from the current SQLite archive.
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- Confirmed the model keeps post-listing returns as labels only.
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- Recorded non-monotonic middle buckets as a limitation rather than overfitting them away.
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