hk-ipo/rules/rule_change_log.md

Rule Change Log

2026-06-15 - Add T0.95 late-order heat stage

Request:

• Treat near-deadline heat as usable when the user can still place an IPO order at T0.95.

Change:

• Added T0_95_final_heat to the analyst skill as a separate actionable late-order stage.
• Added rules/ipo_score_v0_95_final_heat_trial.yaml for stage-safe T0.95 heat scoring and timing discipline.
• Updated the archivist skill and scripts/archive_t0_5_market_heat.py so market-heat snapshots can be explicitly archived as T0_95_final_heat.

Rationale:

• If the user can still place, amend, or cancel an order near the subscription cutoff, near-final heat is a legitimate live decision input.
• Historical final public oversubscription can help calibrate near-final heat buckets, but it remains post-hoc calibration unless the value was visible before the user's executable order cutoff.

Verification:

• Kept the script default stage as T0_5_market_heat for backward compatibility.
• Added explicit --stage T0_95_final_heat support for late-order snapshots.
• Verified with py_compile, script help output, dataset rebuild, and git diff --check.

2026-06-15 - Use Chinese for analyst reports

Request:

• Make analyst reports Chinese by default and record the rule in the analyst skill.

Change:

• Added a Simplified Chinese default language rule to the analyst skill.
• Updated the single-ticker report generator to write Chinese Markdown reports while preserving ticker symbols, stage codes, rule ids, score buckets, and source paths as machine-readable identifiers.
• Regenerated the 06106 T0 report in Chinese.
• Documented the Chinese report default in README.

Rationale:

• The analysis workflow is intended for Chinese-language IPO subscription decisions, while project identifiers still need to remain stable for scripts and audits.

Verification:

• Generated a 06106 dry-run report and checked the Chinese sections.
• Regenerated reports/2026-06-15_06106_T0_prospectus_analysis.md.
• Ran py_compile for the report generator and git diff --check.

2026-06-15 - Add concrete stage dates to reports

Request:

• Every analyst report should note the specific dates behind T0, T1, T2, and D1 for the covered IPO.

Change:

• Added a Stage Calendar section to the single-ticker report generator.
• Required analyst reports to show the ticker-specific T0 subscription window, T1 allotment-result date, T2 grey-market date/window, and D1 listing date.
• Updated the 06106 T0 report to include its concrete stage dates.

Rationale:

• The T0/T1/T2/D1 labels are project analysis stages, so reports should always bind them to actual calendar dates for the IPO under review.

Verification:

• Generated a 06106 dry-run report and checked the stage calendar.
• Ran py_compile for the report generator.
• Ran git diff --check.

2026-06-15 - Clarify short-exit IPO strategy horizon

Request:

• Emphasize that the analyst model is focused on selling allocated IPO shares in T2 grey market or on D1, not long-term holding.

Change:

• Added an explicit T2/D1 sell horizon to the analyst skill.
• Updated ipo_score_v0 targets and holding policy to make D1 sell return the primary modeled label and T2 the intended extension when reliable grey-market data exists.
• Clarified that D5/D20/D60 are review labels only, not holding-period targets.
• Updated single-ticker reports and the report generator to show T2/D1 exit discipline.

Rationale:

• The subscription decision should optimize for immediate IPO exit execution, not a long-term equity thesis.
• This preserves stage safety while aligning report language, model targets, and review labels with the actual trading process.

Verification:

• Generated dry-run single-ticker reports after the template update.
• Rebuilt the analysis model report with the same dataset timestamp to refresh language without changing model rows.
• Ran py_compile for the modified scripts and checked Markdown/report text for the new T2/D1 discipline.

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.csv from the current SQLite archive.
• Refreshed reports/2026-06-15_analysis_model_v0.md with the expanded T1 demand coverage and new empirical calibration.
• Kept the ipo_score_v0 score 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.yaml as the initial transparent scoring baseline.
• Added scripts/build_analysis_dataset.py to generate a feature dataset and calibration report from data/hk_ipo.sqlite.
• Added data/snapshots/analysis_model_v0_dataset.csv as the first model-ready snapshot.
• Added reports/2026-06-15_analysis_model_v0.md to 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.