geometrybase
48b89552fe
Request: - Use the analyst skill to digest downloaded IPO archive data and start building an analysis model. Changes: - Add ipo_score_v0 as the first transparent stage-safe scoring rule set. - Add build_analysis_dataset.py to derive model features, scores, decision bands, and empirical D1 calibration from SQLite. - Generate analysis_model_v0_dataset.csv with 293 scored IPO rows and archived source paths. - Add a model calibration report documenting coverage, T0/T1 bucket performance, usage, and known gaps. - Record the initial model entry in the rule change log and document the command in README. Verification: - Ran py_compile for scripts/build_analysis_dataset.py. - Regenerated the analysis dataset and report with as-of 2026-06-15T13:00:00Z. - Checked CSV row count, source path coverage, and repo-relative path hygiene. - Ran git diff --cached --check. Next useful context: - v0 should be treated as a transparent baseline, with T1 high-score calibration strongest and middle buckets still non-monotonic. - T2 is excluded until a reliable grey-market source is approved.
67 lines
3.2 KiB
Markdown
67 lines
3.2 KiB
Markdown
# HK IPO Analysis Model v0
|
|
|
|
- Model version: `ipo_score_v0`
|
|
- Analysis as of: `2026-06-15T13:00:00Z`
|
|
- Rule file: `rules/ipo_score_v0.yaml`
|
|
- Dataset: `data/snapshots/analysis_model_v0_dataset.csv`
|
|
|
|
## What This Model Does
|
|
|
|
This is the first analyst model built from the downloaded archive. It creates a repeatable feature table, scores each IPO using stage-safe rules, and calibrates the score buckets against archived D1 outcomes. It is intentionally transparent: the output includes every score component and the archived source paths used for each ticker.
|
|
|
|
The model does not use grey-market data in v0 because T2 currently has no approved reproducible source. It also does not use post-listing returns as inputs; returns are labels only.
|
|
|
|
## Data Inventory
|
|
|
|
- IPO rows scored: 293
|
|
- Rows with D1 labels: 273
|
|
- Rows with structured T1 demand fields: 154
|
|
- Rows with prospectus source path: 293
|
|
- Rows with allotment source path: 291
|
|
- Rows with offer size: 293
|
|
- Rows with public oversubscription: 144
|
|
- Rows with international oversubscription: 153
|
|
|
|
## T0 Calibration
|
|
|
|
T0 uses only prospectus-stage structure: offer size, initial public offer percentage, minimum subscription amount, offer price band, and over-allotment availability.
|
|
|
|
| Bucket | N | D1 positive | D1 >= 10% | Avg D1 return | Median D1 return |
|
|
| --- | ---: | ---: | ---: | ---: | ---: |
|
|
| t0_1_to_4 | 60 | 63.3% | 40.0% | 9.6 | 3.1 |
|
|
| t0_5_to_7 | 105 | 73.3% | 51.4% | 40.1 | 13.2 |
|
|
| t0_gte_8 | 72 | 76.4% | 47.2% | 28.6 | 9.6 |
|
|
| t0_lt_1 | 36 | 58.3% | 33.3% | 12.8 | 2.3 |
|
|
|
|
## T1 Calibration
|
|
|
|
T1 adds allotment-stage demand: public subscription, international placing demand, valid application count, application success rate, and HK public offer reallocation.
|
|
|
|
| Bucket | N | D1 positive | D1 >= 10% | Avg D1 return | Median D1 return |
|
|
| --- | ---: | ---: | ---: | ---: | ---: |
|
|
| total_0_to_9 | 35 | 60.0% | 34.3% | 3.5 | 0.2 |
|
|
| total_10_to_17 | 17 | 47.1% | 23.5% | 1.6 | -0.1 |
|
|
| total_18_to_25 | 33 | 72.7% | 54.5% | 28.0 | 14.8 |
|
|
| total_gte_26 | 43 | 97.7% | 95.3% | 101.2 | 88.8 |
|
|
| total_lt_0 | 15 | 73.3% | 20.0% | 0.3 | 3.4 |
|
|
|
|
## Initial Read
|
|
|
|
The strongest v0 T1 bucket is `total_gte_26` with 43 historical D1 observations and a 97.7% D1 positive rate. The model is most useful after allotment results are available; T0 is a watchlist filter rather than a final subscription call.
|
|
|
|
The middle score buckets are not monotonic yet. That is a feature, not a bug report: v0 is exposing where the current rules are too coarse and where missing T1 demand facts weaken calibration. Future rule changes should come from reviewed prediction cards, not from overfitting this initial sample.
|
|
|
|
## Usage
|
|
|
|
1. Run `scripts/build_analysis_dataset.py` after archivist updates the database.
|
|
2. Use `t0_score` for prospectus-stage watchlisting.
|
|
3. Use `total_score`, `decision_band`, and `calibrated_d1_positive_rate` for T1-stage subscription cards.
|
|
4. Treat D1/D5/D20/D60 columns as review labels only, never as prediction inputs.
|
|
|
|
## Known Gaps
|
|
|
|
- T1 demand parsing is incomplete for older HTML-only allotment announcements.
|
|
- Industry and issuer fundamentals are not sufficiently structured for model input.
|
|
- T2 grey-market signal is blocked pending an approved source.
|
|
- Extreme D1 returns should be audited before they drive rule changes.
|