geometrybase
c65b20a1c4
Request: Use the project archivist workflow to update IPO target coverage for the most recent three-year window. Changes: - Add scripts/update_recent_ipo_list.py to discover HKEXnews annual new listing reports, archive XLSX sources, parse subscription-relevant IPO rows, and update SQLite plus snapshots. - Add new_listing_report_entries to preserve annual report row-level evidence. - Archive 2023-2026 Main Board new listing reports and 2024-2026 GEM new listing reports. - Seed 290 report-backed IPO targets for 2023-06-15 through 2026-06-15, skipping 10 non-IPO rows without numeric offer prices. - Refresh ipo_master, missing offering_terms fields, source_refs, ticker_sync_state, and sync_tasks. - Add openpyxl as the XLSX parser dependency and document the archivist refresh flow. - Limit sync summary output while keeping the full queue in SQLite and CSV snapshots. Verification: - Ran update_recent_ipo_list.py for 2023-06-15 to 2026-06-15 with as-of 2026-06-15T07:30:00Z. - Parsed project Python scripts with ast.parse. - Checked SQLite integrity and DB-to-snapshot row counts. - Verified source_refs paths are repo-relative, files exist, and SHA-256 hashes match. - Ran git diff --check and git diff --cached --check. - Checked for Python cache and SQLite transient files. Next useful context: - ipo_master now has 293 tickers; new_listing_report_entries has 290 report-backed targets. - Current sync queue has 2005 open tasks and 42 waiting_until_due tasks for deeper per-ticker archival stages.
129 lines
4.4 KiB
Markdown
129 lines
4.4 KiB
Markdown
# HK IPO
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HK IPO is a project for building a repeatable, auditable research workflow for Hong Kong new listing subscription decisions.
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The project is designed around a feedback loop:
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1. Archive IPO facts and source documents.
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2. Freeze the analysis that was possible at each decision stage.
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3. Compare predictions with post-listing outcomes.
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4. Improve the scoring rules only from reviewed evidence.
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## Goals
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- Maintain a local, Git-tracked history of Hong Kong IPO data.
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- Separate factual archiving from investment judgment.
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- Keep every subscription decision tied to the information available at that time.
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- Review actual IPO outcomes against prior predictions.
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- Build a better IPO scoring process through structured error attribution.
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## Workflow
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Each IPO is evaluated by stage:
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- `T0_prospectus`: prospectus and offer terms only.
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- `T1_allotment`: allotment results, public subscription, placing, allocation, and final pricing.
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- `T2_grey_market`: grey-market result and immediate pre-listing context.
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- `D1`, `D5`, `D20`, `D60`: post-listing review checkpoints.
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The key discipline is to avoid hindsight leakage. A T0 prediction should only use T0 information, even after the IPO has listed.
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## Project Skills
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This repository includes project-local Codex skills under `.codex/skills/`.
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### `archivist`
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Owns facts and source control:
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- archive prospectuses, allotment results, listing facts, and market data;
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- record source URLs, as-of timestamps, repo-relative paths, and file hashes;
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- update the embedded SQLite database;
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- export Git-friendly CSV snapshots.
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It does not make investment recommendations.
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### `analyst`
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Owns IPO judgment and review:
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- produce T0/T1/T2 prediction cards;
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- score IPO candidates;
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- compare multiple IPOs;
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- write research memos and review cards;
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- classify forecast errors;
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- recommend scoring-rule updates.
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It should use archived facts when available and keep prediction cards append-only.
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## Storage Model
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The project is intended to be self-contained and portable across machines. Durable paths should always be relative to the repository root.
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Expected layout:
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```text
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data/
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hk_ipo.sqlite
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raw/
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snapshots/
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memos/
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reports/
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rules/
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schema/
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scripts/
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references/
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```
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Path rules:
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- store paths like `data/raw/06658/prospectus.pdf`;
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- do not store absolute paths;
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- do not store paths with a leading `./`;
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- use POSIX `/` separators;
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- store file hashes for archived source documents when practical.
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SQLite is the embedded source of structured facts. CSV snapshots provide readable Git diffs. Markdown memos preserve the reasoning at each decision point.
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## PDF Text Extraction
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Archived PDFs can be converted into searchable text files:
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```bash
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python3 -m venv .venv
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.venv/bin/python -m pip install -r requirements.txt
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.venv/bin/python scripts/extract_pdf_text.py
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```
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The extractor reads PDF paths from `data/hk_ipo.sqlite`, writes derived text files under `data/extracted_text/`, and exports `data/snapshots/extracted_text_manifest.csv` with page counts, text hashes, and extraction status.
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## Recent IPO Target Refresh
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Use HKEXnews annual new listing reports to seed recent subscription-relevant IPO targets:
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```bash
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.venv/bin/python scripts/update_recent_ipo_list.py --start-date 2023-06-15 --end-date 2026-06-15 --as-of 2026-06-15T07:30:00Z
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```
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The updater archives the HKEXnews XLSX reports under `data/raw/hkex_new_listing_reports/`, records report-backed source references, writes `new_listing_report_entries`, updates `ipo_master` and missing `offering_terms` fields, exports CSV snapshots, and refreshes `sync_tasks`.
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Rows without an IPO offer price, such as transfers of listing, introductions, or de-SPAC transactions, are skipped by default because they are not ordinary public subscription targets.
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## Incremental Archive Sync
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The archivist keeps a per-ticker sync ledger so repeated updates can focus on missing stages:
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```bash
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python3 scripts/update_sync_state.py
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```
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This writes `ticker_sync_state` and `sync_tasks` into `data/hk_ipo.sqlite`, then exports `data/snapshots/ticker_sync_state.csv`, `data/snapshots/sync_tasks.csv`, and `data/snapshots/sync_runs.csv`.
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Use `sync_tasks` as the next-sync queue. Tasks marked `open` are due now; tasks marked `waiting_until_due` are known future updates.
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## Git Discipline
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The repository uses automatic focused commits for completed project changes.
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Before committing, check that unrelated dirty files are not included and that generated durable files use repo-relative paths.
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