geometrybase
eae427d85b
Request: - Provide a way to install or develop a PDF extraction tool for archived HK IPO documents. Changes: - Add requirements.txt with pypdf as the lightweight PDF text extraction dependency. - Add scripts/extract_pdf_text.py to extract text from PDF source_refs into repo-relative data/extracted_text files. - Add extracted text outputs and an extracted_text_manifest snapshot for the six archived HKEXnews PDFs. - Document the extraction workflow in README.md. - Ignore .venv and keep generated SQLite/Python transient files out of git. - Use extracted text to verify the 06106 full prospectus, update source_refs, remove the related data gap, and fill 06106 offering terms. Verification: - Installed python3.14-venv system support, created a local .venv, and installed requirements.txt. - Re-ran scripts/bootstrap_historical_data.py and scripts/extract_pdf_text.py. - Verified extracted text paths and hashes against data/snapshots/extracted_text_manifest.csv. - Verified SQLite integrity and snapshot row counts. - Ran git diff --cached --check and searched durable files for machine-specific absolute paths.
105 lines
3.2 KiB
Markdown
105 lines
3.2 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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## 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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