geometrybase
7cbdd533b0
Request: - Add a repeatable mechanism so HK IPO reports detect issuers that already have Mainland A shares. - Include a third internet/official-exchange cross-check layer beyond structured history and prospectus scans. Changes: - Add listed_share_classes schema support for same-issuer A-share mappings and evidence links. - Add scripts/archive_a_share_mappings.py to scan prospectus extracted text, reject sponsor/portfolio/cornerstone false positives, archive optional official web evidence and A-share/FX quote evidence, and export snapshots on write. - Surface a_share_* fields in the analysis dataset and single-ticker report output. - Update hk-ipo analyst/archivist skill rules and scheduled refresh prompt to require the three-layer A/H mapping check. Verification: - python3 -m py_compile scripts/archive_a_share_mappings.py scripts/build_analysis_dataset.py scripts/generate_ipo_report.py - .venv/bin/python scripts/archive_a_share_mappings.py --as-of 2026-06-24T00:00:00Z --tickers 00668,01688,03661,09630 --dry-run - .venv/bin/python scripts/build_analysis_dataset.py --db /tmp/hk_ipo_ah_dataset_test.sqlite --dataset /tmp/hk_ipo_ah_dataset_test.csv --report /tmp/hk_ipo_ah_model_test.md --as-of 2026-06-24T00:00:00Z - .venv/bin/python scripts/generate_ipo_report.py 09630 --dataset /tmp/hk_ipo_ah_dataset_test.csv --stdout --as-of 2026-06-24T00:00:00Z - git diff --check Next useful context: - Dry-run detected 00668->300866.SZ, 01688->002600.SZ, 03661->300661.SZ, and 09630->688630.SH. - A false positive 01688->300476.SZ from a cornerstone investor parent was rejected by the issuer-context filter.
566 lines
22 KiB
Python
566 lines
22 KiB
Python
#!/usr/bin/env python3
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"""Generate a stage-safe Markdown analyst report for one Hong Kong IPO."""
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from __future__ import annotations
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import argparse
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import csv
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import sys
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from dataclasses import dataclass
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from datetime import datetime, timedelta, timezone
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from pathlib import Path
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from statistics import mean, median
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from typing import Any
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MODEL_RULE_PATH = Path("rules/ipo_score_v0.yaml")
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DEFAULT_DATASET_PATH = Path("data/snapshots/analysis_model_v0_dataset.csv")
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DEFAULT_OUTPUT_DIR = Path("reports")
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T0_STAGE = "T0_prospectus"
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T1_STAGE = "T1_allotment"
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AUTO_STAGE = "auto"
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@dataclass(frozen=True)
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class BucketMetric:
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sample_size: int
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d1_positive_rate: float | None
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d1_strong_rate: float | None
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average_d1_return_pct: float | None
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median_d1_return_pct: float | None
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@dataclass(frozen=True)
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class ScoreComponent:
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name: str
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points: int
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reason: str
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def parse_args() -> argparse.Namespace:
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parser = argparse.ArgumentParser(description=__doc__)
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parser.add_argument("ticker", help="HK ticker, for example 06658 or 6658.")
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parser.add_argument(
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"--stage",
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choices=[AUTO_STAGE, T0_STAGE, T1_STAGE],
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default=AUTO_STAGE,
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help="Prediction stage to generate. Auto uses T1 when structured T1 demand is available.",
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)
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parser.add_argument("--dataset", default=str(DEFAULT_DATASET_PATH), help="Repo-relative model dataset path.")
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parser.add_argument("--output", help="Output Markdown path. Defaults to reports/{date}_{ticker}_{stage}_analysis.md.")
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parser.add_argument("--as-of", help="Report timestamp. Defaults to current UTC time.")
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parser.add_argument("--stdout", action="store_true", help="Print the report instead of writing a file.")
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return parser.parse_args()
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def parse_as_of(value: str | None) -> str:
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if value:
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return datetime.fromisoformat(value.replace("Z", "+00:00")).isoformat().replace("+00:00", "Z")
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return datetime.now(timezone.utc).replace(microsecond=0).isoformat().replace("+00:00", "Z")
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def normalize_ticker(value: str) -> str:
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ticker = value.strip().upper()
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if ticker.endswith(".HK"):
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ticker = ticker[:-3]
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if ticker.isdigit():
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return ticker.zfill(5)
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return ticker
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def load_dataset(path: Path) -> list[dict[str, str]]:
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if not path.exists():
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raise SystemExit(f"Dataset not found: {path.as_posix()}. Run scripts/build_analysis_dataset.py first.")
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with path.open(newline="", encoding="utf-8") as handle:
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return list(csv.DictReader(handle))
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def find_record(rows: list[dict[str, str]], ticker: str, dataset_path: Path) -> dict[str, str]:
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for row in rows:
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if normalize_ticker(row["ticker"]) == ticker:
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return row
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raise SystemExit(
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f"Ticker {ticker} is not in {dataset_path.as_posix()}. "
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"Use archivist to update archived facts, then rerun scripts/build_analysis_dataset.py."
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)
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def as_float(value: Any) -> float | None:
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if value in {None, ""}:
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return None
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return float(value)
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def as_int(value: Any) -> int | None:
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if value in {None, ""}:
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return None
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return int(float(value))
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def as_bool(value: Any) -> bool:
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return str(value).strip() in {"1", "true", "True"}
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def fmt_value(value: Any) -> str:
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if value in {None, ""}:
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return "未记录"
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return str(value)
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def fmt_num(value: float | None, suffix: str = "", decimals: int = 1) -> str:
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if value is None:
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return "未记录"
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return f"{value:,.{decimals}f}{suffix}"
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def fmt_pct_rate(value: float | None) -> str:
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if value is None:
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return "未记录"
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return f"{value * 100:.1f}%"
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def fmt_pct_points(value: float | None) -> str:
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if value is None:
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return "未记录"
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return f"{value:.1f}%"
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def fmt_money_m(value: float | None) -> str:
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if value is None:
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return "未记录"
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return f"HK${value:,.1f}m"
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def fmt_hkd(value: float | None) -> str:
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if value is None:
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return "未记录"
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return f"HK${value:,.2f}"
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def fmt_times(value: float | None) -> str:
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if value is None:
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return "未记录"
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return f"{value:,.2f}x"
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def fmt_int(value: int | None) -> str:
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if value is None:
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return "未记录"
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return f"{value:,}"
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def stock_display_name(record: dict[str, str]) -> str:
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return record.get("stock_short_name") or record.get("company_name_zh") or record.get("company_name_en") or ""
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def company_display_name(record: dict[str, str]) -> str:
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short_name = record.get("stock_short_name") or ""
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chinese_name = record.get("company_name_zh") or ""
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english_name = record.get("company_name_en") or ""
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if short_name and chinese_name and short_name != chinese_name:
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return f"{short_name}（法定中文名：{chinese_name}）"
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if short_name:
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return short_name
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if chinese_name and english_name:
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return f"{chinese_name}（{english_name}）"
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return chinese_name or english_name or "未记录"
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def parse_date(value: str) -> datetime | None:
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if not value:
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return None
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try:
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return datetime.fromisoformat(value)
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except ValueError:
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return None
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def previous_business_date(value: datetime) -> datetime:
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day = value - timedelta(days=1)
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while day.weekday() >= 5:
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day -= timedelta(days=1)
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return day
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def determine_stage(record: dict[str, str], requested_stage: str) -> str:
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if requested_stage == AUTO_STAGE:
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return T1_STAGE if as_bool(record["has_structured_t1"]) else T0_STAGE
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if requested_stage == T1_STAGE and not as_bool(record["has_structured_t1"]):
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raise SystemExit(
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f"{record['ticker']} has no structured T1 demand yet. "
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f"Generate a {T0_STAGE} report or update the archive first."
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)
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return requested_stage
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def output_path_for(ticker: str, stage: str, as_of: str) -> Path:
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date_part = as_of[:10]
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return DEFAULT_OUTPUT_DIR / f"{date_part}_{ticker}_{stage}_analysis.md"
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def parse_components(text: str) -> list[ScoreComponent]:
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components: list[ScoreComponent] = []
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for item in text.split("|"):
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if not item:
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continue
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name, points, reason = item.split(":", 2)
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components.append(ScoreComponent(name=name, points=int(points), reason=reason))
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return components
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def bucket_metric(
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rows: list[dict[str, str]],
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bucket_key: str,
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bucket_value: str,
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require_t1: bool,
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) -> BucketMetric:
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returns: list[float] = []
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for row in rows:
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if require_t1 and not as_bool(row["has_structured_t1"]):
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continue
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if row.get(bucket_key) != bucket_value:
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continue
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d1_return = as_float(row.get("d1_return_pct"))
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if d1_return is not None:
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returns.append(d1_return)
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if not returns:
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return BucketMetric(0, None, None, None, None)
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return BucketMetric(
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sample_size=len(returns),
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d1_positive_rate=sum(value > 0 for value in returns) / len(returns),
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d1_strong_rate=sum(value >= 10 for value in returns) / len(returns),
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average_d1_return_pct=mean(returns),
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median_d1_return_pct=median(returns),
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)
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def t0_decision_band(score: int) -> str:
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if score < 1:
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return "weak_or_avoid"
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if score <= 4:
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return "neutral"
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if score <= 7:
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return "positive_watch"
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return "strong_watch"
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def action_for_decision(decision: str) -> str:
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actions = {
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"weak_or_avoid": "T0 阶段回避，除非后续 T1 认购热度明显改变格局。",
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"neutral": "暂等 T1 分配结果，不在 T0 阶段主动下重注。",
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"positive_watch": "正面观察，但需要等 T1 确认后再决定 T2/D1 退出仓位。",
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"strong_watch": "T0 强关注，仍需等待 T1 认购热度确认后执行 T2/D1 退出纪律。",
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"avoid": "回避申购。",
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"avoid_or_wait": "回避或等待；没有更强催化前不放大仓位。",
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"watch_or_small": "仅在执行条件支持 T2/D1 退出时小额参与。",
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"selective_subscribe": "选择性申购，并严格按 T2/D1 卖出纪律控制仓位。",
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"high_conviction_subscribe": "积极申购，但仍受分配、流动性和 T2/D1 卖出纪律约束。",
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}
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return actions[decision]
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def component_label(name: str) -> str:
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labels = {
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"offer_size": "发行规模",
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"public_pct": "初始公开发售比例",
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"min_subscription": "最低认购金额",
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"offer_price": "发行价",
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"over_allotment": "超额配股权",
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"public_os": "公开认购倍数",
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"international_os": "国际配售认购倍数",
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"valid_applications": "有效申请数",
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"success_rate": "申请成功率",
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"hk_reallocation": "香港公开发售回拨",
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}
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return labels.get(name, name.replace("_", " ").title())
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def components_table(components: list[ScoreComponent]) -> str:
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lines = ["| 评分项 | 分数 | 原因代码 |", "| --- | ---: | --- |"]
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for component in components:
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lines.append(f"| {component_label(component.name)} | {component.points} | `{component.reason}` |")
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return "\n".join(lines)
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def facts_table(record: dict[str, str], stage: str) -> str:
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rows = [
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("板块", fmt_value(record["board"])),
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("状态", fmt_value(record["status"])),
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("上市日期", fmt_value(record["listing_date"])),
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("申购期", f"{fmt_value(record['application_start_date'])} 至 {fmt_value(record['application_end_date'])}"),
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("分配结果日期", fmt_value(record["allotment_results_expected_date"])),
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("上市方式", fmt_value(record["listing_method"])),
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("行业", fmt_value(record["industry_label"])),
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("保荐人", fmt_value(record["sponsors"])),
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("发行价", fmt_hkd(as_float(record["offer_price_hkd"]))),
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("发行规模", fmt_money_m(as_float(record["offer_size_hkd_m"]))),
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("市值", fmt_money_m(as_float(record["market_cap_hkd_m"]))),
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("每手股数", fmt_int(as_int(record["board_lot"]))),
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("最低认购金额", fmt_hkd(as_float(record["min_subscription_amount_hkd"]))),
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("初始公开发售比例", fmt_pct_points(as_float(record["public_offer_pct_initial"]) * 100 if record["public_offer_pct_initial"] else None)),
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("超额配股权股数", fmt_int(as_int(record["over_allotment_offer_shares"]))),
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]
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if stage == T1_STAGE:
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rows.extend(
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[
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("公开认购倍数", fmt_times(as_float(record["public_oversubscription_times"]))),
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("国际配售认购倍数", fmt_times(as_float(record["international_oversubscription_times"]))),
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("有效申请数", fmt_int(as_int(record["valid_applications"]))),
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("成功申请数", fmt_int(as_int(record["successful_applications"]))),
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("申请成功率", fmt_pct_points(as_float(record["application_success_rate"]) * 100 if record["application_success_rate"] else None)),
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("国际配售承配人数", fmt_int(as_int(record["international_placees"]))),
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("香港公开发售回拨倍数", fmt_times(as_float(record["hk_offer_reallocation_multiple"]))),
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]
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)
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lines = ["| 字段 | 数值 |", "| --- | --- |"]
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for label, value in rows:
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lines.append(f"| {label} | {value} |")
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return "\n".join(lines)
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def ah_overlay(record: dict[str, str]) -> str:
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if not record.get("a_share_ticker"):
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return "- 未识别到同一发行人的 A 股或其他内地上市股本。"
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prospectus_path = record.get("a_share_prospectus_source_path") or "data_gap"
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web_path = record.get("a_share_web_source_path") or "data_gap"
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rows = [
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("A 股代码", fmt_value(record.get("a_share_ticker"))),
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("交易所", fmt_value(record.get("a_share_exchange"))),
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("板块", fmt_value(record.get("a_share_board"))),
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("关系", fmt_value(record.get("a_share_relationship"))),
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("A 股公司名", fmt_value(record.get("a_share_company_name"))),
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("A 股上市日", fmt_value(record.get("a_share_listed_date"))),
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("识别方法", fmt_value(record.get("a_share_detection_method"))),
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("映射置信度", fmt_value(record.get("a_share_mapping_confidence"))),
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("招股书证据", f"`{prospectus_path}`" if prospectus_path != "data_gap" else "`data_gap`"),
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("互联网交叉验证", f"`{web_path}`" if web_path != "data_gap" else "`data_gap`"),
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]
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lines = ["| 字段 | 数值 |", "| --- | --- |"]
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for label, value in rows:
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lines.append(f"| {label} | {value} |")
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lines.extend(
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[
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"",
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"- 这是 A/H 或内地上市股本定价场景，不应按纯首次上市 IPO 处理。",
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"- A 股价格可作为估值锚，但 A 股和 H 股通常不能互换或直接套利；短线收益仍取决于香港侧认购热度、流动性、供给和 T2/D1 出口。",
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]
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)
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return "\n".join(lines)
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def stage_calendar_table(record: dict[str, str]) -> str:
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application_start = fmt_value(record["application_start_date"])
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application_end = fmt_value(record["application_end_date"])
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allotment_date = fmt_value(record["allotment_results_expected_date"])
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listing_date = fmt_value(record["listing_date"])
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listed = parse_date(record["listing_date"])
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allotment = parse_date(record["allotment_results_expected_date"])
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if listed:
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previous = previous_business_date(listed)
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if allotment and previous.date() == allotment.date():
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t2_date = f"{previous.date().isoformat()} 分配结果公布后 / D1 前一交易日"
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else:
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t2_date = f"{previous.date().isoformat()}，预计 D1 前一交易日"
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elif allotment:
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t2_date = f"{allotment.date().isoformat()} 分配结果公布后"
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else:
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t2_date = "未记录"
|
||
|
||
rows = [
|
||
(
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||
"T0_prospectus",
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f"{application_start} 至 {application_end}",
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||
"申购前/申购中阶段；只使用招股书和发行条款。",
|
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),
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(
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"T1_allotment",
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allotment_date,
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"分配结果日；使用公开认购热度、国际配售热度和分配事实。",
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),
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(
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"T2_grey_market",
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t2_date,
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"上市前暗盘窗口；只有存在可靠且可执行的数据源时才作为卖出依据。",
|
||
),
|
||
(
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"D1",
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||
listing_date,
|
||
"正式上市首日；T2 数据不可用或不可靠时的默认卖出窗口。",
|
||
),
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]
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lines = ["| 阶段 | 本 IPO 对应日期 | 含义 |", "| --- | --- | --- |"]
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||
for stage, date_text, meaning in rows:
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lines.append(f"| `{stage}` | {date_text} | {meaning} |")
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return "\n".join(lines)
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|
||
|
||
def source_paths(record: dict[str, str], stage: str) -> list[str]:
|
||
paths = []
|
||
if record["prospectus_source_path"]:
|
||
paths.append(record["prospectus_source_path"])
|
||
if stage == T1_STAGE and record["allotment_source_path"]:
|
||
paths.append(record["allotment_source_path"])
|
||
if record.get("a_share_prospectus_source_path"):
|
||
paths.append(record["a_share_prospectus_source_path"])
|
||
if record.get("a_share_web_source_path"):
|
||
paths.append(record["a_share_web_source_path"])
|
||
return paths
|
||
|
||
|
||
def reason_lines(components: list[ScoreComponent], positive: bool) -> list[str]:
|
||
filtered = [component for component in components if (component.points > 0 if positive else component.points < 0)]
|
||
filtered.sort(key=lambda component: component.points, reverse=positive)
|
||
if not filtered:
|
||
return ["- 没有明显正向评分项。" if positive else "- 没有明显负向评分项。"]
|
||
return [f"- {component_label(component.name)}：{component.points:+d} (`{component.reason}`)。" for component in filtered[:5]]
|
||
|
||
|
||
def missing_field_lines(record: dict[str, str], stage: str) -> list[str]:
|
||
required = [
|
||
("industry_label", "行业"),
|
||
("market_cap_hkd_m", "市值"),
|
||
("min_subscription_amount_hkd", "最低认购金额"),
|
||
]
|
||
if stage == T1_STAGE:
|
||
required.extend(
|
||
[
|
||
("public_oversubscription_times", "公开认购倍数"),
|
||
("international_oversubscription_times", "国际配售认购倍数"),
|
||
("valid_applications", "有效申请数"),
|
||
("successful_applications", "成功申请数"),
|
||
]
|
||
)
|
||
missing = [label for key, label in required if not record.get(key)]
|
||
if not missing:
|
||
return ["- 本阶段必需字段没有明显空缺。"]
|
||
return [f"- 缺失或空白字段：{', '.join(missing)}。"]
|
||
|
||
|
||
def build_report(record: dict[str, str], rows: list[dict[str, str]], stage: str, as_of: str) -> str:
|
||
ticker = normalize_ticker(record["ticker"])
|
||
model_version = record["model_version"]
|
||
dataset_as_of = record["analysis_as_of"]
|
||
|
||
if stage == T0_STAGE:
|
||
score = as_int(record["t0_score"]) or 0
|
||
bucket = record["t0_score_bucket"]
|
||
decision = t0_decision_band(score)
|
||
components = parse_components(record["t0_score_breakdown"])
|
||
metric = bucket_metric(rows, "t0_score_bucket", bucket, require_t1=False)
|
||
else:
|
||
score = as_int(record["total_score"]) or 0
|
||
bucket = record["total_score_bucket"]
|
||
decision = record["decision_band"]
|
||
components = parse_components(record["t0_score_breakdown"]) + parse_components(record["t1_score_breakdown"])
|
||
metric = bucket_metric(rows, "total_score_bucket", bucket, require_t1=True)
|
||
|
||
paths = source_paths(record, stage)
|
||
source_lines = [f"- `{path}`" for path in paths] or ["- 本阶段没有记录来源路径。"]
|
||
company_name = company_display_name(record)
|
||
title_name = stock_display_name(record)
|
||
title_prefix = f"{ticker} {title_name}" if title_name else ticker
|
||
|
||
lines = [
|
||
f"# {title_prefix} IPO 分析报告",
|
||
"",
|
||
"## 摘要",
|
||
"",
|
||
f"- 股票代码：`{ticker}`",
|
||
f"- 公司：{company_name}",
|
||
f"- 分析阶段：`{stage}`",
|
||
f"- 报告生成时间：`{as_of}`",
|
||
f"- 模型数据时间：`{dataset_as_of}`",
|
||
f"- 规则版本：`{model_version}`",
|
||
f"- 规则路径：`{MODEL_RULE_PATH.as_posix()}`",
|
||
"- 策略周期：短线 IPO 申购交易；优先在可靠 T2 暗盘卖出，否则默认 D1 卖出。",
|
||
f"- 结论代码：`{decision}`",
|
||
f"- 执行动作：{action_for_decision(decision)}",
|
||
f"- {'T0 分数' if stage == T0_STAGE else '总分'}：`{score}`",
|
||
f"- 分数分桶：`{bucket}`",
|
||
f"- 历史校准 D1 正收益概率：{fmt_pct_rate(metric.d1_positive_rate)}，样本数 {metric.sample_size}",
|
||
"",
|
||
"## 阶段日期表",
|
||
"",
|
||
stage_calendar_table(record),
|
||
"",
|
||
"## 基础事实",
|
||
"",
|
||
facts_table(record, stage),
|
||
"",
|
||
"## A/H 或内地上市股本检查",
|
||
"",
|
||
ah_overlay(record),
|
||
"",
|
||
"## 短线退出模型推断",
|
||
"",
|
||
f"- D1 正收益概率：{fmt_pct_rate(metric.d1_positive_rate)}",
|
||
f"- D1 涨幅不低于 10% 概率：{fmt_pct_rate(metric.d1_strong_rate)}",
|
||
f"- 同分桶历史 D1 平均收益：{fmt_num(metric.average_d1_return_pct, '%')}",
|
||
f"- 同分桶历史 D1 中位收益：{fmt_num(metric.median_d1_return_pct, '%')}",
|
||
"- T2 暗盘卖出收益暂未建模，直到项目确认可靠暗盘数据源。",
|
||
"- D5/D20/D60 只作为复盘标签，不是持仓目标。",
|
||
"",
|
||
"## 评分拆解",
|
||
"",
|
||
components_table(components),
|
||
"",
|
||
"## 正面因素",
|
||
"",
|
||
*reason_lines(components, positive=True),
|
||
"",
|
||
"## 风险与缺口",
|
||
"",
|
||
*reason_lines(components, positive=False),
|
||
*missing_field_lines(record, stage),
|
||
"- T2 暗盘信号暂未使用，因为项目还没有批准可复现的数据源。",
|
||
"- 上市后的 D5/D20/D60 表现只用于后续复盘，不是本模型的持仓周期目标。",
|
||
"",
|
||
"## 触发条件",
|
||
"",
|
||
"- 上调：T1 认购热度显著更强、分配稀缺性更好，或出现有规则支持的新正面催化。",
|
||
"- 下调：公开或国际需求偏弱、供给过大、关键字段质量不足，或市场窗口明显转差。",
|
||
"",
|
||
"## 退出计划",
|
||
"",
|
||
"- 如果申购并获配，且 T2 暗盘数据可靠且可执行，优先按 T2 暗盘卖出计划处理。",
|
||
"- 如果 T2 不可用或不可靠，默认使用 D1 作为卖出窗口。",
|
||
"- 不把 D5/D20/D60 作为本模型的计划持仓周期。",
|
||
"- 后续记录 D1/D5/D20/D60 结果时，只作为复盘标签，不作为倒推预测输入。",
|
||
"",
|
||
"## 来源路径",
|
||
"",
|
||
*source_lines,
|
||
]
|
||
return "\n".join(lines)
|
||
|
||
|
||
def main() -> int:
|
||
args = parse_args()
|
||
ticker = normalize_ticker(args.ticker)
|
||
as_of = parse_as_of(args.as_of)
|
||
dataset_path = Path(args.dataset)
|
||
|
||
rows = load_dataset(dataset_path)
|
||
record = find_record(rows, ticker, dataset_path)
|
||
stage = determine_stage(record, args.stage)
|
||
report = build_report(record, rows, stage, as_of)
|
||
|
||
if args.stdout:
|
||
print(report)
|
||
return 0
|
||
|
||
output_path = Path(args.output) if args.output else output_path_for(ticker, stage, as_of)
|
||
if output_path.exists():
|
||
raise SystemExit(f"Refusing to overwrite existing report: {output_path.as_posix()}")
|
||
output_path.parent.mkdir(parents=True, exist_ok=True)
|
||
output_path.write_text(report + "\n", encoding="utf-8")
|
||
print(f"report written: {output_path.as_posix()}")
|
||
return 0
|
||
|
||
|
||
if __name__ == "__main__":
|
||
sys.exit(main())
|