geometrybase
77b405e4f3
Request: - Analyze HK IPO ticker 01392 with the analyst skill. - Preserve the in-flight 06132 archive/report work already created for the prior request. Changes: - Archived official HKEX prospectus PDFs and extracted text for 01392 and 06132. - Seeded structured T0 facts into the SQLite archive and refreshed CSV snapshots and sync state. - Rebuilt the v0 analysis dataset and model calibration report. - Generated Simplified Chinese T0 prospectus-stage analyst reports for 01392 and 06132. - Adjusted report stage calendars so T2 uses the previous business day before D1 when listing is separated from allocation by a weekend. Verification: - Compiled modified Python scripts with in-memory syntax checks. - Ran SQLite quick_check and foreign_key_check. - Confirmed DB row counts match CSV snapshots for key tables. - Verified 01392/06132 source paths are repo-relative, raw files exist, hashes match, and PDF text manifest rows are ok. - Ran git diff --cached --check. Next useful context: - 01392 T1 is due on 2026-06-18; rerun analyst after allotment results are archived. - 06132 T1 is due on 2026-06-22; rerun analyst after allotment results are archived.
507 lines
19 KiB
Python
507 lines
19 KiB
Python
#!/usr/bin/env python3
|
||
"""Generate a stage-safe Markdown analyst report for one Hong Kong IPO."""
|
||
|
||
from __future__ import annotations
|
||
|
||
import argparse
|
||
import csv
|
||
import sys
|
||
from dataclasses import dataclass
|
||
from datetime import datetime, timedelta, timezone
|
||
from pathlib import Path
|
||
from statistics import mean, median
|
||
from typing import Any
|
||
|
||
|
||
MODEL_RULE_PATH = Path("rules/ipo_score_v0.yaml")
|
||
DEFAULT_DATASET_PATH = Path("data/snapshots/analysis_model_v0_dataset.csv")
|
||
DEFAULT_OUTPUT_DIR = Path("reports")
|
||
|
||
T0_STAGE = "T0_prospectus"
|
||
T1_STAGE = "T1_allotment"
|
||
AUTO_STAGE = "auto"
|
||
|
||
|
||
@dataclass(frozen=True)
|
||
class BucketMetric:
|
||
sample_size: int
|
||
d1_positive_rate: float | None
|
||
d1_strong_rate: float | None
|
||
average_d1_return_pct: float | None
|
||
median_d1_return_pct: float | None
|
||
|
||
|
||
@dataclass(frozen=True)
|
||
class ScoreComponent:
|
||
name: str
|
||
points: int
|
||
reason: str
|
||
|
||
|
||
def parse_args() -> argparse.Namespace:
|
||
parser = argparse.ArgumentParser(description=__doc__)
|
||
parser.add_argument("ticker", help="HK ticker, for example 06658 or 6658.")
|
||
parser.add_argument(
|
||
"--stage",
|
||
choices=[AUTO_STAGE, T0_STAGE, T1_STAGE],
|
||
default=AUTO_STAGE,
|
||
help="Prediction stage to generate. Auto uses T1 when structured T1 demand is available.",
|
||
)
|
||
parser.add_argument("--dataset", default=str(DEFAULT_DATASET_PATH), help="Repo-relative model dataset path.")
|
||
parser.add_argument("--output", help="Output Markdown path. Defaults to reports/{date}_{ticker}_{stage}_analysis.md.")
|
||
parser.add_argument("--as-of", help="Report timestamp. Defaults to current UTC time.")
|
||
parser.add_argument("--stdout", action="store_true", help="Print the report instead of writing a file.")
|
||
return parser.parse_args()
|
||
|
||
|
||
def parse_as_of(value: str | None) -> str:
|
||
if value:
|
||
return datetime.fromisoformat(value.replace("Z", "+00:00")).isoformat().replace("+00:00", "Z")
|
||
return datetime.now(timezone.utc).replace(microsecond=0).isoformat().replace("+00:00", "Z")
|
||
|
||
|
||
def normalize_ticker(value: str) -> str:
|
||
ticker = value.strip().upper()
|
||
if ticker.endswith(".HK"):
|
||
ticker = ticker[:-3]
|
||
if ticker.isdigit():
|
||
return ticker.zfill(5)
|
||
return ticker
|
||
|
||
|
||
def load_dataset(path: Path) -> list[dict[str, str]]:
|
||
if not path.exists():
|
||
raise SystemExit(f"Dataset not found: {path.as_posix()}. Run scripts/build_analysis_dataset.py first.")
|
||
with path.open(newline="", encoding="utf-8") as handle:
|
||
return list(csv.DictReader(handle))
|
||
|
||
|
||
def find_record(rows: list[dict[str, str]], ticker: str, dataset_path: Path) -> dict[str, str]:
|
||
for row in rows:
|
||
if normalize_ticker(row["ticker"]) == ticker:
|
||
return row
|
||
raise SystemExit(
|
||
f"Ticker {ticker} is not in {dataset_path.as_posix()}. "
|
||
"Use archivist to update archived facts, then rerun scripts/build_analysis_dataset.py."
|
||
)
|
||
|
||
|
||
def as_float(value: Any) -> float | None:
|
||
if value in {None, ""}:
|
||
return None
|
||
return float(value)
|
||
|
||
|
||
def as_int(value: Any) -> int | None:
|
||
if value in {None, ""}:
|
||
return None
|
||
return int(float(value))
|
||
|
||
|
||
def as_bool(value: Any) -> bool:
|
||
return str(value).strip() in {"1", "true", "True"}
|
||
|
||
|
||
def fmt_value(value: Any) -> str:
|
||
if value in {None, ""}:
|
||
return "未记录"
|
||
return str(value)
|
||
|
||
|
||
def fmt_num(value: float | None, suffix: str = "", decimals: int = 1) -> str:
|
||
if value is None:
|
||
return "未记录"
|
||
return f"{value:,.{decimals}f}{suffix}"
|
||
|
||
|
||
def fmt_pct_rate(value: float | None) -> str:
|
||
if value is None:
|
||
return "未记录"
|
||
return f"{value * 100:.1f}%"
|
||
|
||
|
||
def fmt_pct_points(value: float | None) -> str:
|
||
if value is None:
|
||
return "未记录"
|
||
return f"{value:.1f}%"
|
||
|
||
|
||
def fmt_money_m(value: float | None) -> str:
|
||
if value is None:
|
||
return "未记录"
|
||
return f"HK${value:,.1f}m"
|
||
|
||
|
||
def fmt_hkd(value: float | None) -> str:
|
||
if value is None:
|
||
return "未记录"
|
||
return f"HK${value:,.2f}"
|
||
|
||
|
||
def fmt_times(value: float | None) -> str:
|
||
if value is None:
|
||
return "未记录"
|
||
return f"{value:,.2f}x"
|
||
|
||
|
||
def fmt_int(value: int | None) -> str:
|
||
if value is None:
|
||
return "未记录"
|
||
return f"{value:,}"
|
||
|
||
|
||
def parse_date(value: str) -> datetime | None:
|
||
if not value:
|
||
return None
|
||
try:
|
||
return datetime.fromisoformat(value)
|
||
except ValueError:
|
||
return None
|
||
|
||
|
||
def previous_business_date(value: datetime) -> datetime:
|
||
day = value - timedelta(days=1)
|
||
while day.weekday() >= 5:
|
||
day -= timedelta(days=1)
|
||
return day
|
||
|
||
|
||
def determine_stage(record: dict[str, str], requested_stage: str) -> str:
|
||
if requested_stage == AUTO_STAGE:
|
||
return T1_STAGE if as_bool(record["has_structured_t1"]) else T0_STAGE
|
||
if requested_stage == T1_STAGE and not as_bool(record["has_structured_t1"]):
|
||
raise SystemExit(
|
||
f"{record['ticker']} has no structured T1 demand yet. "
|
||
f"Generate a {T0_STAGE} report or update the archive first."
|
||
)
|
||
return requested_stage
|
||
|
||
|
||
def output_path_for(ticker: str, stage: str, as_of: str) -> Path:
|
||
date_part = as_of[:10]
|
||
return DEFAULT_OUTPUT_DIR / f"{date_part}_{ticker}_{stage}_analysis.md"
|
||
|
||
|
||
def parse_components(text: str) -> list[ScoreComponent]:
|
||
components: list[ScoreComponent] = []
|
||
for item in text.split("|"):
|
||
if not item:
|
||
continue
|
||
name, points, reason = item.split(":", 2)
|
||
components.append(ScoreComponent(name=name, points=int(points), reason=reason))
|
||
return components
|
||
|
||
|
||
def bucket_metric(
|
||
rows: list[dict[str, str]],
|
||
bucket_key: str,
|
||
bucket_value: str,
|
||
require_t1: bool,
|
||
) -> BucketMetric:
|
||
returns: list[float] = []
|
||
for row in rows:
|
||
if require_t1 and not as_bool(row["has_structured_t1"]):
|
||
continue
|
||
if row.get(bucket_key) != bucket_value:
|
||
continue
|
||
d1_return = as_float(row.get("d1_return_pct"))
|
||
if d1_return is not None:
|
||
returns.append(d1_return)
|
||
if not returns:
|
||
return BucketMetric(0, None, None, None, None)
|
||
return BucketMetric(
|
||
sample_size=len(returns),
|
||
d1_positive_rate=sum(value > 0 for value in returns) / len(returns),
|
||
d1_strong_rate=sum(value >= 10 for value in returns) / len(returns),
|
||
average_d1_return_pct=mean(returns),
|
||
median_d1_return_pct=median(returns),
|
||
)
|
||
|
||
|
||
def t0_decision_band(score: int) -> str:
|
||
if score < 1:
|
||
return "weak_or_avoid"
|
||
if score <= 4:
|
||
return "neutral"
|
||
if score <= 7:
|
||
return "positive_watch"
|
||
return "strong_watch"
|
||
|
||
|
||
def action_for_decision(decision: str) -> str:
|
||
actions = {
|
||
"weak_or_avoid": "T0 阶段回避，除非后续 T1 认购热度明显改变格局。",
|
||
"neutral": "暂等 T1 分配结果，不在 T0 阶段主动下重注。",
|
||
"positive_watch": "正面观察，但需要等 T1 确认后再决定 T2/D1 退出仓位。",
|
||
"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。",
|
||
"avoid": "回避申购。",
|
||
"avoid_or_wait": "回避或等待；没有更强催化前不放大仓位。",
|
||
"watch_or_small": "仅在执行条件支持 T2/D1 退出时小额参与。",
|
||
"selective_subscribe": "选择性申购，并严格按 T2/D1 卖出纪律控制仓位。",
|
||
"high_conviction_subscribe": "积极申购，但仍受分配、流动性和 T2/D1 卖出纪律约束。",
|
||
}
|
||
return actions[decision]
|
||
|
||
|
||
def component_label(name: str) -> str:
|
||
labels = {
|
||
"offer_size": "发行规模",
|
||
"public_pct": "初始公开发售比例",
|
||
"min_subscription": "最低认购金额",
|
||
"offer_price": "发行价",
|
||
"over_allotment": "超额配股权",
|
||
"public_os": "公开认购倍数",
|
||
"international_os": "国际配售认购倍数",
|
||
"valid_applications": "有效申请数",
|
||
"success_rate": "申请成功率",
|
||
"hk_reallocation": "香港公开发售回拨",
|
||
}
|
||
return labels.get(name, name.replace("_", " ").title())
|
||
|
||
|
||
def components_table(components: list[ScoreComponent]) -> str:
|
||
lines = ["| 评分项 | 分数 | 原因代码 |", "| --- | ---: | --- |"]
|
||
for component in components:
|
||
lines.append(f"| {component_label(component.name)} | {component.points} | `{component.reason}` |")
|
||
return "\n".join(lines)
|
||
|
||
|
||
def facts_table(record: dict[str, str], stage: str) -> str:
|
||
rows = [
|
||
("板块", fmt_value(record["board"])),
|
||
("状态", fmt_value(record["status"])),
|
||
("上市日期", fmt_value(record["listing_date"])),
|
||
("申购期", f"{fmt_value(record['application_start_date'])} 至 {fmt_value(record['application_end_date'])}"),
|
||
("分配结果日期", fmt_value(record["allotment_results_expected_date"])),
|
||
("上市方式", fmt_value(record["listing_method"])),
|
||
("行业", fmt_value(record["industry_label"])),
|
||
("保荐人", fmt_value(record["sponsors"])),
|
||
("发行价", fmt_hkd(as_float(record["offer_price_hkd"]))),
|
||
("发行规模", fmt_money_m(as_float(record["offer_size_hkd_m"]))),
|
||
("市值", fmt_money_m(as_float(record["market_cap_hkd_m"]))),
|
||
("每手股数", fmt_int(as_int(record["board_lot"]))),
|
||
("最低认购金额", fmt_hkd(as_float(record["min_subscription_amount_hkd"]))),
|
||
("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)),
|
||
("超额配股权股数", fmt_int(as_int(record["over_allotment_offer_shares"]))),
|
||
]
|
||
if stage == T1_STAGE:
|
||
rows.extend(
|
||
[
|
||
("公开认购倍数", fmt_times(as_float(record["public_oversubscription_times"]))),
|
||
("国际配售认购倍数", fmt_times(as_float(record["international_oversubscription_times"]))),
|
||
("有效申请数", fmt_int(as_int(record["valid_applications"]))),
|
||
("成功申请数", fmt_int(as_int(record["successful_applications"]))),
|
||
("申请成功率", fmt_pct_points(as_float(record["application_success_rate"]) * 100 if record["application_success_rate"] else None)),
|
||
("国际配售承配人数", fmt_int(as_int(record["international_placees"]))),
|
||
("香港公开发售回拨倍数", fmt_times(as_float(record["hk_offer_reallocation_multiple"]))),
|
||
]
|
||
)
|
||
|
||
lines = ["| 字段 | 数值 |", "| --- | --- |"]
|
||
for label, value in rows:
|
||
lines.append(f"| {label} | {value} |")
|
||
return "\n".join(lines)
|
||
|
||
|
||
def stage_calendar_table(record: dict[str, str]) -> str:
|
||
application_start = fmt_value(record["application_start_date"])
|
||
application_end = fmt_value(record["application_end_date"])
|
||
allotment_date = fmt_value(record["allotment_results_expected_date"])
|
||
listing_date = fmt_value(record["listing_date"])
|
||
listed = parse_date(record["listing_date"])
|
||
allotment = parse_date(record["allotment_results_expected_date"])
|
||
if listed:
|
||
previous = previous_business_date(listed)
|
||
if allotment and previous.date() == allotment.date():
|
||
t2_date = f"{previous.date().isoformat()} 分配结果公布后 / D1 前一交易日"
|
||
else:
|
||
t2_date = f"{previous.date().isoformat()}，预计 D1 前一交易日"
|
||
elif allotment:
|
||
t2_date = f"{allotment.date().isoformat()} 分配结果公布后"
|
||
else:
|
||
t2_date = "未记录"
|
||
|
||
rows = [
|
||
(
|
||
"T0_prospectus",
|
||
f"{application_start} 至 {application_end}",
|
||
"申购前/申购中阶段；只使用招股书和发行条款。",
|
||
),
|
||
(
|
||
"T1_allotment",
|
||
allotment_date,
|
||
"分配结果日；使用公开认购热度、国际配售热度和分配事实。",
|
||
),
|
||
(
|
||
"T2_grey_market",
|
||
t2_date,
|
||
"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。",
|
||
),
|
||
(
|
||
"D1",
|
||
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。",
|
||
),
|
||
]
|
||
lines = ["| 阶段 | 本 IPO 对应日期 | 含义 |", "| --- | --- | --- |"]
|
||
for stage, date_text, meaning in rows:
|
||
lines.append(f"| `{stage}` | {date_text} | {meaning} |")
|
||
return "\n".join(lines)
|
||
|
||
|
||
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"])
|
||
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。"]
|
||
|
||
lines = [
|
||
f"# {ticker} IPO 分析报告",
|
||
"",
|
||
"## 摘要",
|
||
"",
|
||
f"- 股票代码：`{ticker}`",
|
||
f"- 公司：{fmt_value(record['company_name_en'])}",
|
||
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),
|
||
"",
|
||
"## 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())
|