216 lines
11 KiB
Python
216 lines
11 KiB
Python
"""akg-factor-bridge CLI。
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python run.py views # 连通性自检：打印视图/表行数
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python run.py apply-views [--dry-run] # 把插槽视图 DDL 应用到基座 PG
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python run.py probe # G1 体检（只读，详见 probe.py）
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python run.py freeze [--date D] # 输入冻结（G0.5，详见 freeze.py）
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python run.py tracks # 赛道覆盖体检 + 成员表快照（G2）
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python run.py plan [--date D] [--top N] # 每日选股计划（R4，读已落库因子表）
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python run.py register # 注册全部因子到 factor_metadata
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python run.py build all --mode history --start 2024-01-01 --end 2025-12-31
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python run.py build akg_heat --mode daily --date 2026-07-24
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python run.py build akg_event --mode history --start 2025-01-01 --end 2026-07-24
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daily 模式：不给 --date 则取基座最新扫描日（盘前链口径，07-30 D 案）；
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start=end=date，跑完自动冻结（--no-freeze 可关）。
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history 模式：需 --start/--end，默认不逐日冻结。
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所有写入幂等（删涉及日期区间再插），可安全重跑。
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"""
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import argparse
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import datetime as dt
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import warnings
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# pandas 用 DBAPI 连接读 SQL 会 warn（无害，功能正常）——静音保持日志干净
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warnings.filterwarnings("ignore", message=".*only supports SQLAlchemy.*")
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import pandas as pd
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import common
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import config
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import db
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import factors
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def cmd_views():
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checks = [
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("PG v_factor_universe", "pg", "SELECT count(*) FROM v_factor_universe"),
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("PG v_factor_consensus", "pg", "SELECT count(*) FROM v_factor_consensus"),
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("PG v_factor_events", "pg", "SELECT count(*) FROM v_factor_events"),
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("PG v_factor_transmission", "pg", "SELECT count(*) FROM v_factor_transmission"),
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("153 stock_fund_heat_scores","heat", "SELECT count(*) FROM stock_fund_heat_scores"),
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("平台 gp_day_data", "price", "SELECT count(*) FROM gp_day_data"),
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("平台 factor_metadata", "factor", "SELECT count(*) FROM factor_metadata"),
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]
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print("连通性自检：")
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for name, src, sql in checks:
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try:
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df = db.read_pg(sql) if src == "pg" else db.read_mysql(src, sql)
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print(f" ✅ {name}: {int(df.iloc[0, 0])}")
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except Exception as e: # noqa: BLE001
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print(f" ❌ {name}: {e!r}")
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# 视图版本自检：v2 才有的列在不在（评审第一批是否已应用）
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print("\n视图版本（2026-07-26 评审 v2）：")
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for label, sql in (
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("v_factor_transmission.n_sources",
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"SELECT n_sources FROM v_factor_transmission LIMIT 1"),
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("v_factor_transmission.mkt_trade_date",
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"SELECT mkt_trade_date FROM v_factor_transmission LIMIT 1"),
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("v_factor_events.source_type",
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"SELECT source_type FROM v_factor_events LIMIT 1"),
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("v_factor_segment_members（第五插槽，G2）",
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"SELECT 1 FROM v_factor_segment_members LIMIT 1")):
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try:
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db.read_pg(sql)
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print(f" ✅ {label}")
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except Exception: # noqa: BLE001
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print(f" ⬜ {label} —— 未就绪")
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fronts = [
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("热度 heat", "heat", "trade_date", "stock_fund_heat_scores"),
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("一致预期 consensus", "pg", "asof_date", "v_factor_consensus"),
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("事件 events", "pg", "disclosure_date", "v_factor_events"),
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("传导 transmission", "pg", "scan_date", "v_factor_transmission"),
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("行情 gp_day_data", "price", "`timestamp`", "gp_day_data"),
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]
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print("\n数据历史深度（min ~ max，distinct 天数——回填范围据此定）：")
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for name, src, col, tbl in fronts:
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sql = f"SELECT MIN({col}), MAX({col}), COUNT(DISTINCT {col}) FROM {tbl}"
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try:
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df = db.read_pg(sql) if src == "pg" else db.read_mysql(src, sql)
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lo, hi, n = df.iloc[0, 0], df.iloc[0, 1], df.iloc[0, 2]
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print(f" {name}: {lo} ~ {hi} ({int(n)} 天)")
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except Exception as e: # noqa: BLE001
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print(f" {name}: ❌ {e!r}")
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print(f"\n当前口径：SUBFACTOR_UNIVERSE={config.SUBFACTOR_UNIVERSE} "
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f"| EVENT_SOURCE_TYPES={sorted(config.EVENT_SOURCE_TYPES)} "
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f"| EVENT_MAX_PER_DOC={config.EVENT_MAX_PER_DOC}")
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_META = {
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"akg_upside": ("astock-kg 预期空间", "分析师一致预期目标价隐含收益率(target_mid/price-1)"),
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"akg_heat": ("astock-kg 热度", "生态日频资金热度分(0~1)"),
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"akg_event": ("astock-kg 事件", "利好利空事件时间衰减加权分(仅公告来源,单文档封顶)"),
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"akg_transmission": ("astock-kg 传导", "板块传导未动成员传导强度(distinct源数×(1-已动比例))"),
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"akg_gate": ("astock-kg 门槛档位",
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"三档置信门槛(07-30拍板): 2=主榜(券商覆盖且upside>=0; 赛道C闸未启用, "
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"转正后再交赛道成员), 1=观察档(无券商覆盖但在图谱传导链上, 无估值锚, "
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"低置信), 0=不采纳(两锚皆无, 或upside<0)。全池出行, 让平台看得见门槛"),
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"akg_score": ("astock-kg 景气度漏斗",
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"先档后分: 主榜=200+传导档位x20+组内分(0.6z(-热度)+0.4z(upside), "
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"两段式07-30拍板, 组内分clip±9.9故档间不重叠); "
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"观察档=100+0.6z(传导)+0.4z(-热度)。仅gate>0出行, 数值直接可排序"),
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}
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def cmd_register():
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print(f"注册因子（共 {len(_META)} 个）：")
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for code, (name, desc) in _META.items():
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common.register(code, name, factors.FACTORS[code], ["astock-kg", code.split("_", 1)[1]], desc)
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def _latest_data_day() -> str:
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"""daily 不传 --date 时的默认日：基座传导台账最新 scan_date（=数据日）。
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盘前链（07-30 D 案）在次日早晨补全上一交易日，"今天"多半还没有数据；
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以基座刚完成的扫描日为准，两边永远对齐。查不到再退回今天。"""
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try:
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df = db.read_pg("SELECT MAX(scan_date) d FROM v_factor_transmission")
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v = None if df.empty else df.iloc[0, 0]
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if v is not None and not pd.isna(v):
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return pd.Timestamp(v).date().isoformat()
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except Exception as e: # noqa: BLE001 —— 视图不可达时退回今天，不阻塞构建
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print(f" (取基座最新扫描日失败，默认改用今天: {e!r})")
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return dt.date.today().isoformat()
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def cmd_build(which, mode, start, end, date, do_freeze=True):
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if mode == "daily":
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d = date or _latest_data_day()
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start = end = d
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if not start or not end:
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raise SystemExit("history 模式需要 --start 与 --end")
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codes = list(factors.FACTORS) if which == "all" else [which]
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frames = {}
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for code in codes:
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if code not in factors.BUILDERS:
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raise SystemExit(f"未知因子: {code}（可选: {list(factors.FACTORS)} 或 all）")
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print(f"[{code}] {mode} {start} ~ {end}")
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try:
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df = factors.BUILDERS[code](start, end)
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frames[f"factor_{code}"] = df
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common.write_factor(factors.FACTORS[code], df, mode)
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except Exception as e: # noqa: BLE001 —— 一路失败不拖累其余（批量容错）
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print(f" ❌ {code} 失败: {e!r}")
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# 日更顺手冻结：输入与输出落在同一目录，任何一行因子值都能被逐步复算。
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# history 模式默认不冻结（逐日冻结应单独跑，避免一次回填写出几百个目录）。
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if do_freeze and mode == "daily":
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try:
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import freeze
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freeze.snapshot(start, extra_frames=frames)
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except Exception as e: # noqa: BLE001 —— 冻结失败不该让因子构建算失败
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print(f" ❌ 冻结失败（因子已落库）: {e!r}")
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def main():
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ap = argparse.ArgumentParser(description="akg-factor-bridge")
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sub = ap.add_subparsers(dest="cmd", required=True)
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sub.add_parser("views")
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sub.add_parser("register")
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sub.add_parser("tracks") # 赛道覆盖体检 + confirmed 成员表快照（G2）
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pl = sub.add_parser("plan") # 每日选股计划（R4）
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pl.add_argument("--date", help="默认取 score 表最新日")
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pl.add_argument("--top", type=int, default=20, help="主榜条数")
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pl.add_argument("--obs-top", type=int, default=10, help="观察档条数")
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pl.add_argument("--theme-cap", type=int, default=5,
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help="每个传导主题最多几条（防单板块刷屏；0=不设限）")
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p = sub.add_parser("probe")
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p.add_argument("--section", choices=["all", "pools", "price", "upside", "corr"],
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default="all",
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help="pools=池结构清单 price=行情年表 upside=分布与q档位 corr=三项相关矩阵")
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av = sub.add_parser("apply-views")
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av.add_argument("--file", default="sql/astock_kg_slot_views.sql")
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av.add_argument("--dry-run", action="store_true", help="只列语句不执行")
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f = sub.add_parser("freeze")
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f.add_argument("--date", help="默认今天")
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b = sub.add_parser("build")
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b.add_argument("factor", help="akg_upside|akg_heat|akg_event|akg_transmission|all")
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b.add_argument("--mode", choices=["daily", "history"], default="daily")
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b.add_argument("--start")
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b.add_argument("--end")
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b.add_argument("--date")
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b.add_argument("--no-freeze", action="store_true", help="daily 模式下跳过输入冻结")
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a = ap.parse_args()
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if a.cmd == "views":
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cmd_views()
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elif a.cmd == "probe":
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import probe # 按需加载：一次性诊断命令，不影响常规链路
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probe.run(a.section)
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elif a.cmd == "apply-views":
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import apply_views
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raise SystemExit(1 if apply_views.apply(a.file, a.dry_run) else 0)
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elif a.cmd == "freeze":
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import freeze
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freeze.snapshot(a.date)
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elif a.cmd == "register":
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cmd_register()
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elif a.cmd == "plan":
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import plan
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plan.generate(a.date, a.top, a.obs_top, a.theme_cap)
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elif a.cmd == "tracks":
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import tracks
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tracks.coverage_report()
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out, df, missing = tracks.snapshot(only_confirmed=True)
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print(f"\nconfirmed 成员表快照: {out}"
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f"（{df['ts_code'].nunique()} 只，{len(df)} 行 股票×赛道）")
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if missing:
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print(f"⚠️ {len(missing)} 个主题在 industry_pools 里没找到（见体检表）")
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elif a.cmd == "build":
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cmd_build(a.factor, a.mode, a.start, a.end, a.date, do_freeze=not a.no_freeze)
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if __name__ == "__main__":
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main()
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