产业链细化逻辑
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"""选股计划逐票对账（只读）——"这只票为什么在/不在计划里"。
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背景：机会线索页（基座前端，环节级雷达，跑全部主题池）与每日选股计划
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（个股级，券商覆盖 ∩ upside≥0 ∩ 十条赛道 ∩ 非风险，传导档优先排序）
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答的不是同一个问题，读数经常"看起来大相径庭"。本工具把任意一只票的
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全部判定摊开：赛道命中（哪路）、覆盖与 upside、热度、传导（谁指向它）、
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gate、score、榜位名次，落榜给出确切原因——审计承诺（下游对接文档
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"每只候选可回溯"）的落地件。
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跑法【桥机 factorevaluation-UTC · ~/akg-factor-bridge】：
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docker compose exec -T akg-factor-bridge python plan_reconcile.py 300750 SH600438 002074.SZ
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docker compose exec -T akg-factor-bridge python plan_reconcile.py # 不带票=打印当日主榜前20/观察档前10
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docker compose exec -T akg-factor-bridge python plan_reconcile.py --date 2026-08-04 300750
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代码三种形态都收（600000.SH / SH600000 / 600000，600/688 归 SH，其余归 SZ）。
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纯只读：因子表 / 基座视图 / yml 全部 SELECT 与本地解析，不写任何东西。
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"""
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from __future__ import annotations
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import re
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import sys
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import pandas as pd
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import common
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import db
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import tracks
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_RISK_RE = re.compile(r"^(\*?S?ST|退市)")
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def _norm_code(s: str) -> str:
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"""任意形态 -> 前缀式（SH600000）。裸 6 位码按交易所惯例补前缀。"""
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s = s.strip().upper()
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if re.fullmatch(r"\d{6}\.(SH|SZ|BJ)", s):
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return common.to_prefix(s)
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if re.fullmatch(r"(SH|SZ|BJ)\d{6}", s):
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return s
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if re.fullmatch(r"\d{6}", s):
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exch = "SH" if s[0] == "6" else ("BJ" if s[0] in "48" else "SZ")
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return f"{exch}{s}"
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raise SystemExit(f"认不出的代码形态: {s!r}（收 600000.SH / SH600000 / 600000）")
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def _factor(table: str, d: str) -> dict[str, float]:
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df = db.read_mysql("factor", f"SELECT stock_code, factor_value FROM {table} "
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f"WHERE trade_date=%s", (d,))
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return {str(r.stock_code).strip(): float(r.factor_value) for r in df.itertuples()}
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def _load(d: str):
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gate = _factor("t_factor_akg_gate", d)
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score = _factor("t_factor_akg_score", d)
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upside = _factor("t_factor_akg_upside", d)
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heat = _factor("t_factor_akg_heat", d)
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tr = _factor("t_factor_akg_transmission", d)
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# 赛道命中：前缀码 -> [(赛道, 命中键, 路)]，图谱路在前（resolve 已排好）
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tdf, _missing = tracks.resolve_members(only_confirmed=True, dedup=False)
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hits: dict[str, list[tuple[str, str, str]]] = {}
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for r in tdf.itertuples():
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hits.setdefault(common.to_prefix(str(r.ts_code)), []).append(
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(r.track, r.theme, r.source_rule))
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# 传导指向：scan_date=d 的候选里谁的 quiet 含这只票
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try:
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tv = db.read_pg(
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"SELECT ts_code, target, n_sources, moved_ratio, mkt_trade_date "
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"FROM v_factor_transmission WHERE scan_date=%s", (d,))
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except Exception as e: # noqa: BLE001 —— 视图不可用只影响传导明细
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print(f"⚠️ v_factor_transmission 读取失败（传导明细缺席）: {e!r}")
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tv = pd.DataFrame(columns=["ts_code", "target", "n_sources",
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"moved_ratio", "mkt_trade_date"])
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tmap: dict[str, list[str]] = {}
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for r in tv.itertuples():
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tmap.setdefault(common.to_prefix(str(r.ts_code)), []).append(
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f"{r.target}(源{int(r.n_sources)}×空间{1 - float(r.moved_ratio):.0%})")
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stale = ""
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if len(tv) and tv["mkt_trade_date"].notna().any():
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md = str(tv["mkt_trade_date"].dropna().iloc[0])[:10]
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if md != d:
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stale = f"（⚠️ 传导快照日 {md} ≠ 档位日 {d}，该日传导降级采信）"
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# 图谱证据 + 简称
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ev: dict[str, dict] = {}
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try:
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mem = db.read_pg("SELECT segment_name, ts_code, chain "
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"FROM v_factor_segment_members WHERE ts_code IS NOT NULL")
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for r in mem.itertuples():
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k = common.to_prefix(str(r.ts_code).strip())
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e = ev.setdefault(k, {"segs": set(), "chains": set()})
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e["segs"].add(str(r.segment_name))
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if r.chain and str(r.chain).strip():
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e["chains"].add(str(r.chain).strip())
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except Exception as e: # noqa: BLE001
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print(f"⚠️ 环节投影读取失败（图谱证据缺席）: {e!r}")
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names: dict[str, str] = {}
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try:
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import json as _json
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pools = db.read_pg("SELECT members FROM industry_pools")
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for _, row in pools.iterrows():
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ms = row["members"]
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if isinstance(ms, str):
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ms = _json.loads(ms)
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for m in ms or []:
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if (m or {}).get("ts_code"):
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names.setdefault(common.to_prefix(m["ts_code"]), m.get("name") or "")
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except Exception as e: # noqa: BLE001
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print(f"⚠️ 简称加载失败: {e!r}")
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# 榜位名次：主榜=score>=150 降序；观察档=100<=score<150 降序
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sc = pd.Series(score)
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main = sc[sc >= 150].sort_values(ascending=False)
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obs = sc[(sc >= 100) & (sc < 150)].sort_values(ascending=False)
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rank_main = {k: i + 1 for i, k in enumerate(main.index)}
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rank_obs = {k: i + 1 for i, k in enumerate(obs.index)}
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return dict(gate=gate, score=score, upside=upside, heat=heat, tr=tr,
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hits=hits, tmap=tmap, stale=stale, ev=ev, names=names,
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main=main, obs=obs, rank_main=rank_main, rank_obs=rank_obs)
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def _fmt(v, pat="{:.2f}"):
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return "—" if v is None else pat.format(v)
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def explain(k: str, L: dict) -> None:
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nm = L["names"].get(k, "")
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g = L["gate"].get(k)
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up = L["upside"].get(k)
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ht = L["heat"].get(k)
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trv = L["tr"].get(k)
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sc = L["score"].get(k)
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hits = L["hits"].get(k, [])
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paths = L["tmap"].get(k, [])
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e = L["ev"].get(k, {"segs": set(), "chains": set()})
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risk = bool(_RISK_RE.match(nm.replace(" ", ""))) if nm else False
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print(f"\n◆ {k} {nm}")
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if hits:
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shown = "；".join(f"{t}←{key}({rule})" for t, key, rule in hits[:4])
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print(f" 赛道命中 {len(hits)} 路：{shown}" + ("…" if len(hits) > 4 else ""))
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else:
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print(" 赛道命中：无（十条赛道三路映射都够不着）")
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print(f" 覆盖/upside：{'有覆盖，upside=' + _fmt(up, '{:+.1%}') if up is not None else '无券商覆盖'}"
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f" 热度：{_fmt(ht)} 传导分：{_fmt(trv)}")
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if paths:
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print(f" 传导指向：{'；'.join(paths[:4])}" + ("…" if len(paths) > 4 else ""))
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if e["segs"]:
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print(f" 图谱证据：环节 {len(e['segs'])} 个（{'、'.join(sorted(e['segs'])[:4])}"
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+ ("…" if len(e["segs"]) > 4 else "") + "）"
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+ (f"，链名 {'、'.join(sorted(e['chains'])[:4])}" if e["chains"] else "，边上无链名"))
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if g == 2.0:
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n = L["rank_main"].get(k)
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# 档界：score=200+档×20+组内分(±9.9) → 强≥230.1、弱≥210.1、无≤209.9
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tier = "强传导" if sc and sc >= 230 else ("弱传导" if sc and sc >= 210 else "无传导")
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print(f" ▶ 判决：主榜第 {n}/{len(L['main'])} 名（score={sc:.1f}，{tier}档）"
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+ ("——计划默认只显示前 20，名次靠后不等于不在计划里" if n and n > 20 else ""))
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elif g == 1.0:
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print(f" ▶ 判决：观察档第 {L['rank_obs'].get(k)}/{len(L['obs'])} 名（score={sc:.1f}，"
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f"无券商覆盖、低置信）")
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elif g == 0.0:
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if risk:
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reason = "重大风险闸（ST/退市族），07-31 拍板默认杜绝"
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elif up is not None and up < 0:
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reason = f"有覆盖但 upside={up:+.1%} < 0——贵了不买是绝对下限"
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elif up is not None and hits:
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reason = "覆盖、upside、赛道三者都过——按机制不该是 0，把本行发我核（疑似口径错位）"
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elif up is not None:
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reason = ("有覆盖、upside≥0，但不在十条赛道（赛道闸拦下）"
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+ ("——图上有环节证据，属映射够不着的错杀池，看首批锚能否接住" if e["segs"] else ""))
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elif not hits and (trv or 0) <= 0:
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reason = "无覆盖、无传导、不在赛道——两锚皆无"
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else:
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reason = "无覆盖且当日不在传导链上（观察档条件差最后一步）"
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print(f" ▶ 判决：不采纳（gate=0）。原因：{reason}")
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else:
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print(" ▶ 判决：当日档位表无此票"
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+ ("（可能不在覆盖池 universe，或当日未出行）" if not hits else "——在赛道却无档位行，把本行发我核"))
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def main() -> int:
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args = [a for a in sys.argv[1:]]
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d = None
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if "--date" in args:
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i = args.index("--date")
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d = args[i + 1]
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del args[i:i + 2]
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if d is None:
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row = db.read_mysql("factor", "SELECT MAX(trade_date) d FROM t_factor_akg_gate")
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v = None if row.empty else row.iloc[0, 0]
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if v is None or pd.isna(v):
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print("档位表为空——先跑当日构建。")
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return 2
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d = pd.Timestamp(v).date().isoformat()
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L = _load(d)
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print(f"选股计划对账 @ 档位日 {d}{L['stale']}")
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print(f"主榜 {len(L['main'])} 只 / 观察档 {len(L['obs'])} 只（150 分界；主榜默认展示前 20）")
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if args:
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for a in args:
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explain(_norm_code(a), L)
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else:
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print("\n—— 主榜前 20（传导档优先，与计划 API 同序）——")
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for i, (k, s) in enumerate(L["main"].head(20).items(), 1):
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hits = L["hits"].get(k, [])
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print(f" {i:>2}. {k} {L['names'].get(k, ''): <6} score={s:.1f} "
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f"upside={_fmt(L['upside'].get(k), '{:+.1%}')} "
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f"热度={_fmt(L['heat'].get(k))} "
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f"赛道={hits[0][0] if hits else '?'}"
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+ (f" 传导指向={L['tmap'][k][0]}" if L["tmap"].get(k) else ""))
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print("\n—— 观察档前 10 ——")
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for i, (k, s) in enumerate(L["obs"].head(10).items(), 1):
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print(f" {i:>2}. {k} {L['names'].get(k, ''): <6} score={s:.1f} "
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f"热度={_fmt(L['heat'].get(k))}"
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+ (f" 传导指向={L['tmap'][k][0]}" if L["tmap"].get(k) else ""))
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print("\n用法：把机会线索卡片上的代表公司码传进来逐票对账，"
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"例如 python plan_reconcile.py 300750 002074 688041")
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return 0
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if __name__ == "__main__":
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raise SystemExit(main())
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