219 lines
9.9 KiB
Python
219 lines
9.9 KiB
Python
"""赛道映射（硬门槛 C 的实现载体，设计 §3.2）。
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config/frontier_tracks.yml 是唯一事实源，三条映射路径并行：
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kg_themes 主题名 → industry_pools（弱锚 fallback，source_rule=pool_theme）
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kg_chains 链名 → 图谱 IN_SEGMENT 边的产业链修饰符（强锚，graph_chain）
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kg_segments 环节名 → 图谱环节投影（强锚，graph_segment）
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后两条读基座第五插槽视图 v_factor_segment_members（akg.project_segments
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每晨 06:10 刷新）。同一只股票多路命中时**图谱路径优先记账**——这就是设计
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文档"两级 source_rule：graph 强 / fallback 弱"的落地。成员表快照落
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data/track_members_<日期>.csv，每行带 source_rule，可审计到入选依据。
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"""
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import datetime as dt
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import json
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import os
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import pandas as pd
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import config
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import db
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try:
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import yaml
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except ImportError: # 镜像未装 PyYAML 时给出可执行的修复指令，而不是裸崩
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yaml = None
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def _need_yaml():
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if yaml is None:
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raise SystemExit(
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"缺 PyYAML：requirements.txt 已加，请在桥机重建镜像——\n"
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" docker compose build akg-factor-bridge && "
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"docker compose up -d akg-factor-bridge")
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def load_yml(path: str | None = None) -> dict:
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_need_yaml()
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with open(path or config.TRACKS_YML, "r", encoding="utf-8") as f:
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return yaml.safe_load(f)
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def _pool_theme_members() -> dict:
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"""industry_pools 当前态：主题名 → 成员列表（dict，含 ts_code/name）。"""
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pools = db.read_pg("SELECT theme, members FROM industry_pools")
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by_theme = {}
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for _, r in pools.iterrows():
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ms = r["members"]
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if isinstance(ms, str):
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ms = json.loads(ms)
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by_theme[str(r["theme"]).strip()] = ms or []
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return by_theme
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def _graph_members():
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"""环节投影（第五插槽）→ 两个索引：环节名→成员、链名→成员（仅上市成员）。
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视图不可用（投影表未建/基座不可达）时返回空索引并提示——图谱路径跳过，
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主题路径照常工作，不挡任何调用方。空串链名（抽取未写修饰的边）在此忽略。
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"""
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try:
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df = db.read_pg(
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"SELECT segment_name, ts_code, member_name, chain "
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"FROM v_factor_segment_members WHERE ts_code IS NOT NULL")
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except Exception as e: # noqa: BLE001
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print(f" (环节投影视图不可用，图谱映射路径跳过: {e!r})")
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return {}, {}
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by_seg, by_chain = {}, {}
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for r in df.itertuples():
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m = (str(r.ts_code), None if pd.isna(r.member_name) else str(r.member_name))
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by_seg.setdefault(str(r.segment_name).strip(), []).append(m)
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if r.chain is not None and not pd.isna(r.chain) and str(r.chain).strip():
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by_chain.setdefault(str(r.chain).strip(), []).append(m)
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return by_seg, by_chain
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def resolve_members(only_confirmed: bool = True, path: str | None = None,
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dedup: bool = True):
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"""yml + 三路数据源 → (成员表, 未命中键清单)。
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成员表列：ts_code, name, track, status, theme, source_rule——theme 列放
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命中它的那个键（主题名/链名/环节名）。图谱路径的行排在主题路径之前，
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去重时同股同赛道保留最先出现的行 ⇒ 图谱锚优先记账。
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未命中 = yml 里写了、数据源里查无此键，元素为 (赛道, 键类型, 键名)。
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"""
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d = load_yml(path)
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by_theme = _pool_theme_members()
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by_seg, by_chain = _graph_members()
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excl = {str(x).strip() for x in (d.get("exclude_themes") or [])}
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rows, missing = [], []
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for tr in d.get("tracks") or []:
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if only_confirmed and tr.get("status") != "confirmed":
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continue
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tname, status = tr["name"], tr.get("status")
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for seg in tr.get("kg_segments") or []: # 图谱路径（强锚）在前
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s = str(seg).strip()
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if s not in by_seg:
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missing.append((tname, "环节", s))
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continue
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for ts, nm in by_seg[s]:
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rows.append((ts, nm, tname, status, s, "graph_segment"))
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for ch in tr.get("kg_chains") or []:
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c = str(ch).strip()
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if c not in by_chain:
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missing.append((tname, "链", c))
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continue
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for ts, nm in by_chain[c]:
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rows.append((ts, nm, tname, status, c, "graph_chain"))
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for theme in tr.get("kg_themes") or []: # 主题路径（fallback）在后
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t = str(theme).strip()
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if t in excl:
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continue
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if t not in by_theme:
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missing.append((tname, "主题", t))
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continue
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for m in by_theme[t]:
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ts = (m or {}).get("ts_code")
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if ts:
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rows.append((ts, m.get("name"), tname, status, t, "pool_theme"))
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df = pd.DataFrame(rows, columns=["ts_code", "name", "track", "status",
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"theme", "source_rule"])
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if dedup:
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df = df.drop_duplicates(["ts_code", "track"])
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return df, missing
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def snapshot(only_confirmed: bool = True, path: str | None = None):
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"""成员表落 data/ 版本化快照（每行带 source_rule 与 updated_at，审计列）。"""
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df, missing = resolve_members(only_confirmed, path)
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os.makedirs("data", exist_ok=True)
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out = f"data/track_members_{dt.date.today().isoformat()}.csv"
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(df.assign(layer="",
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updated_at=dt.datetime.now().isoformat(timespec="seconds"))
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.to_csv(out, index=False))
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return out, df, missing
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def coverage_report(path: str | None = None) -> pd.DataFrame:
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"""赛道覆盖体检：逐赛道给出成员数（含图谱锚数）、各路径命中率，含 candidate。"""
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# dedup=False：命中率要在去重前数——成员完全被同赛道更早键覆盖的键，
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# 去重后一行不剩，会被误计为"没命中"（07-30 实测教训）。
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df, missing = resolve_members(only_confirmed=False, path=path, dedup=False)
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d = load_yml(path)
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print("赛道覆盖体检（主题=industry_pools 当前态；图谱=环节投影；"
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"成员数已去重，命中率按去重前算）：")
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for tr in d.get("tracks") or []:
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sub = df[df["track"] == tr["name"]]
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graph_sub = sub[sub["source_rule"] != "pool_theme"]
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n_all = sub["ts_code"].nunique()
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n_graph = graph_sub["ts_code"].nunique()
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n_tkeys = len(tr.get("kg_themes") or [])
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hit_t = sub[sub["source_rule"] == "pool_theme"]["theme"].nunique()
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n_gkeys = (len(tr.get("kg_segments") or [])
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+ len(tr.get("kg_chains") or []))
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hit_g = graph_sub["theme"].nunique()
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miss = [f"{kind}:{key}" for name, kind, key in missing
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if name == tr["name"]]
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tag = "" if tr.get("status") == "confirmed" else "（candidate）"
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line = (f" {tr['name']}{tag}: 成员 {n_all} 只（图谱锚 {n_graph}）"
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f" | 主题命中 {hit_t}/{n_tkeys}")
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if n_gkeys:
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line += f" | 图谱键命中 {hit_g}/{n_gkeys}"
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if miss:
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line += f" | 未命中: {'、'.join(miss)}"
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print(line)
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conf = df[df["status"] == "confirmed"]
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n_conf = conf["ts_code"].nunique()
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n_conf_graph = conf[conf["source_rule"] != "pool_theme"]["ts_code"].nunique()
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print(f" —— confirmed 合计（去重）: {n_conf} 只，其中带图谱锚 {n_conf_graph} 只")
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return df
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def gate_simulation(path: str | None = None) -> None:
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"""赛道闸演算（只读）：若 ENABLE_TRACK_GATE 置 1，主榜/观察档变成什么规模。
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口径与开闸后的 factors._gate_of 一致：
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主榜' = 现主榜 ∩ 赛道成员；
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观察档' = 现观察档 ∪（无券商预期 ∧ 在赛道 ∧ 现为不采纳 ∧ 非风险股）。
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读最新档位 / upside 因子表与 confirmed 成员表，不写任何东西。"""
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import common
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d = db.read_mysql("factor", "SELECT MAX(trade_date) d FROM t_factor_akg_gate")
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v = None if d.empty else d.iloc[0, 0]
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if v is None or pd.isna(v):
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print("（尚无档位表，跳过赛道闸演算）")
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return
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ds = pd.Timestamp(v).date().isoformat()
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g = db.read_mysql("factor", "SELECT stock_code, factor_value "
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"FROM t_factor_akg_gate WHERE trade_date = %s", (ds,))
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up = db.read_mysql("factor", "SELECT stock_code FROM t_factor_akg_upside "
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"WHERE trade_date = %s", (ds,))
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gate = g.set_index("stock_code")["factor_value"].astype(float)
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covered = set(up["stock_code"]) if not up.empty else set()
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df, _missing = resolve_members(only_confirmed=True, path=path)
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if df.empty:
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print("（赛道成员表为空，跳过演算）")
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return
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by_track = {t: {common.to_prefix(x) for x in sub["ts_code"]}
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for t, sub in df.groupby("track")}
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tset = set().union(*by_track.values())
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try:
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import factors
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risk = factors._risk_set() # noqa: SLF001 —— 同仓自用
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except Exception: # noqa: BLE001 —— 风险名单拿不到时演算照做
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risk = set()
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main_now = set(gate[gate == 2.0].index)
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obs_now = set(gate[gate == 1.0].index)
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main_after = main_now & tset
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obs_extra = {k for k in tset
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if k not in covered and gate.get(k) == 0.0 and k not in risk}
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obs_after = obs_now | obs_extra
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print(f"\n赛道闸演算（截面 {ds}，假想 ENABLE_TRACK_GATE=1）：")
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print(f" 主榜: {len(main_now)} → {len(main_after)}"
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f"（保留 {len(main_after) / max(1, len(main_now)):.0%}）")
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print(f" 观察档: {len(obs_now)} → {len(obs_after)}"
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f"（并入赛道内无覆盖股 +{len(obs_extra)}）")
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parts = [f"{t} {len(main_now & s)}"
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for t, s in sorted(by_track.items(),
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key=lambda kv: -len(main_now & kv[1]))]
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print(f" 逐赛道主榜贡献（跨赛道有重叠）: {' | '.join(parts)}")
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