167 lines
7.7 KiB
Python
167 lines
7.7 KiB
Python
"""逻辑状态四态的覆盖率读数：只读，不写库，不改任何判决。
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回答一个问题：四态这套东西今天在真实数据上能覆盖多少票、各落到哪个状态。
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它是判断"这一路该接多大面"的依据，也是接进计划装配之前必须先看的读数。
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三路各自的取数在这里，归一与合成都调 logic_state 里的纯函数——读数脚本绝不能自己
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另写一套判据，否则读到的就不是系统真会给出的状态。
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甲路 研报论断 基座 PG v_factor_logic（经 sources.logic_claims）
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乙路 产业研判 平台 MySQL t_akg_judgement_snapshot（经 judgement.load_previous）
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丙路 券商行动 平台 MySQL gp_report_rc：两个等长窗口的每股收益预测中位数与机构数
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丁路 公司事件 无数据源，恒定缺失
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丙路的取数口径（三条都要照做，否则读数是错的）：
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一，两个窗口必须等长。不等长会让八成的票假显示覆盖收缩——实测前 135 天对近 45 天时
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有 907 只票误报。
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二，同一财年同一预测期才可比，按 quarter 精确匹配，跨财年比较没有意义。
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三，同一家机构在窗口里可能发多篇，先按机构取最近一篇再算中位数，否则发得勤的
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机构会被重复计入。
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跑法（155 上）：docker exec akg_factor_bridge python3 logic_state_coverage.py [数据日]
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"""
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from __future__ import annotations
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import datetime as dt
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import statistics as st
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import sys
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from collections import Counter, defaultdict
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import common
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import config
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import db
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import judgement
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import logic_state as ls
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import sources
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# 丙路两个窗口各自的长度（自然日）。等长是硬要求，见模块说明第一条。
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BROKER_WINDOW_DAYS = 45
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def broker_paths(codes, ds: str) -> dict:
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"""按票算丙路信号。返回前缀码到 logic_state.signal 的字典（算不出的票不进字典）。"""
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end = dt.date.fromisoformat(ds)
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mid = end - dt.timedelta(days=BROKER_WINDOW_DAYS)
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start = end - dt.timedelta(days=BROKER_WINDOW_DAYS * 2)
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# noqa: SLF001 —— _to_dot 是同仓自用的代码格式转换
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dotted = sorted({sources._to_dot(c) for c in codes if c}) # noqa: SLF001
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if not dotted:
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return {}
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out: dict[str, dict] = {}
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# 一次拉两个窗口的全部行，按票在内存里分窗——逐票查库要发几千次请求。
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marks = ",".join(["%s"] * len(dotted))
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try:
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df = db.read_mysql(
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"factor",
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f"SELECT ts_code, report_date, quarter, org_name, eps FROM gp_report_rc "
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f"WHERE ts_code IN ({marks}) AND report_date > %s AND report_date <= %s "
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f"AND eps IS NOT NULL AND quarter IS NOT NULL",
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tuple(dotted) + (start.isoformat(), end.isoformat()))
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except Exception as e: # noqa: BLE001
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print(f" （券商研报明细表读取失败，丙路整体缺席: {e!r}）")
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return {}
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# 分票、分窗、分财年地堆起来：{票: {财年: {"now": {机构: (日期, 每股收益)}, "prev": ...}}}
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box: dict = defaultdict(lambda: defaultdict(lambda: {"now": {}, "prev": {}}))
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for r in df.itertuples():
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d = sources._ymd(r.report_date) # noqa: SLF001 —— 同仓自用
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if not d:
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continue
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win = "now" if d > mid.isoformat() else "prev"
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k = common.to_prefix(str(r.ts_code).strip())
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q = str(r.quarter).strip()
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org = str(r.org_name or "").strip() or "未署名"
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slot = box[k][q][win]
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# 同一家机构在窗口里发了多篇，只留最近一篇（模块说明第三条）。
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if org not in slot or d > slot[org][0]:
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slot[org] = (d, float(r.eps))
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for k, by_q in box.items():
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# 两个窗口都有料的财年里，取行数最多的那个作可比口径（模块说明第二条）。
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usable = [(q, v) for q, v in by_q.items() if v["now"] and v["prev"]]
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if not usable:
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continue
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q, v = max(usable, key=lambda kv: len(kv[1]["now"]) + len(kv[1]["prev"]))
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now = {"eps": st.median([x[1] for x in v["now"].values()]), "firms": len(v["now"])}
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prev = {"eps": st.median([x[1] for x in v["prev"].values()]), "firms": len(v["prev"])}
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s = ls.from_broker(now, prev, as_of=ds)
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s["refs"] = [{**(s["refs"][0] if s["refs"] else {}), "quarter": q}]
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out[k] = s
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return out
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def main(ds: str | None = None) -> None:
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ds = ds or (dt.date.today() - dt.timedelta(days=1)).isoformat()
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print(f"=== 逻辑状态四态覆盖率读数 · 数据日 {ds} ===\n")
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codes = db.read_pg("SELECT DISTINCT ts_code FROM v_factor_stock_daily "
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"WHERE trade_date = %s", (ds,))["ts_code"].tolist()
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codes = [common.to_prefix(str(c).strip()) for c in codes]
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print(f"当日有行情的票 {len(codes)} 只\n")
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claims = sources.logic_claims(codes, ds, per_stock=200)
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brokers = broker_paths(codes, ds)
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snaps = judgement.load_previous(ds)
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print(f"甲路取到 {len(claims)} 只票的论断；丙路算得出 {len(brokers)} 只票；"
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f"乙路快照 {len(snaps)} 个簇\n")
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# 乙路按环节名对上主题：候选卡按环节，产业研判按主题聚簇，两者不在一个命名空间，
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# 这里只做同名匹配，对不上的票乙路就是缺失。这一路的天花板本来就低（实测 1.4%）。
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by_seg = {(r.get("segment_name") or r.get("subject_name") or "").strip(): r
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for r in snaps.values()}
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seg_of = {}
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try:
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d = db.read_pg("SELECT ts_code, target FROM v_factor_transmission_moved "
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"WHERE scan_date = %s", (ds,))
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for r in d.itertuples():
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seg_of[common.to_prefix(str(r.ts_code).strip())] = str(r.target)
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except Exception as e: # noqa: BLE001
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print(f" （传导视图读取失败，乙路整体缺席: {e!r}）")
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per_path = {ls.PATH_CLAIM: Counter(), ls.PATH_JUDGE: Counter(),
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ls.PATH_BROKER: Counter()}
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states, whys = Counter(), Counter()
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samples: dict = {}
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for k in codes:
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a = ls.from_claims(claims.get(k), ds, stale_days=config.LOGIC_STALE_DAYS)
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b = ls.from_judgement(by_seg.get(seg_of.get(k, "")))
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c = brokers.get(k) or ls.signal(ls.PATH_BROKER, ls.SIG_NONE,
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why="两个窗口里算不出可比的预测")
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per_path[ls.PATH_CLAIM][a["signal"]] += 1
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per_path[ls.PATH_JUDGE][b["signal"]] += 1
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per_path[ls.PATH_BROKER][c["signal"]] += 1
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r = ls.compose([a, b, c, ls.from_events()])
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states[r["state"]] += 1
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if r["why"]:
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whys[f"{r['state']}·{r['why']}"] += 1
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if r["state"] in (ls.STATE_STRONG, ls.STATE_DOUBT) and r["state"] not in samples:
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samples[r["state"]] = (k, r["reasons"][:3])
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if r["state"] == ls.STATE_UNKNOWN and r["why"] == ls.WHY_CONFLICT \
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and "矛盾" not in samples:
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samples["矛盾"] = (k, r["reasons"][:3])
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print("每路各自的信号分布")
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for path, cnt in per_path.items():
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tot = sum(cnt.values())
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line = "、".join(f"{s} {n}（{n / tot:.1%}）" for s, n in cnt.most_common())
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print(f" {path}：{line}")
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print(f" {ls.PATH_EVENT}：缺失 {len(codes)}（100.0%，无数据源）\n")
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print("合成后的四态分布")
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for s, n in states.most_common():
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print(f" {s} {n}（{n / len(codes):.1%}）")
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if whys:
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print("\n 无法判断的子因")
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for w, n in whys.most_common():
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print(f" {w} {n}")
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if samples:
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print("\n样例")
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for tag, (k, rs) in samples.items():
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print(f" {tag} {k}")
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for x in rs:
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print(f" {x}")
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if __name__ == "__main__":
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main(sys.argv[1] if len(sys.argv) > 1 else None)
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