"""每日选股计划（R4 首版）：把 akg_gate / akg_score 变成一份人能读的榜单。

数据全部来自已落库的表，不重算：
  平台因子表  t_factor_akg_score / _gate / _upside / _heat   —— 当日截面
  基座只读视图 v_factor_transmission                          —— 传导证据（主题、源数、已动比例）
  基座 industry_pools                                         —— 股票名称
产出：终端打印 + data/plan/plan_<日期>.md。升降档一节对比前一交易日的档位表，
体现"数据到达本身是信号"（首次覆盖 / 新进传导链即升档）。
"""
import json
import os

import pandas as pd

import common
import db


# 分数编码（与 factors.build_score 一致）：主榜 = 200 + 传导档位×20 + 组内分，
# 观察档 = 100 + 组内分，组内分 clip ±9.9。150 落在两带中间的空档上，用作分界。
_MAIN_MIN = 150.0


def _factor(table: str, ds: str) -> pd.Series:
    df = db.read_mysql(
        "factor",
        f"SELECT stock_code, factor_value FROM {table} WHERE trade_date = %s", (ds,))
    if df.empty:
        return pd.Series(dtype=float)
    return df.set_index("stock_code")["factor_value"].astype(float)


def _latest_date(table: str, upto: str | None = None):
    if upto:
        df = db.read_mysql(
            "factor", f"SELECT MAX(trade_date) d FROM {table} "
                      f"WHERE trade_date <= %s", (upto,))
    else:
        df = db.read_mysql("factor", f"SELECT MAX(trade_date) d FROM {table}")
    v = None if df.empty else df.iloc[0, 0]
    return None if v is None or pd.isna(v) else pd.Timestamp(v).date().isoformat()


def _prev_date(table: str, before: str):
    df = db.read_mysql(
        "factor", f"SELECT MAX(trade_date) d FROM {table} "
                  f"WHERE trade_date < %s", (before,))
    v = None if df.empty else df.iloc[0, 0]
    return None if v is None or pd.isna(v) else pd.Timestamp(v).date().isoformat()


def _names() -> dict:
    out = {}
    pools = db.read_pg("SELECT members FROM industry_pools")
    for _, r in pools.iterrows():
        ms = r["members"]
        if isinstance(ms, str):
            ms = json.loads(ms)
        for m in ms or []:
            ts, name = (m or {}).get("ts_code"), (m or {}).get("name")
            if ts and name:
                out.setdefault(common.to_prefix(ts), str(name))
    return out


def _evidence(ds: str):
    """每股最强一条传导证据：主题、源数、已动比例；另返回涉及的行情快照日。"""
    tr = db.read_pg(
        "SELECT ts_code, target, n_sources, moved_ratio, mkt_trade_date "
        "FROM v_factor_transmission WHERE scan_date = %s", (ds,))
    if tr.empty:
        return {}, set()
    tr["k"] = tr["ts_code"].map(common.to_prefix)
    tr["n_sources"] = pd.to_numeric(tr["n_sources"], errors="coerce").fillna(0)
    tr["moved_ratio"] = pd.to_numeric(tr["moved_ratio"], errors="coerce").fillna(0)
    tr["strength"] = tr["n_sources"] * (1.0 - tr["moved_ratio"])
    tr = tr.sort_values("strength", ascending=False).drop_duplicates("k")
    ev = {r.k: (str(r.target), int(r.n_sources), float(r.moved_ratio))
          for r in tr.itertuples()}
    days = {str(x) for x in tr["mkt_trade_date"].dropna().unique()}
    return ev, days


def _tier_label(score: float) -> str:
    return {0: "无传导", 1: "弱传导", 2: "强传导"}.get(
        int((score - 190.0) // 20), "?")


def _pct(v) -> str:
    return "—" if v is None or pd.isna(v) else f"{v:+.0%}"


def _num(v) -> str:
    return "—" if v is None or pd.isna(v) else f"{v:.2f}"


def _pick(ranked: pd.Series, ev: dict, top: int, theme_cap: int):
    """按分数从高到低取 top 条；每个传导主题最多 theme_cap 条（0=不设限）。

    为什么设限：传导目标是环节级，同环节全体成员共享同一条证据，不设限时
    榜单会被三四个环节刷屏——20 个名额实际只是 4 注。限额后变成
    "多条线索 × 每条线索取最冷最便宜的几只"，被挤掉的仍在完整档位表里。"""
    out, cnt = [], {}
    for k, s in ranked.items():
        e = ev.get(k)
        theme = e[0] if e else "（无传导）"
        if theme_cap and cnt.get(theme, 0) >= theme_cap:
            continue
        cnt[theme] = cnt.get(theme, 0) + 1
        out.append((k, s))
        if len(out) >= top:
            break
    return out


def generate(date: str | None = None, top: int = 20, obs_top: int = 10,
             theme_cap: int = 5) -> str:
    ds = date or _latest_date("t_factor_akg_score")
    if not ds:
        raise SystemExit("t_factor_akg_score 还没有数据——先 build akg_score。")
    score = _factor("t_factor_akg_score", ds)
    gate = _factor("t_factor_akg_gate", ds)
    if score.empty or gate.empty:
        raise SystemExit(f"{ds} 缺 akg_score / akg_gate——先 build 该日再出计划。")
    upside = _factor("t_factor_akg_upside", ds)
    if upside.empty:
        # 晚间 18:40 建当日 upside 表时行情源（~19:50 发布）还没到，当日表常为空。
        # 这里现算兜底：此刻库里已有晚到的当日价，as-of 口径不变（consensus<=当日）。
        import factors
        df_up = factors.build_upside(ds, ds)
        if df_up is not None and not df_up.empty:
            x = df_up.copy()
            x["k"] = x["stock_code"].map(common.to_prefix)
            upside = x.groupby("k")["factor_value"].max().astype(float)
    hd = _latest_date("t_factor_akg_heat", ds)
    heat = _factor("t_factor_akg_heat", hd) if hd else pd.Series(dtype=float)
    names = _names()
    ev, mkt_days = _evidence(ds)

    main = score[score >= _MAIN_MIN].sort_values(ascending=False)
    obs = score[score < _MAIN_MIN].sort_values(ascending=False)

    L = [f"# 每日选股计划 · {ds}", ""]
    L.append(f"主榜 {len(main)} 只 / 观察档 {len(obs)} 只 / 全池档位覆盖 {len(gate)} 只。")
    stale = sorted(d for d in mkt_days if d != ds)
    if stale:
        L.append(f"⚠️ 本日传导用的行情快照 = {'、'.join(stale)}（T−1 口径："
                 f"\"谁已经动了\"看的是上个交易日收盘；拍点方案定版前均如此）。")
    L.append("")

    cap_txt = f"，每主题限额 {theme_cap}" if theme_cap else ""
    main_rows = _pick(main, ev, top, theme_cap)
    L.append(f"## 主榜 Top {len(main_rows)}（有券商预期、目标价不低于现价{cap_txt}）")
    L.append("")
    L.append("| # | 代码 | 名称 | 总分 | 档位 | 传导证据 | 热度 | 预期空间 |")
    L.append("|---|------|------|------|------|----------|------|----------|")
    for i, (k, s) in enumerate(main_rows, 1):
        e = ev.get(k)
        etxt = f"{e[0]}（{e[1]} 源，已动 {e[2]:.0%}）" if e else "—"
        L.append(f"| {i} | {k} | {names.get(k, '—')} | {s:.1f} | {_tier_label(s)} "
                 f"| {etxt} | {_num(heat.get(k))} | {_pct(upside.get(k))} |")
    L.append("")

    obs_rows = _pick(obs, ev, obs_top, theme_cap)
    L.append(f"## 观察档 Top {len(obs_rows)}"
             f"（无券商预期、但在传导链上——没有估值锚，置信度低{cap_txt}）")
    L.append("")
    L.append("| # | 代码 | 名称 | 分 | 传导证据 | 热度 |")
    L.append("|---|------|------|----|----------|------|")
    for i, (k, s) in enumerate(obs_rows, 1):
        e = ev.get(k)
        etxt = f"{e[0]}（{e[1]} 源，已动 {e[2]:.0%}）" if e else "—"
        L.append(f"| {i} | {k} | {names.get(k, '—')} | {s:.1f} "
                 f"| {etxt} | {_num(heat.get(k))} |")
    L.append("")

    L.append("## 今日升降档")
    L.append("")
    prev_ds = _prev_date("t_factor_akg_gate", ds)
    if not prev_ds:
        L.append("（没有更早的档位表可比，升降档从下一个交易日开始。）")
    else:
        prev = _factor("t_factor_akg_gate", prev_ds)
        both = pd.concat([prev.rename("prev"), gate.rename("cur")], axis=1)
        both = both.fillna(-1.0)                    # -1 = 当日不在面板
        up = both[both["cur"] > both["prev"]]
        down = both[both["cur"] < both["prev"]]
        lab = {-1.0: "池外", 0.0: "不采纳", 1.0: "观察档", 2.0: "主榜"}
        L.append(f"对比 {prev_ds}：升档 {len(up)} 只，降档 {len(down)} 只。"
                 f"升档=拿到新锚（首次覆盖 / 新进传导链），本身就是值得看的信号。")

        def _rows(d: pd.DataFrame, cap: int = 15):
            lines = []
            for k, r in d.iterrows():
                lines.append(f"- {k} {names.get(k, '')}："
                             f"{lab.get(r['prev'], '?')} → {lab.get(r['cur'], '?')}")
                if len(lines) >= cap:
                    lines.append(f"- ……共 {len(d)} 只，其余见档位表")
                    break
            return lines

        if not up.empty:
            L.append("")
            L.append("**升档**：")
            L += _rows(up.sort_values("cur", ascending=False))
        if not down.empty:
            L.append("")
            L.append("**降档**：")
            L += _rows(down.sort_values("prev", ascending=False))
    L.append("")
    L.append("---")
    L.append("口径：主榜分 = 200 + 传导档位×20 + 组内分（还没热、还便宜）；"
             "观察档分 = 100 + 0.6z(传导) + 0.4z(−热度)。")

    text = "\n".join(L)
    os.makedirs("data/plan", exist_ok=True)
    out = f"data/plan/plan_{ds}.md"
    with open(out, "w", encoding="utf-8") as f:
        f.write(text + "\n")
    print(text)
    print(f"\n已写入 {out}")
    return out