191 lines
8.0 KiB
Python
191 lines
8.0 KiB
Python
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"""每日选股计划（R4 首版）：把 akg_gate / akg_score 变成一份人能读的榜单。
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数据全部来自已落库的表，不重算：
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平台因子表 t_factor_akg_score / _gate / _upside / _heat —— 当日截面
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基座只读视图 v_factor_transmission —— 传导证据（主题、源数、已动比例）
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基座 industry_pools —— 股票名称
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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，
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体现"数据到达本身是信号"（首次覆盖 / 新进传导链即升档）。
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"""
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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 common
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import db
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# 分数编码（与 factors.build_score 一致）：主榜 = 200 + 传导档位×20 + 组内分，
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# 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。
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_MAIN_MIN = 150.0
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def _factor(table: str, ds: str) -> pd.Series:
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df = db.read_mysql(
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"factor",
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f"SELECT stock_code, factor_value FROM {table} WHERE trade_date = %s", (ds,))
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if df.empty:
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return pd.Series(dtype=float)
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return df.set_index("stock_code")["factor_value"].astype(float)
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def _latest_date(table: str, upto: str | None = None):
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if upto:
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df = db.read_mysql(
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"factor", f"SELECT MAX(trade_date) d FROM {table} "
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f"WHERE trade_date <= %s", (upto,))
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else:
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df = db.read_mysql("factor", f"SELECT MAX(trade_date) d FROM {table}")
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v = None if df.empty else df.iloc[0, 0]
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return None if v is None or pd.isna(v) else pd.Timestamp(v).date().isoformat()
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def _prev_date(table: str, before: str):
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df = db.read_mysql(
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"factor", f"SELECT MAX(trade_date) d FROM {table} "
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f"WHERE trade_date < %s", (before,))
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v = None if df.empty else df.iloc[0, 0]
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return None if v is None or pd.isna(v) else pd.Timestamp(v).date().isoformat()
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def _names() -> dict:
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out = {}
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pools = db.read_pg("SELECT members FROM industry_pools")
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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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for m in ms or []:
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ts, name = (m or {}).get("ts_code"), (m or {}).get("name")
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if ts and name:
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out.setdefault(common.to_prefix(ts), str(name))
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return out
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def _evidence(ds: str):
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"""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。"""
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tr = 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", (ds,))
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if tr.empty:
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return {}, set()
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tr["k"] = tr["ts_code"].map(common.to_prefix)
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tr["n_sources"] = pd.to_numeric(tr["n_sources"], errors="coerce").fillna(0)
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tr["moved_ratio"] = pd.to_numeric(tr["moved_ratio"], errors="coerce").fillna(0)
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tr["strength"] = tr["n_sources"] * (1.0 - tr["moved_ratio"])
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tr = tr.sort_values("strength", ascending=False).drop_duplicates("k")
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ev = {r.k: (str(r.target), int(r.n_sources), float(r.moved_ratio))
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for r in tr.itertuples()}
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days = {str(x) for x in tr["mkt_trade_date"].dropna().unique()}
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return ev, days
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def _tier_label(score: float) -> str:
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return {0: "无传导", 1: "弱传导", 2: "强传导"}.get(
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int((score - 190.0) // 20), "?")
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def _pct(v) -> str:
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return "—" if v is None or pd.isna(v) else f"{v:+.0%}"
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def _num(v) -> str:
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return "—" if v is None or pd.isna(v) else f"{v:.2f}"
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def generate(date: str | None = None, top: int = 20, obs_top: int = 10) -> str:
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ds = date or _latest_date("t_factor_akg_score")
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if not ds:
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raise SystemExit("t_factor_akg_score 还没有数据——先 build akg_score。")
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score = _factor("t_factor_akg_score", ds)
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gate = _factor("t_factor_akg_gate", ds)
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if score.empty or gate.empty:
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raise SystemExit(f"{ds} 缺 akg_score / akg_gate——先 build 该日再出计划。")
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upside = _factor("t_factor_akg_upside", ds)
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hd = _latest_date("t_factor_akg_heat", ds)
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heat = _factor("t_factor_akg_heat", hd) if hd else pd.Series(dtype=float)
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names = _names()
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ev, mkt_days = _evidence(ds)
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main = score[score >= _MAIN_MIN].sort_values(ascending=False)
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obs = score[score < _MAIN_MIN].sort_values(ascending=False)
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L = [f"# 每日选股计划 · {ds}", ""]
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L.append(f"主榜 {len(main)} 只 / 观察档 {len(obs)} 只 / 全池档位覆盖 {len(gate)} 只。")
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stale = sorted(d for d in mkt_days if d != ds)
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if stale:
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L.append(f"⚠️ 本日传导用的行情快照 = {'、'.join(stale)}（T−1 口径："
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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）。")
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L.append("")
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L.append(f"## 主榜 Top {min(top, len(main))}（有券商预期、目标价不低于现价）")
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L.append("")
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L.append("| # | 代码 | 名称 | 总分 | 档位 | 传导证据 | 热度 | 预期空间 |")
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L.append("|---|------|------|------|------|----------|------|----------|")
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for i, (k, s) in enumerate(main.head(top).items(), 1):
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e = ev.get(k)
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etxt = f"{e[0]}（{e[1]} 源，已动 {e[2]:.0%}）" if e else "—"
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L.append(f"| {i} | {k} | {names.get(k, '—')} | {s:.1f} | {_tier_label(s)} "
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f"| {etxt} | {_num(heat.get(k))} | {_pct(upside.get(k))} |")
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L.append("")
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L.append(f"## 观察档 Top {min(obs_top, len(obs))}"
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f"（无券商预期、但在传导链上——没有估值锚，置信度低）")
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L.append("")
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L.append("| # | 代码 | 名称 | 分 | 传导证据 | 热度 |")
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L.append("|---|------|------|----|----------|------|")
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for i, (k, s) in enumerate(obs.head(obs_top).items(), 1):
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e = ev.get(k)
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etxt = f"{e[0]}（{e[1]} 源，已动 {e[2]:.0%}）" if e else "—"
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L.append(f"| {i} | {k} | {names.get(k, '—')} | {s:.1f} "
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f"| {etxt} | {_num(heat.get(k))} |")
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L.append("")
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L.append("## 今日升降档")
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L.append("")
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prev_ds = _prev_date("t_factor_akg_gate", ds)
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if not prev_ds:
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L.append("（没有更早的档位表可比，升降档从下一个交易日开始。）")
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else:
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prev = _factor("t_factor_akg_gate", prev_ds)
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both = pd.concat([prev.rename("prev"), gate.rename("cur")], axis=1)
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both = both.fillna(-1.0) # -1 = 当日不在面板
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up = both[both["cur"] > both["prev"]]
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down = both[both["cur"] < both["prev"]]
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lab = {-1.0: "池外", 0.0: "不采纳", 1.0: "观察档", 2.0: "主榜"}
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L.append(f"对比 {prev_ds}：升档 {len(up)} 只，降档 {len(down)} 只。"
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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。")
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def _rows(d: pd.DataFrame, cap: int = 15):
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lines = []
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for k, r in d.iterrows():
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lines.append(f"- {k} {names.get(k, '')}："
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f"{lab.get(r['prev'], '?')} → {lab.get(r['cur'], '?')}")
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if len(lines) >= cap:
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lines.append(f"- ……共 {len(d)} 只，其余见档位表")
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break
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return lines
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if not up.empty:
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L.append("")
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L.append("**升档**：")
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L += _rows(up.sort_values("cur", ascending=False))
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if not down.empty:
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L.append("")
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L.append("**降档**：")
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L += _rows(down.sort_values("prev", ascending=False))
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L.append("")
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L.append("---")
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L.append("口径：主榜分 = 200 + 传导档位×20 + 组内分（还没热、还便宜）；"
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"观察档分 = 100 + 0.6z(传导) + 0.4z(−热度)。研究用途，不构成投资建议。")
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text = "\n".join(L)
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os.makedirs("data/plan", exist_ok=True)
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out = f"data/plan/plan_{ds}.md"
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with open(out, "w", encoding="utf-8") as f:
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f.write(text + "\n")
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print(text)
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print(f"\n已写入 {out}")
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return out
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