567 lines
28 KiB
Python
567 lines
28 KiB
Python
"""每日选股计划（R4）：数据装配 / Markdown 渲染 / 产出，三段分离。
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collect() -> dict 结构化计划——api.py 直接当 JSON 返回
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render_md() -> str 从 dict 渲染 Markdown
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generate() CLI 与 cron 的入口：collect + render + 落盘 + 打印
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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
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基座 industry_pools —— 股票名称
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升降档一节对比前一交易日的档位表——数据到达本身是信号（首次覆盖 /
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新进传导链即升档）。
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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 card
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import common
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import config
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import db
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import sources
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import version
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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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"members_total, moved 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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# 元组前三项是既有口径（多处按下标取），第四、五项是环节的成员数与已动数（关注环节表用）
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ev = {r.k: (str(r.target), int(r.n_sources), float(r.moved_ratio),
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None if pd.isna(r.members_total) else int(r.members_total),
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None if pd.isna(r.moved) else int(r.moved))
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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 _val(series: pd.Series, k: str):
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v = series.get(k)
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return None if v is None or pd.isna(v) else float(v)
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def _assemble_cards(ds: str, codes: list, ev: dict, upside: pd.Series,
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mkt_days: set, risk: set | None = None) -> tuple[dict, list]:
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"""候选卡装配（2026-09-02 方案第 2.2 节第三项）：对档位表里的全部票（主榜与观察档，
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裁剪之前）读三路证据、逐票判决。规则在 card.py，取数在 sources.py，这里只做对齐。
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返回 (cards, segments_pointed)：cards 按前缀码索引，含判决、理由、缺失、风险、卡内序
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与证据线原值；segments_pointed 是"关注环节"聚合——今日被传导指向的每个环节的源数、
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链符、成员数、已启动成员、领涨者、候选数。这是拍板记录第一项"强传导档降为关注环节"
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在计划文本层的落地。"""
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import factors
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moved = sources.moved_members(ds)
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daily = sources.stock_daily(ds)
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night = sources.night_conclusions(codes, ds)
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if risk is None: # collect 会传入读过一次的名单；单独调用时自己读
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try:
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risk = factors._risk_set() or set() # noqa: SLF001 —— 同仓自用
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except Exception: # noqa: BLE001 —— 风险名单拿不到时不当 ST 处理，与 gate 的宽容一致
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risk = set()
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stale = bool(mkt_days) and any(x != ds for x in mkt_days)
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cards: dict = {}
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for k in codes:
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mv, e, d, n = moved.get(k), ev.get(k), daily.get(k, {}), night.get(k, {})
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theme = (mv or {}).get("theme") or (e[0] if e else None)
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n_sources = (mv or {}).get("n_sources") or (e[1] if e else None)
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up = _val(upside, k)
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evd = {"pointed": bool(mv or e), "theme": theme, "n_sources": n_sources,
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"chain_fit": (mv or {}).get("chain_fit"),
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"pct0": d.get("pct0"), "covered": up is not None, "upside": up,
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"risk_name": k in risk,
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"accum_state": n.get("accum_state"), "accum_score": n.get("accum_score"),
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"accum_age": n.get("accum_age"), "y_signal": n.get("signal"),
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"stale_snapshot": stale}
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j = card.judge(evd, start_pct=config.CARD_START_PCT,
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accum_max_age=config.CARD_ACCUM_MAX_AGE,
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neg_tol=config.UPSIDE_NEG_TOLERANCE)
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cards[k] = {
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**j,
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"theme": theme, "n_sources": n_sources, "chain_fit": evd["chain_fit"],
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"started_source": "moved_view" if mv else None,
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"pct0": d.get("pct0"), "net_z": d.get("net_z"), "heat_chg": d.get("heat_chg"),
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"accum": ({"state": n.get("accum_state"), "score": n.get("accum_score"),
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"age": n.get("accum_age"), "pos_tag": n.get("accum_pos_tag"),
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"date": n.get("conclusion_date")} if n else None),
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"night": ({"signal": n.get("signal"), "support": n.get("support"),
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"pressure": n.get("pressure"), "date": n.get("conclusion_date")}
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if n else None),
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}
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for i, (k, c) in enumerate(sorted(cards.items(), key=lambda kv: card.sort_key(kv[1])), 1):
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c["card_rank"] = i
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segs: dict = {}
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for k, mv in moved.items():
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s = segs.setdefault(mv["theme"], {
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"segment": mv["theme"], "n_sources": mv["n_sources"], "chain_fit": mv["chain_fit"],
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"members_total": mv.get("members_total"), "moved": mv.get("moved"),
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"started": [], "candidates": 0, "leader": None})
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s["started"].append(k)
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if cards.get(k, {}).get("verdict") == card.VERDICT_CANDIDATE:
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s["candidates"] += 1
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for e in ev.values(): # 只有未动名单的环节也是"被指向"，列入但无已启动
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s = segs.setdefault(e[0], {"segment": e[0], "n_sources": e[1], "chain_fit": None,
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"members_total": None, "moved": None,
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"started": [], "candidates": 0, "leader": None})
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if s["members_total"] is None and len(e) > 4:
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s["members_total"], s["moved"] = e[3], e[4]
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for s in segs.values():
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best, best_pct = None, None
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for k in s["started"]:
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# 领涨者按行情视图找，不限于档位表里的票：被指向且已启动但不在主榜与观察档的
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# （无覆盖且不在赛道、或被风险闸挡）也要显示，这是"定位对不对"的读数
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p = (daily.get(k) or {}).get("pct0")
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if p is not None and (best_pct is None or p > best_pct):
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best, best_pct = k, p
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s["leader"] = {"code": best, "pct0": best_pct} if best else None
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s["started_count"] = len(s["started"])
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s["started_in_tiers"] = sum(1 for k in s["started"] if k in cards)
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ordered = sorted(segs.values(), key=lambda s: (-s["candidates"], -(s["n_sources"] or 0),
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-(s["chain_fit"] or 0), s["segment"]))
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return cards, ordered
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def collect(date: str | None = None, top: int = 20, obs_top: int = 10,
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theme_cap: int = 5) -> dict:
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"""装配一天的计划为结构化字典。数据缺失抛 RuntimeError（api 侧转 404）。
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2026-09-02 起附带候选卡：main / observe 的装配、排序、裁剪一字不动（下游 PMS 只读
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这两段的既有字段），每行只是多联入判决类字段；顶层新增 generated_at、plan_version、
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card_counts、candidates（候选单全量，不受裁剪）、watch、segments_pointed。
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`_full` 是全量主榜与观察档行，只给 generate 落快照用，api 返回前会去掉。"""
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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 RuntimeError("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 RuntimeError(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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if upside.empty:
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# 当日 upside 表为空时现算兜底（as-of 口径不变：consensus<=当日、当日收盘价）
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import factors
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df_up = factors.build_upside(ds, ds)
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if df_up is not None and not df_up.empty:
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x = df_up.copy()
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x["k"] = x["stock_code"].map(common.to_prefix)
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upside = x.groupby("k")["factor_value"].max().astype(float)
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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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generated_at = dt.datetime.now().isoformat(timespec="seconds")
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try: # 风险名单只读一次：候选卡与升降档原因共用
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import factors
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risk = factors._risk_set() or set() # noqa: SLF001 —— 同仓自用
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except Exception: # noqa: BLE001
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risk = set()
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cards, segments_pointed = _assemble_cards(
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ds, list(main.index) + list(obs.index), ev, upside, mkt_days, risk=risk)
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def _pick(ranked: pd.Series, n: int):
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"""分数从高到低取 n 条；每个传导主题最多 theme_cap 条（0=不设限）——
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传导目标是环节级、同环节成员共享同一条证据，不限额会被少数环节刷屏。"""
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out, cnt = [], {}
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for k, s in ranked.items():
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e = ev.get(k)
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theme = e[0] if e else "（无传导）"
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if theme_cap and cnt.get(theme, 0) >= theme_cap:
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continue
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cnt[theme] = cnt.get(theme, 0) + 1
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out.append((k, s))
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if len(out) >= n:
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break
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return out
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def _row(rank: int, k: str, s: float, with_tier: bool) -> dict:
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e = ev.get(k)
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c = cards.get(k) or {}
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evidence = ({"theme": e[0], "n_sources": e[1], "moved_ratio": round(e[2], 4)}
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if e else None)
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# 已启动成员在传导视图里没有证据行（视图只摊平未动名单）：只在原本为空时用
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# 已动成员视图的目标环节与源数补上，不覆盖已有值——否则到 PMS 会全落进"无主题"桶。
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if evidence is None and c.get("started_source") == "moved_view" and c.get("theme"):
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evidence = {"theme": c["theme"], "n_sources": c.get("n_sources"),
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"moved_ratio": None, "source": "moved_view"}
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r = {"rank": rank, "code": k, "name": names.get(k),
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"score": round(float(s), 2),
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"evidence": evidence,
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"heat": _val(heat, k), "upside": _val(upside, k)}
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if with_tier:
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r["tier"] = _tier_label(s)
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if c:
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r.update(verdict=c["verdict"], reasons=c["reasons"], missing=c["missing"],
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risk=c["risk"], card_rank=c["card_rank"],
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card={"pct0": c.get("pct0"), "net_z": c.get("net_z"),
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"heat_chg": c.get("heat_chg"), "accum": c.get("accum"),
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"night": c.get("night"), "gates": c.get("gates"),
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"confirm": c.get("confirm")})
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return r
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def _full_rows(ranked: pd.Series, with_tier: bool) -> list:
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return [_row(i, k, s, with_tier) for i, (k, s) in enumerate(ranked.items(), 1)]
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def _by_verdict(v: str) -> list:
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rows = [_row(0, k, score[k], k in main.index) for k, c in cards.items()
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if c.get("verdict") == v]
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rows.sort(key=lambda r: r["card_rank"])
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for i, r in enumerate(rows, 1):
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r["rank"] = i
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return rows
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changes = None
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prev_ds = _prev_date("t_factor_akg_gate", ds)
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if prev_ds:
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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")],
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axis=1).fillna(-1.0) # -1 = 当日不在面板
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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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up_df = both[both["cur"] > both["prev"]].sort_values("cur", ascending=False)
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down_df = both[both["cur"] < both["prev"]].sort_values("prev", ascending=False)
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# ---- 升降原因（07-31 加）：区分首次覆盖 / 估值转正 / 新进传导链等。
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# 依据前一日的 upside / 传导因子表；表空时现算兜底。原因是启发式归类
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# （取最主要的一条），精确审计以档位表与因子表为准。
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prev_up = _factor("t_factor_akg_upside", prev_ds)
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if prev_up.empty:
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try:
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import factors
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dfu = factors.build_upside(prev_ds, prev_ds)
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if dfu is not None and not dfu.empty:
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x = dfu.copy()
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x["k"] = x["stock_code"].map(common.to_prefix)
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prev_up = x.groupby("k")["factor_value"].max().astype(float)
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except Exception: # noqa: BLE001 —— 兜底失败则原因退化为通用文案
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pass
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prev_tr = _factor("t_factor_akg_transmission", prev_ds)
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# 赛道闸与风险闸的成员集合（07-31 修：赛道闸开启后，"不在赛道"曾被
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# 误标成"风险闸/档位调整"、"赛道锚生效"曾被误标成"新进传导链"）。
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# 两个集合都尊重各自开关：闸没开时对应原因自然不会出现。
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tset = None
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try:
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import factors
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tset = factors._track_set() # noqa: SLF001 —— 同仓自用
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except Exception: # noqa: BLE001 —— 拿不到就退化为通用文案
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pass
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def _why(k: str, pg: float, cg: float) -> str:
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if cg == 2.0: # 升入主榜
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pu = _val(prev_up, k)
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if pu is None:
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return "首次覆盖"
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return "估值转正" if pu < 0 else "重获资格"
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if cg == 1.0: # 升入观察档
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if k in ev: # 今天真在传导链上
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pt = _val(prev_tr, k)
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return "新进传导链" if pt is None or pt <= 0 else "档位调整"
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if tset is not None and k in tset:
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return "赛道锚生效" # 赛道成员身份给的锚（闸切换/成员变动）
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return "档位调整"
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if pg == 2.0: # 从主榜降出
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if k in risk:
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return "风险闸"
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cu = _val(upside, k)
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if cu is None:
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return "覆盖脱落"
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if cu < 0:
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return "估值转负"
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if tset is not None and k not in tset:
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return "赛道闸外" # 有覆盖也不贵，但不在十赛道内
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return "档位调整"
|
||
if pg == 1.0: # 从观察档降出
|
||
if k in risk:
|
||
return "风险闸"
|
||
if k not in ev and (tset is None or k not in tset):
|
||
return "离开传导链"
|
||
return "档位调整"
|
||
return "出入面板"
|
||
|
||
def _mv(d: pd.DataFrame):
|
||
return [{"code": k, "name": names.get(k),
|
||
"from": lab.get(r["prev"], "?"), "to": lab.get(r["cur"], "?"),
|
||
"reason": _why(k, r["prev"], r["cur"])}
|
||
for k, r in d.iterrows()]
|
||
|
||
changes = {"base_date": prev_ds,
|
||
"upgrades_total": int(len(up_df)),
|
||
"downgrades_total": int(len(down_df)),
|
||
"upgrades": _mv(up_df.head(15)),
|
||
"downgrades": _mv(down_df.head(15))}
|
||
|
||
card_counts = {v: sum(1 for c in cards.values() if c.get("verdict") == v)
|
||
for v in (card.VERDICT_CANDIDATE, card.VERDICT_WATCH, card.VERDICT_SHOW)}
|
||
return {
|
||
"date": ds,
|
||
"generated_at": generated_at,
|
||
"plan_version": version.git_short_rev(),
|
||
"counts": {"main": int(len(main)), "observe": int(len(obs)),
|
||
"gate_covered": int(len(gate))},
|
||
"market_snapshot_days": sorted(mkt_days),
|
||
"heat_date": hd,
|
||
"theme_cap": theme_cap,
|
||
"main": [_row(i, k, s, True)
|
||
for i, (k, s) in enumerate(_pick(main, top), 1)],
|
||
"observe": [_row(i, k, s, False)
|
||
for i, (k, s) in enumerate(_pick(obs, obs_top), 1)],
|
||
"changes": changes,
|
||
"gate_on": bool(config.ENABLE_TRACK_GATE),
|
||
"encoding": "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)",
|
||
# ---- 候选卡（2026-09-02）：分数与档位之外的另一份产物，不受 top / theme_cap 裁剪 ----
|
||
"card_params": {"start_pct": config.CARD_START_PCT,
|
||
"accum_max_age": config.CARD_ACCUM_MAX_AGE,
|
||
"neg_tol": config.UPSIDE_NEG_TOLERANCE,
|
||
"rules": "候选=环节被指向∧当日涨幅达标∧券商覆盖且非ST∧明确吸筹∧无硬风险；"
|
||
"关注=无硬风险且(只差覆盖 或 门槛全过无确认)；其余仅展示"},
|
||
"card_counts": card_counts,
|
||
"candidates": _by_verdict(card.VERDICT_CANDIDATE),
|
||
"watch": _by_verdict(card.VERDICT_WATCH),
|
||
"segments_pointed": segments_pointed,
|
||
"_full": {"main": _full_rows(main, True), "observe": _full_rows(obs, False)},
|
||
}
|
||
|
||
|
||
def _fmt_pct(v) -> str:
|
||
return "—" if v is None else f"{v:+.0%}"
|
||
|
||
|
||
def _fmt_num(v) -> str:
|
||
return "—" if v is None else f"{v:.2f}"
|
||
|
||
|
||
def _fmt_ev(e) -> str:
|
||
if not e:
|
||
return "—"
|
||
if e.get("moved_ratio") is None: # 已动成员视图补的证据行，没有已动比例
|
||
return f"{e['theme']}（{e.get('n_sources') or '—'} 源，本票已启动）"
|
||
return f"{e['theme']}（{e['n_sources']} 源，已动 {e['moved_ratio']:.0%}）"
|
||
|
||
|
||
def _fmt_pct0(v) -> str:
|
||
return "—" if v is None else f"{v:+.1f}%"
|
||
|
||
|
||
def _fmt_accum(ac: dict) -> str:
|
||
if not ac or not ac.get("state"):
|
||
return "无评分"
|
||
st = str(ac["state"]).split("·")[0]
|
||
age = ac.get("age")
|
||
return f"{st}（{age} 日前）" if isinstance(age, int) else st
|
||
|
||
|
||
def render_md(d: dict) -> str:
|
||
L = [f"# 每日选股计划 · {d['date']}", ""]
|
||
c = d["counts"]
|
||
L.append(f"主榜 {c['main']} 只 / 观察档 {c['observe']} 只 / "
|
||
f"全池档位覆盖 {c['gate_covered']} 只。")
|
||
stale = [x for x in d["market_snapshot_days"] if x != d["date"]]
|
||
if stale:
|
||
L.append(f"注：本日传导用的行情快照 = {'、'.join(stale)}"
|
||
f"（与计划日不同——历史降级日口径）。")
|
||
L.append("")
|
||
|
||
# ---- 候选单与关注环节（2026-09-02）：放在主榜之前，这是新的主产物 ----
|
||
cc = d.get("card_counts") or {}
|
||
cands = d.get("candidates") or []
|
||
L.append(f"## 候选单（环节被指向、当日已启动、券商覆盖且非 ST、明确吸筹、无硬风险；"
|
||
f"共 {cc.get('候选', len(cands))} 只，全量列出不受裁剪）")
|
||
L.append("")
|
||
if not cands:
|
||
L.append("（今日无候选——候选为空不是故障：环节没被指向、成员没启动或没有明确吸筹，都会为空。）")
|
||
else:
|
||
L.append("| # | 代码 | 名称 | 环节 | 源数 | 当日涨幅 | 吸筹 | 预期空间 | 理由 |")
|
||
L.append("|---|------|------|------|------|----------|------|----------|------|")
|
||
for r in cands:
|
||
c = r.get("card") or {}
|
||
ac = c.get("accum") or {}
|
||
ev_ = r.get("evidence") or {}
|
||
L.append(f"| {r['rank']} | {r['code']} | {r['name'] or '—'} | {ev_.get('theme') or '—'} "
|
||
f"| {ev_.get('n_sources') or '—'} | {_fmt_pct0(c.get('pct0'))} "
|
||
f"| {_fmt_accum(ac)} | {_fmt_pct(r.get('upside'))} "
|
||
f"| {'；'.join(r.get('reasons') or [])} |")
|
||
L.append("")
|
||
segs = d.get("segments_pointed") or []
|
||
L.append(f"## 关注环节（今日被传导指向的 {len(segs)} 个环节：定位对不对看这里，挑票看候选单）")
|
||
L.append("")
|
||
if segs:
|
||
L.append("| 环节 | 源数 | 链符 | 成员 | 已启动 | 领涨 | 候选 |")
|
||
L.append("|------|------|------|------|--------|------|------|")
|
||
for s in segs:
|
||
ld = s.get("leader") or {}
|
||
L.append(f"| {s['segment']} | {s.get('n_sources') or '—'} | {_fmt_num(s.get('chain_fit'))} "
|
||
f"| {s.get('members_total') if s.get('members_total') is not None else '—'} "
|
||
f"| {s.get('started_count', 0)} "
|
||
f"| {(ld.get('code') or '—') + (' ' + _fmt_pct0(ld.get('pct0')) if ld.get('code') else '')} "
|
||
f"| {s.get('candidates', 0)} |")
|
||
L.append("")
|
||
watch = d.get("watch") or []
|
||
L.append(f"## 关注单（无硬风险，只差券商覆盖或缺明确吸筹；共 {cc.get('关注', len(watch))} 只，列前 20）")
|
||
L.append("")
|
||
if watch:
|
||
L.append("| # | 代码 | 名称 | 环节 | 当日涨幅 | 吸筹 | 缺什么 |")
|
||
L.append("|---|------|------|------|----------|------|--------|")
|
||
for r in watch[:20]:
|
||
c = r.get("card") or {}
|
||
ev_ = r.get("evidence") or {}
|
||
L.append(f"| {r['rank']} | {r['code']} | {r['name'] or '—'} | {ev_.get('theme') or '—'} "
|
||
f"| {_fmt_pct0(c.get('pct0'))} | {_fmt_accum(c.get('accum') or {})} "
|
||
f"| {'；'.join(r.get('missing') or [])} |")
|
||
L.append("")
|
||
reg = d.get("regime")
|
||
if reg:
|
||
L.append(f"环境标签：{reg.get('status')}，弱势指数 {reg.get('weak_count')}/8"
|
||
f"{'，弱势日' if reg.get('weak_day') else ''}（只展示与复盘分组，不作交易前置）。")
|
||
L.append("")
|
||
|
||
cap_txt = f"，每主题限额 {d['theme_cap']}" if d["theme_cap"] else ""
|
||
gate_txt = "、在十五五赛道内" if d.get("gate_on") else ""
|
||
L.append(f"## 主榜 Top {len(d['main'])}"
|
||
f"（有券商预期、目标价不低于现价{gate_txt}{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 r in d["main"]:
|
||
L.append(f"| {r['rank']} | {r['code']} | {r['name'] or '—'} | {r['score']:.1f} "
|
||
f"| {r['tier']} | {_fmt_ev(r['evidence'])} "
|
||
f"| {_fmt_num(r['heat'])} | {_fmt_pct(r['upside'])} |")
|
||
L.append("")
|
||
|
||
L.append(f"## 观察档 Top {len(d['observe'])}"
|
||
f"（无券商预期、但{'在赛道或传导链上' if d.get('gate_on') else '在传导链上'}"
|
||
f"——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 r in d["observe"]:
|
||
L.append(f"| {r['rank']} | {r['code']} | {r['name'] or '—'} | {r['score']:.1f} "
|
||
f"| {_fmt_ev(r['evidence'])} | {_fmt_num(r['heat'])} |")
|
||
L.append("")
|
||
|
||
L.append("## 今日升降档")
|
||
L.append("")
|
||
ch = d["changes"]
|
||
if not ch:
|
||
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:
|
||
L.append(f"对比 {ch['base_date']}：升档 {ch['upgrades_total']} 只，"
|
||
f"降档 {ch['downgrades_total']} 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。")
|
||
if ch["upgrades"]:
|
||
L.append("")
|
||
L.append("**升档**：")
|
||
L += [f"- {m['code']} {m['name'] or ''}：{m['from']} → {m['to']}"
|
||
f"（{m.get('reason', '—')}）"
|
||
for m in ch["upgrades"]]
|
||
if ch["upgrades_total"] > len(ch["upgrades"]):
|
||
L.append(f"- ……共 {ch['upgrades_total']} 只，其余见档位表")
|
||
if ch["downgrades"]:
|
||
L.append("")
|
||
L.append("**降档**：")
|
||
L += [f"- {m['code']} {m['name'] or ''}：{m['from']} → {m['to']}"
|
||
f"（{m.get('reason', '—')}）"
|
||
for m in ch["downgrades"]]
|
||
if ch["downgrades_total"] > len(ch["downgrades"]):
|
||
L.append(f"- ……共 {ch['downgrades_total']} 只，其余见档位表")
|
||
L.append("")
|
||
L.append("---")
|
||
L.append(f"口径：{d['encoding']}。")
|
||
return "\n".join(L)
|
||
|
||
|
||
def generate(date: str | None = None, top: int = 20, obs_top: int = 10,
|
||
theme_cap: int = 5) -> str:
|
||
try:
|
||
data = collect(date, top, obs_top, theme_cap)
|
||
except RuntimeError as e:
|
||
raise SystemExit(str(e))
|
||
text = render_md(data)
|
||
os.makedirs(config.PLAN_SNAPSHOT_DIR, exist_ok=True)
|
||
out = os.path.join(config.PLAN_SNAPSHOT_DIR, f"plan_{data['date']}.md")
|
||
with open(out, "w", encoding="utf-8") as f:
|
||
f.write(text + "\n")
|
||
# ---- 当日 JSON 快照（2026-09-02 方案第 2.2 节第一项）：主榜与观察档全部行、全部证据线，
|
||
# 不裁剪、不设主题限额；带生成时刻与代码版本。它是复盘与对账的唯一底本；
|
||
# 08:45 的 regime-append 步骤会往里追加 regime 段，/plan 的 regime 段只读这份。----
|
||
full = data.pop("_full", None) or {}
|
||
snap = {**data, "main_shown": data["main"], "observe_shown": data["observe"],
|
||
"main": full.get("main", []), "observe": full.get("observe", []),
|
||
"shown_params": {"top": top, "obs_top": obs_top, "theme_cap": theme_cap}}
|
||
jpath = os.path.join(config.PLAN_SNAPSHOT_DIR, f"plan_{data['date']}.json")
|
||
tmp = jpath + ".tmp"
|
||
with open(tmp, "w", encoding="utf-8") as f:
|
||
json.dump(snap, f, ensure_ascii=False, indent=1, default=str)
|
||
os.replace(tmp, jpath)
|
||
print(text)
|
||
print(f"\n已写入 {out} 与快照 {jpath}"
|
||
f"（主榜 {len(snap['main'])} 行、观察档 {len(snap['observe'])} 行、"
|
||
f"候选 {len(data.get('candidates') or [])} 只，版本 {data.get('plan_version')}）")
|
||
return out
|