649 lines
33 KiB
Python
649 lines
33 KiB
Python
"""每日选股计划（R4）：数据装配 / Markdown 渲染 / 产出，三段分离。
|
||
|
||
collect() -> dict 结构化计划——api.py 直接当 JSON 返回
|
||
render_md() -> str 从 dict 渲染 Markdown
|
||
generate() CLI 与 cron 的入口：collect + render + 落盘 + 打印
|
||
|
||
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
|
||
基座 industry_pools —— 股票名称
|
||
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 /
|
||
新进传导链即升档）。
|
||
|
||
2026-09-03 起（《主观量化系统方案_2026-09-03》第 3.3 节）：候选卡每票多带数据基座的因果论断
|
||
证据线（只展示不进判决，sources.logic_claims）；generate 出计划时把市场四项（两市成交额、广度、
|
||
融资、恐贪，sources.market_context）写进快照的 market 段，与 08:45 追加的 regime 段并列，
|
||
接口 /plan 只从快照读这两段。
|
||
"""
|
||
from __future__ import annotations # 注解不在定义时求值：开发机的 Python 3.9 也能导入本模块跑离线单测
|
||
|
||
import datetime as dt
|
||
import json
|
||
import os
|
||
|
||
import pandas as pd
|
||
|
||
import card
|
||
import common
|
||
import config
|
||
import db
|
||
import sources
|
||
import version
|
||
|
||
|
||
# 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, "
|
||
"members_total, moved 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")
|
||
# 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）
|
||
ev = {r.k: (str(r.target), int(r.n_sources), float(r.moved_ratio),
|
||
None if pd.isna(r.members_total) else int(r.members_total),
|
||
None if pd.isna(r.moved) else int(r.moved))
|
||
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 _val(series: pd.Series, k: str):
|
||
v = series.get(k)
|
||
return None if v is None or pd.isna(v) else float(v)
|
||
|
||
|
||
def _assemble_cards(ds: str, codes: list, ev: dict, upside: pd.Series,
|
||
mkt_days: set, risk: set | None = None) -> tuple[dict, list]:
|
||
"""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，
|
||
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。
|
||
|
||
返回 (cards, segments_pointed)：cards 按前缀码索引，含判决、理由、缺失、风险、卡内序
|
||
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、
|
||
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"
|
||
在计划文本层的落地。"""
|
||
import factors
|
||
|
||
moved = sources.moved_members(ds)
|
||
daily = sources.stock_daily(ds)
|
||
night = sources.night_conclusions(codes, ds)
|
||
# 因果论断（2026-09-03）：数据基座抽取的论断挂在卡上作证据线，只展示不进判决；视图未建时为空。
|
||
logic = sources.logic_claims(codes, ds)
|
||
if risk is None: # collect 会传入读过一次的名单；单独调用时自己读
|
||
try:
|
||
risk = factors._risk_set() or set() # noqa: SLF001 —— 同仓自用
|
||
except Exception: # noqa: BLE001 —— 风险名单拿不到时不当 ST 处理，与 gate 的宽容一致
|
||
risk = set()
|
||
stale = bool(mkt_days) and any(x != ds for x in mkt_days)
|
||
|
||
cards: dict = {}
|
||
for k in codes:
|
||
mv, e, d, n = moved.get(k), ev.get(k), daily.get(k, {}), night.get(k, {})
|
||
theme = (mv or {}).get("theme") or (e[0] if e else None)
|
||
n_sources = (mv or {}).get("n_sources") or (e[1] if e else None)
|
||
up = _val(upside, k)
|
||
evd = {"pointed": bool(mv or e), "theme": theme, "n_sources": n_sources,
|
||
"chain_fit": (mv or {}).get("chain_fit"),
|
||
"pct0": d.get("pct0"), "covered": up is not None, "upside": up,
|
||
"risk_name": k in risk,
|
||
"accum_state": n.get("accum_state"), "accum_score": n.get("accum_score"),
|
||
"accum_age": n.get("accum_age"), "y_signal": n.get("signal"),
|
||
"stale_snapshot": stale, "logic": logic.get(k) or []}
|
||
j = card.judge(evd, start_pct=config.CARD_START_PCT,
|
||
accum_max_age=config.CARD_ACCUM_MAX_AGE,
|
||
neg_tol=config.UPSIDE_NEG_TOLERANCE,
|
||
logic_stale_days=config.LOGIC_STALE_DAYS)
|
||
cards[k] = {
|
||
**j,
|
||
"theme": theme, "n_sources": n_sources, "chain_fit": evd["chain_fit"],
|
||
"started_source": "moved_view" if mv else None,
|
||
"logic_claims": evd["logic"],
|
||
"pct0": d.get("pct0"), "net_z": d.get("net_z"), "heat_chg": d.get("heat_chg"),
|
||
"accum": ({"state": n.get("accum_state"), "score": n.get("accum_score"),
|
||
"age": n.get("accum_age"), "pos_tag": n.get("accum_pos_tag"),
|
||
"date": n.get("conclusion_date")} if n else None),
|
||
"night": ({"signal": n.get("signal"), "support": n.get("support"),
|
||
"pressure": n.get("pressure"), "date": n.get("conclusion_date")}
|
||
if n else None),
|
||
}
|
||
for i, (k, c) in enumerate(sorted(cards.items(), key=lambda kv: card.sort_key(kv[1])), 1):
|
||
c["card_rank"] = i
|
||
|
||
segs: dict = {}
|
||
for k, mv in moved.items():
|
||
s = segs.setdefault(mv["theme"], {
|
||
"segment": mv["theme"], "n_sources": mv["n_sources"], "chain_fit": mv["chain_fit"],
|
||
"members_total": mv.get("members_total"), "moved": mv.get("moved"),
|
||
"started": [], "candidates": 0, "leader": None})
|
||
s["started"].append(k)
|
||
if cards.get(k, {}).get("verdict") == card.VERDICT_CANDIDATE:
|
||
s["candidates"] += 1
|
||
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
|
||
s = segs.setdefault(e[0], {"segment": e[0], "n_sources": e[1], "chain_fit": None,
|
||
"members_total": None, "moved": None,
|
||
"started": [], "candidates": 0, "leader": None})
|
||
if s["members_total"] is None and len(e) > 4:
|
||
s["members_total"], s["moved"] = e[3], e[4]
|
||
for s in segs.values():
|
||
best, best_pct = None, None
|
||
for k in s["started"]:
|
||
# 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
|
||
# （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
|
||
p = (daily.get(k) or {}).get("pct0")
|
||
if p is not None and (best_pct is None or p > best_pct):
|
||
best, best_pct = k, p
|
||
s["leader"] = {"code": best, "pct0": best_pct} if best else None
|
||
s["started_count"] = len(s["started"])
|
||
s["started_in_tiers"] = sum(1 for k in s["started"] if k in cards)
|
||
ordered = sorted(segs.values(), key=lambda s: (-s["candidates"], -(s["n_sources"] or 0),
|
||
-(s["chain_fit"] or 0), s["segment"]))
|
||
return cards, ordered
|
||
|
||
|
||
def collect(date: str | None = None, top: int = 20, obs_top: int = 10,
|
||
theme_cap: int = 5) -> dict:
|
||
"""装配一天的计划为结构化字典。数据缺失抛 RuntimeError（api 侧转 404）。
|
||
|
||
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
|
||
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、
|
||
card_counts、candidates（候选单全量，不受裁剪）、watch、segments_pointed。
|
||
`_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。"""
|
||
ds = date or _latest_date("t_factor_akg_score")
|
||
if not ds:
|
||
raise RuntimeError("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 RuntimeError(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:
|
||
# 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）
|
||
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)
|
||
generated_at = dt.datetime.now().isoformat(timespec="seconds")
|
||
try: # 风险名单只读一次：候选卡与升降档原因共用
|
||
import factors
|
||
risk = factors._risk_set() or set() # noqa: SLF001 —— 同仓自用
|
||
except Exception: # noqa: BLE001
|
||
risk = set()
|
||
cards, segments_pointed = _assemble_cards(
|
||
ds, list(main.index) + list(obs.index), ev, upside, mkt_days, risk=risk)
|
||
|
||
def _pick(ranked: pd.Series, n: int):
|
||
"""分数从高到低取 n 条；每个传导主题最多 theme_cap 条（0=不设限）——
|
||
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。"""
|
||
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) >= n:
|
||
break
|
||
return out
|
||
|
||
def _row(rank: int, k: str, s: float, with_tier: bool) -> dict:
|
||
e = ev.get(k)
|
||
c = cards.get(k) or {}
|
||
evidence = ({"theme": e[0], "n_sources": e[1], "moved_ratio": round(e[2], 4)}
|
||
if e else None)
|
||
# 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
|
||
# 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。
|
||
if evidence is None and c.get("started_source") == "moved_view" and c.get("theme"):
|
||
evidence = {"theme": c["theme"], "n_sources": c.get("n_sources"),
|
||
"moved_ratio": None, "source": "moved_view"}
|
||
r = {"rank": rank, "code": k, "name": names.get(k),
|
||
"score": round(float(s), 2),
|
||
"evidence": evidence,
|
||
"heat": _val(heat, k), "upside": _val(upside, k)}
|
||
if with_tier:
|
||
r["tier"] = _tier_label(s)
|
||
if c:
|
||
# 2026-09-03 新增只联入：basis（判决依据一句话）、logic（因果论断带出处的文字行）、
|
||
# card.chain_fit（链符，入池上下文用）、card.logic_claims（论断原值）、card.failed_gates。
|
||
r.update(verdict=c["verdict"], reasons=c["reasons"], missing=c["missing"],
|
||
risk=c["risk"], card_rank=c["card_rank"],
|
||
basis=c.get("basis"), logic=c.get("logic") or [],
|
||
card={"pct0": c.get("pct0"), "net_z": c.get("net_z"),
|
||
"heat_chg": c.get("heat_chg"), "accum": c.get("accum"),
|
||
"night": c.get("night"), "gates": c.get("gates"),
|
||
"confirm": c.get("confirm"), "failed_gates": c.get("failed_gates"),
|
||
"chain_fit": c.get("chain_fit"),
|
||
"logic_claims": c.get("logic_claims") or []})
|
||
return r
|
||
|
||
def _full_rows(ranked: pd.Series, with_tier: bool) -> list:
|
||
return [_row(i, k, s, with_tier) for i, (k, s) in enumerate(ranked.items(), 1)]
|
||
|
||
def _by_verdict(v: str) -> list:
|
||
rows = [_row(0, k, score[k], k in main.index) for k, c in cards.items()
|
||
if c.get("verdict") == v]
|
||
rows.sort(key=lambda r: r["card_rank"])
|
||
for i, r in enumerate(rows, 1):
|
||
r["rank"] = i
|
||
return rows
|
||
|
||
changes = None
|
||
prev_ds = _prev_date("t_factor_akg_gate", ds)
|
||
if prev_ds:
|
||
prev = _factor("t_factor_akg_gate", prev_ds)
|
||
both = pd.concat([prev.rename("prev"), gate.rename("cur")],
|
||
axis=1).fillna(-1.0) # -1 = 当日不在面板
|
||
lab = {-1.0: "池外", 0.0: "不采纳", 1.0: "观察档", 2.0: "主榜"}
|
||
up_df = both[both["cur"] > both["prev"]].sort_values("cur", ascending=False)
|
||
down_df = both[both["cur"] < both["prev"]].sort_values("prev", ascending=False)
|
||
|
||
# ---- 升降原因（07-31 加）：区分首次覆盖 / 估值转正 / 新进传导链等。
|
||
# 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
|
||
# （取最主要的一条），精确审计以档位表与因子表为准。
|
||
prev_up = _factor("t_factor_akg_upside", prev_ds)
|
||
if prev_up.empty:
|
||
try:
|
||
import factors
|
||
dfu = factors.build_upside(prev_ds, prev_ds)
|
||
if dfu is not None and not dfu.empty:
|
||
x = dfu.copy()
|
||
x["k"] = x["stock_code"].map(common.to_prefix)
|
||
prev_up = x.groupby("k")["factor_value"].max().astype(float)
|
||
except Exception: # noqa: BLE001 —— 兜底失败则原因退化为通用文案
|
||
pass
|
||
prev_tr = _factor("t_factor_akg_transmission", prev_ds)
|
||
# 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
|
||
# 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"）。
|
||
# 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。
|
||
tset = None
|
||
try:
|
||
import factors
|
||
tset = factors._track_set() # noqa: SLF001 —— 同仓自用
|
||
except Exception: # noqa: BLE001 —— 拿不到就退化为通用文案
|
||
pass
|
||
|
||
def _why(k: str, pg: float, cg: float) -> str:
|
||
if cg == 2.0: # 升入主榜
|
||
pu = _val(prev_up, k)
|
||
if pu is None:
|
||
return "首次覆盖"
|
||
return "估值转正" if pu < 0 else "重获资格"
|
||
if cg == 1.0: # 升入观察档
|
||
if k in ev: # 今天真在传导链上
|
||
pt = _val(prev_tr, k)
|
||
return "新进传导链" if pt is None or pt <= 0 else "档位调整"
|
||
if tset is not None and k in tset:
|
||
return "赛道锚生效" # 赛道成员身份给的锚（闸切换/成员变动）
|
||
return "档位调整"
|
||
if pg == 2.0: # 从主榜降出
|
||
if k in risk:
|
||
return "风险闸"
|
||
cu = _val(upside, k)
|
||
if cu is None:
|
||
return "覆盖脱落"
|
||
if cu < 0:
|
||
return "估值转负"
|
||
if tset is not None and k not in tset:
|
||
return "赛道闸外" # 有覆盖也不贵，但不在十赛道内
|
||
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,
|
||
"logic_per_stock": config.LOGIC_CLAIMS_PER_STOCK,
|
||
"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=三门槛全过∧无硬风险∧确认线缺失或陈旧（系统无法判断，交人裁决）；"
|
||
"其余仅展示（只差覆盖、潜在吸筹等非明确状态都不升格，台账 013）；"
|
||
"因果论断只展示不进判决"},
|
||
"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 _fmt_logic(lines, width: int = 60) -> str:
|
||
"""候选单与关注单表格里的因果论断列：第一条截到 width 字，多于一条时带条数；表格里不能有竖线。"""
|
||
lines = [str(x) for x in (lines or []) if x]
|
||
if not lines:
|
||
return "—"
|
||
first = lines[0].replace("|", "／")
|
||
if len(first) > width:
|
||
first = first[:width] + "…"
|
||
return f"{first}（共 {len(lines)} 条）" if len(lines) > 1 else first
|
||
|
||
|
||
def _fmt_yi(v) -> str:
|
||
"""已经是"亿元"的数直接显示。两市成交额由取数层换算好（原表单位是千元），不在这里换算。"""
|
||
return "—" if v is None else f"{v:,.0f} 亿"
|
||
|
||
|
||
def _fmt_amount(v) -> str:
|
||
"""以元为单位的金额换算成亿显示。融资余额用它（那张表的单位是元）。"""
|
||
if v is None:
|
||
return "—"
|
||
return f"{v / 1e8:,.0f} 亿" if abs(v) >= 1e8 else f"{v:,.0f}"
|
||
|
||
|
||
def _fmt_market(m: dict) -> str:
|
||
"""环境段市场四项的一行文字；缺的项写"—"，读数原因在 JSON 的 market.errors 里。"""
|
||
t, b, mg, fg = m.get("turnover") or {}, m.get("breadth") or {}, m.get("margin") or {}, m.get("fear_greed") or {}
|
||
parts = []
|
||
if t:
|
||
ratio = t.get("ratio_vs_prev5")
|
||
parts.append(f"两市成交额 {_fmt_yi(t.get('amount_yi'))}"
|
||
+ (f"（前五日均值的 {ratio:.2f} 倍）" if ratio else "")
|
||
+ (f"，数据日 {t['data_date']}" if t.get("data_date") and t.get("data_date") != m.get("date") else ""))
|
||
else:
|
||
parts.append("两市成交额 —")
|
||
if b:
|
||
med = b.get("pct_median")
|
||
parts.append(f"广度 上涨 {b.get('up')} / 下跌 {b.get('down')} 家，涨停近似 {b.get('limit_up_approx')} 家，"
|
||
f"涨幅中位数 {med:+.2f}%" if med is not None else
|
||
f"广度 上涨 {b.get('up')} / 下跌 {b.get('down')} 家")
|
||
else:
|
||
parts.append("广度 —")
|
||
if mg:
|
||
bal, chg = mg.get("financing_balance"), mg.get("change_percent_5d")
|
||
parts.append(f"融资余额 {_fmt_amount(bal)}"
|
||
+ (f"（五日变化 {chg:+.2f}%）" if chg is not None else "")
|
||
+ (f"，{mg['date']}" if mg.get("date") else ""))
|
||
else:
|
||
parts.append("融资余额 —")
|
||
if fg and fg.get("index_value") is not None:
|
||
parts.append(f"恐贪指数 {fg['index_value']:.0f}" + (f"（{fg['date']}）" if fg.get("date") else ""))
|
||
else:
|
||
parts.append("恐贪指数 —")
|
||
return "市场环境：" + "；".join(parts) + "（只展示与复盘分组，不作交易前置）。"
|
||
|
||
|
||
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 | 因果论断（出处） |")
|
||
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 [])} | {_fmt_logic(r.get('logic'))} |")
|
||
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（三门槛全过、无硬风险，但吸筹确认线缺失或陈旧——系统无法判断，交人裁决；"
|
||
f"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 | 因果论断（出处） |")
|
||
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 [])} | {_fmt_logic(r.get('logic'))} |")
|
||
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("")
|
||
mk = d.get("market")
|
||
if mk:
|
||
L.append(_fmt_market(mk))
|
||
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))
|
||
# 环境段的市场四项（2026-09-03）：出计划时读一次落进快照，接口 /plan 只从快照读，盘中不再取数；
|
||
# 每项读失败为空并把原因记在 market.errors，不阻断。区制段仍由 08:45 的追加步骤写入。
|
||
data["market"] = sources.market_context(data["date"])
|
||
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
|