行业催化：按票所在环节读基座 segment_catalysts，卡上整句与复盘分组，只展示不进判决（台账 051）
Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
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card.py
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card.py
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@ -464,6 +464,26 @@ def news_view(nv) -> str:
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return f"相关快讯（近 3 天 {nv.get('count')} 条，财联社电报点名，不判利好利空）：" + "；".join(parts)
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def industry_catalyst_view(lst) -> str:
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"""行业催化的整句（2026-09-08 台账 051）：票所在环节的行业级催化，量级大→小最多三条。只展示不进判决。"""
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if not isinstance(lst, list) or not lst:
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return "行业催化：暂无（环节评析未标出行业级催化，或材料未到）"
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parts = []
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for x in lst:
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head = f"{(x.get('event_date') or '日期不明')[5:] if x.get('event_date') else '日期不明'} [{x.get('magnitude')}·{x.get('horizon')}·{x.get('direction')}·把握{x.get('confidence')}] {x.get('segment')}：{x.get('title')}"
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if x.get("mechanism"):
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head += f"（{x['mechanism'][:60]}）"
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parts.append(head)
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return f"行业催化（环节级，数据基座按材料标出，{len(lst)} 条，不进判决）：" + "；".join(parts)
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def industry_catalyst_short(lst) -> str:
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if not isinstance(lst, list) or not lst:
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return "—"
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x = lst[0]
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return f"{x.get('magnitude')}·{x.get('horizon')} {str(x.get('title') or '')[:20]}"
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def news_short(nv) -> str:
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if not isinstance(nv, dict) or not nv.get("items"):
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return "—"
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10
plan.py
10
plan.py
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@ -305,6 +305,12 @@ def _assemble_cards(ds: str, codes: list, ev: dict, upside: pd.Series,
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# 相关快讯（2026-09-08 台账 050）：财联社电报近 3 天点名这只票的条目，只当上下文送人看与送研判，
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# 不判正负、不进判决、不进定价状态。表读不到整体缺席不断产。
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news = sources.news_flashes(codes, ds)
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# 行业催化（2026-09-08 台账 051）：数据基座按环节评析材料标出的行业级催化，挂在环节上；
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# 按票所在的被指向环节取，只展示加复盘分组，不进判决、不进逻辑状态。表没建就整体缺席。
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_seg_of = inp["seg_of"]
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ind_cat = sources.catalysts_for_codes(
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{k: _seg_of.get(k, []) for k in codes},
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sources.segment_catalysts(sorted({t for k in codes for t in _seg_of.get(k, [])}), 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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@ -342,6 +348,7 @@ def _assemble_cards(ds: str, codes: list, ev: dict, upside: pd.Series,
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"events": events.get(k), "events_text": card.events_view(events.get(k)),
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"pricing_state": card.pricing_state(ev_fields.get(k)),
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"news": news.get(k), "news_text": card.news_view(news.get(k)),
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"industry_catalyst": ind_cat.get(k), "industry_catalyst_text": card.industry_catalyst_view(ind_cat.get(k)),
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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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"logic_claims": evd["logic"],
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@ -476,6 +483,9 @@ def collect(date: str | None = None, top: int = 20, obs_top: int = 10,
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pricing_text=card.pricing_view(c.get("pricing_state")),
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# 相关快讯（2026-09-08 台账 050）：原值给程序，整句给人；只是上下文，不进判决。
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news=c.get("news"), news_text=c.get("news_text"),
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# 行业催化（2026-09-08 台账 051）：环节级，原值给程序，整句给人；只展示不进判决。
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industry_catalyst=c.get("industry_catalyst"),
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industry_catalyst_text=c.get("industry_catalyst_text"),
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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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@ -449,6 +449,15 @@ def run(since: str, until: str | None, horizons: tuple, start: str, out_dir: str
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if s:
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rows.append({"date": day, "h": h, "list": lst_name, "group": f"催化事件={label}",
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"regime_post": regime_post, "regime_pre": regime_pre, **s})
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# 行业催化有无（2026-09-08 台账 051）：环节级催化，只作观察分组；材料未到时全部落"无"。
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ind_of = {r["code"]: bool(r.get("industry_catalyst")) for r in main_rows + obs_rows}
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for lst_name, lst_codes in (("候选单", cands), ("档位表", list(ind_of))):
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for flag, label in ((True, "有"), (False, "无")):
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codes = [c for c in lst_codes if ind_of.get(c, False) is flag]
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s = summarize(ret, codes, base_all, base_main, caps, cap_base)
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if s:
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rows.append({"date": day, "h": h, "list": lst_name, "group": f"行业催化={label}",
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"regime_post": regime_post, "regime_pre": regime_pre, **s})
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notes.append(f"{day}: 主榜 {len(main_codes)} 观察 {len(obs_rows)} 候选 {len(cands)} "
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f"关注 {len(watch)} 生产 {len(prod)}（{prod_note}）；"
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f"账本 机器通过 {len(ledger['机器通过名单'])} 人批 {len(ledger['人批名单'])} "
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65
sources.py
65
sources.py
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@ -1103,3 +1103,68 @@ def news_flashes(codes, ds: str, *, days: int = NEWS_WINDOW_DAYS, read_mysql=Non
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slot["items"].append(item)
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return out
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# ---------------------------------------------------------------------------
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# 行业催化（2026-09-08，台账 051）：数据基座按环节评析的材料另标出来的"行业级催化事件"，
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# 挂在环节上不挂个股（表 segment_catalysts，基座 PG）。候选卡按票所在的被指向环节取，
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# 只展示、只分组复盘，不进判决、不进逻辑状态。表没建或没材料就整体缺席，不断产。
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# ---------------------------------------------------------------------------
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CATALYST_WINDOW_DAYS = 180
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CATALYST_MAX_PER_SEGMENT = 3
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_MAG_ORDER = {"大": 0, "中": 1, "小": 2}
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def segment_catalysts(segments, ds: str, *, days: int = CATALYST_WINDOW_DAYS, read_pg=None,
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limit_per_segment: int = CATALYST_MAX_PER_SEGMENT) -> dict:
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"""{环节名: [{segment, event_date, title, direction, magnitude, horizon, confidence, mechanism}, ...]}，
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每个环节按量级大→小、事件日新→旧排，最多 limit_per_segment 条。"""
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rp = read_pg or db.read_pg
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want = sorted({str(x) for x in (segments or []) if x})
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if not want:
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return {}
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try:
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since = (dt.date.fromisoformat(ds) - dt.timedelta(days=int(days))).isoformat()
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except ValueError:
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return {}
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try:
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df = rp("SELECT segment_name, event_date, first_seen, title, direction, magnitude, horizon, "
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"confidence, mechanism FROM segment_catalysts "
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"WHERE segment_name = ANY(%s) AND coalesce(event_date, first_seen) >= %s",
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(want, since))
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except Exception as e: # noqa: BLE001 —— 表没建、连接失败：整体缺席
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logging.getLogger("bridge.catalyst").warning("segment_catalysts 读不到: %r", e)
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return {}
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rows = []
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for r in df.to_dict("records"):
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ev = r.get("event_date") or r.get("first_seen")
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rows.append({"segment": str(r.get("segment_name")), "event_date": str(ev)[:10] if ev else None,
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"title": str(r.get("title") or "")[:80], "direction": r.get("direction") or "利好",
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"magnitude": r.get("magnitude") or "小", "horizon": r.get("horizon") or "短期",
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"confidence": r.get("confidence") or "低",
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"mechanism": (str(r.get("mechanism") or "")[:120] or None)})
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rows.sort(key=lambda x: (_MAG_ORDER.get(x["magnitude"], 9), x["event_date"] or ""), reverse=False)
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rows.sort(key=lambda x: (_MAG_ORDER.get(x["magnitude"], 9), -(int((x["event_date"] or "0000-00-00").replace("-", "") or 0))))
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out: dict = {}
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for x in rows:
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slot = out.setdefault(x["segment"], [])
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if len(slot) < limit_per_segment:
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slot.append(x)
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return out
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def catalysts_for_codes(seg_of: dict, cat_by_seg: dict, *, limit: int = 3) -> dict:
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"""按票汇总所在环节的行业催化：同标题去重，量级大→小，最多 limit 条。没有的票不在字典里。"""
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out: dict = {}
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for k, segs in (seg_of or {}).items():
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seen: set = set()
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lst = []
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for seg in segs or []:
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for x in cat_by_seg.get(seg) or []:
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if x["title"] in seen:
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continue
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seen.add(x["title"])
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lst.append(x)
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if lst:
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lst.sort(key=lambda x: (_MAG_ORDER.get(x["magnitude"], 9), -(int((x["event_date"] or "0000-00-00").replace("-", "") or 0))))
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out[k] = lst[:limit]
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return out
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@ -230,11 +230,39 @@ def test_news():
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t("短句是最新一条", card.news_short(out["SH688172"]).startswith("09-08 12:39 燕东微"))
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def test_industry_catalyst():
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"""行业催化（台账 051）：按环节取、按票汇总、量级排序、缺席不断产。"""
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rows = [
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{"segment_name": "端侧AI芯片", "event_date": "2026-09-05", "first_seen": "2026-09-06", "title": "2B 模型媲美百亿模型",
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"direction": "利好", "magnitude": "大", "horizon": "长期", "confidence": "中", "mechanism": "端侧推理可行性大增"},
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{"segment_name": "端侧AI芯片", "event_date": None, "first_seen": "2026-09-01", "title": "某厂扩产",
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"direction": "利好", "magnitude": "小", "horizon": "短期", "confidence": "低", "mechanism": None},
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{"segment_name": "存储", "event_date": "2026-09-07", "first_seen": "2026-09-07", "title": "2B 模型媲美百亿模型",
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"direction": "利好", "magnitude": "中", "horizon": "中期", "confidence": "高", "mechanism": "端侧内存需求上移"},
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]
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calls = []
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def rp(sql, params=None):
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calls.append(params); return _DF(rows)
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by_seg = sources.segment_catalysts(["端侧AI芯片", "存储", "无材料环节"], "2026-09-08", read_pg=rp)
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t("按环节名与 180 天窗口查", calls[0][0] == ["存储", "无材料环节", "端侧AI芯片"] and calls[0][1] == "2026-03-12")
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t("量级大的排前、没事件日的用首见日", by_seg["端侧AI芯片"][0]["magnitude"] == "大" and by_seg["端侧AI芯片"][1]["event_date"] == "2026-09-01")
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t("没材料的环节不在字典里", "无材料环节" not in by_seg)
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per = sources.catalysts_for_codes({"SH600000": ["端侧AI芯片", "存储"], "SZ000001": ["无材料环节"], "SZ000002": []}, by_seg)
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t("按票汇总同标题去重、量级大→小", [x["title"] for x in per["SH600000"]] == ["2B 模型媲美百亿模型", "某厂扩产"])
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t("所在环节没有催化的票不在字典里", "SZ000001" not in per and "SZ000002" not in per)
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v = card.industry_catalyst_view(per["SH600000"])
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t("整句带量级持续期方向与环节名", v.startswith("行业催化（环节级") and "[大·长期·利好·把握中] 端侧AI芯片" in v)
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t("缺席整句与短句", "暂无" in card.industry_catalyst_view(None) and card.industry_catalyst_short([]) == "—")
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def boom(*a, **k): raise RuntimeError("no table")
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t("表读不到整体缺席不断产", sources.segment_catalysts(["端侧AI芯片"], "2026-09-08", read_pg=boom) == {})
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def main():
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test_events()
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test_event_day_fields()
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test_pricing_state()
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test_news()
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test_industry_catalyst()
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print("ALL OK — 四类事件 / 事件日字段 / 定价状态四情形 / 卡上文字 / 相关快讯 全部通过")
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