feat(data): A档全量落地——热度三件+股东户数收尾(用户批「按此方式实施,nas验证后推vps」);①xueqiu_hot雪球5633行+sina_sector新浪行业49行对账源进ak-events 19:30(同款快照语义)②hot_rank东财人气榜fetcher/注册先行,wrapper挂载留待恢复窗测量拍板(墙未定论不带不确定单元上线防缺日告警噪音)③gdhs股东户数进ak-weekly周六窗=per-period新形状:build_gdhs_units只枚举距今>90天季度末(披露守卫,防未披露期拉空走真空+done语义被marker锁死永不重拉的09-02设计坑),单次全市场5342行,marker去重无新期零网络;vintage缺日名单+2快照族(hot_rank留位);+6测试(注册/None/df契约/gdhs守卫两形态/缺日名单)215全绿 [vps]

scripts/data_platform/akshare_static_download.py

@@ -143,7 +143,7 @@ PER_STOCK_TYPES = (
     "financial_abstract", # stock_financial_abstract(symbol="600519")
     # top_holders 单独 (per-stock × per-period)
 )
-# 模式 B: per-date 类型 (11 类, margin_szse 跳过)
+# 模式 B: per-date 类型 (14 类, margin_szse 跳过)
 PER_DATE_TYPES = (
     "dragon_tiger",  # stock_lhb_detail_em(start_date, end_date)
     "block_trade",   # stock_dzjy_mrmx(symbol="A股", start_date, end_date)
@@ -164,6 +164,12 @@ PER_DATE_TYPES = (
     "fund_flow_concept",   # 新浪 stock_fund_flow_concept('即时') 概念资金流 ~387 行
     "ths_industry",        # 同花顺 stock_board_industry_summary_ths() 行业一览 90 行×12 列
     "ths_concept",         # 同花顺 stock_board_concept_name_ths() 概念名单 ~375 行
+    # 热度/对账三件 (09-02 用户批全量落地; 快照语义同上). hot_rank=东财 emappdata
+    # 集群 (墙待测, fetcher/注册先行, wrapper 挂载等恢复窗测量拍板);
+    # xueqiu_hot/sina_sector 双机实测无墙.
+    "hot_rank",            # 东财 stock_hot_rank_em() 人气榜 ~100 行
+    "xueqiu_hot",          # 雪球 stock_hot_follow_xq('最热门') 关注热度 ~5633 行
+    "sina_sector",         # 新浪 stock_sector_spot() 行业 49 行 (同花顺 90 行的对账源)
 )
 # 模式 C: per-period 类型 (2 类)
 PER_PERIOD_TYPES = (
@@ -177,6 +183,12 @@ ONE_SHOT_TYPES = (
 )
 # top_holders 特殊: per-stock × per-period
 TOP_HOLDERS = "top_holders"
+# 股东户数 特殊: per-period (季度末) 单次全市场快照, 09-02 入列 ak-weekly。
+# ⚠️ 披露守卫: 只枚举距今 >GDHS_DISCLOSE_DAYS 的季度末 —— 未披露期拉回空 df
+# 会走「真空写空+done」语义永不重拉 (09-02 设计坑), 守卫保证枚举出来的期
+# 数据必然已披露完, 空了才是真空。
+GDHS = "gdhs"
+GDHS_DISCLOSE_DAYS = 90  # 季度末后 90 天 = 披露期结束, 剩余未披露的属长尾
 # 北交所代码段 (920新段 + 83/87/43历史段): akshare 东财 stock_gdfx_free_top_10_em
 # 不支持北交所, build_top_holders_units 阶段直接跳过 (避免每只×20期×3retry 失败风暴).
 BJ_PREFIXES = ("920", "83", "87", "43")
@@ -192,6 +204,7 @@ ALL_TYPES = (
     + PER_DATE_TYPES
     + PER_PERIOD_TYPES
     + ONE_SHOT_TYPES
+    + (GDHS,)
 )
 
 
@@ -682,6 +695,41 @@ def fetch_ths_concept(date: str) -> Optional[pd.DataFrame]:
     return df
 
 
+def fetch_hot_rank(date: str) -> Optional[pd.DataFrame]:
+    """stock_hot_rank_em() — 东财人气榜快照 (~100 行)。
+
+    emappdata 集群墙待恢复窗测量定论; 失败走 None 语义 (不写不标), 无害。
+    """
+    df, _status = call_ak_with_retry(
+        ak.stock_hot_rank_em, f"hot_rank/{date}",
+    )
+    return df
+
+
+def fetch_xueqiu_hot(date: str) -> Optional[pd.DataFrame]:
+    """stock_hot_follow_xq('最热门') — 雪球关注热度快照 (~5633 行)。"""
+    df, _status = call_ak_with_retry(
+        ak.stock_hot_follow_xq, f"xueqiu_hot/{date}", symbol="最热门",
+    )
+    return df
+
+
+def fetch_sina_sector(date: str) -> Optional[pd.DataFrame]:
+    """stock_sector_spot() — 新浪行业快照 (49 行, 同花顺行业榜对账源)。"""
+    df, _status = call_ak_with_retry(
+        ak.stock_sector_spot, f"sina_sector/{date}",
+    )
+    return df
+
+
+def fetch_gdhs(period: str) -> Optional[pd.DataFrame]:
+    """stock_zh_a_gdhs(symbol=period) — 股东户数全市场按期单次 (~5342 行)。"""
+    df, _status = call_ak_with_retry(
+        ak.stock_zh_a_gdhs, f"gdhs/{period}", symbol=period,
+    )
+    return df
+
+
 # ======================== per-period fetch 函数 (2 类) ========================
 
 def fetch_forecast(period: str) -> Optional[pd.DataFrame]:
@@ -790,6 +838,32 @@ def download_one_unit(
 
 # ======================== 主循环 (通用, 适用所有四种模式) ========================
 
+def build_gdhs_units(
+    now: Optional[datetime.date] = None,
+) -> List[Tuple[str, Callable[[], pd.DataFrame]]]:
+    """构造 gdhs units: 近 3 年季度末 × 披露守卫, 每期 1 unit。
+
+    只枚举距今 >GDHS_DISCLOSE_DAYS 的季度末 (披露完毕), 防止未披露期拉回
+    空 df 走「真空写空+done」语义后被 marker 锁死永不重拉。marker 去重:
+    已抓期 skip, 无新期时零网络调用 (ak-weekly 每周六空转)。
+    """
+    now = now or datetime.date.today()
+    cutoff = now - datetime.timedelta(days=GDHS_DISCLOSE_DAYS)
+    periods = []
+    for y in range(now.year - 2, now.year + 1):
+        for (m, d) in [(3, 31), (6, 30), (9, 30), (12, 31)]:
+            p = datetime.date(y, m, d)
+            if p <= cutoff:
+                periods.append(f"{y}{m:02d}{d:02d}")
+    units = [(f"{p}_{GDHS}", partial(fetch_gdhs, p)) for p in periods]
+    logger.info(
+        "[%s] 季度末 %d 个 (披露守卫 >%d 天, %s..%s)",
+        GDHS, len(units), GDHS_DISCLOSE_DAYS,
+        periods[0] if periods else "-", periods[-1] if periods else "-",
+    )
+    return units
+
+
 def run_one_type(
     data_type: str,
     units: List[Tuple[str, Callable[[], pd.DataFrame]]],
@@ -1129,10 +1203,17 @@ def run_type_dispatch(
             "fund_flow_concept": fetch_fund_flow_concept,
             "ths_industry": fetch_ths_industry,
             "ths_concept": fetch_ths_concept,
+            "hot_rank": fetch_hot_rank,
+            "xueqiu_hot": fetch_xueqiu_hot,
+            "sina_sector": fetch_sina_sector,
         }
         units = build_per_date_units(t, fetch_map[t], args)
         return run_one_type(t, units, args)
 
+    if t == GDHS:
+        units = build_gdhs_units()
+        return run_one_type(t, units, args)
+
     if t in PER_PERIOD_TYPES:
         fetch_map = {
             "forecast": fetch_forecast,