feat(data): A档第二批接入——新浪资金流×2+同花顺行业/概念榜进ak-events 19:30(用户批方案A);东财push2墙死后免费无墙替代源双机实测秒级零限流(新浪行业资金流90行带净额/领涨股+概念387行,同花顺行业90行×12列+概念375行);快照族语义显式化:端点无date参数无历史回补,19:30采集=收盘终态,节假日拉到上交易日快照接受重复由消费方按交易日历过滤;vintage缺日检查扩全panel族并分档——可回补族(zt_pool×3)近窗洞给回补指引,快照族洞即永久告警查当晚日志无回补指引,旧洞不刷告警防疲劳;+5测试(注册/None/df契约×2+快照族永久洞语义)29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 (7 类, margin_szse 跳过)
+# 模式 B: per-date 类型 (11 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)
@@ -156,6 +156,14 @@ PER_DATE_TYPES = (
     "zt_pool",       # stock_zt_pool_em(date) 涨停池
     "zt_pool_zbgc",  # stock_zt_pool_zbgc_em(date) 炸板池
     "zt_pool_dtgc",  # stock_zt_pool_dtgc_em(date) 跌停池
+    # 行业/概念截面快照 (2026-09-02 用户批 A 档接入; 东财 push2 clist 墙死后的
+    # 免费/无墙替代源, 双机实测秒级零限流)。⚠️ 快照型端点无 date 参数、无历史
+    # 回补, date 仅命名文件 (19:30 采集=收盘终态); 节假日拉到上一交易日快照
+    # (无自日期列可判), 接受重复, 消费方按交易日历过滤。
+    "fund_flow_industry",  # 新浪 stock_fund_flow_industry('即时') 行业资金流 90 行 (净额/领涨股)
+    "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
 )
 # 模式 C: per-period 类型 (2 类)
 PER_PERIOD_TYPES = (
@@ -642,6 +650,38 @@ def fetch_zt_pool_dtgc(date: str) -> Optional[pd.DataFrame]:
     return df
 
 
+def fetch_fund_flow_industry(date: str) -> Optional[pd.DataFrame]:
+    """stock_fund_flow_industry(symbol='即时') — 新浪行业资金流快照 (90 行)。"""
+    df, _status = call_ak_with_retry(
+        ak.stock_fund_flow_industry, f"fund_flow_industry/{date}", symbol="即时",
+    )
+    return df
+
+
+def fetch_fund_flow_concept(date: str) -> Optional[pd.DataFrame]:
+    """stock_fund_flow_concept(symbol='即时') — 新浪概念资金流快照 (~387 行)。"""
+    df, _status = call_ak_with_retry(
+        ak.stock_fund_flow_concept, f"fund_flow_concept/{date}", symbol="即时",
+    )
+    return df
+
+
+def fetch_ths_industry(date: str) -> Optional[pd.DataFrame]:
+    """stock_board_industry_summary_ths() — 同花顺行业一览快照 (90 行×12 列)。"""
+    df, _status = call_ak_with_retry(
+        ak.stock_board_industry_summary_ths, f"ths_industry/{date}",
+    )
+    return df
+
+
+def fetch_ths_concept(date: str) -> Optional[pd.DataFrame]:
+    """stock_board_concept_name_ths() — 同花顺概念名单快照 (~375 行)。"""
+    df, _status = call_ak_with_retry(
+        ak.stock_board_concept_name_ths, f"ths_concept/{date}",
+    )
+    return df
+
+
 # ======================== per-period fetch 函数 (2 类) ========================
 
 def fetch_forecast(period: str) -> Optional[pd.DataFrame]:
@@ -1085,6 +1125,10 @@ def run_type_dispatch(
             "zt_pool": fetch_zt_pool,
             "zt_pool_zbgc": fetch_zt_pool_zbgc,
             "zt_pool_dtgc": fetch_zt_pool_dtgc,
+            "fund_flow_industry": fetch_fund_flow_industry,
+            "fund_flow_concept": fetch_fund_flow_concept,
+            "ths_industry": fetch_ths_industry,
+            "ths_concept": fetch_ths_concept,
         }
         units = build_per_date_units(t, fetch_map[t], args)
         return run_one_type(t, units, args)