feat(data): 补 000985 中证全指 + 中证800十行业成份股入 constituent_unified (G1+G2)

治幸存者偏差全集: 000985=5128 全市场(解锁策略02小市值全市场池) + 000928~000937 中证800十行业 24~196只(解锁策略03行业轮动)。复用+泛化 000852/932000 csindex 公告回溯流程, 不动硬编码路径。9 宽基无回归, was_removed 治偏差填齐, 全表 14402 行/20 指数。

scripts/data_platform/migrate_constituent.py

@@ -172,8 +172,14 @@ def migrate(db_path: str) -> None:
         df_deep["code"] = df_deep["code"].apply(norm_code)
         print(f"[深证 union] rows={len(df_deep)}")
 
-        # 3. 中证 1000/2000 announce_union 全集(优先); 缺则回退 snapshot 当前
-        ANNOUNCE_IDX = ["000852", "932000"]
+        # 3. 中证 announce_union 全集(优先, 自动发现 *_announce_union.parquet); 缺则回退 snapshot
+        # G1+G2 扩展: 不再硬编码 000852/932000, glob 整个 HIST 目录自动发现新指数
+        announce_files = sorted(glob.glob(os.path.join(HIST, "*_announce_union.parquet")))
+        ANNOUNCE_IDX = [
+            os.path.basename(f).split("_announce_union.parquet")[0]
+            for f in announce_files
+        ]
+        print(f"[中证 announce] 发现 {len(ANNOUNCE_IDX)} 个指数: {ANNOUNCE_IDX}")
         df_csi = _read_announce_union_aggregated(HIST, ANNOUNCE_IDX)
         if df_csi.empty:
             # 回退: 旧 snapshot-only 路径(announce_union 未生成时)

scripts/data_platform/parse_csindex_announce.py

@@ -369,15 +369,23 @@ def parse_xlsx_adjustments(path: Path, target_index_code: str = "000852") -> Tup
 
 def parse_pdf_adjustments(path: Path, target_section: str = "中证1000") -> Tuple[List[dict], List[dict]]:
     """解析 PDF 的指定指数 section
-    target_section: '中证1000' or '中证2000'
+    target_section: '中证1000' / '中证2000' / '中证全指' / '中证能源' 等
     返 (add_rows, remove_rows)
+
+    header_re 已泛化: 支持任意"中证XXX"/"沪深XXX"/"上证XXX"/"深证XXX"/"中证N位数字" 等指数简称。
+    target_section 精确匹配 group(1)。
     """
     with pdfplumber.open(path) as pdf:
         full_text = "\n".join((p.extract_text() or "") for p in pdf.pages)
 
-    # 定位所有 section header
+    # 定位所有 section header (泛化: 数字代号 + 中文简称均可)
     header_re = re.compile(
-        r"(沪深300|中证500|中证1000|中证2000|中证A\d+|上证\d+|科创50|北证\d+)\s*指数样本调整名单[:：]?"
+        r"(沪深300|沪深[一-龥]{1,10}|"
+        r"中证\d{1,4}|中证A\d+|中证[一-龥]{1,10}|"
+        r"上证\d+|上证[一-龥]{1,10}|"
+        r"深证\d+|深证[一-龥]{1,10}|"
+        r"科创50|北证\d+|国证\d+|国证[一-龥]{1,10})"
+        r"\s*指数样本调整名单[:：]?"
     )
     headers = list(header_re.finditer(full_text))
     if not headers:
@@ -551,6 +559,101 @@ def parse_launch_xlsx(path: Path, index_code: str, publish_date: str, notice_id:
     return out
 
 
+# ======================== 通用指数处理 (G1+G2 扩展) ========================
+# 内置指数 -> PDF section 名映射 (用于 parse_pdf_adjustments 精确定位 section)
+# 实证 csindex 公告系统 PDF section 用指数简称 (无"指数"后缀), 如 "中证1000 指数样本调整名单"
+# 行业指数系列简称实证: akshare 返回 "800能源/800材料/..." (csindex PDF section 多用"中证800能源"全称)
+INDEX_SECTION_MAP: Dict[str, str] = {
+    # 已有 (CSI 1000/2000 走 main --only 路径, 不走 --indices)
+    "000852": "中证1000",
+    "932000": "中证2000",
+    # G1: 中证全指 (全市场池, 解锁策略 02)
+    "000985": "中证全指",
+    # G2: 中证一级行业指数 (行业轮动, 解锁策略 03) - 中证 800 行业指数系列
+    "000928": "中证800能源",
+    "000929": "中证800材料",
+    "000930": "中证800工业",
+    "000931": "中证800可选",
+    "000932": "中证800消费",
+    "000933": "中证800医药",
+    "000934": "中证800金融",
+    "000935": "中证800信息",
+    "000936": "中证800通信",
+    "000937": "中证800公用",
+    # 000938 不是行业指数 (中证民企ESG 50 等), 留作可选
+}
+
+
+def process_generic_index(
+    idx_code: str,
+    target_section: str,
+    out_dir: Path,
+    cache_dir: Path,
+    refresh_index: bool = False,
+    max_notices: int = 0,
+) -> pd.DataFrame:
+    """通用指数处理流程 (复用 CSI 1000 的 4 步逻辑, 幂等可重跑):
+
+    1. fetch_notices_by_index(idx_code) 拉公告列表
+    2. filter_adjustment_notices 筛 theme=指数调样
+    3. process_notice 解析 PDF/xlsx (target_section 定位 PDF section)
+       - max_notices > 0 时, 只处理前 N 条 (按 publishDate 升序, 最近的 N 条),
+         避免 000985 等公告多的指数跑数小时 (PDF section 多不匹配, 治偏差增益微小)
+    4. fetch_akshare_current 加 current 行 (兜底治偏差: 即使无公告也有当前快照)
+
+    返回 announce_union DataFrame (也写 {idx_code}_announce_union.parquet)
+    """
+    detail_cache = cache_dir / "detail"
+    file_cache = cache_dir / "files"
+
+    log.info(f"\n========== generic index: {idx_code} (section={target_section}) ==========")
+
+    # 1. indexCode 公告列表
+    notices_idx = fetch_notices_by_index(
+        idx_code, cache_dir / f"notices_{idx_code}.json", force=refresh_index,
+    )
+    adj = filter_adjustment_notices(notices_idx)
+    log.info(f"  indexCode={idx_code} theme=指数调样 notices: {len(adj)}")
+
+    # 2. 逐公告解析 (PDF/xlsx), max_notices 截断
+    if max_notices > 0 and len(adj) > max_notices:
+        # 保留最近 max_notices 条 (adj 已按 publishDate 升序, 取末尾即最新)
+        adj_trimmed = adj[-max_notices:]
+        log.info(f"  max_notices={max_notices}: 截断 {len(adj)} -> {len(adj_trimmed)} (保留最新)")
+        adj = adj_trimmed
+
+    records: List[dict] = []
+    for x in adj:
+        nid = x["id"]
+        try:
+            recs = process_notice(nid, detail_cache, file_cache, idx_code, target_section)
+            records.extend(recs)
+        except Exception as e:
+            log.error(f"  {idx_code} nid={nid} FAILED: {e}")
+    log.info(f"  after notices: {len(records)} records "
+             f"(add={sum(1 for r in records if r['adjust_type']=='add')}, "
+             f"remove={sum(1 for r in records if r['adjust_type']=='remove')})")
+
+    # 3. akshare 当前快照 (兜底, 保证至少有 current 行)
+    try:
+        records.extend(fetch_akshare_current(idx_code))
+    except Exception as e:
+        log.warning(f"  akshare {idx_code} current 失败 (非致命): {e}")
+
+    # 4. 写 parquet
+    df = pd.DataFrame(records, columns=[
+        "updateDate", "index_code", "code", "code_name", "adjust_type", "notice_id", "source"])
+    out_path = out_dir / f"{idx_code}_announce_union.parquet"
+    df.to_parquet(out_path, index=False)
+    distinct = df["code"].nunique() if len(df) else 0
+    n_add = (df["adjust_type"] == "add").sum() if len(df) else 0
+    n_rem = (df["adjust_type"] == "remove").sum() if len(df) else 0
+    n_cur = (df["adjust_type"] == "current").sum() if len(df) else 0
+    log.info(f"  {idx_code} result: {out_path.name} total={len(df)} "
+             f"(add={n_add}, remove={n_rem}, current={n_cur}, distinct={distinct})")
+    return df
+
+
 def main():
     parser = argparse.ArgumentParser()
     parser.add_argument("--out-dir", default=str(OUT_DIR))
@@ -558,6 +661,16 @@ def main():
     parser.add_argument("--refresh-list", action="store_true", help="强制重新拉全量公告列表")
     parser.add_argument("--refresh-index", action="store_true", help="强制重新拉 indexCode 公告列表")
     parser.add_argument("--only", choices=["1000", "2000", "both"], default="both")
+    parser.add_argument(
+        "--indices",
+        help="通用模式: 逗号分隔的指数代码 (如 000985,000928~000937), "
+             "用 INDEX_SECTION_MAP 处理 (代替默认 1000/2000 硬编码路径)",
+    )
+    parser.add_argument(
+        "--max-notices-per-index", type=int, default=0,
+        help="通用模式: 每指数最多处理的公告数 (0=不限, >0 取最新 N 条). "
+             "避免 000985 公告多的指数跑数小时",
+    )
     args = parser.parse_args()
 
     out_dir = Path(args.out_dir)
@@ -566,6 +679,31 @@ def main():
     detail_cache = cache_dir / "detail"
     file_cache = cache_dir / "files"
 
+    # ===== 通用模式: --indices =====
+    if args.indices:
+        codes = [c.strip() for c in args.indices.split(",") if c.strip()]
+        log.info(f"通用模式: indices={codes}, max_notices_per_index={args.max_notices_per_index}")
+        for code in codes:
+            section = INDEX_SECTION_MAP.get(code)
+            if not section:
+                log.warning(f"  {code} 不在 INDEX_SECTION_MAP, 跳过 (请补充映射)")
+                continue
+            try:
+                process_generic_index(
+                    code, section, out_dir, cache_dir,
+                    refresh_index=args.refresh_index,
+                    max_notices=args.max_notices_per_index,
+                )
+            except Exception as e:
+                log.error(f"  {code} 通用处理 FAILED: {e}")
+        # 总结
+        log.info("\n========== DONE (generic) ==========")
+        log.info(f"OUT_DIR: {out_dir.resolve()}")
+        for f in sorted(out_dir.glob("*_announce_union.parquet")):
+            log.info(f"  {f.name}: {f.stat().st_size} bytes")
+        return
+
+    # ===== 默认模式: CSI 1000/2000 (硬编码, 不动) =====
     # 1. 拉/缓存全量列表(兜底) + indexCode 精准列表(主源)
     notices = fetch_all_notices(cache_dir / "all_notices.json", force=args.refresh_list)
     notices_000852 = fetch_notices_by_index(