chore: baseline — ORM migration + systemd deploy + MCP server + daily check

- ORM migration: models/ops 重构
- deploy: 标准化 systemd timer/service 部署体系
- MCP server: 5 个只读工具(任务/状态/数据集)
- daily check: 数据一致性巡检
- watch: task_watch 后台监控
- kline_daily: 麦蕊优先,时间门禁15:00
- 删除: task_industry_sector, task_sector_features

app/core/config.py

@@ -30,6 +30,12 @@ if _HAS_DOTENV:
 class Settings(BaseSettings):
     """应用配置。所有字段都可以通过环境变量或 .env 文件覆盖。"""
 
+    # Runtime mode: "docker" | "systemd"
+    #   docker  : 单容器运行，依赖进程内 scheduler 触发所有同步任务
+    #   systemd : Linux 宿主机运行，由 systemd timer/service 触发任务，
+    #             进程内 scheduler 必须关闭，避免双调度器重叠
+    runtime_mode: str = "docker"
+
     # Scheduler
     scheduler_tick_seconds: int = 5
     scheduler_timezone: str = "Asia/Shanghai"

app/core/datasource/utils.py

@@ -1,15 +1,146 @@
 """数据源公共工具：代码转换、K 线标准化、日期过滤。
 
-参考 dashboard/api/services/datasource/fetch_kline.py，但适配新项目。"""
+参考 dashboard/api/services/datasource/fetch_kline.py，但适配新项目。
+"""
 from __future__ import annotations
 
+import logging
 import re
+import threading
+from datetime import datetime, time as dtime, timedelta
+from typing import Any, Callable, TypeVar
 
 import pandas as pd
 
+logger = logging.getLogger("datasource.utils")
+
 KLINE_COLS = ["trade_date", "open", "high", "low", "close", "volume"]
 KLINE_5MIN_COLS = ["bar_time", "open", "high", "low", "close", "volume", "amount", "turnover_rate"]
 
+T = TypeVar("T")
+
+_THREAD_POOL: dict[int, threading.Thread] = {}
+
+
+def call_with_timeout(
+    func: Callable[..., T],
+    *args: Any,
+    timeout: float = 20.0,
+    on_timeout: T | None = None,
+    description: str = "",
+    **kwargs: Any,
+) -> T | None:
+    """用 daemon 线程跑 `func(*args, **kwargs)`,超时返回 `on_timeout`。
+
+    Why: 之前用 ThreadPoolExecutor，但超时后 `future.cancel()` 无法终止
+    已在运行的线程，pool 的 `shutdown(wait=True)` 会等待卡死的函数返回，
+    把 20s 超时变成无限阻塞——这是 kline_daily 3406 只股票批量失败的根因。
+
+    改用 daemon 线程：超时后直接返回，线程成为孤儿进程，不会阻塞任何调用方。
+    daemon=True 确保进程退出时不会等待这些孤儿线程。
+
+    Args:
+        func:        要跑的同步函数
+        *args/**kwargs: 透传给 func
+        timeout:     秒数,默认 20s(对标 mairui/sina 的 urlopen timeout)
+        on_timeout:  超时返回的占位值(默认 None,调用方需处理 None)
+        description: 日志里的调用描述(如 'xueqiu.kline SH600519')
+
+    Returns: func 的返回值,或 on_timeout(超时时)
+    """
+    result: list[T | None | BaseException] = [None]
+    done = threading.Event()
+
+    def _wrapper() -> None:
+        try:
+            result[0] = func(*args, **kwargs)
+        except BaseException as e:
+            result[0] = e
+        finally:
+            done.set()
+
+    t = threading.Thread(
+        target=_wrapper,
+        daemon=True,
+        name=f"ds-timeout-{description or func.__name__}",
+    )
+    t.start()
+
+    if not done.wait(timeout=timeout):
+        logger.warning(
+            "[%s] %s 超时 (>%ss), 返回 on_timeout",
+            description or func.__name__, description or func.__name__, timeout,
+        )
+        return on_timeout
+
+    if isinstance(result[0], BaseException):
+        raise result[0]  # type: ignore[misc]
+    return result[0]
+
+
+def effective_market_date(
+    now: datetime | None = None,
+    holidays: set[str] | None = None,
+) -> str:
+    """返回"数据生效日"(最近已完成交易日)。
+
+    15:30 是 A 股收盘时刻;之后今天的数据才完整,之前应取昨天。
+    然后向前回退直到找到一个交易日（跳过周末和法定假期）。
+    用于数据源给"无明确来源日期"的数据(snowball quote_detail 的股本快照等)
+    打 trade_date 时用 — 永远不要用 datetime.now() 直接当 trade_date。
+
+    Why: 任务运行日不等于数据交易日。例如 7月9日 早盘 9:30 跑 share_snapshot,
+    此时拿到的股本快照实际对应 7月8日 收盘,不能标 7月9日。早期代码用
+    `datetime.now().strftime("%Y-%m-%d")` 导致 share 表里 7月9日 跑出来的
+    行标成 7月9日,与实际数据日不符,统计/回溯会出错。
+
+    2026-07-12 增强: 加入交易日历回退逻辑。如果 15:30 后是法定节假日
+    （如春节/国庆），会向前回退到最后一个交易日，不会把节假日当天当 trade_date。
+
+    Args:
+        now:      测试用注入点(默认 datetime.now())
+        holidays: 已知的 A 股休市日集合（YYYY-MM-DD 格式）。
+                  默认从 config 表 trading_calendar_holidays 加载。
+
+    Returns: 'YYYY-MM-DD' 字符串（永远是合法的交易日）
+    """
+    t = now or datetime.now()
+    if t.time() >= dtime(15, 0):
+        candidate = t
+    else:
+        candidate = t - timedelta(days=1)
+
+    # 加载交易日历（如果调用方未提供）
+    if holidays is None:
+        try:
+            from app.core.db import ops as db_ops
+            h_row = db_ops.fetch_config_by_key("trading_calendar_holidays")
+            if h_row and h_row.get("category") == "general":
+                import json
+                vals = json.loads(h_row.get("value", "") or "[]")
+                if isinstance(vals, list):
+                    holidays = {h for h in vals if isinstance(h, str)}
+        except Exception:
+            pass
+
+    if holidays is None:
+        holidays = set()
+
+    # 向前回退直到找到交易日
+    max_iter = 30  # 安全上限，避免死循环
+    while max_iter > 0:
+        date_str = candidate.strftime("%Y-%m-%d")
+        if candidate.weekday() < 5 and date_str not in holidays:
+            return date_str
+        candidate -= timedelta(days=1)
+        max_iter -= 1
+
+    # 兜底（正常情况下不会走到这里）
+    t2 = now or datetime.now()
+    if t2.time() >= dtime(15, 30):
+        return t2.strftime("%Y-%m-%d")
+    return (t2 - timedelta(days=1)).strftime("%Y-%m-%d")
+
 
 def to_code6(code: str) -> str:
     """'SH600000' / 'sh.600000' / '600000' / 'SH600000.SH' → '600000'"""

app/core/db/models.py

@@ -141,7 +141,6 @@ class Stock(ORMBase):
     exchange: Mapped[str] = mapped_column(String(8), nullable=False, default="")
     list_date: Mapped[Optional[date]] = mapped_column(Date)
     listing_status: Mapped[str] = mapped_column(String(16), nullable=False, default="normal")
-    industry: Mapped[Optional[str]] = mapped_column(String(64), default="")
     total_share: Mapped[float] = mapped_column(Float, default=0)
     float_share: Mapped[float] = mapped_column(Float, default=0)
     share_updated_at: Mapped[Optional[date]] = mapped_column(Date)
@@ -258,92 +257,7 @@ class Share(ORMBase):
     float_share: Mapped[float] = mapped_column(Float, nullable=False)
 
 
-# ──────────────────────── 10. sectors ────────────────────────
-class Sectors(ORMBase):
-    """行业字典（baostock 行业分类 / 申万 / 中证 等多种 taxonomy）。"""
-    __tablename__ = "sectors"
-    __table_args__ = (
-        Index("idx_sectors_sector_name", "sector_name"),
-        {"schema": "market_data"},
-    )
-
-    sector_key: Mapped[str] = mapped_column(String(64), primary_key=True)
-    sector_name: Mapped[str] = mapped_column(String(64), nullable=False, default="")
-    taxonomy: Mapped[Optional[str]] = mapped_column(String(64), default="")
-    level: Mapped[Optional[str]] = mapped_column(String(16), default="")
-    source: Mapped[Optional[str]] = mapped_column(String(32), default="")
-    enabled: Mapped[int] = mapped_column(Integer, default=1)
-    updated_at: Mapped[Optional[datetime]] = _updated_at()
-
-
-# ──────────────────────── 11. stock_sector_map ────────────────────────
-class StockSectorMap(ORMBase):
-    """股票-行业多对多映射（一张股票可对应多个行业 / 概念板块）。"""
-    __tablename__ = "stock_sector_map"
-    __table_args__ = (
-        Index("idx_stock_sector_map_sector_key", "sector_key"),
-        {"schema": "market_data"},
-    )
-
-    stock_code: Mapped[str] = mapped_column(String(10), primary_key=True)
-    sector_key: Mapped[str] = mapped_column(String(64), nullable=False, default="")
-    updated_at: Mapped[Optional[datetime]] = _updated_at()
-
-
-# ──────────────────────── 12. industry ────────────────────────
-class Industry(ORMBase):
-    """股票-行业映射（baostock 源，证监会分类标准）。"""
-    __tablename__ = "industry"
-    __table_args__ = (
-        Index("idx_industry_industry_name", "industry_name"),
-        {"schema": "market_data"},
-    )
-
-    code: Mapped[str] = mapped_column(String(10), primary_key=True)
-    industry_name: Mapped[Optional[str]] = mapped_column(String(64), default="")
-    industry_classification: Mapped[Optional[str]] = mapped_column(String(32), default="")
-    update_date: Mapped[Optional[date]] = mapped_column(Date)
-    updated_at: Mapped[Optional[datetime]] = _updated_at()
-
-
-# ──────────────────────── 13. sector_indices ────────────────────────
-class SectorIndices(ORMBase):
-    """行业日线（基点 100，复合收益）。"""
-    __tablename__ = "sector_indices"
-    __table_args__ = (
-        PrimaryKeyConstraint("trade_date", "sector_name"),
-        Index("idx_sector_indices_sector_name", "sector_name"),
-        {"schema": "market_data"},
-    )
-
-    trade_date: Mapped[date] = mapped_column(Date, nullable=False)
-    sector_name: Mapped[str] = mapped_column(String(64), nullable=False)
-    close: Mapped[float] = mapped_column(Float, default=0)
-    sector_amplitude: Mapped[float] = mapped_column(Float, default=0)
-
-
-# ──────────────────────── 14. sector_features_daily ────────────────────────
-class SectorFeaturesDaily(ORMBase):
-    """行业日特征：sector_ret / sector_amplitude / close / EMA / score。"""
-    __tablename__ = "sector_features_daily"
-    __table_args__ = (
-        PrimaryKeyConstraint("trade_date", "sector_name"),
-        Index("idx_sector_features_sector_name", "sector_name"),
-        {"schema": "market_data"},
-    )
-
-    trade_date: Mapped[date] = mapped_column(Date, nullable=False)
-    sector_name: Mapped[str] = mapped_column(String(64), nullable=False)
-    sector_ret: Mapped[float] = mapped_column(Float, default=0)
-    sector_amplitude: Mapped[float] = mapped_column(Float, default=0)
-    close: Mapped[float] = mapped_column(Float, default=0)
-    ema10: Mapped[float] = mapped_column(Float, default=0)
-    ema20: Mapped[float] = mapped_column(Float, default=0)
-    ema200: Mapped[float] = mapped_column(Float, default=0)
-    score: Mapped[int] = mapped_column(Integer, default=0)
-
-
-# ──────────────────────── 15. market_regime_daily ────────────────────────
+# ──────────────────────── 12. market_regime_daily ────────────────────────
 class MarketRegimeDaily(ORMBase):
     """市场情绪衍生指标（基于本地 kline 聚合）。"""
     __tablename__ = "market_regime_daily"
@@ -599,6 +513,80 @@ class StockNodeMap(ORMBase):
     updated_at: Mapped[Optional[datetime]] = _updated_at()
 
 
+# ──────────────────────── 22. kline_stock_macd_daily ────────────────────────
+class KlineStockMACDDaily(ORMBase):
+    """个股日 K 级别 MACD 指标（mairui /hsstock/history/macd 直拉）。
+
+    字段（mairui 原始字段名，与 K 线口径一致）：
+      diff  = DIF（快慢 EMA 差）
+      dea   = DEA（DIF 的 9 日 EMA，即 signal line）
+      macd  = MACD 柱（2 ×（DIF − DEA））
+      ema12 / ema26 = 计算 DIF 用的两条 EMA
+    stock_code 用 hermes 格式（SH600519），与项目其他表一致。
+    """
+    __tablename__ = "kline_stock_macd_daily"
+    __table_args__ = (
+        PrimaryKeyConstraint("stock_code", "trade_date"),
+        Index("idx_kline_stock_macd_date", "trade_date"),
+        {"schema": "market_data"},
+    )
+
+    stock_code: Mapped[str] = mapped_column(String(10), nullable=False)
+    trade_date: Mapped[date] = mapped_column(Date, nullable=False)
+    diff: Mapped[Optional[float]] = mapped_column(Float)
+    dea: Mapped[Optional[float]] = mapped_column(Float)
+    macd: Mapped[Optional[float]] = mapped_column(Float)
+    ema12: Mapped[Optional[float]] = mapped_column(Float)
+    ema26: Mapped[Optional[float]] = mapped_column(Float)
+    source: Mapped[Optional[str]] = mapped_column(String(32), default="mairui")
+    updated_at: Mapped[Optional[datetime]] = _updated_at()
+
+
+# ──────────────────────── 23. kline_stock_kdj_daily ────────────────────────
+class KlineStockKDJDaily(ORMBase):
+    """个股日 K 级别 KDJ 指标（mairui /hsstock/history/kdj 直拉）。
+
+    k / d / j 三条随机指标线。stock_code 用 hermes 格式（SH600519）。
+    """
+    __tablename__ = "kline_stock_kdj_daily"
+    __table_args__ = (
+        PrimaryKeyConstraint("stock_code", "trade_date"),
+        Index("idx_kline_stock_kdj_date", "trade_date"),
+        {"schema": "market_data"},
+    )
+
+    stock_code: Mapped[str] = mapped_column(String(10), nullable=False)
+    trade_date: Mapped[date] = mapped_column(Date, nullable=False)
+    k: Mapped[Optional[float]] = mapped_column(Float)
+    d: Mapped[Optional[float]] = mapped_column(Float)
+    j: Mapped[Optional[float]] = mapped_column(Float)
+    source: Mapped[Optional[str]] = mapped_column(String(32), default="mairui")
+    updated_at: Mapped[Optional[datetime]] = _updated_at()
+
+
+# ──────────────────────── 24. kline_stock_boll_daily ────────────────────────
+class KlineStockBOLLDaily(ORMBase):
+    """个股日 K 级别 BOLL 布林带指标（mairui /hsstock/history/boll 直拉）。
+
+    mairui 原始字段：u=上轨, m=中轨, d=下轨。落库列名统一为 upper/mid/lower。
+    stock_code 用 hermes 格式（SH600519）。
+    """
+    __tablename__ = "kline_stock_boll_daily"
+    __table_args__ = (
+        PrimaryKeyConstraint("stock_code", "trade_date"),
+        Index("idx_kline_stock_boll_date", "trade_date"),
+        {"schema": "market_data"},
+    )
+
+    stock_code: Mapped[str] = mapped_column(String(10), nullable=False)
+    trade_date: Mapped[date] = mapped_column(Date, nullable=False)
+    upper: Mapped[Optional[float]] = mapped_column(Float)
+    mid: Mapped[Optional[float]] = mapped_column(Float)
+    lower: Mapped[Optional[float]] = mapped_column(Float)
+    source: Mapped[Optional[str]] = mapped_column(String(32), default="mairui")
+    updated_at: Mapped[Optional[datetime]] = _updated_at()
+
+
 __all__ = [
     # 1-2
     "Config",
@@ -612,13 +600,7 @@ __all__ = [
     "Kline5Min",
     "Moneyflow",
     "Share",
-    # 10-12
-    "Sectors",
-    "StockSectorMap",
-    "Industry",
-    # 13-15
-    "SectorIndices",
-    "SectorFeaturesDaily",
+    # 10-11
     "MarketRegimeDaily",
     # 16
     "TickTrade",
@@ -631,4 +613,8 @@ __all__ = [
     "NodeCategory",
     "Node",
     "StockNodeMap",
+    # 22-24 (2026-07-08 mairui 技术指标 MACD/KDJ/BOLL - 日 K 级别)
+    "KlineStockMACDDaily",
+    "KlineStockKDJDaily",
+    "KlineStockBOLLDaily",
 ]

app/core/db/ops.py

@@ -22,10 +22,12 @@ from sqlalchemy.dialects.postgresql import insert as pg_insert
 from app.core.db.models import (
     Config,
     DatasetRegistry,
-    Industry,
     Kline5Min,
     KlineIndex,
     KlineStock,
+    KlineStockBOLLDaily,
+    KlineStockKDJDaily,
+    KlineStockMACDDaily,
     KlineStockMADaily,
     LonghubangDaily,
     LonghubangSeat,
@@ -34,13 +36,9 @@ from app.core.db.models import (
     Moneyflow,
     Node,
     NodeCategory,
-    SectorFeaturesDaily,
-    SectorIndices,
-    Sectors,
     Share,
     Stock,
     StockNodeMap,
-    StockSectorMap,
     SyncHistory,
     TickTrade,
 )
@@ -451,19 +449,16 @@ def upsert_stock(
     exchange: str,
     list_date: str = "",
     listing_status: str = "normal",
-    industry: str = "",
 ) -> None:
     """upsert 一只股票基础信息（单条）。
 
     注意：MySQL 9.7.0 在 ON DUPLICATE KEY UPDATE 阶段对 DATE 字段的 VALUES()/new.col
-    求值存在 bug（会强制把空串塞进去，触发 1292 严格模式错误）。所以这里：
-    - list_date 字段在首次 INSERT 时写入；UPDATE 分支不更新（保持原值或 NULL）
-    - industry 字段也避开这个 bug
+    求值存在 bug（会强制把空串塞进去，触发 1292 严格模式错误）。所以这里
+    list_date 字段在首次 INSERT 时写入；UPDATE 分支不更新（保持原值或 NULL）。
 
     批量写入请用 upsert_stocks_bulk()，性能高 10-20 倍。
     """
     del list_date  # 显式不接受 list_date 更新（旧值保留）
-    del industry   # 同上
     with get_session() as s:
         stmt = _pg_upsert(
             Stock,
@@ -477,7 +472,7 @@ def upsert_stock(
 def upsert_stocks_bulk(rows: list[dict[str, Any]], chunk_size: int = 500) -> int:
     """批量 upsert 股票基础信息。
 
-    每条 row 需有: code, name, exchange, listing_status（可选 list_date / industry）
+    每条 row 需有: code, name, exchange, listing_status（可选 list_date）
     性能：~5000 只股票从 ~50s 降到 ~3s。
     """
     if not rows:
@@ -499,7 +494,7 @@ def upsert_stocks_bulk(rows: list[dict[str, Any]], chunk_size: int = 500) -> int
             stmt = _pg_upsert(
                 Stock, values,
                 conflict_keys=["code"],
-                update_cols=["name", "exchange", "list_date", "listing_status", "industry"],
+                update_cols=["name", "exchange", "list_date", "listing_status"],
             )
             s.execute(stmt)
             total += len(chunk)
@@ -507,15 +502,25 @@ def upsert_stocks_bulk(rows: list[dict[str, Any]], chunk_size: int = 500) -> int
 
 
 def update_stock_share_snapshot(code: str, total_share: float, float_share: float, trade_date: str = "") -> None:
+    """更新 stocks 表的股本字段。
+
+    Args:
+        trade_date: 数据源返回的股本快照"as-of"交易日(用于 share 表写入)。
+                    注意:这个参数**不会**写到 stocks.share_updated_at 字段 —
+                    那个字段的语义是"我们什么时候拉到的"(运行日,date.today()),
+                    不是数据交易日。把 trade_date 写到 share_updated_at 会让
+                    task_share_snapshot / task_stocks_basic 的 dedup 检查
+                    (share_updated_at == today) 失效,见 task 层 fix。
+    """
+    del trade_date  # 显式不接受 — 见 docstring
     with get_session() as s:
-        snap_date = _to_date_str(trade_date) if trade_date else None
         s.execute(
             update(Stock)
             .where(Stock.code == code)
             .values(
                 total_share=float(total_share),
                 float_share=float(float_share),
-                share_updated_at=snap_date,
+                share_updated_at=date.today(),  # "拉取日",不是"数据日"
             )
         )
 
@@ -543,7 +548,6 @@ def _stock_row_to_dict(r: Stock) -> dict[str, Any]:
         "exchange": r.exchange or "",
         "list_date": _to_date_str(r.list_date),
         "listing_status": r.listing_status or "normal",
-        "industry": r.industry or "",
         "total_share": r.total_share if r.total_share is not None else 0,
         "float_share": r.float_share if r.float_share is not None else 0,
         "share_updated_at": _to_date_str(r.share_updated_at),
@@ -674,9 +678,13 @@ def get_kline_5min_snapshots() -> dict[str, Optional[datetime]]:
     返回：{stock_code (6位): max(bar_time) 或 None}
     用于 kline_5min 任务的"全量/增量"统一规划。
 
-    DB 里 stock_code 形如 "000001.SZ"（mairui 写入格式），函数剥掉
-    交易所后缀统一为 6 位。bar_time 是 DATETIME 字段，SQLAlchemy 2.x
-    native 返回 datetime；如果是 str（方言边界情况）手动解析。
+    DB 里 stock_code 是 hermes 格式 "SH600000"（带 SH/SZ/BJ 前缀，无点），
+    2026-07-09 之前误以为 mairui 原始格式 "000001.SZ"，用 split(".")[0] 剥
+    不到 → 全部股票被 skip → task 把整市场当"全量"重跑(实际是增量)。
+    修:用正则剥 SH/SZ/BJ 前缀。
+
+    bar_time 是 DATETIME 字段，SQLAlchemy 2.x native 返回 datetime；如果是
+    str（方言边界情况）手动解析。
     """
     _ensure_schema()
     out: dict[str, Optional[datetime]] = {}
@@ -686,7 +694,13 @@ def get_kline_5min_snapshots() -> dict[str, Optional[datetime]]:
             .group_by(Kline5Min.stock_code)
         ).all()
         for raw_code, latest in rows:
-            code6 = str(raw_code or "").strip().split(".")[0]
+            # 剥 SH/SZ/BJ 前缀(hermes 格式),容错 6 位裸码
+            code6 = str(raw_code or "").strip()
+            for prefix in ("SH", "SZ", "BJ"):
+                if code6.startswith(prefix):
+                    code6 = code6[len(prefix):]
+                    break
+            code6 = code6.split(".")[0]  # 兜底剥 "000001.SZ" 老格式
             if len(code6) != 6 or not code6.isdigit():
                 continue
             if isinstance(latest, datetime):
@@ -834,143 +848,6 @@ def replace_all_stock_node_map(rows: list[dict[str, Any]]) -> None:
             s.execute(stmt)
 
 
-# ── 行业 / 概念板块 ─────────────────────────────────────────────────────
-
-
-def replace_all_industries(rows: list[dict[str, Any]]) -> None:
-    """全量替换 industry 表。rows: code, industry_name, industry_classification, update_date"""
-    if not rows:
-        return
-    with get_session() as s:
-        s.execute(delete(Industry))
-        values = [
-            {
-                "code": str(r.get("code", "")).zfill(6),
-                "industry_name": r.get("industry_name") or None,
-                "industry_classification": r.get("industry_classification") or None,
-                "update_date": _to_date_str(r.get("update_date")),
-            }
-            for r in rows
-        ]
-        if values:
-            stmt = _pg_upsert(Industry, values,
-                                   conflict_keys=["code"],
-                                   update_cols=["industry_name", "industry_classification", "update_date"])
-            s.execute(stmt)
-
-
-def fetch_all_industries() -> list[dict[str, Any]]:
-    _ensure_schema()
-    with get_session() as s:
-        rows = s.execute(
-            select(Industry.code, Industry.industry_name, Industry.industry_classification, Industry.update_date)
-        ).all()
-        return [
-            {
-                "code": r.code,
-                "industry_name": r.industry_name,
-                "industry_classification": r.industry_classification,
-                "update_date": _to_date_str(r.update_date),
-            }
-            for r in rows
-        ]
-
-
-def replace_all_sectors(rows: list[dict[str, Any]]) -> None:
-    """rows: sector_key, sector_name, taxonomy, level, source, enabled"""
-    if not rows:
-        return
-    with get_session() as s:
-        s.execute(delete(Sectors))
-        values = [
-            {
-                "sector_key": r.get("sector_key", ""),
-                "sector_name": r.get("sector_name", ""),
-                "taxonomy": r.get("taxonomy", ""),
-                "level": r.get("level", ""),
-                "source": r.get("source", ""),
-                "enabled": int(r.get("enabled", 1)),
-            }
-            for r in rows
-        ]
-        if values:
-            stmt = _pg_upsert(Sectors, values,
-                                   conflict_keys=["sector_key"],
-                                   update_cols=["sector_name", "taxonomy", "level", "source", "enabled"])
-            s.execute(stmt)
-
-
-def replace_all_stock_sector_map(rows: list[dict[str, Any]]) -> None:
-    """rows: stock_code, sector_key"""
-    if not rows:
-        return
-    with get_session() as s:
-        s.execute(delete(StockSectorMap))
-        values = [
-            {
-                "stock_code": str(r.get("stock_code", "")).zfill(6),
-                "sector_key": r.get("sector_key", ""),
-            }
-            for r in rows
-        ]
-        if values:
-            stmt = _pg_upsert(StockSectorMap, values,
-                                   conflict_keys=["stock_code"],
-                                   update_cols=["sector_key"])
-            s.execute(stmt)
-
-
-# ── 行业聚合（衍生）─────────────────────────────────────────────────────
-
-
-def replace_all_sector_indices(rows: list[dict[str, Any]]) -> None:
-    """rows: trade_date, sector_name, close, sector_amplitude"""
-    if not rows:
-        return
-    with get_session() as s:
-        values = [
-            {
-                "trade_date": _to_date_str(r.get("trade_date")),
-                "sector_name": r.get("sector_name", ""),
-                "close": float(r.get("close") or 0),
-                "sector_amplitude": float(r.get("sector_amplitude") or 0),
-            }
-            for r in rows
-        ]
-        if values:
-            stmt = _pg_upsert(SectorIndices, values,
-                                   conflict_keys=["trade_date", "sector_name"],
-                                   update_cols=["close", "sector_amplitude"])
-            s.execute(stmt)
-
-
-def replace_all_sector_features(rows: list[dict[str, Any]]) -> None:
-    """rows: trade_date, sector_name, sector_ret, sector_amplitude, close, ema10, ema20, ema200, score"""
-    if not rows:
-        return
-    with get_session() as s:
-        values = [
-            {
-                "trade_date": _to_date_str(r.get("trade_date")),
-                "sector_name": r.get("sector_name", ""),
-                "sector_ret": float(r.get("sector_ret") or 0),
-                "sector_amplitude": float(r.get("sector_amplitude") or 0),
-                "close": float(r.get("close") or 0),
-                "ema10": float(r.get("ema10") or 0),
-                "ema20": float(r.get("ema20") or 0),
-                "ema200": float(r.get("ema200") or 0),
-                "score": int(r.get("score") or 0),
-            }
-            for r in rows
-        ]
-        if values:
-            stmt = _pg_upsert(SectorFeaturesDaily, values,
-                                   conflict_keys=["trade_date", "sector_name"],
-                                   update_cols=["sector_ret", "sector_amplitude", "close",
-                                                "ema10", "ema20", "ema200", "score"])
-            s.execute(stmt)
-
-
 # ── 市场情绪（衍生）─────────────────────────────────────────────────────
 
 
@@ -1031,6 +908,51 @@ def upsert_kline_stock_ma_daily_rows(rows: list[dict[str, Any]]) -> None:
         s.execute(stmt)
 
 
+def upsert_kline_stock_macd_daily_rows(rows: list[dict[str, Any]]) -> int:
+    """rows: stock_code, trade_date, diff, dea, macd, ema12, ema26, source"""
+    if not rows:
+        return 0
+    with get_session() as s:
+        return _bulk_upsert_orm(s, KlineStockMACDDaily, rows)
+
+
+def upsert_kline_stock_kdj_daily_rows(rows: list[dict[str, Any]]) -> int:
+    """rows: stock_code, trade_date, k, d, j, source"""
+    if not rows:
+        return 0
+    with get_session() as s:
+        return _bulk_upsert_orm(s, KlineStockKDJDaily, rows)
+
+
+def upsert_kline_stock_boll_daily_rows(rows: list[dict[str, Any]]) -> int:
+    """rows: stock_code, trade_date, upper, mid, lower, source"""
+    if not rows:
+        return 0
+    with get_session() as s:
+        return _bulk_upsert_orm(s, KlineStockBOLLDaily, rows)
+
+
+_INDICATOR_MODEL = {
+    "macd": KlineStockMACDDaily,
+    "kdj": KlineStockKDJDaily,
+    "boll": KlineStockBOLLDaily,
+}
+
+
+def get_indicator_max_date(indicator: str, code: str) -> Optional[str]:
+    """某只票某指标已入库的最大 trade_date（YYYY-MM-DD），无则 None。"""
+    model = _INDICATOR_MODEL.get(indicator.lower())
+    if model is None:
+        raise ValueError(f"未知指标: {indicator!r}")
+    _ensure_schema()
+    with get_session() as s:
+        d = s.execute(
+            select(func.max(model.trade_date))
+            .where(model.stock_code == code)
+        ).scalar()
+        return _to_date_str(d) if d else None
+
+
 def pd_isna(v: Any) -> bool:
     """避免直接 import pandas（开销大），手写 nan/None 检查。"""
     if v is None:
@@ -1042,11 +964,26 @@ def pd_isna(v: Any) -> bool:
 
 
 def get_stock_kline_max_date(code: str) -> Optional[str]:
+    """某只票 kline_stock 已入库的最大 trade_date (YYYY-MM-DD),无则 None。
+
+    code 接受 6 位 ("600000") 或 hermes 格式 ("SH600000") — DB 里实际存的是
+    hermes 格式,2026-07-09 之前用 `where KlineStock.stock_code == '600000'`
+    永远查不到(hermes 有 SH/SZ/BJ 前缀),导致 incremental 把全市场当"无数据"
+    重跑。修:同时查 6 位和 hermes 两种格式,取较新结果。
+    """
     _ensure_schema()
+    c6 = str(code or "").strip()
+    hermes = f"SH{c6}" if c6.startswith(("5", "6", "9")) else (
+        f"SZ{c6}" if c6.startswith(("0", "2", "3")) else (
+        f"BJ{c6}" if c6.startswith(("4", "8")) else c6
+    ))
+    candidates = [c6]
+    if hermes != c6:
+        candidates.append(hermes)
     with get_session() as s:
         d = s.execute(
             select(func.max(KlineStock.trade_date))
-            .where(KlineStock.stock_code == code)
+            .where(KlineStock.stock_code.in_(candidates))
         ).scalar()
         return _to_date_str(d) if d else None
 

app/core/scheduler/scheduler.py

@@ -323,17 +323,6 @@ DEFAULT_SCHEDULES: list[tuple[str, dict, str]] = [
         },
         "每个交易日 21:35 拉取个股资金流（mairui 21:30 发布）",
     ),
-    (
-        "schedule_industry_sector",
-        {
-            "name": "周一行业映射",
-            "time": "09:30",
-            "condition": "trading_day",
-            "job": "industry_sector",
-            "enabled": True,
-        },
-        "每个交易日 09:30 全量更新股票-行业映射（开销大，可改为 weekly）",
-    ),
     (
         "schedule_market_regime",
         {
@@ -345,17 +334,6 @@ DEFAULT_SCHEDULES: list[tuple[str, dict, str]] = [
         },
         "每个交易日 16:15 聚合市场情绪（基于本地日K线）",
     ),
-    (
-        "schedule_share_snapshot",
-        {
-            "name": "盘后股本快照",
-            "time": "16:20",
-            "condition": "trading_day",
-            "job": "share_snapshot",
-            "enabled": True,
-        },
-        "每个交易日 16:20 刷新个股总股本/流通股本快照",
-    ),
     (
         "schedule_longhubang",
         {
@@ -389,6 +367,17 @@ DEFAULT_SCHEDULES: list[tuple[str, dict, str]] = [
         },
         "每个交易日 16:30 基于 kline_stock 计算 MA5/10/20/60 (本地派生，~30s @5213 只)",
     ),
+    (
+        "schedule_mairui_indicators",
+        {
+            "name": "日 K 技术指标 MACD/KDJ/BOLL",
+            "time": "16:40",
+            "condition": "trading_day",
+            "job": "mairui_indicators",
+            "enabled": True,
+        },
+        "每个交易日 16:40 从 mairui 直拉 MACD/KDJ/BOLL 增量 (3 指标 × 全市场，增量约数分钟)",
+    ),
 ]
 
 
@@ -428,8 +417,8 @@ def register_sync_jobs() -> None:
 
     for dataset_id in [
         "stock_basic", "kline_daily", "kline_index", "kline_5min",
-        "moneyflow", "industry_sector", "share_snapshot", "market_regime",
-        "longhubang", "stock_node", "mairui_ma_daily",
+        "moneyflow", "share_snapshot", "market_regime",
+        "longhubang", "stock_node", "mairui_ma_daily", "mairui_indicators",
     ]:
 
         def _make_job(did=dataset_id):

app/core/sync/base.py

@@ -34,13 +34,13 @@ def _is_market_closed() -> bool:
 
 
 def _effective_sync_end() -> str:
-    """若盘后则今天，否则昨天。"""
-    today = datetime.now()
-    if today.time() >= dtime(15, 30):
-        return today.strftime("%Y-%m-%d")
-    # 昨天
-    from datetime import timedelta
-    return (today - timedelta(days=1)).strftime("%Y-%m-%d")
+    """若盘后则今天，否则昨天。
+
+    委派到 app.core.datasource.utils.effective_market_date() 统一实现，
+    该函数加载交易日历，会跳过节假日返回最近交易日。
+    """
+    from app.core.datasource.utils import effective_market_date
+    return effective_market_date()
 
 
 class SyncTask:

app/core/sync/registry.py

@@ -94,30 +94,6 @@ SYNC_DEFINITIONS: list[dict[str, Any]] = [
         "dependency_ids": ["stock_basic"],
         "sort_order": 50,
     },
-    {
-        "dataset_id": "industry_sector",
-        "name": "股票-行业映射",
-        "description": "股票-行业映射 + 行业字典（Baostock）",
-        "storage_uri": "PG market_data.industry + sectors + stock_sector_map",
-        "storage_layer": "pg",
-        "management_role": "基础字典",
-        "source": "Baostock query_stock_industry",
-        "sync_script": "app.tasks.task_industry_sector:run",
-        "dependency_ids": ["stock_basic"],
-        "sort_order": 60,
-    },
-    {
-        "dataset_id": "sector_features",
-        "name": "行业聚合特征",
-        "description": "由 kline_stock + industry 衍生：行业日收益 / 行业指数 close / EMA10-20-200 / score。纯本地计算，无外部 API。",
-        "storage_uri": "PG market_data.sector_indices + sector_features_daily",
-        "storage_layer": "pg",
-        "management_role": "衍生源",
-        "source": "本地计算（kline_stock + industry）",
-        "sync_script": "app.tasks.task_sector_features:run",
-        "dependency_ids": ["kline_daily", "industry_sector"],
-        "sort_order": 65,
-    },
     {
         "dataset_id": "share_snapshot",
         "name": "股本快照",
@@ -178,6 +154,18 @@ SYNC_DEFINITIONS: list[dict[str, Any]] = [
         "dependency_ids": ["kline_daily"],
         "sort_order": 90,
     },
+    {
+        "dataset_id": "mairui_indicators",
+        "name": "日 K 技术指标 MACD/KDJ/BOLL (mairui)",
+        "description": "mairui /hsstock/history/{macd,kdj,boll} 直拉，写 3 张表；每交易日 16:40 增量。",
+        "storage_uri": "PG market_data.kline_stock_macd_daily + kdj_daily + boll_daily",
+        "storage_layer": "pg",
+        "management_role": "原始源",
+        "source": "mairui /hsstock/history/{macd,kdj,boll}/{symbol}/d/n",
+        "sync_script": "app.tasks.task_mairui_indicators:run",
+        "dependency_ids": ["stock_basic"],
+        "sort_order": 91,
+    },
 ]