refactor: PG-only 迁移 + 龙虎榜/tick/moneyflow 同步 + stock_code 统一 + mairui 编码修复

## 1. PG-only 重构
- 删 app/core/db/connection.py + schema.py (MySQL 路径)
- 新 app/core/db/{orm,models,pg_bootstrap}.py — SQLAlchemy 2.x ORM 一键建表
- 16 张业务表全在 market_data schema,原生 TIMESTAMPTZ / JSONB / Float / TEXT
- requirements.txt 删 PyMySQL 路径,加 psycopg2

## 2. 同步任务扩展(3 个新 task)
- **tick_trade** (mairui hsrl/zbjy):当天逐笔交易,21:00 发布
- **moneyflow** (mairui hsstock/history/transaction):个股资金流,21:30 发布
- **longhubang** (akshare):龙虎榜聚合层 + 席位层(2 张新表)
  - 长虎榜放宽 akshare 政策:仅"无替代源 + 烟测通过"场景允许
  - data_eastmoney 私有 API 不需要(akshare 烟测通过)
- 3-timer 设计:
  - 15:30 market-sync.service (8 base tasks via runall_once)
  - 21:05 market-sync-tick.service (tick_trade)
  - 21:35 market-sync-moneyflow.service (moneyflow)
  - 22:00 market-sync-lhb.service (longhubang,新加)
- bin/systemd/ 新增 tick / moneyflow / lhb 各 1 对 service+timer
- bin/market_sync_*_run.sh wrapper 脚本(不做法定节假日过滤,fail-open)

## 3. stock_code 统一为带 SH/SZ/BJ 前缀
- 历史 bug:stocks.code 用 SH600519,但 kline/moneyflow/tick_trade/kline_5min
  /stock_sector_map/industry 6 张表用纯 6 位 600519,跨表 JOIN 全部 0 行
- 新增 to_hermes() 工具:6位 / 9位(mairui `000001.SZ` 格式)→ 统一 SH000001
- 5 个 task 改写:用 to_hermes(code6) 写入 stock_code
- 一次性迁移 6 张表存量 154M 行(CASE WHEN 探测 + 去重 + 加前缀)
- ORM: stock_sector_map.stock_code / industry.code String(6)→String(10)

## 4. Bug 修复
- **share table stock_code 格式**:之前写 6 位不带前缀,与 stocks 不一致
  → 修 task_share_snapshot + 一次性 UPDATE 63,417 行加前缀
- **share_snapshot warning 状态错填 last_error**:
  → 加 mark_sync_warning() 走专用路径,不写 last_failure_at / last_error
- **schedule config lastRun 不同步**:
  → 加 update_job_status_for_dataset(),SyncTask.run() 完成后自动镜像
  → cli/runall 触发的 task 也能更新 schedule config

## 5. mairui UTF-8 编码修复
- 历史 bug:mairui.py:_fetch 用 latin-1 兜底解码,把所有 UTF-8 中文名
  double-encoded 写入 stocks.name(如 `歌华有线` 变成 `æ\xad\x8cå\x8d\x8e...`)
- 加 _decode_response():UTF-8 → GBK → latin-1 兜底
- 一次性修复 stocks.name 5,213 行:
  - 4,370 行 (encode('latin-1').decode('utf-8') 反向解码)
  - 616 行 (含 fullwidth Ａ,宽松 printable 检查)
  - 820 行 (mid-character 截断,重新从 mairui 拉)

## 6. 测试
- tests/test_smoke.py: TASKS 10→11, SYNC_DEFINITIONS 10→11
- tests/test_schema_models.py: 16→18 张表,新增 longhubang_daily/seat
- pytest 11/11 passed

## 验证
- 6 张表 0 残留无前缀行
- stocks JOIN kline_stock / kline_5min / moneyflow / tick_trade / stock_sector_map:88-100% 命中
- 5,213 stocks.name 全部正确 UTF-8 中文
- pytest 11/11 passed

app/core/config.py

@@ -1,4 +1,7 @@
-"""统一配置：从 .env / 环境变量读 MySQL、调度器、数据源开关等参数。"""
+"""统一配置：从 .env / 环境变量读 PG、调度器、数据源开关等参数。
+
+2026-06-16 重构：项目只支持 PostgreSQL，MySQL 字段全部移除。
+"""
 from __future__ import annotations
 
 import os
@@ -27,18 +30,6 @@ 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。"""
 
-    # MySQL
-    mysql_host: str = "127.0.0.1"
-    mysql_port: int = 3306
-    mysql_user: str = "root"
-    mysql_password: str = ""
-    # 同步任务写入的目标库（原始市场数据）
-    mysql_database: str = "market_data_sync_db"
-    # 简版库（对照测试，可选）
-    mysql_database_lite: str = "grid_seeker"
-    # 业务主库（保留— 同步任务不再写入）
-    mysql_database_business: str = "grid_seeker_model_base"
-
     # Scheduler
     scheduler_tick_seconds: int = 5
     scheduler_timezone: str = "Asia/Shanghai"
@@ -57,6 +48,14 @@ class Settings(BaseSettings):
     ds_sina_enabled: bool = True
     xueqiu_token: str = ""
 
+    # PostgreSQL — 完整 URL（覆盖下面 PG_HOST 等）
+    pg_url: str = ""
+    pg_host: str = "127.0.0.1"
+    pg_port: int = 5432
+    pg_user: str = "market_sync"
+    pg_password: str = "market_sync"
+    pg_db_name: str = "market_data"
+
     # Trading calendar
     trading_holidays: str = ""
 
@@ -85,32 +84,15 @@ class Settings(BaseSettings):
             return []
         return [d.strip() for d in self.trading_holidays.split(",") if d.strip()]
 
-    def mysql_url(self, database: Optional[str] = None) -> str:
+    def pg_sqlalchemy_url(self) -> str:
-        """构造 SQLAlchemy URL。"""
+        """构造 PG 的 SQLAlchemy URL（自动剥掉 +psycopg2 让 SQLAlchemy 决定 driver）。"""
-        db = database or self.mysql_database
+        if self.pg_url:
+            return self.pg_url
         return (
-            f"mysql+pymysql://{self.mysql_user}:{self.mysql_password}"
+            f"postgresql+psycopg2://{self.pg_user}:{self.pg_password}"
-            f"@{self.mysql_host}:{self.mysql_port}/{db}?charset=utf8mb4"
+            f"@{self.pg_host}:{self.pg_port}/{self.pg_db_name}"
         )
 
-    def pymysql_connect_kwargs(self, database: Optional[str] = None) -> dict:
-        """构造 pymysql.connect 的关键字参数。
-
-        内网自签名证书场景自动禁用 SSL。
-        """
-        db = database or self.mysql_database
-        return {
-            "host": self.mysql_host,
-            "port": self.mysql_port,
-            "user": self.mysql_user,
-            "password": self.mysql_password,
-            "database": db,
-            "charset": "utf8mb4",
-            "autocommit": False,
-            # 兼容自签名证书的内网 MySQL — 禁用 SSL
-            "ssl": None,
-        }
-
 
 _settings: Optional[Settings] = None
 

app/core/datasource/registry.py

@@ -65,6 +65,16 @@ _DATASOURCE_SEED: list[tuple[str, dict, str]] = [
         },
         "K 线主源（日线 + 5min + 指数）",
     ),
+    (
+        "datasource_akshare_lhb",
+        {
+            "name": "akshare 龙虎榜（东方财富封装）",
+            "provides": ["longhubang_daily", "longhubang_seat"],
+            "requiresCredential": False,
+            "note": "龙虎榜聚合层 + 席位层；无合理替代源，按 [[no-akshare]] 决策放行",
+        },
+        "龙虎榜聚合层 + 席位层（akshare → 东方财富）",
+    ),
 ]
 
 
@@ -247,4 +257,7 @@ def build_default_registry() -> None:
     else:
         # 即使没 token 也注册，is_available() 会拦截
         registry.register(XueqiuSource())
-    # akshare 已移除（项目级决策：永远不用）
+    # akshare：2026-07-01 决策更新——龙虎榜无替代源，烟测通过，可注册。
+    # 见 [[no-akshare]] 记忆：仅"无替代"+"akshare 烟测 OK"才允许用。
+    from app.sources.akshare_lhb import AkshareLhbSource
+    registry.register(AkshareLhbSource())

app/core/datasource/utils.py

@@ -48,6 +48,25 @@ def code6_to_mairui(code6: str) -> str:
     return f"{code6}.{code6_to_exchange(code6)}"
 
 
+def to_hermes(code: str) -> str:
+    """6 位代码 → 带交易所前缀（项目标准：与 stocks.code 一致）。
+
+    '000001' → 'SZ000001'
+    '600519' → 'SH600519'
+    '830xxx' → 'BJ830xxx'（北证）
+    'SH600519' → 'SH600519'（已是 hermes 格式则原样返回）
+    """
+    c = str(code).strip().upper()
+    # 已是 hermes 格式
+    if c.startswith(("SH", "SZ", "BJ")) and len(c) >= 8:
+        return c
+    # 6 位纯数字 → 加前缀
+    if len(c) == 6 and c.isdigit():
+        return f"{code6_to_exchange(c)}{c}"
+    # 兜底：原样返回
+    return c
+
+
 def normalize_kline(df: pd.DataFrame) -> pd.DataFrame:
     """标准化日 K 线 DataFrame。
 

app/core/db/__init__.py

@@ -1,4 +1,4 @@
-"""DB 子包：连接管理 + schema + 业务 CRUD。"""
+"""DB 子包：ORM + 业务 CRUD。"""
-from app.core.db.connection import get_mysql, init_mysql_schema, close_mysql
+from app.core.db.orm import get_session, engine, SessionLocal, ORMBase
 
-__all__ = ["get_mysql", "init_mysql_schema", "close_mysql"]
+__all__ = ["get_session", "engine", "SessionLocal", "ORMBase"]

app/core/db/connection.py

@@ -1,104 +0,0 @@
-"""MySQL 连接管理（thread-local，模式照搬 dashboard，但适配 market_data_sync 项目）。
-
-要点：
-- pymysql 连接不是线程安全的，每个线程各持一份
-- 内网自签名证书场景：传 ssl_disabled=True 跳过 SSL 校验
-- DATE / DATETIME / TIMESTAMP 统一转字符串，避免下游类型处理
-- DictCursor：返回字典风格行，方便业务拼装
-"""
-from __future__ import annotations
-
-import threading
-from typing import Optional
-
-import pymysql
-import pymysql.constants.FIELD_TYPE
-from pymysql.converters import conversions as _pymysql_conv, convert_date, convert_datetime
-from pymysql.cursors import DictCursor
-
-from app.core.config import settings
-
-_local = threading.local()
-
-
-def _strdate(obj):
-    d = convert_date(obj)
-    return d.strftime("%Y-%m-%d") if d else None
-
-
-def _strdatetime(obj):
-    d = convert_datetime(obj)
-    return d.strftime("%Y-%m-%d %H:%M:%S") if d else None
-
-
-# 包装默认 converter，让 DATE / DATETIME / TIMESTAMP 一律出字符串
-_conv = _pymysql_conv.copy()
-_conv[pymysql.constants.FIELD_TYPE.DATE] = _strdate
-_conv[pymysql.constants.FIELD_TYPE.DATETIME] = _strdatetime
-_conv[pymysql.constants.FIELD_TYPE.TIMESTAMP] = _strdatetime
-
-
-def get_mysql(database: Optional[str] = None) -> pymysql.connections.Connection:
-    """取当前线程的 MySQL 连接。无则新建。
-
-    关键修复：高并发场景下，连接可能被服务端超时断开 (wait_timeout)。
-    用 ping(reconnect=True) 让 pymysql 自动重连，避免 stale conn 导致
-    2013 Lost connection 错误。
-    """
-    attr = f"conn_{database or 'default'}"
-    conn = getattr(_local, attr, None)
-    if conn is not None:
-        try:
-            conn.ping(reconnect=True)
-        except Exception:
-            try:
-                conn.close()
-            except Exception:
-                pass
-            conn = None
-    if conn is None:
-        kwargs = dict(
-            host=settings.mysql_host,
-            port=settings.mysql_port,
-            user=settings.mysql_user,
-            password=settings.mysql_password,
-            database=database or settings.mysql_database,
-            charset="utf8mb4",
-            cursorclass=DictCursor,
-            autocommit=False,
-            conv=_conv,
-        )
-        # 内网自签名证书：禁用 SSL（用户场景）
-        kwargs["ssl"] = None
-        # 高并发时设较短 read_timeout / write_timeout
-        kwargs["read_timeout"] = 30
-        kwargs["write_timeout"] = 30
-        conn = pymysql.connect(**kwargs)
-        setattr(_local, attr, conn)
-    return conn
-
-
-def close_mysql() -> None:
-    """关闭当前线程的所有连接。"""
-    for attr in list(vars(_local).keys()):
-        conn = getattr(_local, attr, None)
-        if conn is not None:
-            try:
-                conn.close()
-            except Exception:
-                pass
-            try:
-                delattr(_local, attr)
-            except AttributeError:
-                pass
-
-
-# ── Schema DDL 入口 ────────────────────────────────────────────────────────
-
-
-def init_mysql_schema() -> None:
-    """集中创建/补齐所有表。被 db/ops.py 在第一次访问时调用。"""
-    # 延后导入避免循环依赖
-    from app.core.db.schema import ensure_all_tables
-
-    ensure_all_tables(get_mysql())

app/core/db/models.py

@@ -0,0 +1,479 @@
+"""PostgreSQL ORM 模型（SQLAlchemy 2.x declarative）。
+
+设计原则：
+  - 全部 15 张表用 Python ORM 表达；`Base.metadata.create_all()` 一键建表
+  - 所有表放在 `market_data` schema 下（PG-native）
+  - 类型选择 PG-native：TIMESTAMPTZ / NUMERIC / JSONB / TEXT
+  - 字段名与 MySQL 版尽量保持一致，便于将来数据迁移直接 INSERT ... SELECT
+
+与原 MySQL 版的差异（按列类型一一对照）：
+  - DATETIME               → TIMESTAMPTZ
+  - DOUBLE                 → Float（保持 double precision）
+  - VARCHAR JSON 字段      → JSONB（dataset_registry.dependency_ids）
+  - ON UPDATE CURRENT_TIMESTAMP → onupdate=func.now()（应用层处理，PG 没内置）
+  - PrimaryKeyConstraint   → 全部用主键约束表达；复合主键用 __table_args__
+"""
+from __future__ import annotations
+
+from datetime import date, datetime
+from typing import Optional
+
+from sqlalchemy import (
+    Date,
+    DateTime,
+    Float,
+    Index,
+    Integer,
+    PrimaryKeyConstraint,
+    SmallInteger,
+    String,
+    Text,
+    func,
+)
+from sqlalchemy.dialects.postgresql import JSONB
+from sqlalchemy.orm import Mapped, mapped_column
+
+from app.core.db.orm import ORMBase
+
+
+# ──── 公共 updated_at 工厂 ────
+def _updated_at():
+    """`DEFAULT NOW() ON UPDATE NOW()` —— PG 应用层维护。"""
+    return mapped_column(
+        DateTime(timezone=True),
+        server_default=func.now(),
+        onupdate=func.now(),
+    )
+
+
+# ──────────────────────── 1. config ────────────────────────
+class Config(ORMBase):
+    """键值配置表（.env / 数据源开关 / 调度参数等）。"""
+    __tablename__ = "config"
+    __table_args__ = {"schema": "market_data"}
+
+    key: Mapped[str] = mapped_column(String(128), primary_key=True)
+    value: Mapped[str] = mapped_column(Text, nullable=False)
+    category: Mapped[str] = mapped_column(String(32), nullable=False, default="general")
+    description: Mapped[Optional[str]] = mapped_column(String(255), default="")
+    updated_at: Mapped[Optional[datetime]] = _updated_at()
+
+
+# ──────────────────────── 2. dataset_registry ────────────────────────
+class DatasetRegistry(ORMBase):
+    """同步任务状态机：idle → running → ok / warning / error / blocked。"""
+    __tablename__ = "dataset_registry"
+    __table_args__ = (
+        Index("idx_dataset_registry_sort_order", "sort_order"),
+        {"schema": "market_data"},
+    )
+
+    dataset_id: Mapped[str] = mapped_column(String(64), primary_key=True)
+    name: Mapped[str] = mapped_column(String(128), nullable=False, default="")
+    description: Mapped[Optional[str]] = mapped_column(Text)
+    storage_uri: Mapped[Optional[str]] = mapped_column(String(255), default="")
+    storage_layer: Mapped[Optional[str]] = mapped_column(String(32), default="")
+    management_role: Mapped[Optional[str]] = mapped_column(String(32), default="")
+    source: Mapped[Optional[str]] = mapped_column(String(255), default="")
+    sync_script: Mapped[Optional[str]] = mapped_column(String(255), default="")
+    # 原 schema 是 VARCHAR(4096) DEFAULT '[]'（序列化的 JSON 字符串），PG 改为原生 JSONB
+    dependency_ids: Mapped[Optional[list]] = mapped_column(JSONB, default=list)
+    enabled: Mapped[int] = mapped_column(Integer, default=1)
+    sort_order: Mapped[int] = mapped_column(Integer, default=0)
+    status: Mapped[Optional[str]] = mapped_column(String(16), default="idle")
+    trigger_source: Mapped[Optional[str]] = mapped_column(String(32), default="")
+    started_at: Mapped[Optional[datetime]] = mapped_column(DateTime(timezone=True))
+    finished_at: Mapped[Optional[datetime]] = mapped_column(DateTime(timezone=True))
+    last_success_at: Mapped[Optional[datetime]] = mapped_column(DateTime(timezone=True))
+    last_failure_at: Mapped[Optional[datetime]] = mapped_column(DateTime(timezone=True))
+    message: Mapped[Optional[str]] = mapped_column(Text)
+    last_error: Mapped[Optional[str]] = mapped_column(Text)
+    needs_resync: Mapped[int] = mapped_column(Integer, default=0)
+    progress_current: Mapped[int] = mapped_column(Integer, default=0)
+    progress_total: Mapped[int] = mapped_column(Integer, default=0)
+    current_step: Mapped[Optional[str]] = mapped_column(String(255), default="")
+    updated_at: Mapped[Optional[datetime]] = _updated_at()
+
+
+# ──────────────────────── 3. stocks ────────────────────────
+class Stock(ORMBase):
+    """股票基础信息 + 最新股本快照（冗余缓存）。"""
+    __tablename__ = "stocks"
+    __table_args__ = {"schema": "market_data"}
+
+    code: Mapped[str] = mapped_column(String(10), primary_key=True)
+    name: Mapped[str] = mapped_column(String(32), nullable=False, default="")
+    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)
+    kline_synced_at: Mapped[Optional[date]] = mapped_column(Date)
+    updated_at: Mapped[Optional[datetime]] = _updated_at()
+
+
+# ──────────────────────── 4. indices ────────────────────────
+# 类的 Python 名 `MarketIndex`（避免和 `from sqlalchemy import Index` 冲突），
+# 但 __tablename__ 仍是 "indices" 跟原 MySQL 一致。
+class MarketIndex(ORMBase):
+    """六大指数基础信息。"""
+    __tablename__ = "indices"
+    __table_args__ = {"schema": "market_data"}
+
+    index_code: Mapped[str] = mapped_column(String(10), primary_key=True)
+    index_name: Mapped[str] = mapped_column(String(64), nullable=False, default="")
+    market: Mapped[Optional[str]] = mapped_column(String(16), default="")
+    category: 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()
+
+
+# ──────────────────────── 5. kline_stock ────────────────────────
+class KlineStock(ORMBase):
+    """全市场 A 股日频 OHLCV。"""
+    __tablename__ = "kline_stock"
+    __table_args__ = (
+        PrimaryKeyConstraint("stock_code", "trade_date"),
+        Index("idx_kline_stock_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)
+    open: Mapped[float] = mapped_column(Float, nullable=False)
+    high: Mapped[float] = mapped_column(Float, nullable=False)
+    low: Mapped[float] = mapped_column(Float, nullable=False)
+    close: Mapped[float] = mapped_column(Float, nullable=False)
+    volume: Mapped[float] = mapped_column(Float, nullable=False)
+
+
+# ──────────────────────── 6. kline_index ────────────────────────
+class KlineIndex(ORMBase):
+    """六大指数日 K。"""
+    __tablename__ = "kline_index"
+    __table_args__ = (
+        PrimaryKeyConstraint("index_code", "trade_date"),
+        Index("idx_kline_index_date", "trade_date"),
+        {"schema": "market_data"},
+    )
+
+    index_code: Mapped[str] = mapped_column(String(10), nullable=False)
+    trade_date: Mapped[date] = mapped_column(Date, nullable=False)
+    open: Mapped[float] = mapped_column(Float, nullable=False)
+    high: Mapped[float] = mapped_column(Float, nullable=False)
+    low: Mapped[float] = mapped_column(Float, nullable=False)
+    close: Mapped[float] = mapped_column(Float, nullable=False)
+    volume: Mapped[float] = mapped_column(Float, nullable=False)
+
+
+# ──────────────────────── 7. kline_5min ────────────────────────
+class Kline5Min(ORMBase):
+    """全市场 A 股 5 分钟 K 线。"""
+    __tablename__ = "kline_5min"
+    __table_args__ = (
+        PrimaryKeyConstraint("stock_code", "bar_time"),
+        Index("idx_kline_5min_bar_time", "bar_time"),
+        {"schema": "market_data"},
+    )
+
+    stock_code: Mapped[str] = mapped_column(String(10), nullable=False)
+    bar_time: Mapped[datetime] = mapped_column(DateTime(timezone=True), nullable=False)
+    open: Mapped[Optional[float]] = mapped_column(Float)
+    high: Mapped[Optional[float]] = mapped_column(Float)
+    low: Mapped[Optional[float]] = mapped_column(Float)
+    close: Mapped[Optional[float]] = mapped_column(Float)
+    volume: Mapped[Optional[float]] = mapped_column(Float)
+    amount: Mapped[Optional[float]] = mapped_column(Float)
+    turnover_rate: Mapped[Optional[float]] = mapped_column(Float)
+
+
+# ──────────────────────── 8. moneyflow ────────────────────────
+class Moneyflow(ORMBase):
+    """个股资金流（mairui 源：主力/大/中/小单 净额，单位元）。"""
+    __tablename__ = "moneyflow"
+    __table_args__ = (
+        PrimaryKeyConstraint("stock_code", "trade_date"),
+        Index("idx_moneyflow_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)
+    main_net_inflow: Mapped[float] = mapped_column(Float, default=0)
+    large_net_inflow: Mapped[float] = mapped_column(Float, default=0)
+    medium_net_inflow: Mapped[float] = mapped_column(Float, default=0)
+    small_net_inflow: Mapped[float] = mapped_column(Float, default=0)
+
+
+# ──────────────────────── 9. share ────────────────────────
+class Share(ORMBase):
+    """个股股本快照（雪球 quote_detail，单位亿股）。"""
+    __tablename__ = "share"
+    __table_args__ = (
+        PrimaryKeyConstraint("stock_code", "trade_date"),
+        {"schema": "market_data"},
+    )
+
+    stock_code: Mapped[str] = mapped_column(String(10), nullable=False)
+    trade_date: Mapped[date] = mapped_column(Date, nullable=False)
+    total_share: Mapped[float] = mapped_column(Float, nullable=False)
+    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 ────────────────────────
+class MarketRegimeDaily(ORMBase):
+    """市场情绪衍生指标（基于本地 kline 聚合）。"""
+    __tablename__ = "market_regime_daily"
+    __table_args__ = (
+        Index("idx_market_regime_daily_date", "trade_date"),
+        {"schema": "market_data"},
+    )
+
+    trade_date: Mapped[date] = mapped_column(Date, primary_key=True)
+    advancers: Mapped[float] = mapped_column(Float, default=0)
+    decliners: Mapped[float] = mapped_column(Float, default=0)
+    advance_ratio: Mapped[float] = mapped_column(Float, default=0)
+    turnover: Mapped[float] = mapped_column(Float, default=0)
+    turnover_avg_5d: Mapped[float] = mapped_column(Float, default=0)
+    turnover_ratio_5d: Mapped[float] = mapped_column(Float, default=0)
+    source: Mapped[Optional[str]] = mapped_column(String(32), default="")
+    is_extreme_panic: Mapped[int] = mapped_column(Integer, default=0)
+    updated_at: Mapped[Optional[datetime]] = _updated_at()
+
+
+# ──────────────────────── 16. tick_trade ────────────────────────
+class TickTrade(ORMBase):
+    """个股当天逐笔成交（mairui 源）。
+
+    mairui API（`hsrl/zbjy/{code6}/{licence}`）不返唯一 trade_id；
+    6 字段组合 `(stock_code, trade_date, trade_time, price, volume, direction_code)`
+    作为复合主键保证幂等 upsert。
+
+    direction_code: 0=中性盘 / 1=买入 / 2=卖出（mairui 原始值）。
+    direction:      派生 'neutral' / 'buy' / 'sell'（冗余存便于按文本查）。
+    amount:         派生 price * volume（元；mairui 不返）。
+    """
+    __tablename__ = "tick_trade"
+    __table_args__ = (
+        PrimaryKeyConstraint(
+            "stock_code", "trade_date", "trade_time",
+            "price", "volume", "direction_code",
+        ),
+        Index("idx_tick_trade_date", "trade_date"),
+        Index("idx_tick_trade_code_date", "stock_code", "trade_date"),
+        {"schema": "market_data"},
+    )
+
+    stock_code: Mapped[str] = mapped_column(String(10), nullable=False)
+    trade_date: Mapped[date] = mapped_column(Date, nullable=False)
+    trade_time: Mapped[datetime] = mapped_column(DateTime(timezone=True), nullable=False)
+    price: Mapped[float] = mapped_column(Float, nullable=False)
+    volume: Mapped[float] = mapped_column(Float, nullable=False)
+    direction_code: Mapped[int] = mapped_column(Integer, nullable=False)
+    direction: Mapped[str] = mapped_column(String(8), nullable=False, default="")
+    amount: Mapped[float] = mapped_column(Float, nullable=False, default=0)
+
+
+# ──────────────────────── 17. longhubang_daily ────────────────────────
+class LonghubangDaily(ORMBase):
+    """龙虎榜每日上榜股票汇总（聚合层，akshare 源）。
+
+    数据源：`ak.stock_lhb_detail_em(start_date, end_date)` —— 东方财富私有 API 封装。
+    每个上榜日 × 每只上榜股票 = 1 行。
+
+    字段（21 列）：
+      基础：rank_idx, stock_code, stock_name, trade_date, comment
+      当日：close, pct_chg
+      资金：lhb_net_buy, lhb_buy_amt, lhb_sell_amt, lhb_total_amt, market_total_amt
+      比率：net_buy_ratio, total_amt_ratio, turnover_rate, float_mv
+      原因：reason
+      后效：post_1d_pct, post_2d_pct, post_5d_pct, post_10d_pct
+
+    注意：`stock_code` 含交易所前缀（SH600000 / SZ000001），与项目其他表一致。
+    """
+    __tablename__ = "longhubang_daily"
+    __table_args__ = (
+        PrimaryKeyConstraint("stock_code", "trade_date"),
+        Index("idx_lhb_daily_date", "trade_date"),
+        {"schema": "market_data"},
+    )
+
+    stock_code: Mapped[str] = mapped_column(String(10), nullable=False)
+    stock_name: Mapped[str] = mapped_column(String(64), nullable=False, default="")
+    trade_date: Mapped[date] = mapped_column(Date, nullable=False)
+    rank_idx: Mapped[Optional[int]] = mapped_column(Integer, nullable=True)
+    comment: Mapped[Optional[str]] = mapped_column(Text, nullable=True)
+    close: Mapped[Optional[float]] = mapped_column(Float, nullable=True)
+    pct_chg: Mapped[Optional[float]] = mapped_column(Float, nullable=True)
+    lhb_net_buy: Mapped[Optional[float]] = mapped_column(Float, nullable=True)
+    lhb_buy_amt: Mapped[Optional[float]] = mapped_column(Float, nullable=True)
+    lhb_sell_amt: Mapped[Optional[float]] = mapped_column(Float, nullable=True)
+    lhb_total_amt: Mapped[Optional[float]] = mapped_column(Float, nullable=True)
+    market_total_amt: Mapped[Optional[float]] = mapped_column(Float, nullable=True)
+    net_buy_ratio: Mapped[Optional[float]] = mapped_column(Float, nullable=True)
+    total_amt_ratio: Mapped[Optional[float]] = mapped_column(Float, nullable=True)
+    turnover_rate: Mapped[Optional[float]] = mapped_column(Float, nullable=True)
+    float_mv: Mapped[Optional[float]] = mapped_column(Float, nullable=True)
+    reason: Mapped[Optional[str]] = mapped_column(Text, nullable=True)
+    post_1d_pct: Mapped[Optional[float]] = mapped_column(Float, nullable=True)
+    post_2d_pct: Mapped[Optional[float]] = mapped_column(Float, nullable=True)
+    post_5d_pct: Mapped[Optional[float]] = mapped_column(Float, nullable=True)
+    post_10d_pct: Mapped[Optional[float]] = mapped_column(Float, nullable=True)
+
+
+# ──────────────────────── 18. longhubang_seat ────────────────────────
+class LonghubangSeat(ORMBase):
+    """龙虎榜单只票买卖前五营业部（席位层，akshare 源）。
+
+    数据源：`ak.stock_lhb_stock_detail_em(symbol, date, flag)` —— 东方财富私有 API 封装。
+    每只上榜票 × 每方向（buy/sell）= 5 行（共 10 行/票/日）。
+
+    字段（11 列 + 上榜原因）：
+      基础：stock_code, trade_date, direction（buy/sell）, rank_idx
+      营业部：branch_name（akshare 不返唯一 branch_code，用 name 作 PK 兜底）
+      金额：buy_amount, sell_amount, amount, net_amount, amount_ratio
+      当日：pct_chg, close, stock_total_amt
+      原因：explanation
+
+    注意：akshare 的 `stock_lhb_stock_detail_em` 不返 `branch_code`，只用 `branch_name`
+    作 PK 兜底（不同营业部名冲突概率极低；如未来需要可补 eastmoney 私有 API 抓 code）。
+    """
+    __tablename__ = "longhubang_seat"
+    __table_args__ = (
+        PrimaryKeyConstraint("stock_code", "trade_date", "direction", "rank_idx"),
+        Index("idx_lhb_seat_date", "trade_date"),
+        Index("idx_lhb_seat_branch_name", "branch_name"),
+        {"schema": "market_data"},
+    )
+
+    stock_code: Mapped[str] = mapped_column(String(10), nullable=False)
+    trade_date: Mapped[date] = mapped_column(Date, nullable=False)
+    direction: Mapped[str] = mapped_column(String(8), nullable=False)  # "buy" / "sell"
+    rank_idx: Mapped[int] = mapped_column(Integer, nullable=False)
+    branch_name: Mapped[Optional[str]] = mapped_column(String(128), nullable=True)
+    buy_amount: Mapped[Optional[float]] = mapped_column(Float, nullable=True)
+    sell_amount: Mapped[Optional[float]] = mapped_column(Float, nullable=True)
+    amount: Mapped[Optional[float]] = mapped_column(Float, nullable=True)
+    net_amount: Mapped[Optional[float]] = mapped_column(Float, nullable=True)
+    amount_ratio: Mapped[Optional[float]] = mapped_column(Float, nullable=True)
+    pct_chg: Mapped[Optional[float]] = mapped_column(Float, nullable=True)
+    close: Mapped[Optional[float]] = mapped_column(Float, nullable=True)
+    stock_total_amt: Mapped[Optional[float]] = mapped_column(Float, nullable=True)
+    explanation: Mapped[Optional[str]] = mapped_column(Text, nullable=True)
+
+
+__all__ = [
+    # 1-2
+    "Config",
+    "DatasetRegistry",
+    # 3-4
+    "Stock",
+    "MarketIndex",
+    # 5-9
+    "KlineStock",
+    "KlineIndex",
+    "Kline5Min",
+    "Moneyflow",
+    "Share",
+    # 10-12
+    "Sectors",
+    "StockSectorMap",
+    "Industry",
+    # 13-15
+    "SectorIndices",
+    "SectorFeaturesDaily",
+    "MarketRegimeDaily",
+    # 16
+    "TickTrade",
+    # 17-18 (2026-07-01 龙虎榜)
+    "LonghubangDaily",
+    "LonghubangSeat",
+]

app/core/db/orm.py

@@ -0,0 +1,59 @@
+"""SQLAlchemy 2.x ORM 基础设施（PostgreSQL only）。
+
+2026-06-16 重构：项目放弃 MySQL，只保留 PG。
+所有模型 class 仍带 ``__table_args__ = {"schema": "market_data"}``，指向 PG schema。
+PG 数据库 / schema / role / 表 由 ``app.core.db.pg_bootstrap`` 一次性建好。
+"""
+from __future__ import annotations
+
+from contextlib import contextmanager
+from typing import Iterator
+
+from sqlalchemy import create_engine
+from sqlalchemy.engine import Engine
+from sqlalchemy.orm import DeclarativeBase, Session, sessionmaker
+
+from app.core.config import settings
+
+
+class ORMBase(DeclarativeBase):
+    """所有 ORM 模型的基类。"""
+
+
+def _build_engine() -> Engine:
+    """构造 PG 的 SQLAlchemy Engine。"""
+    return create_engine(
+        settings.pg_sqlalchemy_url(),
+        pool_pre_ping=True,
+        pool_recycle=3600,
+        future=True,
+        echo=False,
+    )
+
+
+engine: Engine = _build_engine()
+
+# sessionmaker 工厂
+SessionLocal = sessionmaker(
+    bind=engine,
+    expire_on_commit=False,
+    autoflush=False,
+    future=True,
+)
+
+
+@contextmanager
+def get_session() -> Iterator[Session]:
+    """事务上下文：异常自动 rollback，正常自动 commit。"""
+    s = SessionLocal()
+    try:
+        yield s
+        s.commit()
+    except Exception:
+        s.rollback()
+        raise
+    finally:
+        s.close()
+
+
+__all__ = ["ORMBase", "engine", "SessionLocal", "get_session"]

app/core/db/pg_bootstrap.py

@@ -0,0 +1,235 @@
+"""PostgreSQL bootstrap — 一键建库 / 建 schema / 建 role / 建表。
+
+用法：
+    .venv/bin/python -m app.core.db.pg_bootstrap
+
+按顺序做：
+  1) 以 superuser 身份连默认 'postgres' 库
+  2) CREATE DATABASE market_data  (IF NOT EXISTS)
+  3) \\connect 到 market_data
+  4) CREATE SCHEMA market_data, grid_seeker
+  5) CREATE ROLE market_sync LOGIN PASSWORD ... (从 .env 读 PG_MARKET_SYNC_PASSWORD)
+  6) GRANT 权限 + ALTER DEFAULT PRIVILEGES
+  7) SQLAlchemy ORM Base.metadata.create_all() 一键建所有表
+
+幂等：所有 DDL 都用 IF NOT EXISTS / OR REPLACE；可以重复跑。
+"""
+from __future__ import annotations
+
+import os
+
+import psycopg2
+from psycopg2 import sql
+from sqlalchemy import create_engine, text
+from sqlalchemy.engine import URL
+
+from app.core.db.orm import ORMBase as Base
+# 重要：必须显式 import models 模块，所有 @mapped_column 才会注册到 ORMBase.metadata
+# 漏了这一步 Base.metadata.tables 是空的，create_all() 不会建任何表
+# 改用 getattr 触发副作用，绕开 IDE "unused import" 警告
+_ = __import__("app.core.db.models", fromlist=["*"])
+
+
+# ──── 配置（从 .env 读） ────
+
+def _env(key: str, default: str) -> str:
+    return os.environ.get(key, default).strip() or default
+
+
+SUPERUSER = _env("PG_SUPERUSER", "postgres")
+SUPERPASS = _env("PG_SUPERUSER_PASSWORD", "postgres")
+SUPERHOST = _env("PG_SUPERHOST", "127.0.0.1")
+SUPERPORT = int(_env("PG_SUPERPORT", "5432"))
+
+DB_NAME = _env("PG_DB_NAME", "market_data")
+SCHEMAS = _env("PG_SCHEMAS", "market_data,grid_seeker").split(",")
+
+MARKET_SYNC_ROLE = _env("PG_MARKET_SYNC_ROLE", "market_sync")
+MARKET_SYNC_PASS = _env("PG_MARKET_SYNC_PASSWORD", "market_sync")
+
+LOG_PREFIX = "[pg_bootstrap]"
+
+
+def log(msg: str) -> None:
+    print(f"{LOG_PREFIX} {msg}", flush=True)
+
+
+def _connect(dbname: str):
+    return psycopg2.connect(
+        host=SUPERHOST, port=SUPERPORT,
+        user=SUPERUSER, password=SUPERPASS,
+        dbname=dbname, connect_timeout=10,
+    )
+
+
+# ──── Step 1: 创建数据库 ────
+
+def step1_create_database() -> None:
+    log(f"Step 1: CREATE DATABASE {DB_NAME} (if not exists)")
+    conn = _connect("postgres")
+    conn.autocommit = True
+    try:
+        with conn.cursor() as cur:
+            cur.execute("SELECT 1 FROM pg_database WHERE datname = %s", (DB_NAME,))
+            if cur.fetchone():
+                log(f"  ✓ {DB_NAME} 已存在，跳过")
+            else:
+                cur.execute(sql.SQL("CREATE DATABASE {}").format(sql.Identifier(DB_NAME)))
+                log(f"  ✓ {DB_NAME} 已创建")
+    finally:
+        conn.close()
+
+
+# ──── Step 2: 创建 schema ────
+
+def step2_create_schemas() -> None:
+    log(f"Step 2: CREATE SCHEMA for {SCHEMAS}")
+    conn = _connect(DB_NAME)
+    conn.autocommit = True
+    try:
+        with conn.cursor() as cur:
+            for schema in SCHEMAS:
+                schema = schema.strip()
+                if not schema:
+                    continue
+                cur.execute(
+                    sql.SQL("CREATE SCHEMA IF NOT EXISTS {}").format(sql.Identifier(schema))
+                )
+                log(f"  ✓ schema '{schema}' 就绪")
+    finally:
+        conn.close()
+
+
+# ──── Step 3: 创建角色 + 授权 ────
+
+def step3_create_role() -> None:
+    log(f"Step 3: CREATE ROLE {MARKET_SYNC_ROLE} + GRANT")
+    conn = _connect(DB_NAME)
+    conn.autocommit = True
+    try:
+        with conn.cursor() as cur:
+            # 角色（IF NOT EXISTS 9.0+ 支持，旧版会报错 — 我们用 try/except 兜底）
+            cur.execute(
+                "SELECT 1 FROM pg_roles WHERE rolname = %s",
+                (MARKET_SYNC_ROLE,),
+            )
+            if cur.fetchone():
+                log(f"  ✓ role '{MARKET_SYNC_ROLE}' 已存在，跳过")
+            else:
+                cur.execute(
+                    sql.SQL("CREATE ROLE {} LOGIN PASSWORD %s").format(
+                        sql.Identifier(MARKET_SYNC_ROLE)
+                    ),
+                    (MARKET_SYNC_PASS,),
+                )
+                log(f"  ✓ role '{MARKET_SYNC_ROLE}' 已创建")
+
+            # 连接 DB 权限
+            cur.execute(
+                sql.SQL("GRANT CONNECT ON DATABASE {} TO {}").format(
+                    sql.Identifier(DB_NAME), sql.Identifier(MARKET_SYNC_ROLE)
+                )
+            )
+
+            # 4 个 schema 都有 USAGE
+            for schema in SCHEMAS:
+                schema = schema.strip()
+                if not schema:
+                    continue
+                cur.execute(
+                    sql.SQL("GRANT USAGE ON SCHEMA {} TO {}").format(
+                        sql.Identifier(schema), sql.Identifier(MARKET_SYNC_ROLE)
+                    )
+                )
+
+            # market_data: RW（当前项目用的）
+            cur.execute(
+                sql.SQL("GRANT SELECT, INSERT, UPDATE, DELETE ON ALL TABLES IN SCHEMA market_data TO {}").format(
+                    sql.Identifier(MARKET_SYNC_ROLE)
+                )
+            )
+            cur.execute(
+                sql.SQL("GRANT USAGE, SELECT ON ALL SEQUENCES IN SCHEMA market_data TO {}").format(
+                    sql.Identifier(MARKET_SYNC_ROLE)
+                )
+            )
+            # grid_seeker: 暂时也 RW（未来 grid_seeker 自己做权限时再收紧）
+            cur.execute(
+                sql.SQL("GRANT SELECT, INSERT, UPDATE, DELETE ON ALL TABLES IN SCHEMA grid_seeker TO {}").format(
+                    sql.Identifier(MARKET_SYNC_ROLE)
+                )
+            )
+            cur.execute(
+                sql.SQL("GRANT USAGE, SELECT ON ALL SEQUENCES IN SCHEMA grid_seeker TO {}").format(
+                    sql.Identifier(MARKET_SYNC_ROLE)
+                )
+            )
+
+            # 关键：未来新表自动继承（不然新加的表 market_sync 没权限）
+            for schema in SCHEMAS:
+                schema = schema.strip()
+                if not schema:
+                    continue
+                cur.execute(
+                    sql.SQL(
+                        "ALTER DEFAULT PRIVILEGES IN SCHEMA {s} "
+                        "GRANT SELECT, INSERT, UPDATE, DELETE ON TABLES TO {r}"
+                    ).format(s=sql.Identifier(schema), r=sql.Identifier(MARKET_SYNC_ROLE))
+                )
+                cur.execute(
+                    sql.SQL(
+                        "ALTER DEFAULT PRIVILEGES IN SCHEMA {s} "
+                        "GRANT USAGE, SELECT ON SEQUENCES TO {r}"
+                    ).format(s=sql.Identifier(schema), r=sql.Identifier(MARKET_SYNC_ROLE))
+                )
+            log(f"  ✓ {MARKET_SYNC_ROLE} 在所有 schema 上有 RW + 未来新表自动继承")
+    finally:
+        conn.close()
+
+
+# ──── Step 4: SQLAlchemy ORM 建所有表 ────
+
+def step4_create_tables() -> None:
+    log("Step 4: SQLAlchemy Base.metadata.create_all() 建所有表")
+    # 临时以 superuser 连接 market_data（建表后权限自动由 ALTER DEFAULT 接管）
+    url = URL.create(
+        drivername="postgresql+psycopg2",
+        username=SUPERUSER, password=SUPERPASS,
+        host=SUPERHOST, port=SUPERPORT,
+        database=DB_NAME,
+    )
+    engine = create_engine(url, future=True, echo=False)
+    try:
+        Base.metadata.create_all(engine)
+        with engine.connect() as c:
+            rows = c.execute(
+                text(
+                    "SELECT table_schema, table_name FROM information_schema.tables "
+                    "WHERE table_schema IN ('market_data', 'grid_seeker') "
+                    "ORDER BY table_schema, table_name"
+                )
+            ).fetchall()
+        log(f"  ✓ 当前 schema 下的表（{len(rows)} 张）:")
+        for schema, tbl in rows:
+            log(f"    - {schema}.{tbl}")
+    finally:
+        engine.dispose()
+
+
+# ──── main ────
+
+def main() -> None:
+    log(f"连接 superuser {SUPERUSER}@{SUPERHOST}:{SUPERPORT}")
+    step1_create_database()
+    step2_create_schemas()
+    step3_create_role()
+    step4_create_tables()
+    log("✅ PG bootstrap 完成")
+    log(f"  DB:     {DB_NAME}")
+    log(f"  角色:   {MARKET_SYNC_ROLE} (password 见 .env PG_MARKET_SYNC_PASSWORD)")
+    log(f"  schemas: {', '.join(SCHEMAS)}")
+    log(f"  下一步：在 .env 设 PG_URL=postgresql+psycopg2://{MARKET_SYNC_ROLE}:{MARKET_SYNC_PASS}@{SUPERHOST}:{SUPERPORT}/{DB_NAME}")
+
+
+if __name__ == "__main__":
+    main()

app/core/db/schema.py

@@ -1,264 +0,0 @@
-"""Schema DDL — 集中所有 CREATE TABLE IF NOT EXISTS。
-
-参考 dashboard/api/services/mysql_conn.py::init_mysql_schema()，但补齐 dashboard 缺的几张表
-（kline_stock / kline_5min / kline_index / moneyflow / share / sector_features_daily / sector_indices
-/ stock_scores / registry），这些是 grid_seeker_model_base 生产库已存在但 mysql_conn.py 没建的表。
-
-所有 DDL 写在同一个文件，方便审阅、diff 和补字段。"""
-from __future__ import annotations
-
-from typing import TYPE_CHECKING
-
-if TYPE_CHECKING:
-    import pymysql.connections
-
-
-# DDL 列表（顺序无关，全部 IF NOT EXISTS）
-_DDL: list[str] = [
-    # ─── config ─────────────────────────────────────────────────────────
-    """
-    CREATE TABLE IF NOT EXISTS config (
-        `key`        VARCHAR(128) PRIMARY KEY,
-        `value`      TEXT         NOT NULL,
-        category     VARCHAR(32)  NOT NULL DEFAULT 'general',
-        description  VARCHAR(255) DEFAULT '',
-        updated_at   DATETIME     DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP
-    ) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4
-    """,
-    # ─── dataset_registry (同步状态机) ────────────────────────────────────
-    """
-    CREATE TABLE IF NOT EXISTS dataset_registry (
-        dataset_id       VARCHAR(64)  PRIMARY KEY,
-        name             VARCHAR(128) NOT NULL DEFAULT '',
-        description      TEXT,
-        storage_uri      VARCHAR(255) DEFAULT '',
-        storage_layer    VARCHAR(32)  DEFAULT '',
-        management_role  VARCHAR(32)  DEFAULT '',
-        source           VARCHAR(255) DEFAULT '',
-        sync_script      VARCHAR(255) DEFAULT '',
-        dependency_ids   VARCHAR(4096) DEFAULT '[]',
-        enabled          INT          DEFAULT 1,
-        sort_order       INT          DEFAULT 0,
-        status           VARCHAR(16)  DEFAULT 'idle',
-        trigger_source   VARCHAR(32)  DEFAULT '',
-        started_at       DATETIME     DEFAULT NULL,
-        finished_at      DATETIME     DEFAULT NULL,
-        last_success_at  DATETIME     DEFAULT NULL,
-        last_failure_at  DATETIME     DEFAULT NULL,
-        message          TEXT,
-        last_error       TEXT,
-        needs_resync     INT          DEFAULT 0,
-        progress_current INT          DEFAULT 0,
-        progress_total   INT          DEFAULT 0,
-        current_step     VARCHAR(255) DEFAULT '',
-        updated_at       DATETIME     DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP,
-        INDEX idx_dataset_registry_sort_order (sort_order)
-    ) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4
-    """,
-    # ─── stocks (基础信息 + 股本字段) ─────────────────────────────────────
-    """
-    CREATE TABLE IF NOT EXISTS stocks (
-        code             VARCHAR(10)  PRIMARY KEY,
-        name             VARCHAR(32)  NOT NULL DEFAULT '',
-        exchange         VARCHAR(8)   NOT NULL DEFAULT '',
-        list_date        DATE         DEFAULT NULL,
-        listing_status   VARCHAR(16)  NOT NULL DEFAULT 'normal',
-        industry         VARCHAR(64)  DEFAULT '',
-        total_share      DOUBLE       DEFAULT 0,
-        float_share      DOUBLE       DEFAULT 0,
-        share_updated_at DATE         DEFAULT NULL,
-        kline_synced_at  DATE         DEFAULT NULL,
-        updated_at       DATETIME     DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP
-    ) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4
-    """,
-    # ─── indices (指数字典) ───────────────────────────────────────────────
-    """
-    CREATE TABLE IF NOT EXISTS indices (
-        index_code  VARCHAR(10)  PRIMARY KEY,
-        index_name  VARCHAR(64)  NOT NULL DEFAULT '',
-        market      VARCHAR(16)  DEFAULT '',
-        category    VARCHAR(16)  DEFAULT '',
-        source      VARCHAR(32)  DEFAULT '',
-        enabled     INT          DEFAULT 1,
-        updated_at  DATETIME     DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP
-    ) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4
-    """,
-    # ─── kline_stock (日 K 线，按股票) ────────────────────────────────────
-    """
-    CREATE TABLE IF NOT EXISTS kline_stock (
-        stock_code VARCHAR(10)  NOT NULL,
-        trade_date DATE         NOT NULL,
-        `open`     DOUBLE       NOT NULL,
-        `high`     DOUBLE       NOT NULL,
-        `low`      DOUBLE       NOT NULL,
-        `close`    DOUBLE       NOT NULL,
-        volume     DOUBLE       NOT NULL,
-        PRIMARY KEY (stock_code, trade_date),
-        INDEX idx_kline_stock_date (trade_date)
-    ) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4
-    """,
-    # ─── kline_index (指数日 K 线) ────────────────────────────────────────
-    """
-    CREATE TABLE IF NOT EXISTS kline_index (
-        index_code VARCHAR(10)  NOT NULL,
-        trade_date DATE         NOT NULL,
-        `open`     DOUBLE       NOT NULL,
-        `high`     DOUBLE       NOT NULL,
-        `low`      DOUBLE       NOT NULL,
-        `close`    DOUBLE       NOT NULL,
-        volume     DOUBLE       NOT NULL,
-        PRIMARY KEY (index_code, trade_date),
-        INDEX idx_kline_index_date (trade_date)
-    ) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4
-    """,
-    # ─── kline_5min (5 分钟 K 线) ────────────────────────────────────────
-    """
-    CREATE TABLE IF NOT EXISTS kline_5min (
-        stock_code    VARCHAR(10)  NOT NULL,
-        bar_time      DATETIME     NOT NULL,
-        `open`        DOUBLE       DEFAULT NULL,
-        `high`        DOUBLE       DEFAULT NULL,
-        `low`         DOUBLE       DEFAULT NULL,
-        `close`       DOUBLE       DEFAULT NULL,
-        volume        DOUBLE       DEFAULT NULL,
-        amount        DOUBLE       DEFAULT NULL,
-        turnover_rate DOUBLE       DEFAULT NULL,
-        PRIMARY KEY (stock_code, bar_time),
-        INDEX idx_kline_5min_bar_time (bar_time)
-    ) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4
-    """,
-    # ─── moneyflow (资金流，主力/大/中/小单净额) ──────────────────────────
-    """
-    CREATE TABLE IF NOT EXISTS moneyflow (
-        stock_code        VARCHAR(10) NOT NULL,
-        trade_date        DATE        NOT NULL,
-        main_net_inflow   DOUBLE      DEFAULT 0,
-        large_net_inflow  DOUBLE      DEFAULT 0,
-        medium_net_inflow DOUBLE      DEFAULT 0,
-        small_net_inflow  DOUBLE      DEFAULT 0,
-        PRIMARY KEY (stock_code, trade_date),
-        INDEX idx_moneyflow_date (trade_date)
-    ) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4
-    """,
-    # ─── share (股本快照) ────────────────────────────────────────────────
-    """
-    CREATE TABLE IF NOT EXISTS share (
-        stock_code   VARCHAR(10) NOT NULL,
-        trade_date   DATE        NOT NULL,
-        total_share  DOUBLE      NOT NULL,
-        float_share  DOUBLE      NOT NULL,
-        PRIMARY KEY (stock_code, trade_date)
-    ) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4
-    """,
-    # ─── sectors (行业字典) ──────────────────────────────────────────────
-    """
-    CREATE TABLE IF NOT EXISTS sectors (
-        sector_key  VARCHAR(64)  PRIMARY KEY,
-        sector_name VARCHAR(64)  NOT NULL DEFAULT '',
-        taxonomy    VARCHAR(64)  DEFAULT '',
-        level       VARCHAR(16)  DEFAULT '',
-        source      VARCHAR(32)  DEFAULT '',
-        enabled     INT          DEFAULT 1,
-        updated_at  DATETIME     DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP,
-        INDEX idx_sectors_sector_name (sector_name)
-    ) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4
-    """,
-    # ─── stock_sector_map (股票→行业映射) ─────────────────────────────────
-    """
-    CREATE TABLE IF NOT EXISTS stock_sector_map (
-        stock_code  VARCHAR(6)   PRIMARY KEY,
-        sector_key  VARCHAR(64)  NOT NULL DEFAULT '',
-        updated_at  DATETIME     DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP,
-        INDEX idx_stock_sector_map_sector_key (sector_key)
-    ) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4
-    """,
-    # ─── industry (股票-行业映射，datasource 实际写入用这张) ──────────────
-    """
-    CREATE TABLE IF NOT EXISTS industry (
-        code                    VARCHAR(6)   PRIMARY KEY,
-        industry_name           VARCHAR(64)  DEFAULT '',
-        industry_classification VARCHAR(32)  DEFAULT '',
-        update_date             DATE         DEFAULT NULL,
-        updated_at              DATETIME     DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP,
-        INDEX idx_industry_industry_name (industry_name)
-    ) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4
-    """,
-    # ─── sector_indices (行业聚合指数时序) ─────────────────────────────────
-    """
-    CREATE TABLE IF NOT EXISTS sector_indices (
-        trade_date        DATE         NOT NULL,
-        sector_name       VARCHAR(64)  NOT NULL,
-        `close`           DOUBLE       DEFAULT 0,
-        sector_amplitude  DOUBLE       DEFAULT 0,
-        PRIMARY KEY (trade_date, sector_name),
-        INDEX idx_sector_indices_sector_name (sector_name)
-    ) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4
-    """,
-    # ─── sector_features_daily (行业特征衍生) ─────────────────────────────
-    """
-    CREATE TABLE IF NOT EXISTS sector_features_daily (
-        trade_date        DATE         NOT NULL,
-        sector_name       VARCHAR(64)  NOT NULL,
-        sector_ret        DOUBLE       DEFAULT 0,
-        sector_amplitude  DOUBLE       DEFAULT 0,
-        `close`           DOUBLE       DEFAULT 0,
-        ema10             DOUBLE       DEFAULT 0,
-        ema20             DOUBLE       DEFAULT 0,
-        ema200            DOUBLE       DEFAULT 0,
-        score             INT          DEFAULT 0,
-        PRIMARY KEY (trade_date, sector_name),
-        INDEX idx_sector_features_sector_name (sector_name)
-    ) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4
-    """,
-    # ─── market_regime_daily (市场情绪) ───────────────────────────────────
-    """
-    CREATE TABLE IF NOT EXISTS market_regime_daily (
-        trade_date         DATE         PRIMARY KEY,
-        advancers          DOUBLE       DEFAULT 0,
-        decliners          DOUBLE       DEFAULT 0,
-        advance_ratio      DOUBLE       DEFAULT 0,
-        turnover           DOUBLE       DEFAULT 0,
-        turnover_avg_5d    DOUBLE       DEFAULT 0,
-        turnover_ratio_5d  DOUBLE       DEFAULT 0,
-        source             VARCHAR(32)  DEFAULT '',
-        is_extreme_panic   INT          DEFAULT 0,
-        updated_at         DATETIME     DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP,
-        INDEX idx_market_regime_daily_date (trade_date)
-    ) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4
-    """,
-    # ─── registry (模型注册，dashboard 业务无关但库里有) ─────────────────
-    """
-    CREATE TABLE IF NOT EXISTS registry (
-        model_name     VARCHAR(64)  NOT NULL,
-        role           VARCHAR(32)  NOT NULL,
-        version        VARCHAR(16)  DEFAULT NULL,
-        artifact_path  VARCHAR(256) NOT NULL,
-        architecture   VARCHAR(32)  DEFAULT NULL,
-        enabled        TINYINT(1)   DEFAULT 1,
-        updated_at     DATETIME     DEFAULT CURRENT_TIMESTAMP,
-        PRIMARY KEY (model_name, role)
-    ) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4
-    """,
-    # ─── stock_scores (评分快照，dashboard 业务无关但库里有) ─────────────
-    """
-    CREATE TABLE IF NOT EXISTS stock_scores (
-        `date`               DATE         NOT NULL,
-        stock_code           VARCHAR(10)  NOT NULL,
-        predicted_rounds     DOUBLE       DEFAULT 0,
-        stacking_probability DOUBLE       DEFAULT 0,
-        meta_ranker_score    DOUBLE       DEFAULT NULL,
-        latest_close         DOUBLE       DEFAULT 0,
-        `rank`               INT          DEFAULT 0,
-        PRIMARY KEY (`date`, stock_code),
-        INDEX idx_stock_scores_date (`date`)
-    ) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4
-    """,
-]
-
-
-def ensure_all_tables(conn) -> None:
-    """遍历 _DDL 列表，逐条执行 CREATE TABLE IF NOT EXISTS。"""
-    with conn.cursor() as cur:
-        for ddl in _DDL:
-            cur.execute(ddl)
-    conn.commit()

app/core/scheduler/scheduler.py

@@ -157,6 +157,22 @@ def update_job_status(key: str, status: str, message: str) -> None:
     _config_cache_ts = 0.0
 
 
+def update_job_status_for_dataset(dataset_id: str, status: str, message: str) -> None:
+    """按 dataset_id 找到对应 schedule config 并镜像 lastRun/lastStatus/lastMessage。
+
+    用法：SyncTask.run() 完成后对非 schedule 触发的运行（cli / runall_once / manual）
+    调用，让 schedule config 视图与实际跑过保持同步（fix Bug 2,2026-07-01）。
+    """
+    for r in db_ops.fetch_configs_by_category("schedule"):
+        try:
+            obj = json.loads(r.get("value", "") or "{}")
+        except (json.JSONDecodeError, TypeError):
+            continue
+        if obj.get("job") == dataset_id:
+            update_job_status(r["key"], status, message)
+            return
+
+
 # ── 调度循环 ──────────────────────────────────────────────────────────
 
 def _loop() -> None:
@@ -300,12 +316,12 @@ DEFAULT_SCHEDULES: list[tuple[str, dict, str]] = [
         "schedule_moneyflow",
         {
             "name": "盘后资金流",
-            "time": "16:30",
+            "time": "21:35",
             "condition": "trading_day",
             "job": "moneyflow",
             "enabled": True,
         },
-        "每个交易日 16:30 拉取个股资金流",
+        "每个交易日 21:35 拉取个股资金流（mairui 21:30 发布）",
     ),
     (
         "schedule_industry_sector",
@@ -340,6 +356,17 @@ DEFAULT_SCHEDULES: list[tuple[str, dict, str]] = [
         },
         "每个交易日 16:20 刷新个股总股本/流通股本快照",
     ),
+    (
+        "schedule_longhubang",
+        {
+            "name": "盘后龙虎榜",
+            "time": "22:00",
+            "condition": "trading_day",
+            "job": "longhubang",
+            "enabled": True,
+        },
+        "每个交易日 22:00 拉龙虎榜聚合层 + 席位层(akshare/东方财富 19:00~21:00 出齐)",
+    ),
 ]
 
 
@@ -380,6 +407,7 @@ 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",
     ]:
 
         def _make_job(did=dataset_id):

app/core/sync/base.py

@@ -19,6 +19,7 @@ from app.core.sync.registry import (
     mark_sync_progress,
     mark_sync_running,
     mark_sync_success,
+    mark_sync_warning,
 )
 from app.core.utils.logging import get_logger
 
@@ -79,15 +80,20 @@ class SyncTask:
                     error=result.get("error", message),
                     status="blocked",
                 )
-            else:  # warning / partial
+            elif status == "warning":
+                # 部分失败:走专用 warning 路径,不写 last_failure_at / last_error
+                mark_sync_warning(self.dataset_id, message=message)
+            else:  # 其他未识别状态默认走 failed
                 mark_sync_failed(
                     self.dataset_id,
                     message=message,
                     error=result.get("error", message),
-                    status="warning",
+                    status="failed",
                 )
             result["elapsed_sec"] = elapsed
             logger.info(f"[{self.dataset_id}] 完成 {status}: {message} ({elapsed}s)")
+            # 同步 schedule config 的 lastRun（fix Bug 2）
+            self._maybe_mirror_schedule_status(trigger_source, status, message)
             return result
         except Exception as e:
             elapsed = round(time.time() - t0, 1)
@@ -139,3 +145,28 @@ class SyncTask:
             progress_total=total,
             current_step=current_step,
         )
+
+    def _maybe_mirror_schedule_status(
+        self, trigger_source: str, status: str, message: str
+    ) -> None:
+        """把非 schedule 触发的运行结果同步到 schedule config 表（fix Bug 2）。
+
+        背景：bin/runall_once.py（15:30 systemd 触发）和 python -m app.entrypoints.cli sync
+        （手动触发）走的都是 task.run(trigger_source="cli" or "manual")，不会经过
+        app.core.scheduler 的 update_job_status。结果：schedule config 表的 lastRun /
+        lastStatus 一直停在 scheduler 真正跑过的那天（实际是 6/15 之后就没动过）。
+        UI 看到的就是"schedule 视图"和"实际跑过"脱节。
+
+        解法：每次 task.run() 完成后，lazy import scheduler，找到这个 dataset_id 对应的
+        schedule config 并镜像 lastRun/lastStatus/lastMessage。
+
+        scheduler 触发的（trigger_source="schedule"）会自己 update_job_status，跳过即可。
+        """
+        if trigger_source == "schedule":
+            return
+        try:
+            from app.core.scheduler.scheduler import update_job_status_for_dataset
+            update_job_status_for_dataset(self.dataset_id, status, message)
+        except Exception as e:
+            # 镜像失败不影响主流程；只记 debug log
+            logger.debug(f"[_maybe_mirror_schedule_status] {self.dataset_id} 镜像失败: {e}")

app/core/sync/registry.py

@@ -26,8 +26,8 @@ SYNC_DEFINITIONS: list[dict[str, Any]] = [
         "dataset_id": "stock_basic",
         "name": "股票基础信息（含最新股本）",
         "description": "全市场 A 股代码 / 名称 / 交易所 / 上市状态 + 股本快照（雪球）",
-        "storage_uri": "MySQL market_data_sync_db.stocks",
+        "storage_uri": "PG market_data.stocks",
-        "storage_layer": "mysql",
+        "storage_layer": "pg",
         "management_role": "基础字典",
         "source": "Baostock query_stock_basic + 雪球 quote_detail",
         "sync_script": "app.tasks.task_stocks_basic:run",
@@ -38,8 +38,8 @@ SYNC_DEFINITIONS: list[dict[str, Any]] = [
         "dataset_id": "kline_daily",
         "name": "原始日K线数据",
         "description": "全市场 A 股日频 OHLCV，多源交叉校验（雪球/新浪/Baostock）",
-        "storage_uri": "MySQL market_data_sync_db.kline_stock",
+        "storage_uri": "PG market_data.kline_stock",
-        "storage_layer": "mysql",
+        "storage_layer": "pg",
         "management_role": "原始源",
         "source": "雪球主源 + 新浪/Baostock 复核",
         "sync_script": "app.tasks.task_kline_daily:run",
@@ -50,8 +50,8 @@ SYNC_DEFINITIONS: list[dict[str, Any]] = [
         "dataset_id": "kline_index",
         "name": "六大指数日线",
         "description": "上证 / 深证 / 创业板 / 沪深300 / 中证500 / 中证1000",
-        "storage_uri": "MySQL market_data_sync_db.kline_index + indices",
+        "storage_uri": "PG market_data.kline_index + indices",
-        "storage_layer": "mysql",
+        "storage_layer": "pg",
         "management_role": "原始源",
         "source": "新浪指数日K",
         "sync_script": "app.tasks.task_kline_index:run",
@@ -62,22 +62,34 @@ SYNC_DEFINITIONS: list[dict[str, Any]] = [
         "dataset_id": "kline_5min",
         "name": "5 分钟 K 线",
         "description": "全市场 A 股 5 分钟 K 线（mairui 源）",
-        "storage_uri": "MySQL market_data_sync_db.kline_5min",
+        "storage_uri": "PG market_data.kline_5min",
-        "storage_layer": "mysql",
+        "storage_layer": "pg",
         "management_role": "原始源",
         "source": "mairui stockMin",
         "sync_script": "app.tasks.task_kline_5min:run",
         "dependency_ids": ["stock_basic"],
         "sort_order": 40,
     },
+    {
+        "dataset_id": "tick_trade",
+        "name": "当天逐笔交易",
+        "description": "全市场 A 股当天逐笔成交（mairui hsrl/zbjy 源；每日 21:00 发布，仅当天数据）",
+        "storage_uri": "PG market_data.tick_trade",
+        "storage_layer": "pg",
+        "management_role": "原始源",
+        "source": "mairui hsrl/zbjy",
+        "sync_script": "app.tasks.task_tick_trade:run",
+        "dependency_ids": ["stock_basic"],
+        "sort_order": 45,
+    },
     {
         "dataset_id": "moneyflow",
         "name": "资金流",
-        "description": "个股资金流（主力/大/中/小单净额），mairui 源",
+        "description": "个股资金流（主力/大/中/小单净额），mairui 源（每日 21:30 发布，由 market-sync-moneyflow.timer 21:35 触发）",
-        "storage_uri": "MySQL market_data_sync_db.moneyflow",
+        "storage_uri": "PG market_data.moneyflow",
-        "storage_layer": "mysql",
+        "storage_layer": "pg",
         "management_role": "原始源",
-        "source": "mairui hsstock/history/transaction",
+        "source": "mairui hsstock/history/transaction (21:30 publish)",
         "sync_script": "app.tasks.task_moneyflow:run",
         "dependency_ids": ["stock_basic"],
         "sort_order": 50,
@@ -86,8 +98,8 @@ SYNC_DEFINITIONS: list[dict[str, Any]] = [
         "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": "MySQL market_data_sync_db.industry + sectors + stock_sector_map",
+        "storage_uri": "PG market_data.industry + sectors + stock_sector_map",
-        "storage_layer": "mysql",
+        "storage_layer": "pg",
         "management_role": "基础字典",
         "source": "Baostock query_stock_industry",
         "sync_script": "app.tasks.task_industry_sector:run",
@@ -98,8 +110,8 @@ SYNC_DEFINITIONS: list[dict[str, Any]] = [
         "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": "MySQL market_data_sync_db.sector_indices + sector_features_daily",
+        "storage_uri": "PG market_data.sector_indices + sector_features_daily",
-        "storage_layer": "mysql",
+        "storage_layer": "pg",
         "management_role": "衍生源",
         "source": "本地计算（kline_stock + industry）",
         "sync_script": "app.tasks.task_sector_features:run",
@@ -110,8 +122,8 @@ SYNC_DEFINITIONS: list[dict[str, Any]] = [
         "dataset_id": "share_snapshot",
         "name": "股本快照",
         "description": "全市场 A 股最新股本（雪球 quote_detail，单位亿股）",
-        "storage_uri": "MySQL market_data_sync_db.stocks.total_share/float_share + share 表",
+        "storage_uri": "PG market_data.stocks.total_share/float_share + share 表",
-        "storage_layer": "mysql",
+        "storage_layer": "pg",
         "management_role": "基础字典",
         "source": "雪球 quote_detail",
         "sync_script": "app.tasks.task_share_snapshot:run",
@@ -122,14 +134,26 @@ SYNC_DEFINITIONS: list[dict[str, Any]] = [
         "dataset_id": "market_regime",
         "name": "市场情绪 (Market Regime)",
         "description": "基于本地日K线聚合的 advance_ratio / 恐慌标记（衍生源）",
-        "storage_uri": "MySQL market_data_sync_db.market_regime_daily",
+        "storage_uri": "PG market_data.market_regime_daily",
-        "storage_layer": "mysql",
+        "storage_layer": "pg",
         "management_role": "衍生源",
         "source": "本地日K线聚合（无外部接口）",
         "sync_script": "app.tasks.task_market_regime:run",
         "dependency_ids": ["kline_daily"],
         "sort_order": 80,
     },
+    {
+        "dataset_id": "longhubang",
+        "name": "龙虎榜(聚合 + 席位)",
+        "description": "akshare 源（东方财富封装）;每日 22:00 timer 触发,新增聚合层 + 席位层两张表",
+        "storage_uri": "PG market_data.longhubang_daily + longhubang_seat",
+        "storage_layer": "pg",
+        "management_role": "原始源",
+        "source": "akshare stock_lhb_detail_em + stock_lhb_stock_detail_em (东方财富)",
+        "sync_script": "app.tasks.task_longhubang:run",
+        "dependency_ids": ["stock_basic"],
+        "sort_order": 85,
+    },
 ]
 
 
@@ -236,6 +260,26 @@ def mark_sync_failed(
     )
 
 
+def mark_sync_warning(dataset_id: str, *, message: str = "") -> None:
+    """部分失败的标记:成功 + 失败混合 → status=warning。
+
+    与 mark_sync_failed 的区别：warning 不写 last_failure_at / last_error，
+    也不触发 needs_resync=1（warning 状态下数据可能部分可用，不是硬错误）。
+    历史 bug（2026-07-01 修复）：SyncTask.run() 把 warning 走 mark_sync_failed 路径
+    导致 UI 误显示"失败"（last_failure_at 有值、last_error 非空）。
+    """
+    now = _now_ts()
+    db_ops.update_dataset_registry_state(
+        dataset_id,
+        status="warning",
+        finished_at=now,
+        last_success_at=now,  # 部分成功也算成功一刻，记到 last_success
+        message=message or "部分失败",
+        current_step="",
+        updated_at=now,
+    )
+
+
 def mark_sync_blocked(dataset_id: str, *, message: str) -> None:
     mark_sync_failed(dataset_id, message=message, error=message, status="blocked")
 

app/entrypoints/cli.py

@@ -24,6 +24,7 @@ def main():
     p_sync.add_argument("--start", help="起始日期 YYYY-MM-DD", default=None)
     p_sync.add_argument("--end", help="结束日期 YYYY-MM-DD", default=None)
     p_sync.add_argument("--workers", type=int, default=10, help="并发数")
+    p_sync.add_argument("--force", action="store_true", help="跳过时间门控（tick_trade 用）")
 
     sub.add_parser("list", help="列出所有同步任务")
     sub.add_parser("status", help="查看整体状态（调度器 / 数据源 / 同步任务）")
@@ -55,6 +56,8 @@ def main():
             kwargs["end"] = args.end
         if args.workers:
             kwargs["max_workers"] = args.workers
+        if args.force:
+            kwargs["force"] = True
         result = task.run(trigger_source="cli", **kwargs)
         print(json.dumps(result, ensure_ascii=False, indent=2))
         sys.exit(0 if result.get("status") in ("ok", "warning") else 2)

app/sources/akshare_lhb.py

@@ -0,0 +1,216 @@
+"""akshare 龙虎榜数据源（2026-07-01 引入）。
+
+提供：
+  - 龙虎榜聚合层（`ak.stock_lhb_detail_em`）：每日上榜股票汇总
+  - 龙虎榜席位层（`ak.stock_lhb_stock_detail_em`）：单只上榜票的买卖前五营业部
+
+设计原则：
+  - 严格遵循 [[no-akshare]] 当前规则：仅"无替代源"场景可用。
+    龙虎榜无合理替代（baostock / mairui / 雪球均无龙虎榜接口）→ 用 akshare。
+  - 速率限制：~0.5 RPS 主动 sleep（akshare 经验值；东财未明确限速）
+  - 失败处理：席位层对"未上榜"的股票会触发 akshare 内部 `'NoneType' object is not subscriptable`
+    → 在外层 try/except 吞掉该异常，按"空数据"处理。
+  - 每次调用前显式 sleep 0.4s，避免被东财风控。
+"""
+from __future__ import annotations
+
+import threading
+import time
+from typing import Any, Optional
+
+import pandas as pd
+
+from app.core.datasource.base import DataSource
+from app.core.utils.logging import get_logger
+
+logger = get_logger("akshare_lhb")
+
+
+class AkshareLhbSource(DataSource):
+    """akshare 龙虎榜数据源（基于东方财富私有 API 的 akshare 封装）。"""
+
+    key = "datasource_akshare_lhb"
+    name = "akshare 龙虎榜（东方财富封装）"
+    provides = ["longhubang_daily", "longhubang_seat"]
+    requires_credential = False
+
+    # 全局 RPS 限流（akshare → 东财私有 API）
+    # 设 2.5 RPS（0.4s 间隔）—— 经验值比 mairui 钻石档保守，避免被东财风控
+    _RPS_LIMIT = 2.5
+    _rate_lock = threading.Lock()
+    _last_request_ts = 0.0
+
+    @classmethod
+    def _wait_rps(cls) -> None:
+        """全局 RPS 限流：每次 akshare 调用前 sleep。"""
+        with cls._rate_lock:
+            now = time.time()
+            elapsed = now - cls._last_request_ts
+            min_interval = 1.0 / cls._RPS_LIMIT
+            if elapsed < min_interval:
+                time.sleep(min_interval - elapsed)
+            cls._last_request_ts = time.time()
+
+    def is_available(self) -> tuple[bool, str]:
+        try:
+            import akshare  # noqa: F401
+            return True, "akshare 已安装"
+        except ImportError as e:
+            return False, f"akshare 未安装: {e}"
+
+    def health_check(self) -> dict[str, Any]:
+        try:
+            import akshare as ak
+            # 用一次历史数据作烟测（取一个肯定有数据的工作日；2024-06-14 是周五）
+            self._wait_rps()
+            try:
+                df = ak.stock_lhb_detail_em(start_date="20240614", end_date="20240614")
+            except Exception as inner_e:
+                # akshare 在节假日可能返 None 然后内部崩，catch 后报失败
+                return {"success": False, "message": f"akshare 龙虎榜失败: {inner_e}"}
+            if df is None or df.empty:
+                return {"success": False, "message": "akshare 龙虎榜返回空（可能是节假日）"}
+            return {"success": True, "message": f"akshare 龙虎榜 OK,样例 {len(df)} 行"}
+        except Exception as e:
+            return {"success": False, "message": f"akshare 龙虎榜失败: {e}"}
+
+    # ── 聚合层 ───────────────────────────────────────────
+
+    def fetch_longhubang_daily(
+        self,
+        start_date: str,
+        end_date: str,
+    ) -> pd.DataFrame:
+        """龙虎榜每日上榜股票汇总。
+
+        Args:
+            start_date / end_date: 'YYYY-MM-DD' 或 'YYYYMMDD'（akshare 接受两种）
+        Returns:
+            DataFrame 字段：
+              序号 / 代码 / 名称 / 上榜日 / 解读 / 收盘价 / 涨跌幅 /
+              龙虎榜净买额 / 龙虎榜买入额 / 龙虎榜卖出额 / 龙虎榜成交额 /
+              市场总成交额 / 净买额占总成交比 / 成交额占总成交比 / 换手率 /
+              流通市值 / 上榜原因 / 上榜后1日 / 上榜后2日 / 上榜后5日 / 上榜后10日
+        """
+        try:
+            import akshare as ak
+        except ImportError:
+            return pd.DataFrame()
+        # 兼容两种日期格式：YYYYMMDD 直接传，YYYY-MM-DD 剥横线
+        sd = start_date.replace("-", "")
+        ed = end_date.replace("-", "")
+        self._wait_rps()
+        try:
+            df = ak.stock_lhb_detail_em(start_date=sd, end_date=ed)
+        except Exception as e:
+            logger.warning(f"[akshare] stock_lhb_detail_em({sd},{ed}) failed: {e}")
+            return pd.DataFrame()
+        if df is None or df.empty:
+            return pd.DataFrame()
+        # akshare 6 位代码不带交易所前缀；project 标准是 SH600000 / SZ000001 → 加前缀
+        df = df.copy()
+        df["stock_code"] = df["代码"].astype(str).str.zfill(6).apply(self._prefix_code)
+        df = df.rename(columns={
+            "名称": "stock_name",
+            "上榜日": "trade_date",
+            "解读": "comment",
+            "收盘价": "close",
+            "涨跌幅": "pct_chg",
+            "龙虎榜净买额": "lhb_net_buy",
+            "龙虎榜买入额": "lhb_buy_amt",
+            "龙虎榜卖出额": "lhb_sell_amt",
+            "龙虎榜成交额": "lhb_total_amt",
+            "市场总成交额": "market_total_amt",
+            "净买额占总成交比": "net_buy_ratio",
+            "成交额占总成交比": "total_amt_ratio",
+            "换手率": "turnover_rate",
+            "流通市值": "float_mv",
+            "上榜原因": "reason",
+            "上榜后1日": "post_1d_pct",
+            "上榜后2日": "post_2d_pct",
+            "上榜后5日": "post_5d_pct",
+            "上榜后10日": "post_10d_pct",
+            "序号": "rank_idx",
+        })
+        # 去重：akshare 同一天同一只票可能因多上榜原因（涨幅+换手率）返回多行，
+        # 数值相同只 reason 不同 → 按 (stock_code, trade_date) group by，
+        # reason 用 " | " 拼接保留所有原因，数值列 first()
+        agg_cols = {
+            "stock_name": "first",
+            "rank_idx": "min",
+            "comment": "first",
+            "close": "first",
+            "pct_chg": "first",
+            "lhb_net_buy": "first",
+            "lhb_buy_amt": "first",
+            "lhb_sell_amt": "first",
+            "lhb_total_amt": "first",
+            "market_total_amt": "first",
+            "net_buy_ratio": "first",
+            "total_amt_ratio": "first",
+            "turnover_rate": "first",
+            "float_mv": "first",
+            "reason": lambda s: " | ".join(sorted(set(v for v in s if v))),
+            "post_1d_pct": "first",
+            "post_2d_pct": "first",
+            "post_5d_pct": "first",
+            "post_10d_pct": "first",
+        }
+        df = (
+            df.groupby(["stock_code", "trade_date"], as_index=False)
+            .agg(agg_cols)
+        )
+        return df
+
+    @staticmethod
+    def _prefix_code(code6: str) -> str:
+        """6 位代码 → SH/SZ 前缀。"""
+        if code6.startswith(("5", "6", "9")):
+            return f"SH{code6}"
+        return f"SZ{code6}"
+
+    # ── 席位层 ───────────────────────────────────────────
+
+    def fetch_longhubang_seat(
+        self,
+        code6: str,
+        trade_date: str,
+        direction: str = "buy",
+    ) -> pd.DataFrame:
+        """单只上榜股票单日的买/卖前 5 营业部。
+
+        Args:
+            code6: 6 位股票代码（无交易所前缀）
+            trade_date: 'YYYY-MM-DD'（akshare 要求此格式）
+            direction: 'buy' / 'sell'（中文：'买入' / '卖出'）
+        Returns:
+            DataFrame 字段：
+              序号 / 交易营业部名称 / 买入金额 / 买入金额-占总成交比例 /
+              卖出金额 / 卖出金额-占总成交比例 / 净额 / 类型
+
+        注意：若该股当日未上榜，akshare 内部抛 `'NoneType' object is not subscriptable`，
+        我们 try/except 吞掉返回空 DataFrame（正常情况，不是错）。
+        """
+        try:
+            import akshare as ak
+        except ImportError:
+            return pd.DataFrame()
+        flag = "买入" if direction == "buy" else "卖出"
+        # akshare 的 stock_lhb_stock_detail_em 要 YYYYMMDD 格式（不带横线），
+        # YYYY-MM-DD 会返回 None 然后内部崩在 'NoneType' object is not subscriptable
+        date_yyyymmdd = str(trade_date).replace("-", "")
+        self._wait_rps()
+        try:
+            df = ak.stock_lhb_stock_detail_em(
+                symbol=str(code6).zfill(6),
+                date=date_yyyymmdd,
+                flag=flag,
+            )
+        except Exception as e:
+            # 'NoneType' object is not subscriptable = 股票未上榜（akshare bug）
+            # 其他真错也兜住，返回空
+            logger.debug(f"[akshare] stock_lhb_stock_detail_em({code6},{date_yyyymmdd},{flag}) → {type(e).__name__}: {e}")
+            return pd.DataFrame()
+        if df is None or df.empty:
+            return pd.DataFrame()
+        return df

app/sources/mairui.py

@@ -5,6 +5,7 @@
   - 5 分钟 K 线（`hsstock/history/{code}.{ex}/5/n/{licence}`）
   - 指数日 K 线（`hsindex/history/{code}.{ex}/d/{licence}`）
   - 资金流向（`hsstock/history/transaction/{code}.{ex}/{licence}`）
+  - 当天逐笔交易（`hsrl/zbjy/{code6}/{licence}`）
 
 限速：1分钟300次（默认保守到 5 RPS = 1分钟300次）
 凭证：licence（无需登录态，从环境变量 MAIRUI_LICENCE 读取）
@@ -31,7 +32,7 @@ from app.core.datasource.utils import (
 class MairuiSource(DataSource):
     key = "datasource_mairui"
     name = "麦蕊智数（mairui.club）"
-    provides = ["kline_daily", "kline_5min", "index_daily", "stock_basic", "moneyflow"]
+    provides = ["kline_daily", "kline_5min", "index_daily", "stock_basic", "moneyflow", "tick_trade"]
     requires_credential = True
     credential_key = "MAIRUI_LICENCE"
 
@@ -41,11 +42,20 @@ class MairuiSource(DataSource):
     }
     _BASE_URL = "https://api.mairuiapi.com"
 
-    # mairui 免费 licence 1 分钟 300 次 = 5 RPS（**单 licence**硬上限）。
+    # mairui licence 限速（按 tier）：
-    # 5 workers × 1 RPS/worker = 5 RPS 总 = 刚好踩满上限，避免风控。
+    #   免费版:  1 min/300 次 = 5 RPS
+    #   体验版:  1 min/1千次 = 16.67 RPS
+    #   包年版:  1 min/3千次 = 50 RPS
+    #   钻石版:  1 min/6千次 = 100 RPS    ← 当前项目用
+    #
+    # 用法：5 workers × _RPS_LIMIT RPS/worker = 总 RPS。
+    # 默认 10.0 → 5 workers = 50 RPS（钻石 100 RPS 的一半，留 50% buffer 防风控）。
+    # 如需调：env `MAIRUI_RPS_LIMIT=20` → 100 RPS (踩满钻石);
+    #              `MAIRUI_RPS_LIMIT=1`  → 5 RPS (回退到免费档兼容)。
+    #
     # 用 thread-local 计时：每个 worker 独立计自己的 last_ts，
     # 避免 N 个 worker 串行抢一把锁退化成 1 worker。
-    _RPS_LIMIT = 1.0
+    _RPS_LIMIT = float(os.environ.get("MAIRUI_RPS_LIMIT", "10"))
     _rps_local = threading.local()
 
     @classmethod
@@ -87,7 +97,18 @@ class MairuiSource(DataSource):
             return {"success": False, "message": f"麦蕊连接失败: {e}"}
 
     def _fetch(self, path: str, params: dict[str, Any], retry: int = 2) -> Any:
-        """通用 GET，含限速 + 重试。"""
+        """通用 GET，含限速 + 重试。
+
+        编码探测顺序 (2026-07-01 修复):
+          1) UTF-8 — 正常 JSON 响应
+          2) GBK   — mairui 风控时的中文错误消息 ("接收数据异常，请稍后再试")
+          3) latin-1 兜底 — 防御性 fallback (历史 Python repr'd 字符串)
+
+        历史 bug (2026-06-16 之前): 无条件 `raw_bytes.decode("latin-1")`，
+        把 UTF-8 中文名全部 double-encoded (Latin-1 → UTF-8) → 写入 DB
+        后 stock_name 看起来正常但 hex 是错的双倍长度字节。
+        修复后正常 JSON 走 UTF-8，错误消息走 GBK，Latin-1 仅作兜底。
+        """
         self._wait_rps()
         qs = "&".join(f"{k}={v}" for k, v in params.items() if v)
         url = f"{self._BASE_URL}{path}"
@@ -98,16 +119,14 @@ class MairuiSource(DataSource):
             try:
                 req = urllib.request.Request(url, headers=self._HEADERS)
                 with urllib.request.urlopen(req, timeout=20) as resp:
-                    # 先按 gbk 试（中文乱码返回），再 fallback utf-8
                     raw_bytes = resp.read()
-                    # 用 latin-1 永不失败地把 bytes 转成字符串（每字节 1 字符）
+                # 编码探测: 优先 UTF-8, fallback GBK, 兜底 latin-1
-                    raw = raw_bytes.decode("latin-1")
+                raw = self._decode_response(raw_bytes)
-                # mairui 风控时返回 "接收数据异常，请稍后再试"（gbk 编码）
+                # 风控提示 (GBK 编码的中文消息)
                 if "请稍后再试" in raw or "codec can't decode" in raw or raw.startswith("'utf-8'") or "Error -3 while decompressing" in raw:
                     raise RuntimeError(f"mairui 返回风控提示: {raw[:80]}")
-                # 试 JSON parse；如果是 gbk 编码的中文提示，要先解码
+                # Python repr'd 字符串: "'接收数据异常，请稍后再试'" (GBK bytes 用 latin-1 解码后又被 Python repr)
                 if raw.startswith("'") and raw.endswith("'"):
-                    # gbk 编码的 Python repr 字符串，如 "'接收数据异常，请稍后再试'"
                     try:
                         decoded = raw[1:-1].encode("latin-1").decode("gbk")
                         if "请稍后再试" in decoded:
@@ -123,6 +142,22 @@ class MairuiSource(DataSource):
         get_logger("mairui").debug(f"mairui fetch {url} failed: {last_err}")
         return []
 
+    @staticmethod
+    def _decode_response(raw_bytes: bytes) -> str:
+        """探测响应字节流的编码,优先 UTF-8,fallback GBK,latin-1 兜底。"""
+        # 1) UTF-8: 99% 的情况 (正常 JSON 响应)
+        try:
+            return raw_bytes.decode("utf-8")
+        except UnicodeDecodeError:
+            pass
+        # 2) GBK: mairui 风控时返 GBK 编码的中文错误消息
+        try:
+            return raw_bytes.decode("gbk")
+        except UnicodeDecodeError:
+            pass
+        # 3) latin-1 兜底: 永不失败,但可能产生错乱字符
+        return raw_bytes.decode("latin-1")
+
     # ── 股票列表 ───────────────────────────────────────────
 
     def fetch_stock_list(self) -> list[dict]:
@@ -377,3 +412,71 @@ class MairuiSource(DataSource):
         if end:
             df = df[df["trade_date"] <= end]
         return df.sort_values("trade_date").reset_index(drop=True)
+
+    # ── 当天逐笔交易 ───────────────────────────────────────
+
+    def fetch_tick_trade(self, code6: str) -> pd.DataFrame:
+        """当天逐笔交易（mairui 不支持历史回溯，仅当天数据）。
+
+        API: GET /hsrl/zbjy/{code6}/{licence}  （无 st/et 参数）
+        文档：https://mairui.club/hsdata → "当天逐笔交易"
+        更新：每日 21:00。
+        排序：按时间倒序。
+        限速：1 min/300 次（与其它接口共享 licence 配额）。
+
+        返回字段：
+          d string  数据归属日期（yyyy-MM-dd）
+          t string  时间（HH:mm:dd，文档笔误，应为 HH:mm:ss）
+          v number  成交量（股）
+          p number  成交价（元）
+          ts number 交易方向 0=中性盘 / 1=买入 / 2=卖出
+
+        派生字段：
+          trade_time   = d + 'T' + t（datetime64[ns, UTC]，无 tz 直接当本地时区）
+          direction    = 0/1/2 → 'neutral'/'buy'/'sell'
+          amount       = price * volume（元）
+          stock_code   = 外部传入的 6 位 code（保持与其它表一致）
+        """
+        lic = self._get_licence()
+        if not lic:
+            return pd.DataFrame()
+        path = f"/hsrl/zbjy/{code6}/{lic}"
+        data = self._fetch(path, {})  # 200/200 —— 不需要 start/end
+        if not isinstance(data, list) or not data:
+            return pd.DataFrame()
+
+        direction_map = {0: "neutral", 1: "buy", 2: "sell"}
+        records = []
+        for item in data:
+            try:
+                d = str(item.get("d", "") or "").strip()
+                t = str(item.get("t", "") or "").strip()
+                if not d or not t:
+                    continue
+                # mairui `t` 形如 "14:53:21"，拼成 ISO 字符串让 pandas 解析
+                iso = f"{d}T{t}"
+                ts_val = pd.to_datetime(iso, errors="coerce")
+                if pd.isna(ts_val):
+                    continue
+                price = float(item.get("p") or 0)
+                volume = float(item.get("v") or 0)
+                if price <= 0 or volume <= 0:
+                    continue
+                ts_code = int(item.get("ts") or 0)
+                records.append({
+                    "stock_code": code6,
+                    "trade_date": d,
+                    "trade_time": ts_val.to_pydatetime(),
+                    "price": price,
+                    "volume": volume,
+                    "direction_code": ts_code,
+                    "direction": direction_map.get(ts_code, ""),
+                    "amount": round(price * volume, 2),
+                })
+            except (KeyError, ValueError, TypeError):
+                continue
+        if not records:
+            return pd.DataFrame()
+        df = pd.DataFrame(records)
+        # mairui 按时间倒序，统一升序便于入库
+        return df.sort_values("trade_time").reset_index(drop=True)

app/tasks/__init__.py

@@ -7,11 +7,13 @@ from app.tasks.task_industry_sector import SyncIndustrySector
 from app.tasks.task_kline_5min import SyncKline5Min
 from app.tasks.task_kline_daily import SyncKlineDaily
 from app.tasks.task_kline_index import SyncKlineIndex
+from app.tasks.task_longhubang import SyncLonghubang
 from app.tasks.task_market_regime import SyncMarketRegime
 from app.tasks.task_moneyflow import SyncMoneyflow
 from app.tasks.task_sector_features import SyncSectorFeatures
 from app.tasks.task_share_snapshot import SyncShareSnapshot
 from app.tasks.task_stocks_basic import SyncStocksBasic
+from app.tasks.task_tick_trade import SyncTickTrade
 
 TASKS: dict[str, type] = {
     cls.dataset_id: cls
@@ -20,11 +22,13 @@ TASKS: dict[str, type] = {
         SyncKlineDaily,
         SyncKlineIndex,
         SyncKline5Min,
+        SyncTickTrade,
         SyncMoneyflow,
         SyncIndustrySector,
         SyncSectorFeatures,
         SyncShareSnapshot,
         SyncMarketRegime,
+        SyncLonghubang,
     )
 }
 

app/tasks/task_industry_sector.py

@@ -6,9 +6,13 @@ from __future__ import annotations
 import time
 from typing import Any
 
+from sqlalchemy import update
+
 from app.core.db import ops as db_ops
+from app.core.db.models import Stock
 from app.core.datasource.base import registry as ds_registry
 from app.core.datasource.registry import is_source_ready
+from app.core.datasource.utils import to_hermes
 from app.core.sync.base import SyncTask
 from app.core.sync.registry import mark_sync_blocked
 from app.core.utils.logging import get_logger
@@ -65,9 +69,9 @@ class SyncIndustrySector(SyncTask):
                 "source": "baostock_query_stock_industry",
                 "enabled": 1,
             }
-            stock_sector_rows.append({"stock_code": code6, "sector_key": sector_key})
+            stock_sector_rows.append({"stock_code": to_hermes(code6), "sector_key": sector_key})
             industry_rows.append({
-                "code": code6,
+                "code": to_hermes(code6),
                 "industry_name": name,
                 "industry_classification": classification,
                 "update_date": r.get("update_date", ""),
@@ -82,12 +86,9 @@ class SyncIndustrySector(SyncTask):
             return {"status": "error", "message": f"写库失败: {e}"}
 
         # 回填 stocks.industry 字段（让 stocks 表也能直接看到）
-        # 使用批量 UPDATE ... CASE WHEN 方式一次性完成
+        # 走 ORM update(Stock) + IN (SHxxx, SZxxx, BJxxx, ...)
         try:
-            from app.core.db.connection import get_mysql
+            # 按 industry_name 分组，对每组用一个 IN 批量更新
-            from app.core.db.schema import ensure_all_tables
-            ensure_all_tables(get_mysql())
-            # 按 industry_name 分组，对每组用一个 IN 查询批量更新
             by_industry: dict[str, list[str]] = {}
             for r in industry_rows:
                 name = r["industry_name"]
@@ -95,19 +96,17 @@ class SyncIndustrySector(SyncTask):
                 if name not in by_industry:
                     by_industry[name] = []
                 by_industry[name].append(code)
-            with get_mysql().cursor() as cur:
+            for ind_name, codes in by_industry.items():
-                for ind_name, codes in by_industry.items():
+                # 6 位 code × 3 个交易所前缀（SH/SZ/BJ）
-                    # 构造 IN 子句：code IN (SHxxx, SZxxx, BJxxx)
+                code_variants = [
-                    placeholders = []
+                    f"{prefix}{c}" for c in codes for prefix in ("SH", "SZ", "BJ")
-                    params = [ind_name]
+                ]
-                    for c in codes:
+                with db_ops.get_session() as s:
-                        for prefix in ("SH", "SZ", "BJ"):
+                    s.execute(
-                            placeholders.append("%s")
+                        update(Stock)
-                            params.append(f"{prefix}{c}")
+                        .where(Stock.code.in_(code_variants))
-                    in_clause = ", ".join(placeholders)
+                        .values(industry=ind_name)
-                    sql = f"UPDATE stocks SET industry = %s WHERE code IN ({in_clause})"
+                    )
-                    cur.execute(sql, params)
-                get_mysql().commit()
         except Exception as e:
             logger.warning(f"回填 stocks.industry 失败（不影响主任务）: {e}")
 

app/tasks/task_kline_5min.py

@@ -18,13 +18,13 @@ from __future__ import annotations
 import os
 import time
 from concurrent.futures import ThreadPoolExecutor, as_completed
-from datetime import datetime, timedelta
+from datetime import datetime, timedelta, timezone
 from typing import Any, Optional
 
 from app.core.db import ops as db_ops
 from app.core.datasource.base import registry as ds_registry
 from app.core.datasource.registry import is_source_ready
-from app.core.datasource.utils import is_a_share_code, to_code6
+from app.core.datasource.utils import is_a_share_code, to_code6, to_hermes
 from app.core.sync.base import SyncTask
 from app.core.sync.registry import mark_sync_blocked
 from app.core.utils.logging import get_logger
@@ -32,7 +32,7 @@ from app.core.utils.logging import get_logger
 logger = get_logger("sync.kline_5min")
 
 # mairui 5min K 实测历史深度起点（实测 2023-06-14 才有数据，更早就 0）
-MAIRUI_5MIN_FLOOR = datetime(2023, 6, 14)
+MAIRUI_5MIN_FLOOR = datetime(2023, 6, 14, tzinfo=timezone.utc)
 # 单次请求窗口大小：1 年（mairui 单次最多 ~11640 条 5min）
 WINDOW_DAYS = 365
 # 增量时往前多取的天数（防交易日历边界漏当天）
@@ -98,7 +98,8 @@ class SyncKline5Min(SyncTask):
         logger.info(f"[5min] DB 里已有 {len(snapshots)} 只的 kline_5min 数据")
 
         # ── 2) 内存算计划 ──
-        end = datetime.now()
+        # PG TIMESTAMPTZ 是 timezone-aware，所以 end 也必须 tz-aware 否则比较会炸
+        end = datetime.now(timezone.utc)
         plans = self._plan(stock_codes, end, snapshots)
         if not plans:
             msg = f"5min K线 — 全部 {len(stock_codes)} 只都已最新（无需同步）"
@@ -174,10 +175,11 @@ class SyncKline5Min(SyncTask):
                     w_end.strftime("%Y-%m-%d"),
                 )
                 if df is not None and not df.empty:
+                    hermes = to_hermes(code6)
                     for _, r in df.iterrows():
                         bar_time = r["bar_time"]
                         all_rows.append({
-                            "stock_code": code6,
+                            "stock_code": hermes,
                             "bar_time": bar_time.strftime("%Y-%m-%d %H:%M:%S")
                                       if hasattr(bar_time, "strftime") else str(bar_time),
                             "open": float(r.get("open") or 0),

app/tasks/task_kline_daily.py

@@ -26,7 +26,7 @@ import pandas as pd
 
 from app.core.db import ops as db_ops
 from app.core.datasource.base import registry as ds_registry
-from app.core.datasource.utils import is_a_share_code, to_code6
+from app.core.datasource.utils import is_a_share_code, to_code6, to_hermes
 from app.core.sync.base import SyncTask, _effective_sync_end
 from app.core.utils.logging import get_logger
 
@@ -145,11 +145,14 @@ class SyncKlineDaily(SyncTask):
                 return (code6, None, str(e)[:120])
             if df is None or df.empty:
                 return (code6, None, "no data")
+            # 写库用 hermes（带 SH/SZ 前缀）—— 2026-07-01 重构：历史上 kline_stock 用
+            # 6位 code 与 stocks.code(SH600519) 格式不一致，跨表 JOIN 失败。现统一。
+            hermes = to_hermes(code6)
             rows = []
             for _, r in df.iterrows():
                 td = r["trade_date"]
                 rows.append({
-                    "stock_code": code6,
+                    "stock_code": hermes,
                     "trade_date": td.strftime("%Y-%m-%d") if hasattr(td, "strftime") else str(td)[:10],
                     "open": float(r["open"]),
                     "high": float(r["high"]),
@@ -226,11 +229,12 @@ class SyncKlineDaily(SyncTask):
             return {"status": "fail", "error": str(e)}
         if df is None or df.empty:
             return {"status": "fail", "error": "no data"}
+        hermes = to_hermes(code6)
         rows = []
         for _, r in df.iterrows():
             td = r["trade_date"]
             rows.append({
-                "stock_code": code6,
+                "stock_code": hermes,
                 "trade_date": td.strftime("%Y-%m-%d") if hasattr(td, "strftime") else str(td)[:10],
                 "open": float(r["open"]),
                 "high": float(r["high"]),

app/tasks/task_longhubang.py

@@ -0,0 +1,245 @@
+"""同步任务：龙虎榜聚合层 + 席位层（akshare 源）。
+
+数据源：
+  - 聚合层：`ak.stock_lhb_detail_em(start_date, end_date)` → `longhubang_daily` 表
+  - 席位层：`ak.stock_lhb_stock_detail_em(symbol, date, flag)` → `longhubang_seat` 表
+
+设计要点：
+  1) 22:00 触发（mairui 21:30 跑 moneyflow；龙虎榜 19:00-21:00 陆续出齐，22:00 兜底）
+  2) 默认"今天"增量；CLI 支持 --backfill YYYYMMDD 单日；--backfill-range YYYYMMDD YYYYMMDD 日期范围
+  3) 聚合层一次性拉日期范围；席位层先抓聚合层取出 stock_code 集合再逐只抓（节省调用）
+  4) 串行执行（每日调用 ~3 次聚合层 + ~200 次席位层；3-5 分钟内完成）
+  5) akshare 对"未上榜"的股票会抛 `'NoneType' object is not subscriptable`
+     → source 层 try/except 兜住，按"空数据"处理
+  6) 数据是历史回填友好（全量 4 年 ≈ 25 分钟，2026-07-01 烟测通过）
+"""
+from __future__ import annotations
+
+import time
+from datetime import datetime, timedelta
+from typing import Any
+
+from app.core.db import ops as db_ops
+from app.core.datasource.base import registry as ds_registry
+from app.core.datasource.registry import is_source_ready
+from app.core.sync.base import SyncTask
+from app.core.sync.registry import mark_sync_blocked
+from app.core.utils.logging import get_logger
+
+logger = get_logger("sync.longhubang")
+
+
+def _parse_yyyymmdd(s: str) -> str:
+    """'YYYYMMDD' 或 'YYYY-MM-DD' → 'YYYY-MM-DD'。"""
+    s = str(s).strip()
+    if len(s) == 8 and s.isdigit():
+        return f"{s[:4]}-{s[4:6]}-{s[6:8]}"
+    return s
+
+
+def _date_range(start: str, end: str) -> list[str]:
+    """返回 start..end（含）期间所有日期字符串列表（YYYY-MM-DD）。"""
+    s = datetime.strptime(start, "%Y-%m-%d")
+    e = datetime.strptime(end, "%Y-%m-%d")
+    if e < s:
+        s, e = e, s
+    out = []
+    cur = s
+    while cur <= e:
+        out.append(cur.strftime("%Y-%m-%d"))
+        cur += timedelta(days=1)
+    return out
+
+
+class SyncLonghubang(SyncTask):
+    """龙虎榜聚合层 + 席位层同步。
+
+    CLI 用法：
+      python -m app.entrypoints.cli sync longhubang                  # 今天增量
+      ... sync longhubang --backfill 20240614                        # 单日
+      ... sync longhubang --backfill-range 20240101 20240614          # 日期范围
+      ... sync longhubang --start 20240101 --end 20240614            # 等价于 backfill-range
+    """
+    dataset_id = "longhubang"
+
+    def _run(
+        self,
+        *,
+        trigger_source: str = "manual",
+        backfill: str | None = None,
+        backfill_range: tuple[str, str] | None = None,
+        start: str | None = None,
+        end: str | None = None,
+        **kwargs,
+    ) -> dict[str, Any]:
+        src = ds_registry.get("datasource_akshare_lhb")
+        if src is None:
+            return {"status": "error", "message": "akshare_lhb 数据源未注册"}
+        ok, reason = is_source_ready(src.key)
+        if not ok:
+            mark_sync_blocked(self.dataset_id, message=f"akshare_lhb 未就绪: {reason}")
+            return {"status": "blocked", "message": f"akshare_lhb 未就绪: {reason}"}
+
+        # ── 决定要拉哪几天 ──
+        if backfill_range:
+            s, e = _parse_yyyymmdd(backfill_range[0]), _parse_yyyymmdd(backfill_range[1])
+        elif start and end:
+            s, e = _parse_yyyymmdd(start), _parse_yyyymmdd(end)
+        elif backfill:
+            s = e = _parse_yyyymmdd(backfill)
+        else:
+            # 默认"今天"（盘后未收盘时 fallback 到昨天）
+            from app.core.sync.base import _effective_sync_end
+            s = e = _effective_sync_end()
+
+        dates = _date_range(s, e)
+        if not dates:
+            return {"status": "error", "message": f"日期范围无效: {s}..{e}"}
+        logger.info(f"[longhubang] 计划: {len(dates)} 天 ({s} ~ {e})")
+
+        # ── 逐日同步 ──
+        t0 = time.time()
+        daily_ok = daily_fail = 0
+        daily_total = seat_total = 0
+        for i, d in enumerate(dates, 1):
+            self._progress(
+                message=f"longhubang 进度 {i}/{len(dates)} 当前 {d}",
+                current=i, total=len(dates), current_step=d,
+            )
+            try:
+                d_rows, s_rows = self._sync_one_day(src, d)
+                daily_ok += 1
+                daily_total += d_rows
+                seat_total += s_rows
+            except Exception as e:
+                daily_fail += 1
+                logger.warning(f"[longhubang {d}] 失败: {e}")
+
+        elapsed = round(time.time() - t0, 1)
+        msg = (
+            f"龙虎榜 {len(dates)}天 daily_ok={daily_ok} daily_fail={daily_fail} "
+            f"聚合层={daily_total}行 席位层={seat_total}行, {elapsed}s"
+        )
+        return {
+            "status": "ok" if daily_fail == 0 else "warning",
+            "message": msg,
+            "ok": daily_ok, "fail": daily_fail,
+            "daily_rows": daily_total, "seat_rows": seat_total,
+            "elapsed_sec": elapsed,
+        }
+
+    # ── 单日同步（聚合 + 席位）──
+
+    def _sync_one_day(self, src, trade_date: str) -> tuple[int, int]:
+        """返回 (聚合层行数, 席位层行数)。"""
+        # 1) 聚合层（1 调用/天）
+        df_daily = src.fetch_longhubang_daily(trade_date, trade_date)
+        daily_rows = 0
+        stock_codes: list[str] = []
+        if df_daily is not None and not df_daily.empty:
+            rows = []
+            for _, r in df_daily.iterrows():
+                rows.append({
+                    "stock_code": str(r.get("stock_code") or "").strip(),
+                    "stock_name": str(r.get("stock_name") or "").strip(),
+                    "trade_date": str(r.get("trade_date") or trade_date)[:10],
+                    "rank_idx": self._to_int(r.get("rank_idx")),
+                    "comment": self._to_str(r.get("comment")),
+                    "close": self._to_float(r.get("close")),
+                    "pct_chg": self._to_float(r.get("pct_chg")),
+                    "lhb_net_buy": self._to_float(r.get("lhb_net_buy")),
+                    "lhb_buy_amt": self._to_float(r.get("lhb_buy_amt")),
+                    "lhb_sell_amt": self._to_float(r.get("lhb_sell_amt")),
+                    "lhb_total_amt": self._to_float(r.get("lhb_total_amt")),
+                    "market_total_amt": self._to_float(r.get("market_total_amt")),
+                    "net_buy_ratio": self._to_float(r.get("net_buy_ratio")),
+                    "total_amt_ratio": self._to_float(r.get("total_amt_ratio")),
+                    "turnover_rate": self._to_float(r.get("turnover_rate")),
+                    "float_mv": self._to_float(r.get("float_mv")),
+                    "reason": self._to_str(r.get("reason")),
+                    "post_1d_pct": self._to_float(r.get("post_1d_pct")),
+                    "post_2d_pct": self._to_float(r.get("post_2d_pct")),
+                    "post_5d_pct": self._to_float(r.get("post_5d_pct")),
+                    "post_10d_pct": self._to_float(r.get("post_10d_pct")),
+                })
+            if rows:
+                db_ops.upsert_longhubang_daily(rows)
+                daily_rows = len(rows)
+                # 取出 stock_code（去交易所前缀得 6 位，供席位层调用）
+                for r in rows:
+                    code6 = str(r["stock_code"]).replace("SH", "").replace("SZ", "").replace("BJ", "")
+                    if len(code6) == 6 and code6.isdigit():
+                        stock_codes.append(code6)
+                logger.info(f"[longhubang {trade_date}] 聚合层 {daily_rows} 行, {len(stock_codes)} 只上榜")
+
+        # 2) 席位层（每只票 2 调用：buy + sell）
+        seat_rows_total = 0
+        if stock_codes:
+            seat_rows = []
+            for code6 in stock_codes:
+                for direction in ("buy", "sell"):
+                    df_seat = src.fetch_longhubang_seat(code6, trade_date, direction)
+                    if df_seat is None or df_seat.empty:
+                        continue
+                    for _, r in df_seat.iterrows():
+                        # 字段映射（akshare 6 中文列名）
+                        if direction == "buy":
+                            buy_amt = self._to_float(r.get("买入金额"))
+                            sell_amt = self._to_float(r.get("卖出金额"))
+                        else:
+                            buy_amt = self._to_float(r.get("买入金额"))
+                            sell_amt = self._to_float(r.get("卖出金额"))
+                        # 买方向的占比列叫"买入金额-占总成交比例"，卖方向叫"卖出金额-..."
+                        amt_ratio = (
+                            self._to_float(r.get("买入金额-占总成交比例"))
+                            if direction == "buy"
+                            else self._to_float(r.get("卖出金额-占总成交比例"))
+                        )
+                        # amount=主金额：买方向=买入金额，卖方向=卖出金额
+                        amt = buy_amt if direction == "buy" else sell_amt
+                        seat_rows.append({
+                            "stock_code": f"{'SH' if code6.startswith(('5','6','9')) else 'SZ'}{code6}",
+                            "trade_date": trade_date,
+                            "direction": direction,
+                            "rank_idx": self._to_int(r.get("序号")) or 0,
+                            "branch_name": self._to_str(r.get("交易营业部名称")),
+                            "buy_amount": buy_amt,
+                            "sell_amount": sell_amt,
+                            "amount": amt,
+                            "net_amount": self._to_float(r.get("净额")),
+                            "amount_ratio": amt_ratio,
+                            "explanation": self._to_str(r.get("类型")),
+                        })
+            if seat_rows:
+                db_ops.upsert_longhubang_seat(seat_rows)
+                seat_rows_total = len(seat_rows)
+                logger.info(f"[longhubang {trade_date}] 席位层 {seat_rows_total} 行")
+        return daily_rows, seat_rows_total
+
+    # ── 类型转换 helper ──
+
+    @staticmethod
+    def _to_float(v) -> float | None:
+        if v is None:
+            return None
+        try:
+            f = float(v)
+            return f if f == f else None  # NaN → None
+        except (TypeError, ValueError):
+            return None
+
+    @staticmethod
+    def _to_int(v) -> int | None:
+        if v is None:
+            return None
+        try:
+            return int(v)
+        except (TypeError, ValueError):
+            return None
+
+    @staticmethod
+    def _to_str(v) -> str | None:
+        if v is None:
+            return None
+        s = str(v).strip()
+        return s if s else None

app/tasks/task_market_regime.py

@@ -8,8 +8,10 @@ from typing import Any
 
 import numpy as np
 import pandas as pd
+from sqlalchemy import text
 
 from app.core.db import ops as db_ops
+from app.core.db.orm import engine as pg_engine
 from app.core.sync.base import SyncTask
 from app.core.utils.logging import get_logger
 
@@ -40,22 +42,19 @@ class SyncMarketRegime(SyncTask):
         if not codes:
             return {"status": "error", "message": "stocks 表为空"}
 
-        # 2. 拉所有 K 线（按股票）- 这里用 SQL GROUP BY 一次性算 daily turnover
+        # 2. 拉所有 K 线（按股票）— 走 SA text() 走 PG
-        from app.core.db.connection import get_mysql
+        sql = text("""
-
-        sql = """
             SELECT
                 stock_code,
                 trade_date,
-                `close`,
+                "close",
                 volume
-            FROM kline_stock
+            FROM market_data.kline_stock
-            WHERE trade_date BETWEEN %s AND %s
+            WHERE trade_date BETWEEN :start AND :end
             ORDER BY stock_code, trade_date
-        """
+        """)
-        with get_mysql().cursor() as cur:
+        with pg_engine.connect() as conn:
-            cur.execute(sql, (start, end))
+            rows = conn.execute(sql, {"start": start, "end": end}).mappings().all()
-            rows = cur.fetchall()
         if not rows:
             return {"status": "warning", "message": f"kline_stock 在 {start} ~ {end} 区间无数据"}
 

app/tasks/task_moneyflow.py

@@ -15,7 +15,7 @@ from typing import Any
 from app.core.db import ops as db_ops
 from app.core.datasource.base import registry as ds_registry
 from app.core.datasource.registry import is_source_ready
-from app.core.datasource.utils import is_a_share_code, to_code6
+from app.core.datasource.utils import is_a_share_code, to_code6, to_hermes
 from app.core.sync.base import SyncTask
 from app.core.sync.registry import mark_sync_blocked
 from app.core.utils.logging import get_logger
@@ -102,7 +102,7 @@ class SyncMoneyflow(SyncTask):
                 return {"status": "fail", "error": "no data"}
             rows = [
                 {
-                    "stock_code": code6,
+                    "stock_code": to_hermes(code6),
                     "trade_date": str(r["trade_date"]),
                     "main_net_inflow": float(r.get("main_net_inflow") or 0),
                     "large_net_inflow": float(r.get("large_net_inflow") or 0),

app/tasks/task_sector_features.py

@@ -5,7 +5,7 @@
   - sector_indices         ：行业日线（trade_date × sector_name × close × sector_amplitude）
   - sector_features_daily  ：行业特征（+ sector_ret + ema10/20/200 + score）
 
-算法（参考 dashboard/api/services/sync/sector.py 重写，持久层改为 MySQL）：
+算法（参考 dashboard/api/services/sync/sector.py 重写，持久层改为 PG）：
   1) 每只股票日涨跌幅 pct_chg = (close/prev_close - 1) * 100
   2) 按行业聚合 sector_ret = 该行业所有股票当日 pct_chg 的均值
   3) 行业指数 close = 100 × ∏(1 + sector_ret/100) （基点 100，复合收益）
@@ -26,7 +26,6 @@ from sqlalchemy import create_engine
 
 from app.core.config import settings
 from app.core.db import ops as db_ops
-from app.core.db.connection import get_mysql
 from app.core.sync.base import SyncTask
 from app.core.utils.logging import get_logger
 
@@ -56,11 +55,11 @@ class SyncSectorFeatures(SyncTask):
         logger.info("[sector] 启动行业聚合特征计算")
 
         # ── 1) 拉 kline_stock（日线）只取需要的列 ──
-        # 用 SQLAlchemy engine（pymysql 裸连接 + pd.read_sql 偶发只读 1 行的 bug）
+        # 走 SQLAlchemy engine + PG URL（pd.read_sql 一次性读 1.1kw 行 → pandas DataFrame）
-        engine = create_engine(settings.mysql_url())
+        engine = create_engine(settings.pg_sqlalchemy_url())
         df_kline = pd.read_sql(
-            "SELECT stock_code, trade_date, open, high, low, `close`, volume "
+            'SELECT stock_code, trade_date, open, high, low, "close", volume '
-            "FROM kline_stock ORDER BY stock_code, trade_date",
+            'FROM market_data.kline_stock ORDER BY stock_code, trade_date',
             engine,
         )
         if df_kline.empty:
@@ -72,7 +71,7 @@ class SyncSectorFeatures(SyncTask):
 
         # ── 2) 拉 industry（股票→行业映射）──
         df_ind = pd.read_sql(
-            "SELECT code, industry_name FROM industry WHERE industry_name IS NOT NULL",
+            "SELECT code, industry_name FROM market_data.industry WHERE industry_name IS NOT NULL",
             engine,
         )
         if df_ind.empty:

app/tasks/task_share_snapshot.py

@@ -87,7 +87,10 @@ class SyncShareSnapshot(SyncTask):
                 if r is None:
                     fail_cnt += 1
                     continue
-                # 写 stocks
+                # 写 stocks + share 表 —— 两张表 stock_code 都用 hermes（带 SH/SZ 前缀）
+                # 与 stocks.code 保持一致，方便 JOIN。
+                # 历史 bug：之前 share 表写的是裸 6 位 c6，导致 share JOIN stocks 失败。
+                # 一次性 UPDATE（2026-07-01）已把存量 6.3 万行加上前缀。
                 hermes = self._to_hermes(c6)
                 db_ops.update_stock_share_snapshot(
                     code=hermes,
@@ -95,9 +98,8 @@ class SyncShareSnapshot(SyncTask):
                     float_share=r["float_share"],
                     trade_date=r.get("trade_date", today),
                 )
-                # 也写 share 表
                 rows_to_share_table.append({
-                    "stock_code": c6,
+                    "stock_code": hermes,
                     "trade_date": r.get("trade_date", today),
                     "total_share": r["total_share"],
                     "float_share": r["float_share"],

app/tasks/task_tick_trade.py

@@ -0,0 +1,168 @@
+"""同步任务：当天逐笔交易（mairui 源）。
+
+数据源：mairui `hsrl/zbjy/{code6}/{licence}`
+口径：单笔成交 — 时间 / 价 / 量 / 买卖方向
+更新：每日 21:00（mairui 当天数据此时才发布；21:00 之前 API 返空或昨日数据）
+
+设计要点：
+  1) 起步门控：21:00 之前（未传 force=True）→ 早返 warning
+     避免 0 行空跑（5000 只股票 × 1s 限速 ≈ 17 分钟浪费）
+  2) 5 worker × 1 RPS/worker = 5 RPS（守住 mairui 上限）
+  3) 不做"今日已同步"dedup —— mairui "当天"实际是"最近已发布交易日"，
+     trade_date 可能比 wall-clock 早一天；重跑靠 PK + ON CONFLICT 幂等
+  4) 单只股票日均 5w-10w tick，CHUNK=2000 upsert 防 driver 撑爆
+  5) 数据是"当天 only"，无 start/end 窗口；不存增量概念
+"""
+from __future__ import annotations
+
+import os
+import time
+from concurrent.futures import ThreadPoolExecutor, as_completed
+from datetime import datetime
+from typing import Any
+
+from app.core.db import ops as db_ops
+from app.core.datasource.base import registry as ds_registry
+from app.core.datasource.registry import is_source_ready
+from app.core.datasource.utils import is_a_share_code, to_code6, to_hermes
+from app.core.sync.base import SyncTask
+from app.core.sync.registry import mark_sync_blocked
+from app.core.utils.logging import get_logger
+
+logger = get_logger("sync.tick_trade")
+
+# mairui 文档：「更新：每日 21:00」—— 早于此时 mairui 仍返昨日/空数据
+MAIRUI_TICK_TRADE_PUBLISH_HOUR = 21
+MAIRUI_TICK_TRADE_PUBLISH_MIN = 0
+# 21:00 之后再跑（留 5 分钟缓冲，等 mairui 完整入库）
+TICK_TRADE_RUN_GATE = (MAIRUI_TICK_TRADE_PUBLISH_HOUR, MAIRUI_TICK_TRADE_PUBLISH_MIN + 5)
+
+
+def _passes_publish_gate(now: Optional[datetime] = None, *, force: bool = False) -> tuple[bool, str]:
+    """判断当前时间是否已过 mairui 21:00 数据发布门控。
+
+    force=True 跳过门控（手动 / 测试用）。
+    """
+    if force:
+        return True, "force=True 跳过门控"
+    n = now or datetime.now()
+    gate = n.replace(hour=TICK_TRADE_RUN_GATE[0], minute=TICK_TRADE_RUN_GATE[1], second=0, microsecond=0)
+    if n < gate:
+        return False, f"mairui 逐笔数据 {MAIRUI_TICK_TRADE_PUBLISH_HOUR:02d}:{MAIRUI_TICK_TRADE_PUBLISH_MIN:02d} 发布；当前 {n.strftime('%H:%M')} 早于门控，skip"
+    return True, "ok"
+
+
+class SyncTickTrade(SyncTask):
+    dataset_id = "tick_trade"
+
+    def _run(
+        self,
+        *,
+        trigger_source: str = "manual",
+        codes: list[str] | None = None,
+        max_workers: int = 5,
+        force: bool = False,
+        **kwargs,
+    ) -> dict[str, Any]:
+        mr = ds_registry.get("datasource_mairui")
+        if mr is None:
+            return {"status": "error", "message": "mairui 数据源未注册"}
+        ok, reason = is_source_ready(mr.key)
+        if not ok:
+            mark_sync_blocked(self.dataset_id, message=f"mairui 未就绪: {reason}")
+            return {"status": "blocked", "message": f"mairui 未就绪: {reason}"}
+
+        # ── 21:00 门控（force=True 跳过）──
+        gate_ok, gate_msg = _passes_publish_gate(force=force)
+        if not gate_ok:
+            logger.info(f"[tick_trade] 门控未过: {gate_msg}")
+            return {"status": "warning", "message": gate_msg, "ok": 0, "fail": 0, "rows": 0, "elapsed_sec": 0.0}
+
+        # ── 取股票列表 ──
+        if codes:
+            stock_codes = [to_code6(c) for c in codes]
+        else:
+            stock_codes = [c for c in db_ops.iter_stock_codes(active_only=True) if is_a_share_code(c)]
+        limit_raw = os.environ.get("MARKET_DATA_STOCK_LIMIT", "").strip()
+        if limit_raw.isdigit() and int(limit_raw) > 0:
+            stock_codes = stock_codes[: int(limit_raw)]
+        if not stock_codes:
+            return {"status": "error", "message": "无股票代码"}
+
+        # 注：不做"今日已同步"dedup。原因：
+        #   - mairui 的"当天"实际是"最近一个已发布的交易日"（21:00 之前是昨日），
+        #     用 today_str 做去重会失效（即使刚跑完，trade_date 还是昨日）。
+        #   - PK 含 trade_date，重跑是 ON CONFLICT DO UPDATE，安全且幂等。
+        #   - 17 分钟全市场开销可接受；非交易日用 `MARKET_DATA_STOCK_LIMIT=0` 跳。
+
+        total = len(stock_codes)
+        if total == 0:
+            return {"status": "ok", "message": "无股票需要同步", "ok": 0, "fail": 0, "rows": 0, "elapsed_sec": 0.0}
+
+        # ── 预估耗时：5 RPS 上限 ──
+        est_sec = total / 5.0
+        logger.info(f"[tick_trade] 计划: {total} 只, 预估 {est_sec:.0f}s ({est_sec/60:.1f}min)")
+        self._progress(
+            message=f"计划: {total} 只, 预估 {est_sec/60:.1f}min @5RPS",
+            current=0, total=total, current_step="planning",
+        )
+
+        # ── 执行 ──
+        t0 = time.time()
+        ok_cnt = fail_cnt = 0
+        rows_total = 0
+        with ThreadPoolExecutor(max_workers=max_workers) as pool:
+            futures = {pool.submit(self._sync_one, mr, c6): c6 for c6 in stock_codes}
+            for i, future in enumerate(as_completed(futures), 1):
+                c6 = futures[future]
+                try:
+                    res = future.result()
+                    if res["status"] == "ok":
+                        ok_cnt += 1
+                        rows_total += res["rows"]
+                    else:
+                        fail_cnt += 1
+                        if res.get("error"):
+                            logger.warning(f"[tick_trade {c6}] {res['error']}")
+                except Exception as e:
+                    fail_cnt += 1
+                    logger.warning(f"[tick_trade {c6}] {e}")
+                if i % 50 == 0 or i == total:
+                    self._progress(
+                        message=f"tick_trade 进度 {i}/{total} OK:{ok_cnt} FAIL:{fail_cnt} 行:{rows_total}",
+                        current=i, total=total, current_step=c6,
+                    )
+        elapsed = round(time.time() - t0, 1)
+        msg = f"逐笔 {ok_cnt}成 {fail_cnt}败 共{rows_total}行, {elapsed}s"
+        return {
+            "status": "ok" if fail_cnt == 0 else "warning",
+            "message": msg,
+            "ok": ok_cnt, "fail": fail_cnt, "rows": rows_total, "elapsed_sec": elapsed,
+        }
+
+    def _sync_one(self, mr, code6: str) -> dict:
+        try:
+            df = mr.fetch_tick_trade(code6)
+        except Exception as e:
+            return {"status": "fail", "error": str(e)}
+        if df is None or df.empty:
+            return {"status": "fail", "error": "no data (mairui 21:00 前可能为空)"}
+        try:
+            rows = [
+                {
+                    "stock_code": to_hermes(code6),
+                    "trade_date": str(r["trade_date"]),
+                    "trade_time": r["trade_time"].strftime("%Y-%m-%d %H:%M:%S")
+                                 if hasattr(r["trade_time"], "strftime") else str(r["trade_time"]),
+                    "price": float(r.get("price") or 0),
+                    "volume": float(r.get("volume") or 0),
+                    "direction_code": int(r.get("direction_code") or 0),
+                    "direction": str(r.get("direction") or ""),
+                    "amount": float(r.get("amount") or 0),
+                }
+                for _, r in df.iterrows()
+            ]
+            db_ops.upsert_tick_trade(rows)
+            return {"status": "ok", "rows": len(rows)}
+        except Exception as e:
+            return {"status": "fail", "error": f"db write: {e}"}

bin/archive/migrate_mysql_to_pg.py

@@ -0,0 +1,288 @@
+"""[DEPRECATED 2026-06-16] MySQL → PostgreSQL 数据迁移工具。
+
+⚠️ 项目从 2026-06-16 起放弃 MySQL，只支持 PostgreSQL。
+   本脚本已归档到 ``bin/archive/``，仅作历史参考 / 应急回滚。
+   如果你想从 MySQL 备份恢复数据到 PG，可以临时把它移回 ``bin/`` 用一次。
+
+同步目标：把 MySQL `market_data_sync_db` 库 15 张表的数据搬到 PG `market_data.schema` 下。
+
+策略（不重新建表，建表由 pg_bootstrap 负责）：
+  - 用 pymysql 读 MySQL 流式分页（避免 1 亿行一次占满内存）
+  - 用 psycopg2.copy_from 或 executemany 写 PG（按表选择最高效方式）
+  - 顺序：先小表（配置/字典）→ 后大表（kline_5min 等）
+
+数据量预估（按当时 MySQL 实测，2026-06-16）：
+  config:                  20 行
+  dataset_registry:        15 行
+  industry:              5,207 行
+  sectors:                  83 行
+  stock_sector_map:     5,207 行
+  indices:                  6 行
+  market_regime_daily:  2,046 行
+  share:                6,141 行
+  stocks:               5,210 行
+  sector_indices:     103,084 行
+  sector_features_daily: 416,521 行
+  kline_index:          37,480 行
+  moneyflow:        3,188,301 行
+  kline_stock:     11,605,541 行
+  kline_5min:     121,945,849 行   ← 最大，约 1.2 亿
+
+用法（应急时）：
+    mv bin/archive/migrate_mysql_to_pg.py bin/migrate_mysql_to_pg.py
+    .venv/bin/python -m bin.migrate_mysql_to_pg
+"""
+from __future__ import annotations
+
+import argparse
+import sys
+import time
+from io import StringIO
+from typing import Iterable
+
+import pymysql
+import psycopg2
+from dotenv import load_dotenv
+
+load_dotenv(".env", override=False)
+
+# 15 张表的列定义（MySQL → PG 列名映射）
+# 大部分列名一致；少数 PG 用 JSONB（dataset_registry.dependency_ids）需要 JSON 解析
+TABLES = [
+    # (table_name, mysql_order_by, list_of_column_names)
+    ("config",                   "key",      ["key", "value", "category", "description", "updated_at"]),
+    ("dataset_registry",         "dataset_id", ["dataset_id", "name", "description", "storage_uri", "storage_layer",
+                                             "management_role", "source", "sync_script", "dependency_ids",
+                                             "enabled", "sort_order", "status", "trigger_source",
+                                             "started_at", "finished_at", "last_success_at", "last_failure_at",
+                                             "message", "last_error", "needs_resync", "progress_current",
+                                             "progress_total", "current_step", "updated_at"]),
+    ("indices",                  "index_code", ["index_code", "index_name", "market", "category", "source", "enabled", "updated_at"]),
+    ("industry",                 "code",      ["code", "industry_name", "industry_classification", "update_date", "updated_at"]),
+    ("sectors",                  "sector_key", ["sector_key", "sector_name", "taxonomy", "level", "source", "enabled", "updated_at"]),
+    ("stock_sector_map",         "stock_code", ["stock_code", "sector_key", "updated_at"]),
+    ("stocks",                   "code",      ["code", "name", "exchange", "list_date", "listing_status", "industry",
+                                             "total_share", "float_share", "share_updated_at", "kline_synced_at",
+                                             "updated_at"]),
+    ("market_regime_daily",      "trade_date", ["trade_date", "advancers", "decliners", "advance_ratio", "turnover",
+                                             "turnover_avg_5d", "turnover_ratio_5d", "source", "is_extreme_panic",
+                                             "updated_at"]),
+    ("share",                    "stock_code,trade_date", ["stock_code", "trade_date", "total_share", "float_share"]),
+    ("sector_indices",           "trade_date,sector_name", ["trade_date", "sector_name", "close", "sector_amplitude"]),
+    ("sector_features_daily",    "trade_date,sector_name", ["trade_date", "sector_name", "sector_ret", "sector_amplitude",
+                                                       "close", "ema10", "ema20", "ema200", "score"]),
+    ("kline_index",              "index_code,trade_date", ["index_code", "trade_date", "open", "high", "low", "close", "volume"]),
+    ("moneyflow",                "stock_code,trade_date", ["stock_code", "trade_date", "main_net_inflow",
+                                                       "large_net_inflow", "medium_net_inflow", "small_net_inflow"]),
+    ("kline_stock",              "stock_code,trade_date", ["stock_code", "trade_date", "open", "high", "low", "close", "volume"]),
+    ("kline_5min",               "stock_code,bar_time",   ["stock_code", "bar_time", "open", "high", "low", "close",
+                                                       "volume", "amount", "turnover_rate"]),
+]
+
+
+def mysql_conn():
+    return pymysql.connect(
+        host=__import__("os").environ.get("MYSQL_HOST", "127.0.0.1"),
+        port=int(__import__("os").environ.get("MYSQL_PORT", "3306")),
+        user=__import__("os").environ.get("MYSQL_USER", "root"),
+        password=__import__("os").environ.get("MYSQL_PASSWORD", ""),
+        database=__import__("os").environ.get("MYSQL_DATABASE", "market_data_sync_db"),
+        charset="utf8mb4",
+        cursorclass=pymysql.cursors.SSDictCursor,  # 服务端 cursor，流式不撑内存
+    )
+
+
+def pg_conn():
+    import os as _os
+    pg_url = _os.environ.get("PG_URL", "").strip()
+    # SQLAlchemy 格式 → psycopg2 格式（剥掉 +psycopg2）
+    if pg_url.startswith("postgresql+psycopg2://"):
+        pg_url = pg_url.replace("postgresql+psycopg2://", "postgresql://", 1)
+    if not pg_url:
+        host = _os.environ.get("PG_HOST", "127.0.0.1")
+        port = _os.environ.get("PG_PORT", "5432")
+        user = _os.environ.get("PG_USER", "market_sync")
+        pw = _os.environ.get("PG_PASSWORD", "market_sync")
+        db = _os.environ.get("PG_DB_NAME", "market_data")
+        pg_url = f"postgresql://{user}:{pw}@{host}:{port}/{db}"
+    return psycopg2.connect(pg_url)
+
+
+def _convert_value(col: str, v):
+    """MySQL → PG 类型兼容转换。"""
+    if v is None:
+        return None
+    if col == "dependency_ids" and isinstance(v, str):
+        # MySQL 存的是 Python list repr（如 ['stock_info']），不是合法 JSON。
+        # 先后备方案：先 json.loads（合法 JSON），失败再 ast.literal_eval（Python repr）。
+        import ast
+        import json
+        if not v.strip():
+            return []
+        # 试 1: 当 JSON 解析
+        try:
+            return json.loads(v)
+        except json.JSONDecodeError:
+            pass
+        # 试 2: 当 Python list/tuple repr 解析
+        try:
+            parsed = ast.literal_eval(v)
+            if isinstance(parsed, (list, tuple)):
+                return list(parsed)
+        except (ValueError, SyntaxError):
+            pass
+        # 试 3: 逗号分隔的纯字符串列表（兜底）
+        return [s.strip().strip("'\"") for s in v.strip("[]").split(",") if s.strip()]
+    if isinstance(v, str) and v == "":
+        return None
+    return v
+
+
+def _copy_from_path(conn_pg, table_name: str, columns: list[str], tmp_path: str) -> int:
+    """从已写好的 temp file COPY 到 PG。
+
+    temp file 由调用方在游标活跃时填充好。
+    """
+    cur = conn_pg.cursor()
+    with open(tmp_path, "rb") as fb:
+        full_table = f"market_data.{table_name}"
+        cur.copy_expert(
+            f"COPY {full_table} ({','.join(columns)}) FROM STDIN WITH (FORMAT text, NULL '')",
+            fb,
+        )
+    conn_pg.commit()
+    return 0  # 实际行数已经在外层统计了
+
+
+def _truncate_pg_table(conn_pg, table_name: str) -> None:
+    """清空 PG 表。用 DELETE 而不是 TRUNCATE（TRUNCATE 需要额外权限）。
+
+    DELETE 在大表上慢，但仅迁移时用一次可以接受。
+    """
+    cur = conn_pg.cursor()
+    cur.execute(f"DELETE FROM market_data.{table_name}")
+    conn_pg.commit()
+
+
+def migrate_table(mysql, pg, table_name: str, order_by: str, columns: list[str],
+                 chunk_size: int = 100_000) -> int:
+    """单表迁移：先 TRUNCATE，再流式读 MySQL + COPY 写 PG。"""
+    t0 = time.time()
+    col_list = ", ".join(f"`{c}`" for c in columns)
+
+    with mysql.cursor() as cur:
+        cur.execute(f"SELECT COUNT(*) FROM {table_name}")
+        n_total = cur.fetchone()["COUNT(*)"]
+
+    if n_total == 0:
+        print(f"  {table_name:<25} 0 行（跳过）")
+        return 0
+
+    # 先清空 PG 表（确保是干净迁移，不重复）
+    _truncate_pg_table(pg, table_name)
+
+    # 读 MySQL（流式：SSCursor 不缓存全表）→ 写 PG 临时文件 → COPY
+    # 关键：迭代 + 写 temp file 必须在 with 块内完成（cur 关闭后再迭代会失败）
+    print(f"  {table_name:<25} {n_total:>12,} 行 ...", end=" ", flush=True)
+    import json as _json
+    import tempfile
+    JSONB_COLS = {"dependency_ids"}
+    tmp = tempfile.NamedTemporaryFile(
+        mode="w", encoding="utf-8", prefix=f"migrate_{table_name}_", suffix=".tsv",
+        delete=False,
+    )
+    tmp_path = tmp.name
+    written = 0
+    try:
+        with mysql.cursor() as cur:
+            order_by_quoted = ", ".join(f"`{c.strip()}`" for c in order_by.split(","))
+            cur.execute(f"SELECT {col_list} FROM `{table_name}` ORDER BY {order_by_quoted}")
+            for r in cur:  # SSCursor：一次一行，不缓存全表
+                cells = []
+                for col, v in zip(columns, (r.get(col) for col in columns)):
+                    if v is None:
+                        cells.append("")
+                    elif col in JSONB_COLS and not isinstance(v, str):
+                        cells.append(_json.dumps(v, ensure_ascii=False))
+                    else:
+                        s = str(v)
+                        cells.append(s.replace("\\", "\\\\").replace("\t", "\\t").replace("\n", "\\n"))
+                tmp.write("\t".join(cells))
+                tmp.write("\n")
+                written += 1
+        # with 块结束，cur 已关闭。temp file 写完。关 file 准备 COPY。
+        tmp.flush()
+        tmp.close()
+        # COPY 从磁盘 temp file 读
+        with open(tmp_path, "rb") as fb:
+            cur_pg = pg.cursor()
+            full_table = f"market_data.{table_name}"
+            cur_pg.copy_expert(
+                f"COPY {full_table} ({','.join(columns)}) FROM STDIN WITH (FORMAT text, NULL '')",
+                fb,
+            )
+            pg.commit()
+    finally:
+        try:
+            import os as _os
+            _os.unlink(tmp_path)
+        except OSError:
+            pass
+    elapsed = time.time() - t0
+    print(f"→ {written:>12,} 行  ({elapsed:.1f}s)")
+    return written
+
+
+def main():
+    p = argparse.ArgumentParser()
+    p.add_argument("--only", help="逗号分隔的表名列表，只迁指定的")
+    p.add_argument("--chunk-size", type=int, default=100_000)
+    p.add_argument("--skip", help="逗号分隔要跳过的表")
+    args = p.parse_args()
+
+    only = set((args.only or "").split(",")) if args.only else None
+    skip = set((args.skip or "").split(",")) if args.skip else set()
+
+    targets = [
+        t for t in TABLES
+        if (only is None or t[0] in only) and t[0] not in skip
+    ]
+
+    print(f"=== MySQL → PG 数据迁移 ===")
+    print(f"目标表: {len(targets)} 张")
+    print(f"源: MySQL market_data_sync_db (127.0.0.1:3306)")
+    print(f"目标: PG market_data 库 market_data schema")
+    print()
+
+    mysql = mysql_conn()
+    pg = pg_conn()
+
+    grand_total = 0
+    grand_t0 = time.time()
+    for table_name, order_by, columns in targets:
+        try:
+            n = migrate_table(mysql, pg, table_name, order_by, columns, args.chunk_size)
+            grand_total += n
+        except Exception as e:
+            print(f"  ✗ {table_name} 失败: {e}")
+    grand_elapsed = time.time() - grand_t0
+    print()
+    print(f"=== 全部完成：{grand_total:,} 行 / 耗时 {grand_elapsed:.1f}s ===")
+
+    # 简单校验：每张表行数对比
+    print("\n=== 校验：MySQL vs PG 行数对比 ===")
+    with mysql.cursor() as cur, pg.cursor() as cur_pg:
+        for table_name, _, _ in targets:
+            cur.execute(f"SELECT COUNT(*) AS n FROM {table_name}")
+            n_mysql = cur.fetchone()["n"]
+            cur_pg.execute(f"SELECT COUNT(*) FROM market_data.{table_name}")
+            n_pg = cur_pg.fetchone()[0]
+            ok = "✓" if n_mysql == n_pg else "✗"
+            print(f"  {ok} {table_name:<25} MySQL={n_mysql:>12,}  PG={n_pg:>12,}")
+
+    mysql.close()
+    pg.close()
+
+
+if __name__ == "__main__":
+    main()

bin/market_sync_lhb_run.sh

@@ -0,0 +1,29 @@
+#!/bin/bash
+# systemd 调度的入口（专跑龙虎榜）
+#
+# 设计：
+#   - 与 market_sync_moneyflow_run.sh 同构,只跑 longhubang 一个 task
+#   - 22:00 触发（龙虎榜 19:00~21:00 陆续出齐；与 moneyflow 21:35 错开避免并发限速）
+#   - 单日调用 ~3 次聚合层 + ~200 次席位层，3-5 分钟内完成
+#   - 单独日志到 logs/lhb_<ts>.log
+#   - exit code 透传
+set -u
+
+PROJECT_ROOT="/home/gao/Development/quant_home/market_sync"
+LOG_DIR="$PROJECT_ROOT/logs"
+mkdir -p "$LOG_DIR"
+
+TS=$(date +%Y%m%d_%H%M%S)
+LOG="$LOG_DIR/lhb_${TS}.log"
+
+echo "[market-sync-lhb] start ts=$TS log=$LOG" | tee -a "$LOG"
+
+# 注：不做法定节假日过滤 —— 与其他 3 个 timer 一致的 fail-open 原则。
+# 非交易日 akshare 仍可能返当周数据 → task 报 warning，不丢历史。
+
+# ── 跑 longhubang ──
+cd "$PROJECT_ROOT"
+.venv/bin/python -m app.entrypoints.cli sync longhubang 2>&1 | tee -a "$LOG"
+EXIT_CODE=${PIPESTATUS[0]}
+echo "[market-sync-lhb] done exit_code=$EXIT_CODE" | tee -a "$LOG"
+exit $EXIT_CODE

bin/market_sync_moneyflow_run.sh

@@ -0,0 +1,29 @@
+#!/bin/bash
+# systemd 调度的入口（专跑 moneyflow）
+#
+# 设计：
+#   - 与 market_sync_tick_run.sh 同构,只跑 moneyflow 一个 task
+#   - mairui 文档：「更新：每日 21:30」—— systemd 21:35 触发,留 5min buffer
+#   - CLI 不传 --force：让 task 走 21:30 时间门控（moneyflow 暂无门控；为一致仍走通路径）
+#   - 单独日志到 logs/moneyflow_<ts>.log
+#   - exit code 透传
+set -u
+
+PROJECT_ROOT="/home/gao/Development/quant_home/market_sync"
+LOG_DIR="$PROJECT_ROOT/logs"
+mkdir -p "$LOG_DIR"
+
+TS=$(date +%Y%m%d_%H%M%S)
+LOG="$LOG_DIR/moneyflow_${TS}.log"
+
+echo "[market-sync-moneyflow] start ts=$TS log=$LOG" | tee -a "$LOG"
+
+# 注：不做法定节假日过滤 —— 与 market_sync_run.sh 同样的 fail-open 原则。
+# 大不了非交易日 mairui 返回空 → task 报 warning，不丢历史。
+
+# ── 跑 moneyflow ──
+cd "$PROJECT_ROOT"
+.venv/bin/python -m app.entrypoints.cli sync moneyflow 2>&1 | tee -a "$LOG"
+EXIT_CODE=${PIPESTATUS[0]}
+echo "[market-sync-moneyflow] done exit_code=$EXIT_CODE" | tee -a "$LOG"
+exit $EXIT_CODE

bin/market_sync_run.sh

@@ -0,0 +1,92 @@
+#!/bin/bash
+# systemd 调度的入口：跑一次全量同步 + 配套结构化 watch
+#
+# 设计：
+#   - 后台启动 runall_once.py（输出重定向到时间戳 log）
+#   - 同时后台启动 structured_watch.sh 盯进程（13 checkpoint × 递增间隔）
+#   - wrapper 自身 fast-poll runall PID（每 5s 一次）
+#   - runall 一退出就 SIGTERM watch（不靠 watch 自己的 checkpoint 轮询，避开 30min 长 sleep）
+#   - watch 自然结束后整个脚本退出
+#   - 退出码透传 runall_once.py 的 exit code，systemd 据此判断成功 / 失败
+#
+# 为什么不只用 structured_watch：
+#   - 看 [[background-watcher-pitfalls]] 记忆：先承诺后验证是三大坑之一
+#   - structured_watch.sh 的 schedule 在后期是 30min / 60min 间隔
+#   - runall 死后，watch 最坏要等 60min 才发现进程没了
+#   - wrapper fast-poll 让响应时间 < 5s
+#
+# 为什么不让 systemd TimeoutStartSec= 自动收尾：
+#   - 超时是 SIGTERM → SIGKILL，但 SIGKILL 不给 runall / watch 写日志的机会
+#   - 自己 fast-poll 能优雅收尾（先 SIGTERM watch 留出写日志时间，再 SIGKILL 兜底）
+set -u
+
+PROJECT_ROOT="/home/gao/Development/quant_home/market_sync"
+LOG_DIR="$PROJECT_ROOT/logs"
+mkdir -p "$LOG_DIR"
+
+TS=$(date +%Y%m%d_%H%M%S)
+RUNALL_LOG="$LOG_DIR/runall_${TS}.log"
+WATCH_LOG="$LOG_DIR/watch_${TS}.log"
+
+echo "[market-sync-run] start ts=$TS"
+echo "[market-sync-run] runall_log=$RUNALL_LOG"
+echo "[market-sync-run] watch_log=$WATCH_LOG"
+
+# 注：不做法定节假日过滤。理由（fail-open 原则）：
+#   - 节假日配置（TRADING_HOLIDAYS / config 表）一旦出错（误把今天加进去、
+#     漏更新本年度、笔误等）= 直接漏一天数据，且没声音
+#   - 大不了非交易日跑出来 mairui/baostock 返回空数据，task 报 warning 但不丢历史
+#   - 周末过滤由 systemd timer `OnCalendar=Mon..Fri` 完成，足够
+# ── 1) 后台启动 runall_once.py ──
+cd "$PROJECT_ROOT"
+.venv/bin/python bin/runall_once.py > "$RUNALL_LOG" 2>&1 &
+RUNALL_PID=$!
+echo "[market-sync-run] runall pid=$RUNALL_PID"
+
+# ── 2) 后台启动 structured_watch.sh（独立进程，不阻塞 wrapper）
+#       PATTERN 不含 "python" → 触发 watch 内部 case 第二分支，前缀 [p]ython.* 拼出
+"$PROJECT_ROOT/bin/structured_watch.sh" \
+    "runall_once" \
+    "$RUNALL_LOG" \
+    "$WATCH_LOG" \
+    "market-sync-full" > /dev/null 2>&1 &
+WATCH_PID=$!
+echo "[market-sync-run] watch pid=$WATCH_PID"
+
+# ── 3) wrapper 自身 fast-poll runall PID（每 5s）
+#       这是关键修复：runall 死后 < 5s 内就触发 watch 退出
+RUNALL_EXIT_CODE=1
+while kill -0 "$RUNALL_PID" 2>/dev/null; do
+    sleep 5
+done
+
+# runall 已退出，取 exit code
+wait "$RUNALL_PID" 2>/dev/null
+RUNALL_EXIT_CODE=$?
+echo "[market-sync-run] runall 已退出 exit_code=$RUNALL_EXIT_CODE"
+
+# ── 4) 通知 watch 退出（如果还在跑）──
+if kill -0 "$WATCH_PID" 2>/dev/null; then
+    echo "[market-sync-run] 通知 watch 退出（pid=$WATCH_PID）"
+    kill -TERM "$WATCH_PID" 2>/dev/null || true
+    # 给 watch 30s 自然退出（不要立即 KILL，留它写最后日志）
+    for _ in 1 2 3 4 5 6; do
+        sleep 5
+        kill -0 "$WATCH_PID" 2>/dev/null || break
+    done
+    if kill -0 "$WATCH_PID" 2>/dev/null; then
+        echo "[market-sync-run] ⚠️ watch 30s 内未退出，强制 kill -9"
+        kill -KILL "$WATCH_PID" 2>/dev/null || true
+    fi
+fi
+
+# ── 5) 输出 summary ──
+SUMMARY="$LOG_DIR/runall_summary.json"
+if [ -f "$SUMMARY" ]; then
+    echo "[market-sync-run] === summary ==="
+    cat "$SUMMARY"
+    echo "[market-sync-run] === /summary ==="
+fi
+
+echo "[market-sync-run] done exit_code=$RUNALL_EXIT_CODE"
+exit "$RUNALL_EXIT_CODE"

bin/market_sync_tick_run.sh

@@ -0,0 +1,30 @@
+#!/bin/bash
+# systemd 调度的入口（专跑 tick_trade）
+#
+# 设计：
+#   - 比 market_sync_run.sh 简单：只跑一个 task (tick_trade)
+#   - 21:00 门控由 task 内部处理；systemd 触发时间 21:05 留 5min buffer
+#   - CLI 加 --force 跳过 21:00 门控（systemd 触发时已是 21:05+，force 不会真正"跳过"
+#     任何东西，但保持与 runall_once 同步任务的 force 语义一致）
+#   - 单独日志到 logs/tick_<ts>.log
+#   - exit code 透传（systemd 据此判成功 / 失败）
+set -u
+
+PROJECT_ROOT="/home/gao/Development/quant_home/market_sync"
+LOG_DIR="$PROJECT_ROOT/logs"
+mkdir -p "$LOG_DIR"
+
+TS=$(date +%Y%m%d_%H%M%S)
+LOG="$LOG_DIR/tick_${TS}.log"
+
+echo "[market-sync-tick] start ts=$TS log=$LOG" | tee -a "$LOG"
+
+# 注：不做法定节假日过滤 —— 与 market_sync_run.sh 同样的 fail-open 原则。
+# 大不了非交易日 mairui 返回空 → task 报 warning，不丢历史。
+
+# ── 跑 tick_trade（force=True 跳过 21:00 门控，因为 systemd 触发时间 21:05 已过门控）──
+cd "$PROJECT_ROOT"
+.venv/bin/python -m app.entrypoints.cli sync tick_trade --force 2>&1 | tee -a "$LOG"
+EXIT_CODE=${PIPESTATUS[0]}
+echo "[market-sync-tick] done exit_code=$EXIT_CODE" | tee -a "$LOG"
+exit $EXIT_CODE

bin/run_remaining.sh

@@ -1,6 +1,7 @@
 #!/bin/bash
-# 跑剩余三个任务：moneyflow → share_snapshot → market_regime
+# 跑剩余任务：tick_trade → moneyflow → share_snapshot → market_regime
 # 跳过 kline_5min（全量回填 6 年太慢，下次有空再补）
+# tick_trade 用 --force 跳过 21:00 门控（人工补跑场景）
 set -e
 cd "$(dirname "$0")/.."
 export PYTHONPATH="$(pwd)"
@@ -9,11 +10,12 @@ mkdir -p logs
 # 先清理上次可能留下的卡死记录（recover_interrupted_syncs 启动时自动处理，但显式更稳）
 echo "=== 启动时间: $(date) ===" | tee logs/runall_remaining.log
 
-for TASK in moneyflow share_snapshot market_regime; do
+for TASK in "tick_trade --force" moneyflow share_snapshot market_regime; do
   echo "" | tee -a logs/runall_remaining.log
   echo "=== [$TASK] 开始 $(date) ===" | tee -a logs/runall_remaining.log
   T0=$(date +%s)
-  if .venv/bin/python -m app.entrypoints.cli sync "$TASK" 2>&1 | tee -a logs/runall_remaining.log; then
+  # shellcheck disable=SC2086
+  if .venv/bin/python -m app.entrypoints.cli sync $TASK 2>&1 | tee -a logs/runall_remaining.log; then
     T1=$(date +%s)
     echo "=== [$TASK] 完成，耗时 $((T1 - T0))s ===" | tee -a logs/runall_remaining.log
   else

bin/runall_once.py

@@ -1,10 +1,14 @@
 """一次性跑完所有 sync task（按依赖顺序）。
 
-跳过 industry_sector（已 ok）。
+按 sort_order 触发：stock_basic → kline_daily → kline_index → kline_5min
-按顺序触发：stock_basic → kline_daily → kline_index → kline_5min → moneyflow
+                 → industry_sector → sector_features
-          → share_snapshot → market_regime
+                 → share_snapshot → market_regime
 
-每个 task 跑完写日志 + 把 result 落到 summary.json。
+每个 task 跑完写日志 + 把 result 落到 logs/runall_summary.json。
+
+注：以下 task **不**在 runall 链中 —— 各自有独立的 systemd timer：
+  - tick_trade   : mairui 21:00 发布 → market-sync-tick.timer (21:05)
+  - moneyflow    : mairui 21:30 发布 → market-sync-moneyflow.timer (21:35)
 """
 from __future__ import annotations
 
@@ -28,20 +32,21 @@ seed_sync_registry()
 
 from app.tasks import get_task
 
-# 顺序执行，便于排查
+# 顺序执行，按 sort_order 走（便于排查 + 避免外部 API 限流）
+# 注：tick_trade / moneyflow 由独立 timer 跑（21:05 / 21:35）
 TASKS = [
     "stock_basic",
-    "industry_sector",  # 已 ok，但再跑一次全量也 OK (用 SKIP 标志)
     "kline_index",
     "kline_daily",
     "kline_5min",
-    "moneyflow",
+    "industry_sector",
+    "sector_features",
     "share_snapshot",
     "market_regime",
 ]
 
-# industry_sector 跑过一次（291s 全量）已 ok，跳过
+# 不跳任何 task — 全量跑
-SKIP = {"industry_sector"}
+SKIP: set[str] = set()
 
 results = {}
 t_all = time.time()

bin/systemd/market-sync-lhb.service

@@ -0,0 +1,33 @@
+[Unit]
+Description=Market data 龙虎榜 sync (akshare 源, daily 22:00)
+Documentation=file:///home/gao/Development/quant_home/market_sync/bin/market_sync_lhb_run.sh
+After=network-online.target market-sync.service market-sync-tick.service market-sync-moneyflow.service
+Wants=network-online.target
+
+[Service]
+Type=oneshot
+WorkingDirectory=/home/gao/Development/quant_home/market_sync
+User=gao
+Group=gao
+
+# 入口脚本：单独跑 longhubang 一个 task
+# 龙虎榜数据 19:00~21:00 陆续出齐 → 22:00 触发（与 moneyflow 21:35 错开避免并发）
+ExecStart=/home/gao/Development/quant_home/market_sync/bin/market_sync_lhb_run.sh
+
+# 硬上限 1h（每日 ~200 次 akshare 调用 + DB upsert，3-5 分钟内完成，留 buffer）
+TimeoutStartSec=3600
+
+# 不要 Restart=（oneshot 失败就让 OnFailure= 发通知，别自动重跑——
+# 重跑会撞外部 API 限流窗口，浪费资源）
+# Restart=no 是 oneshot 默认值
+
+# 日志走 journald（journalctl -u market-sync-lhb.service -f）
+StandardOutput=journal
+StandardError=journal
+SyslogIdentifier=market-sync-lhb
+
+# 环境（不读 /etc/environment，只带这几个；.env 由 cli 内部 load_dotenv）
+Environment=PYTHONUNBUFFERED=1
+
+[Install]
+WantedBy=multi-user.target

bin/systemd/market-sync-lhb.timer

@@ -0,0 +1,16 @@
+[Unit]
+Description=Schedule 龙虎榜 sync — Mon..Fri 22:00 Asia/Shanghai
+# market-sync-lhb.service 是这个 timer 的执行单元
+# 龙虎榜数据 19:00~21:00 陆续出齐 → 22:00 触发（与 moneyflow 21:35 错开）
+
+[Timer]
+# 龙虎榜出齐 → 22:00 触发
+OnCalendar=Mon..Fri 22:00:00 Asia/Shanghai
+# 系统关机 / 错过执行时，下次开机补跑一次（避免漏数据）
+Persistent=true
+# 单位（service）的精确名称
+Unit=market-sync-lhb.service
+# 不要 AccuracySec（默认 1min 漂移够用，避免 22:00:00 整点打堆）
+
+[Install]
+WantedBy=timers.target

bin/systemd/market-sync-moneyflow.service

@@ -0,0 +1,33 @@
+[Unit]
+Description=Market data moneyflow sync (mairui transaction, daily 21:35)
+Documentation=file:///home/gao/Development/quant_home/market_sync/bin/market_sync_moneyflow_run.sh
+After=network-online.target market-sync.service market-sync-tick.service
+Wants=network-online.target
+
+[Service]
+Type=oneshot
+WorkingDirectory=/home/gao/Development/quant_home/market_sync
+User=gao
+Group=gao
+
+# 入口脚本：单独跑 moneyflow 一个 task
+# mairui 文档：「更新：每日 21:30」—— 21:35 触发留 5min buffer
+ExecStart=/home/gao/Development/quant_home/market_sync/bin/market_sync_moneyflow_run.sh
+
+# 硬上限 2h（5206 只 × 50 RPS = 104s 理论上限，留 buffer 给重试 / 慢响应）
+TimeoutStartSec=7200
+
+# 不要 Restart=（oneshot 失败就让 OnFailure= 发通知，别自动重跑——
+# 重跑会撞外部 API 限流窗口，浪费 1.5h）
+# Restart=no 是 oneshot 默认值
+
+# 日志走 journald（journalctl -u market-sync-moneyflow.service -f）
+StandardOutput=journal
+StandardError=journal
+SyslogIdentifier=market-sync-moneyflow
+
+# 环境（不读 /etc/environment，只带这几个；.env 由 cli 内部 load_dotenv）
+Environment=PYTHONUNBUFFERED=1
+
+[Install]
+WantedBy=multi-user.target

bin/systemd/market-sync-moneyflow.timer

@@ -0,0 +1,16 @@
+[Unit]
+Description=Schedule moneyflow sync — Mon..Fri 21:35 Asia/Shanghai
+# market-sync-moneyflow.service 是这个 timer 的执行单元
+# mairui 文档：「更新：每日 21:30」—— 21:35 触发留 5 分钟 buffer
+
+[Timer]
+# mairui 21:30 发布数据 → 21:35 触发
+OnCalendar=Mon..Fri 21:35:00 Asia/Shanghai
+# 系统关机 / 错过执行时，下次开机补跑一次（避免漏数据）
+Persistent=true
+# 单位（service）的精确名称
+Unit=market-sync-moneyflow.service
+# 不要 AccuracySec（默认 1min 漂移够用，避免 21:35:00 整点打堆）
+
+[Install]
+WantedBy=timers.target

bin/systemd/market-sync-tick.service

@@ -0,0 +1,33 @@
+[Unit]
+Description=Market data tick-by-tick trade sync (mairui hsrl/zbjy, daily 21:05)
+Documentation=file:///home/gao/Development/quant_home/market_sync/bin/market_sync_tick_run.sh
+After=network-online.target market-sync.service
+Wants=network-online.target
+
+[Service]
+Type=oneshot
+WorkingDirectory=/home/gao/Development/quant_home/market_sync
+User=gao
+Group=gao
+
+# 入口脚本：单独跑 tick_trade 一个 task（mairui 21:00 发布数据，
+# 这个 service 在 21:05 触发，留 5min buffer；force=True 绕过 task 内部 21:00 门控）
+ExecStart=/home/gao/Development/quant_home/market_sync/bin/market_sync_tick_run.sh
+
+# 硬上限 2h（5000 只 × 1 RPS ≈ 17min，留 buffer 给 retry / 慢任务）
+TimeoutStartSec=7200
+
+# 不要 Restart=（oneshot 失败就让 OnFailure= 发通知，别自动重跑——
+# 重跑会撞外部 API 限流窗口，浪费 1.5h）
+# Restart=no 是 oneshot 默认值
+
+# 日志走 journald（journalctl -u market-sync-tick.service -f）
+StandardOutput=journal
+StandardError=journal
+SyslogIdentifier=market-sync-tick
+
+# 环境（不读 /etc/environment，只带这几个；.env 由 cli 内部 load_dotenv）
+Environment=PYTHONUNBUFFERED=1
+
+[Install]
+WantedBy=multi-user.target

bin/systemd/market-sync-tick.timer

@@ -0,0 +1,17 @@
+[Unit]
+Description=Schedule tick_trade sync — Mon..Fri 21:05 Asia/Shanghai
+# market-sync-tick.service 是这个 timer 的执行单元
+# 触发顺序：market-sync.service (15:30) 之后 market-sync-tick.service (21:05)
+# 但 systemd timer 独立触发，After= 只是声明依赖关系（非强制）
+
+[Timer]
+# mairui 文档：「更新：每日 21:00」—— 21:05 触发留 5 分钟 buffer
+OnCalendar=Mon..Fri 21:05:00 Asia/Shanghai
+# 系统关机 / 错过执行时，下次开机补跑一次（避免漏数据）
+Persistent=true
+# 单位（service）的精确名称
+Unit=market-sync-tick.service
+# 不要 AccuracySec（默认 1min 漂移够用，避免 21:05:00 整点打堆）
+
+[Install]
+WantedBy=timers.target

bin/systemd/market-sync.service

@@ -0,0 +1,32 @@
+[Unit]
+Description=Market Data Full Sync (PostgreSQL, 9 sync tasks end-to-end)
+Documentation=file:///home/gao/Development/quant_home/market_sync/bin/market_sync_run.sh
+After=network-online.target
+Wants=network-online.target
+
+[Service]
+Type=oneshot
+WorkingDirectory=/home/gao/Development/quant_home/market_sync
+User=gao
+Group=gao
+
+# 入口脚本（runall_once.py + structured_watch.sh 二合一）
+ExecStart=/home/gao/Development/quant_home/market_sync/bin/market_sync_run.sh
+
+# 硬上限 2h（实际 ~80min，留 buffer 给 retry / 慢任务）
+TimeoutStartSec=7200
+
+# 不要 Restart=（oneshot 失败就让 OnFailure= 发通知，别自动重跑——
+# 重跑会撞外部 API 限流窗口，浪费 1.5h）
+# Restart=no 是 oneshot 默认值
+
+# 日志走 journald（journalctl -u market-sync.service -f）
+StandardOutput=journal
+StandardError=journal
+SyslogIdentifier=market-sync
+
+# 环境（不读 /etc/environment，只带这几个；.env 由 runall_once.py 内部 load_dotenv）
+Environment=PYTHONUNBUFFERED=1
+
+[Install]
+WantedBy=multi-user.target

bin/systemd/market-sync.timer

@@ -0,0 +1,15 @@
+[Unit]
+Description=Schedule market data full sync — Mon..Fri 15:30 Asia/Shanghai
+# market-sync.service 是这个 timer 的执行单元
+
+[Timer]
+# A 股收盘时间 15:00，30 分钟 buffer 让最后一笔 tick 落地
+OnCalendar=Mon..Fri 15:30:00 Asia/Shanghai
+# 系统关机 / 错过执行时，下次开机补跑一次（避免漏数据）
+Persistent=true
+# 单位（service）的精确名称
+Unit=market-sync.service
+# 不要 AccuracySec（默认 1min 漂移够用，避免 15:30:00 整点打堆）
+
+[Install]
+WantedBy=timers.target

requirements.txt

@@ -20,7 +20,10 @@ requests>=2.30.0
 tenacity>=8.0.0
 
 # Datasources — A 股免费源
-# akshare 永远不使用（项目级决策）
+# akshare 仅用于"无稳定替代源"的数据集（2026-07-01 决策）。
+# 当前仅龙虎榜使用（聚合层 stock_lhb_detail_em + 席位层 stock_lhb_stock_detail_em）。
+# 5min K / moneyflow / kline_daily 等已有 baostock / mairui 替代，禁 akshare。
+akshare>=1.18.0
 baostock>=0.9.0
 # 雪球非官方 SDK：需要 XUEQIU_TOKEN（环境变量）才能拿到股本 / 日K 复核
 # 没装 → share / kline_daily 任务静默失败（ImportError 被 except 吞）

scripts/run_all_sync.sh

@@ -57,6 +57,10 @@ case "${1:-all}" in
     4|kline_daily)        run_step "4_kline_daily" "kline_daily" "--workers 20" ;;
     5|share_snapshot)     run_step "5_share_snapshot" "share_snapshot" "--workers 10" ;;
     6|market_regime)      run_step "6_market_regime" "market_regime" ;;
+    7|kline_5min)         run_step "7_kline_5min" "kline_5min" "--workers 10" ;;
+    8|moneyflow)          run_step "8_moneyflow" "moneyflow" "--workers 10" ;;
+    9|sector_features)    run_step "9_sector_features" "sector_features" ;;
+    10|tick_trade)        run_step "10_tick_trade" "tick_trade" "--workers 5 --force" ;;
     all)
         run_step "1_stock_basic" "stock_basic" || exit 1
         run_step "2_industry_sector" "industry_sector" || exit 1
@@ -64,10 +68,14 @@ case "${1:-all}" in
         run_step "4_kline_daily" "kline_daily" "--workers 20" || exit 1
         run_step "5_share_snapshot" "share_snapshot" "--workers 10" || exit 1
         run_step "6_market_regime" "market_regime" || exit 1
-        echo "$LOG_PREFIX ✓ 全部 6 步完成" | tee -a "$RUN_LOG"
+        run_step "7_kline_5min" "kline_5min" "--workers 10" || exit 1
+        run_step "8_moneyflow" "moneyflow" "--workers 10" || exit 1
+        run_step "9_sector_features" "sector_features" || exit 1
+        run_step "10_tick_trade" "tick_trade" "--workers 5 --force" || exit 1
+        echo "$LOG_PREFIX ✓ 全部 10 步完成" | tee -a "$RUN_LOG"
         ;;
     *)
-        echo "usage: $0 [1|2|3|4|5|6|all]"
+        echo "usage: $0 [1|2|3|4|5|6|7|8|9|10|all]"
         exit 1
         ;;
 esac

tests/test_schema_models.py

@@ -0,0 +1,38 @@
+"""ORM 模型 metadata 一致性测试（PostgreSQL only，2026-06-16 重构后）。
+
+历史背景：原 test_schema_models.py 还测了 schema.py vs models.py 的 DDL 漂移。
+schema.py 在这次重构中已删除（MySQL DDL 不再需要），所以只保留 ORM metadata 完整性测试。
+"""
+from __future__ import annotations
+
+
+def test_orm_metadata_registers_all_tables():
+    """ORMBase.metadata 应该注册 18 张 PG 表（项目主业务表）。"""
+    from app.core.db import models  # 触发全部模型 import
+    from app.core.db.orm import ORMBase
+
+    # 注意：SA 2.x 的 MetaData.tables 既能按 bare name 也能按 schema-qualified name 索引
+    # （keyed 容器），所以用 values() 拿 Table 对象再用 .name 拿 bare 名
+    bare_names = {t.name for t in ORMBase.metadata.tables.values()}
+    assert len(bare_names) == 18, f"期望 18 张 ORM 表，实际 {len(bare_names)}: {bare_names}"
+    expected = {
+        "config", "dataset_registry", "stocks", "indices",
+        "kline_stock", "kline_index", "kline_5min", "moneyflow", "share",
+        "sectors", "stock_sector_map", "industry",
+        "sector_indices", "sector_features_daily", "market_regime_daily",
+        "tick_trade",
+        "longhubang_daily", "longhubang_seat",  # 2026-07-01 龙虎榜
+    }
+    assert bare_names == expected, f"ORM 表名集合与期望不符: {bare_names ^ expected}"
+
+
+def test_orm_tables_in_market_data_schema():
+    """所有 ORM 表应该在 market_data PG schema 下（不是 public，不是 None）。"""
+    from app.core.db import models  # 触发全部模型 import
+    from app.core.db.orm import ORMBase
+
+    bad = []
+    for table in ORMBase.metadata.tables.values():
+        if table.schema != "market_data":
+            bad.append((table.name, table.schema))
+    assert not bad, f"以下表 schema 不是 market_data: {bad}"

tests/test_smoke.py

@@ -8,7 +8,7 @@ import pytest
 def test_import_config():
     from app.core.config import settings
     assert settings is not None
-    assert hasattr(settings, "mysql_host")
+    assert hasattr(settings, "pg_host")
 
 
 def test_import_data_source_base():
@@ -35,9 +35,10 @@ def test_tasks_registry():
     from app.tasks import TASKS, get_task
     assert isinstance(TASKS, dict)
     # 注：每次新增 task 都要更新这里的数字
-    # 当前 9 个：stock_basic, kline_daily, kline_index, kline_5min, moneyflow,
+    # 当前 11 个：stock_basic, kline_daily, kline_index, kline_5min, tick_trade,
-    #         industry_sector, sector_features, share_snapshot, market_regime
+    #          moneyflow, industry_sector, sector_features, share_snapshot, market_regime,
-    assert len(TASKS) == 9
+    #          longhubang
+    assert len(TASKS) == 11
     # get_task 应该返回实例
     task = get_task("kline_daily")
     assert task.dataset_id == "kline_daily"
@@ -45,10 +46,18 @@ def test_tasks_registry():
 
 def test_sync_definitions():
     from app.core.sync.registry import SYNC_DEFINITIONS
-    assert len(SYNC_DEFINITIONS) == 9
+    assert len(SYNC_DEFINITIONS) == 11
     # 所有 dataset_id 应唯一
     ids = [d["dataset_id"] for d in SYNC_DEFINITIONS]
-    assert len(set(ids)) == 9
+    assert len(set(ids)) == 11
+    # tick_trade 应在其中且 sort_order=45
+    tt = next(d for d in SYNC_DEFINITIONS if d["dataset_id"] == "tick_trade")
+    assert tt["sort_order"] == 45
+    assert "stock_basic" in tt["dependency_ids"]
+    # longhubang 应在其中且 sort_order=85
+    lh = next(d for d in SYNC_DEFINITIONS if d["dataset_id"] == "longhubang")
+    assert lh["sort_order"] == 85
+    assert "stock_basic" in lh["dependency_ids"]
 
 
 def test_build_default_registry():
@@ -60,8 +69,9 @@ def test_build_default_registry():
     assert "datasource_xinlang" in keys
     assert "datasource_baostock" in keys
     assert "datasource_mairui" in keys
-    # akshare 已移除（项目级决策：永远不用）
+    # akshare：2026-07-01 决策更新——龙虎榜无替代源，烟测通过，可注册。
-    assert "datasource_akshare" not in keys
+    # 见 [[no-akshare]] 记忆：仅"无替代"+"akshare 烟测 OK"才允许用。
+    assert "datasource_akshare_lhb" in keys
     assert "datasource_xueqiu" in keys