提交

2026-09-09 11:35:02 +08:00
parent bc1c72d558
commit d656c05b3d
35 changed files with 711 additions and 3194 deletions
--- a/backend/.env
+++ b/backend/.env
@@ -1,16 +0,0 @@
-DATABASE_URL=postgresql+asyncpg://postgres:Cirry0115@cirry.cn:5432/stock
-# TUSHARE_TOKEN=22edda0afe44c0609a187ff1ac0bb2a8fc61430f490ec19f7fec8390
-TUSHARE_TOKEN=2f7dbca732cdb762eb61bf3ca1b58f0c19a12732346ae06b3f60f4f5
-# 15000 积分档走 quicksync 镜像（官方接口对该 token 返回 40101）
-TUSHARE_API_URL=http://api.quicksync.cn
-DATA_ADJUST=qfq
-DATA_DEFAULT_START=20200101
-
-# ---- Redis 读缓存（股票列表/筛选项；留空则不缓存直查数据库）----
-REDIS_URL=redis://default:26d5c71d57344f37b8b4ddb567f2652f0c7ef41c774284ad@cirry.cn:6379
-
-# ---- LLM（智能选股；DeepSeek，OpenAI 兼容协议；/anthropic 后缀会被 _endpoint 自动归一）----
-# key 在 https://bigmodel.cn 控制台获取，格式形如 xxxxxxxx.yyyyyyyy（id.secret）
-LLM_BASE_URL=https://api.deepseek.com
-LLM_API_KEY=sk-b09acbd6c0ca4f94818b6deb039d6515
-LLM_MODEL=deepseek-chat
--- a/backend/app/api.py
+++ b/backend/app/api.py
--- a/backend/app/auth_api.py
+++ b/backend/app/auth_api.py
@@ -1,6 +1,8 @@
 """只登录、不注册的鉴权 API。"""
 from __future__ import annotations

+import time
+from collections import deque
 from datetime import timedelta

 from fastapi import APIRouter, Cookie, Depends, HTTPException, Request, Response, status
@@ -49,6 +51,33 @@ def clear_session_cookie(response: Response) -> None:
    )


+# ---------- 登录按 IP 限速（进程内滑动窗口，无外部依赖） ----------
+# 补充账户级锁定（auth_max_failed_logins）：本层拦多 IP 分布爆破，也稀释
+# 「故意输错 5 次锁死他人账户」的滥用面。反代部署时 host 是代理 IP，需改读 X-Forwarded-For。
+_LOGIN_WINDOW = 60.0
+_LOGIN_MAX_PER_WINDOW = 15
+_login_attempts: dict[str, deque[float]] = {}
+_login_gc_at = 0.0
+
+
+def _login_rate_limited(ip: str) -> bool:
+    """超限返回 True；未超限记录本次尝试（成功失败都计）。"""
+    global _login_gc_at
+    now = time.monotonic()
+    q = _login_attempts.setdefault(ip, deque())
+    while q and q[0] <= now - _LOGIN_WINDOW:
+        q.popleft()
+    if len(q) >= _LOGIN_MAX_PER_WINDOW:
+        return True
+    q.append(now)
+    if now - _login_gc_at > 3600:  # 顺手回收陈旧 entry，防长跑内存增长
+        _login_gc_at = now
+        stale = now - _LOGIN_WINDOW * 10
+        for k in [k for k, v in _login_attempts.items() if not v or v[-1] <= stale]:
+            del _login_attempts[k]
+    return False
+
+
@router.post("/login", response_model=LoginResponse)
 async def login(
    payload: LoginRequest,
@@ -56,6 +85,10 @@ async def login(
    response: Response,
    db: AsyncSession = Depends(get_session),
 ) -> LoginResponse:
+    ip = request.client.host if request.client else "?"
+    if _login_rate_limited(ip):
+        raise HTTPException(status_code=429, detail="登录尝试过于频繁，请稍后再试")
+
    now = utcnow()
    username = payload.username.strip()
    user = (await db.execute(select(User).where(User.username == username))).scalar_one_or_none()
--- a/backend/app/backtest/events.py
+++ b/backend/app/backtest/events.py
@@ -11,6 +11,7 @@ match=all(连续满足)/any(曾经满足)，多条件之间取 AND。
 """
 from __future__ import annotations

+import asyncio
 from datetime import date, datetime, timedelta

 import numpy as np
@@ -120,6 +121,69 @@ def _stats_block(trades: list[dict]) -> dict:
    }


+def _scan_batch(
+    candle_rows: list,
+    code_by_symbol: dict[str, str],
+    f_map: dict,
+    name_map: dict[str, str],
+    spec: EventBacktestSpec,
+    start_ts: datetime,
+    trades_limit: int,
+) -> list[dict]:
+    """纯 CPU：一批 bar -> 交易明细（放线程池跑，不阻塞事件循环）。"""
+    trades: list[dict] = []
+    if not candle_rows:
+        return trades
+    bars = pd.DataFrame(
+        candle_rows, columns=["symbol", "ts", "open", "high", "low", "close"]
+    )
+    for symbol, g in bars.groupby("symbol", sort=False):
+        if len(g) < 30:
+            continue
+        g = g.reset_index(drop=True)
+        ts_code_l = code_by_symbol[symbol]
+        cache: dict = {"_families": set()}
+        mask = _signal_mask(g, spec, cache)
+        if not mask.any():
+            continue
+        for sig_i in np.flatnonzero(mask.to_numpy()):
+            ts_sig = g.at[sig_i, "ts"]
+            # 信号必须落在回测窗口内（buffer 区只用于指标配热）
+            if ts_sig < start_ts:
+                continue
+            ie = _entry_exit_indices(int(sig_i), spec, len(g))
+            if ie is None:
+                continue
+            entry_i, exit_i = ie
+            e_row, x_row = g.iloc[entry_i], g.iloc[exit_i]
+            e_price = _price_at(e_row, "open" if spec.entry_timing == "next_open" else "close")
+            x_price = _price_at(x_row, "open" if spec.exit_timing == "open" else "close")
+            if not e_price or not x_price:
+                continue
+            f_in = f_map.get((ts_code_l, e_row["ts"].date()), 1.0)
+            f_out = f_map.get((ts_code_l, x_row["ts"].date()), 1.0)
+            ret_pct = (x_price * f_out) / (e_price * f_in) * 100 - 100
+            trades.append({
+                "ts_code": ts_code_l,
+                "name": name_map.get(ts_code_l),
+                "entry_date": e_row["ts"], "entry_price": round(e_price, 3),
+                "exit_date": x_row["ts"], "exit_price": round(x_price, 3),
+                "ret_pct": round(float(ret_pct), 3),
+            })
+            if len(trades) >= trades_limit:
+                return trades
+    return trades
+
+
+def _summarize(trades: list[dict]) -> tuple[dict, list[dict]]:
+    """纯 CPU：汇总统计 + 最好/最差样本（同样下线程池）。"""
+    stats = _stats_block(trades)
+    # 明细样本：最好 100 + 最差 100（其余统计已覆盖）
+    trades_sorted = sorted(trades, key=lambda t: t["ret_pct"], reverse=True)
+    sample = trades_sorted[:100] + (trades_sorted[-100:] if len(trades_sorted) > 100 else [])
+    return stats, sample
+
+
 async def run_event_backtest(
    session: AsyncSession,
    spec: EventBacktestSpec,
@@ -195,53 +259,15 @@ async def run_event_backtest(
        )).all()
        f_map = {(r[0], r[1].date()): float(r[2]) for r in adj_rows if r[2]}

-        bars = pd.DataFrame(
-            candle_rows, columns=["symbol", "ts", "open", "high", "low", "close"]
-        )
-        for symbol, g in bars.groupby("symbol", sort=False):
-            if len(g) < 30:
-                continue
-            g = g.reset_index(drop=True)
-            ts_code_l = code_by_symbol[symbol]
-            cache: dict = {"_families": set()}
-            mask = _signal_mask(g, spec, cache)
-            if not mask.any():
-                continue
-            for sig_i in np.flatnonzero(mask.to_numpy()):
-                ts_sig = g.at[sig_i, "ts"]
-                # 信号必须落在回测窗口内（buffer 区只用于指标配热）
-                if ts_sig < start_ts:
-                    continue
-                ie = _entry_exit_indices(int(sig_i), spec, len(g))
-                if ie is None:
-                    continue
-                entry_i, exit_i = ie
-                e_row, x_row = g.iloc[entry_i], g.iloc[exit_i]
-                e_price = _price_at(e_row, "open" if spec.entry_timing == "next_open" else "close")
-                x_price = _price_at(x_row, "open" if spec.exit_timing == "open" else "close")
-                if not e_price or not x_price:
-                    continue
-                f_in = f_map.get((ts_code_l, e_row["ts"].date()), 1.0)
-                f_out = f_map.get((ts_code_l, x_row["ts"].date()), 1.0)
-                ret_pct = (x_price * f_out) / (e_price * f_in) * 100 - 100
-                trades.append({
-                    "ts_code": ts_code_l,
-                    "name": name_map.get(ts_code_l),
-                    "entry_date": e_row["ts"], "entry_price": round(e_price, 3),
-                    "exit_date": x_row["ts"], "exit_price": round(x_price, 3),
-                    "ret_pct": round(float(ret_pct), 3),
-                })
-                if len(trades) >= MAX_TRADES:
-                    break
-            if len(trades) >= MAX_TRADES:
-                break
+        # pandas 全市场扫描是同步 CPU 重计算，丢线程池跑（await 期间事件循环可服务其他请求）
+        trades.extend(await asyncio.to_thread(
+            _scan_batch, candle_rows, code_by_symbol, f_map, name_map,
+            spec, start_ts, MAX_TRADES - len(trades),
+        ))
        if len(trades) >= MAX_TRADES:
            break

-    stats = _stats_block(trades)
-    # 明细样本：最好 100 + 最差 100（其余统计已覆盖）
-    trades_sorted = sorted(trades, key=lambda t: t["ret_pct"], reverse=True)
-    sample = trades_sorted[:100] + (trades_sorted[-100:] if len(trades_sorted) > 100 else [])
+    stats, sample = await asyncio.to_thread(_summarize, trades)
    return {
        "spec": spec,
        "universe": ts_code or "all",
--- a/backend/app/cache.py
+++ b/backend/app/cache.py
@@ -1,7 +1,8 @@
 """Redis 读缓存（可选基础设施）。

- REDIS_URL 留空、连接失败或超时：所有操作静默退化为「无缓存」，接口照常直查数据库，
-  且本进程内禁用重试（避免每个请求都陪跑一次连接超时）。
+- REDIS_URL 留空、连接失败或超时：所有操作静默退化为「无缓存」，接口照常直查数据库。
+  故障后熔断 _RECOVERY_SECONDS（期间所有请求直连，不陪跑连接超时），到期自动放行
+  一次探测——成功即完全恢复，仍失败则重新熔断（Redis 属加速件，坏了不能拖慢接口）。
 - 失效策略：TTL 自然过期 + 版本号（INCR）作废。自选股增删等写操作只 INCR 版本 key，
  旧缓存 key 里带着旧版本号，无需 SCAN 批量删除。
 - 只缓存「读多写少、可容忍短暂陈旧」的聚合数据（股票列表、筛选项等）；
@@ -13,6 +14,7 @@
 """
 from __future__ import annotations

+import asyncio
 import hashlib
 import json
 import time
@@ -23,12 +25,13 @@ import redis.asyncio as aioredis
 from .config import settings

 _pool: aioredis.ConnectionPool | None = None
-_disabled = False  # 一次失败后本进程禁用（Redis 属加速件，坏了不能拖慢接口）
+_RECOVERY_SECONDS = 60.0  # 熔断时长：期间直连不试 Redis，到期放行一次探测
+_disabled_until = 0.0  # 熔断截止的 monotonic 时刻；0 = 未熔断


 def _client() -> aioredis.Redis | None:
-    global _pool, _disabled
-    if not settings.redis_url or _disabled:
+    global _pool
+    if not settings.redis_url or time.monotonic() < _disabled_until:
        return None
    if _pool is None:
        _pool = aioredis.ConnectionPool.from_url(
@@ -43,8 +46,8 @@ def _client() -> aioredis.Redis | None:


 def _bail() -> None:
-    global _disabled
-    _disabled = True
+    global _disabled_until
+    _disabled_until = time.monotonic() + _RECOVERY_SECONDS


 def digest(*parts: Any) -> str:
@@ -84,7 +87,21 @@ def local_set(key: str, raw: str, ttl: int) -> None:
    _local_store[key] = (time.monotonic() + max(1, min(ttl, 120)), raw)
    _local_bytes += len(raw) + 64  # 连同 dict/tuple 开销粗略计入
    while _local_store and (len(_local_store) > _LOCAL_MAX_ENTRIES or _local_bytes > _LOCAL_MAX_BYTES):
-        _local_bytes -= len(_local_store.popitem(last=False)[1][1]) + 64
+        # dict 是插入序：next(iter(...)) 即最旧键（dict.popitem 不支持参数，别写成 OrderedDict 的写法）
+        oldest = next(iter(_local_store))
+        _local_bytes -= len(_local_store.pop(oldest)[1]) + 64
+
+
+# --- 后台写（fire-and-forget）------------------------------------------------
+# 读路径拿到响应后异步写 Redis、不阻塞返回。统一入口：任务挂全局集合防 GC，
+# cache_set 内部自带异常静默（缓存层尽力而为），优雅停机丢最后一次写无害（TTL 兜底）。
+_bg_tasks: set[asyncio.Task] = set()
+
+
+def set_bg(key: str, value: Any, ttl: int) -> None:
+    t = asyncio.create_task(cache_set(key, value, ttl))
+    _bg_tasks.add(t)
+    t.add_done_callback(_bg_tasks.discard)


 # --- 版本号本地缓存：热请求连 Redis GET ver:xx 都省掉 ------------------------
--- a/backend/app/config.py
+++ b/backend/app/config.py
@@ -56,6 +56,10 @@ class Settings(BaseSettings):
    screener_default_limit: int = 200  # 选股结果条数上限
    screener_sync_interval: float = 0.35  # 全市场批量调用间隔（秒），Tushare 控频

+    # ---- 夜间定时任务（收盘后自动同步 + 会话清理；见 app/scheduler.py）----
+    nightly_sync_enabled: bool = True
+    nightly_sync_hour: int = 18        # 本地时间整点，触发在 :05（收盘后日线已生成）
+
    # A股交易成本（基准日 2026-08）——做成可配置参数，便于将来按生效日期版本化
    stamp_duty_rate: float = 0.0005      # 印花税 0.05%，单边卖出（2023-08-28 减半）
    transfer_fee_rate: float = 0.00001   # 过户费 0.001%，沪深双边（2022 调整）
--- a/backend/app/data/company.py
+++ b/backend/app/data/company.py
@@ -9,7 +9,7 @@ from __future__ import annotations

 import asyncio
 import time
-from datetime import datetime, timedelta, timezone
+from datetime import datetime

 from sqlalchemy import select
 from sqlalchemy.dialects.postgresql import insert as pg_insert
@@ -18,12 +18,10 @@ from sqlalchemy.ext.asyncio import AsyncSession
 from ..config import settings
 from ..db import async_session
 from ..models import StockCompany
+from .sync_utils import call_retry, f_clean, fresh, get_pro_lazy, s_clean, utcnow

 _REFRESH_DAYS = 30

-# 频率超限特征（等待 62s 重试一次；与 screener.market_sync / data.etf_sync._call_retry 同款语义）
-_RATE_MARKS = ("频率超限", "每分钟")
-
 # 显式列出全部字段：introduction/office/main_business/business_scope 文档标注默认不显示，
 # 不传 fields 时 tushare 不返回这四列（实测 000001.SZ）
 _FIELDS = (
@@ -32,87 +30,38 @@ _FIELDS = (
    "employees,main_business,business_scope"
 )

-_pro = None  # 惰性单例（get_pro 每次都 ts.set_token 写文件，没必要重复）
-
-
-def _get_pro():
-    if not settings.tushare_token:
-        raise RuntimeError("未配置 TUSHARE_TOKEN，无法拉取公司简介（backend/.env）")
-    global _pro
-    if _pro is None:
-        from .tushare_provider import get_pro
-
-        _pro = get_pro()
-    return _pro
-
-
-def _call_retry(fn, *args, **kwargs):
-    """同步调用 tushare 接口；「每分钟」级频率超限等 62s 重试一次。"""
-    try:
-        return fn(*args, **kwargs)
-    except Exception as e:  # noqa: BLE001
-        msg = str(e)
-        if any(m in msg for m in _RATE_MARKS) and "小时" not in msg:
-            time.sleep(62)
-            return fn(*args, **kwargs)
-        raise
-
-
-def _utcnow() -> datetime:
-    return datetime.now(timezone.utc).replace(tzinfo=None)
-
-
-def _fresh(updated_at: datetime | None) -> bool:
-    return updated_at is not None and updated_at >= _utcnow() - timedelta(days=_REFRESH_DAYS)
-
-
-def _s(v) -> str | None:
-    """pandas NaN / 空串 / None -> None，其余 strip。"""
-    if v is None or (isinstance(v, float) and v != v):
-        return None
-    s = str(v).strip()
-    return s or None
-
-
-def _f(v) -> float | None:
-    try:
-        f = float(v)
-    except (TypeError, ValueError):
-        return None
-    return None if f != f else f  # NaN -> None
-

 def _i(v) -> int | None:
-    f = _f(v)
+    f = f_clean(v)
    return None if f is None else int(f)


 def fetch_company_sync(ts_code: str) -> dict | None:
    """同步拉单只公司简介（需在 to_thread 里跑）；返回行 dict，无此股返回 None。"""
    time.sleep(settings.screener_sync_interval)
-    df = _call_retry(_get_pro().stock_company, ts_code=ts_code, fields=_FIELDS)
+    df = call_retry(get_pro_lazy().stock_company, ts_code=ts_code, fields=_FIELDS)
    if df is None or df.empty:
        return None
    r = df.iloc[0]
    return {
        "ts_code": ts_code,
-        "com_name": _s(r.get("com_name")),
-        "com_id": _s(r.get("com_id")),
-        "chairman": _s(r.get("chairman")),
-        "manager": _s(r.get("manager")),
-        "secretary": _s(r.get("secretary")),
-        "reg_capital": _f(r.get("reg_capital")),
-        "setup_date": _s(r.get("setup_date")),
-        "province": _s(r.get("province")),
-        "city": _s(r.get("city")),
-        "introduction": _s(r.get("introduction")),
-        "website": _s(r.get("website")),
-        "email": _s(r.get("email")),
-        "office": _s(r.get("office")),
+        "com_name": s_clean(r.get("com_name")),
+        "com_id": s_clean(r.get("com_id")),
+        "chairman": s_clean(r.get("chairman")),
+        "manager": s_clean(r.get("manager")),
+        "secretary": s_clean(r.get("secretary")),
+        "reg_capital": f_clean(r.get("reg_capital")),
+        "setup_date": s_clean(r.get("setup_date")),
+        "province": s_clean(r.get("province")),
+        "city": s_clean(r.get("city")),
+        "introduction": s_clean(r.get("introduction")),
+        "website": s_clean(r.get("website")),
+        "email": s_clean(r.get("email")),
+        "office": s_clean(r.get("office")),
        "employees": _i(r.get("employees")),
-        "main_business": _s(r.get("main_business")),
-        "business_scope": _s(r.get("business_scope")),
-        "updated_at": _utcnow(),
+        "main_business": s_clean(r.get("main_business")),
+        "business_scope": s_clean(r.get("business_scope")),
+        "updated_at": utcnow(),
    }


@@ -151,7 +100,7 @@ async def get_company(session: AsyncSession, ts_code: str) -> dict | None:
    """
    row = (await session.execute(
        select(StockCompany).where(StockCompany.ts_code == ts_code))).scalar_one_or_none()
-    if row is not None and _fresh(row.updated_at):
+    if row is not None and fresh(row.updated_at, _REFRESH_DAYS):
        return _row_dict(row) if row.com_name is not None else None  # 墓碑 -> None
    # 释放请求会话持有的连接：后面可能隔着 1-2s 的 tushare 调用，别长占连接池。
    # 用 close() 而非 rollback()：rollback 会把会话身份映射里的实例全部 expire——
@@ -165,7 +114,7 @@ async def get_company(session: AsyncSession, ts_code: str) -> dict | None:
        async with async_session() as s2:  # 锁内重读 + 写入走新会话
            row = (await s2.execute(
                select(StockCompany).where(StockCompany.ts_code == ts_code))).scalar_one_or_none()
-            if row is not None and _fresh(row.updated_at):
+            if row is not None and fresh(row.updated_at, _REFRESH_DAYS):
                return _row_dict(row) if row.com_name is not None else None
            try:
                fetched = await asyncio.to_thread(fetch_company_sync, ts_code)
@@ -174,5 +123,5 @@ async def get_company(session: AsyncSession, ts_code: str) -> dict | None:
                if row is not None and row.com_name is not None:
                    return _row_dict(row)
                raise
-            await _upsert(s2, fetched or {"ts_code": ts_code, "updated_at": _utcnow()})
+            await _upsert(s2, fetched or {"ts_code": ts_code, "updated_at": utcnow()})
            return fetched
--- a/backend/app/data/etf_sync.py
+++ b/backend/app/data/etf_sync.py
@@ -20,7 +20,7 @@ from __future__ import annotations

 import asyncio
 import time
-from datetime import datetime, timedelta, timezone
+from datetime import datetime, timedelta

 from sqlalchemy import delete, func, select, text
 from sqlalchemy.dialects.postgresql import insert as pg_insert
@@ -29,6 +29,7 @@ from sqlalchemy.ext.asyncio import AsyncSession
 from .. import cache
 from ..config import settings
 from . import etf_provider
+from .sync_utils import call_retry, get_pro_lazy, utcnow

 # 进程内单例任务状态（uvicorn 单进程场景够用）
 _state: dict = {
@@ -46,34 +47,6 @@ _lock = asyncio.Lock()
 _BATCH = 3000       # upsert 分批行数（asyncpg 单语句参数上限 32766，10 列/行）
 # fund_daily 返回全市场基金 ~2100 行，一天一批远小于上限

-# 频率超限特征（等待 62s 重试一次；与 screener.market_sync._call_retry 同款语义）
-_RATE_MARKS = ("频率超限", "每分钟")
-
-
-def _call_retry(fn, *args, **kwargs):
-    """同步调用 tushare 接口；「每分钟」级频率超限等 62s 重试一次。"""
-    try:
-        return fn(*args, **kwargs)
-    except Exception as e:  # noqa: BLE001
-        msg = str(e)
-        if any(m in msg for m in _RATE_MARKS) and "小时" not in msg:
-            time.sleep(62)
-            return fn(*args, **kwargs)
-        raise
-
-
-def _get_pro():
-    """token 检查 + 返回 pro api 客户端（同步对象，调用需 to_thread 包裹）。"""
-    if not settings.tushare_token:
-        raise RuntimeError("未配置 TUSHARE_TOKEN，无法同步 ETF 日线（backend/.env）")
-    from .tushare_provider import get_pro
-
-    return get_pro()
-
-
-def _utcnow() -> datetime:
-    return datetime.now(timezone.utc).replace(tzinfo=None)
-

 def _parse_d(s: str) -> datetime:
    return datetime.strptime(str(s), "%Y%m%d")
@@ -85,7 +58,7 @@ async def _sync_spot(session: AsyncSession) -> int:

    async with etf_provider.new_client() as client:
        rows = await etf_provider.fetch_etf_spot(client)
-    now = _utcnow()
+    now = utcnow()
    stmt = pg_insert(EtfBasic).values([{**r, "updated_at": now} for r in rows])
    stmt = stmt.on_conflict_do_update(
        index_elements=["ts_code"],
@@ -108,7 +81,7 @@ async def _sync_spot(session: AsyncSession) -> int:
 def _fetch_day_sync(pro, d: str) -> list[dict]:
    """拉某交易日全市场场内基金日线（fund_daily；未生成的日期返回空）。"""
    time.sleep(settings.screener_sync_interval)
-    df = _call_retry(pro.fund_daily, trade_date=d)
+    df = call_retry(pro.fund_daily, trade_date=d)
    if df is None or df.empty:
        return []
    rows = []
@@ -128,7 +101,7 @@ def _fetch_day_sync(pro, d: str) -> list[dict]:
 def _fetch_symbol_sync(pro, ts_code: str, start: str | None, end: str | None) -> list[dict]:
    """按 ts_code 增量/全量拉单只 ETF 日线（start=None 即上市以来全量）。"""
    time.sleep(settings.screener_sync_interval)
-    df = _call_retry(pro.fund_daily, ts_code=ts_code, start_date=start, end_date=end)
+    df = call_retry(pro.fund_daily, ts_code=ts_code, start_date=start, end_date=end)
    if df is None or df.empty:
        return []
    df = df.sort_values("trade_date")
@@ -209,7 +182,7 @@ async def _run_sync(full: bool) -> None:
    from ..models import Candle, EtfBasic, TradeCalendar

    try:
-        pro = await asyncio.to_thread(_get_pro)
+        pro = await asyncio.to_thread(get_pro_lazy)

        # 1) 快照 -> etf_basic
        _state["step"] = "正在拉取 ETF 列表"
--- a/backend/app/data/fetcher.py
+++ b/backend/app/data/fetcher.py
@@ -113,6 +113,9 @@ async def sync_symbol(
    )
    await session.execute(stmt)
    await session.commit()
+    # 作废 candles 相关读缓存（preview 等）——不 bump 的话旧版本号的缓存要等 TTL 自然过期
+    from .. import cache
+    await cache.bump_version("candles")
    return {"symbol": code, "bars": len(bars), "source": used}


--- a/backend/app/data/index_global.py
+++ b/backend/app/data/index_global.py
@@ -13,13 +13,13 @@ from __future__ import annotations

 import asyncio
 import json
-import math
 import time
 from datetime import date, datetime, timedelta

 from .. import cache
 from ..config import settings
 from ..domain import Bar
+from .sync_utils import d8_iso, f_clean

 # ---- 静态元数据表（tushare index_global 支持的全部 21 个指数，展示顺序即文档顺序）----
 # region: americas 美洲 / europe 欧洲 / asia 亚太（含港股与富时A50）
@@ -82,22 +82,6 @@ class GlobalIndexError(RuntimeError):
    """全部国际指数都拉不到（token/网络故障）——接口层转 503。"""


-def _f(v) -> float | None:
-    """pandas 值 -> float；NaN/None -> None。"""
-    if v is None:
-        return None
-    try:
-        f = float(v)
-    except (TypeError, ValueError):
-        return None
-    return None if math.isnan(f) else f
-
-
-def _d(v) -> str | None:
-    """YYYYMMDD -> 'YYYY-MM-DD'（字符串便于 JSON 缓存）。"""
-    return datetime.strptime(str(v), "%Y%m%d").date().isoformat() if v else None
-
-
 def is_cn_index(code: str) -> bool:
    return "." in code

@@ -128,14 +112,14 @@ def _fetch_quote_sync(pro, ts_code: str) -> dict:
    tail = df.tail(_SPARK_DAYS)
    last = df.iloc[-1]
    return {
-        "close": _f(last["close"]),
-        "change": _f(last.get("change")),
-        "pct_chg": _f(last.get("pct_chg")),
-        "open": _f(last.get("open")),
-        "high": _f(last.get("high")),
-        "low": _f(last.get("low")),
-        "pre_close": _f(last.get("pre_close")),
-        "trade_date": _d(last["trade_date"]),
+        "close": f_clean(last["close"]),
+        "change": f_clean(last.get("change")),
+        "pct_chg": f_clean(last.get("pct_chg")),
+        "open": f_clean(last.get("open")),
+        "high": f_clean(last.get("high")),
+        "low": f_clean(last.get("low")),
+        "pre_close": f_clean(last.get("pre_close")),
+        "trade_date": d8_iso(last["trade_date"]),
        "spark": [round(float(c), 4) for c in tail["close"]],
        "spark_dates": [str(d) for d in tail["trade_date"]],
    }
@@ -249,8 +233,8 @@ def _fetch_global_bars_sync(ts_code: str) -> list[Bar]:
    df = pd.concat(frames).drop_duplicates(subset="trade_date").sort_values("trade_date")
    bars: list[Bar] = []
    for _, r in df.iterrows():
-        vol = _f(r.get("vol"))
-        amt = _f(r.get("amount"))
+        vol = f_clean(r.get("vol"))
+        amt = f_clean(r.get("amount"))
        bars.append(
            Bar(
                ts=datetime.strptime(str(r["trade_date"]), "%Y%m%d"),
@@ -318,9 +302,9 @@ def _fetch_basic_sync(ts_code: str) -> dict:
        "market": r.get("market"),
        "publisher": r.get("publisher"),
        "category": r.get("category"),
-        "base_date": _d(r.get("base_date")),
-        "base_point": _f(r.get("base_point")),
-        "list_date": _d(r.get("list_date")),
+        "base_date": d8_iso(r.get("base_date")),
+        "base_point": f_clean(r.get("base_point")),
+        "list_date": d8_iso(r.get("list_date")),
    }


@@ -354,10 +338,10 @@ def _fetch_valuation_sync(ts_code: str, days: int) -> list[dict]:
    rows = []
    for _, r in df.sort_values("trade_date").iterrows():
        rows.append({
-            "trade_date": _d(r["trade_date"]),
-            "pe": _f(r.get("pe")), "pe_ttm": _f(r.get("pe_ttm")), "pb": _f(r.get("pb")),
-            "turnover_rate": _f(r.get("turnover_rate")),
-            "total_mv": _f(r.get("total_mv")), "float_mv": _f(r.get("float_mv")),
+            "trade_date": d8_iso(r["trade_date"]),
+            "pe": f_clean(r.get("pe")), "pe_ttm": f_clean(r.get("pe_ttm")), "pb": f_clean(r.get("pb")),
+            "turnover_rate": f_clean(r.get("turnover_rate")),
+            "total_mv": f_clean(r.get("total_mv")), "float_mv": f_clean(r.get("float_mv")),
        })
    return rows

@@ -395,7 +379,7 @@ def _fetch_weights_sync(ts_code: str) -> dict | None:
        latest_date = df.iloc[0]["trade_date"]
        rows = df[df["trade_date"] == latest_date]
        return {
-            "trade_date": _d(latest_date),
+            "trade_date": d8_iso(latest_date),
            "total": int(len(rows)),
            "items": [
                {"con_code": str(r["con_code"]), "weight": round(float(r["weight"]), 4)}
--- a/backend/app/data/market_overview.py
+++ b/backend/app/data/market_overview.py
@@ -17,7 +17,6 @@
 from __future__ import annotations

 import asyncio
-import math
 import time
 from datetime import date, datetime, timedelta

@@ -26,6 +25,7 @@ import pandas as pd

 from .. import cache
 from ..config import settings
+from .sync_utils import d8_iso, f_clean

 # (tushare代码, 名称, 地区, 腾讯符号) —— 展示顺序即列表顺序
 # 首页聚焦中美（港股/国际指数在 /indexes 国际指数页）；标普500 腾讯符号是 s_usINX（不是 s_usSPX）
@@ -57,22 +57,6 @@ class MarketOverviewError(RuntimeError):
    """所有指数都拉不到（token/网络故障）——接口层转 503。"""


-def _f(v) -> float | None:
-    """pandas 值 -> float；NaN/None -> None（否则 JSON 里会出现 NaN）。"""
-    if v is None:
-        return None
-    try:
-        f = float(v)
-    except (TypeError, ValueError):
-        return None
-    return None if math.isnan(f) else f
-
-
-def _d(v) -> str | None:
-    """YYYYMMDD -> 'YYYY-MM-DD'（字符串便于 JSON 缓存；pydantic 响应模型自动 coerce）。"""
-    return datetime.strptime(str(v), "%Y%m%d").date().isoformat() if v else None
-
-
 def _get_pro():
    if not settings.tushare_token:
        raise MarketOverviewError("未配置 TUSHARE_TOKEN，无法获取大盘行情（backend/.env）")
@@ -165,10 +149,10 @@ def _quote_from_df(df: pd.DataFrame) -> dict | None:
    tail = df.tail(_SPARK_DAYS)
    last = df.iloc[-1]
    return {
-        "close": _f(last["close"]),
-        "change": _f(last.get("change")),
-        "pct_chg": _f(last.get("pct_chg")),
-        "trade_date": _d(last["trade_date"]),
+        "close": f_clean(last["close"]),
+        "change": f_clean(last.get("change")),
+        "pct_chg": f_clean(last.get("pct_chg")),
+        "trade_date": d8_iso(last["trade_date"]),
        "spark": [round(float(c), 4) for c in tail["close"]],
        "spark_dates": [str(d) for d in tail["trade_date"]],
    }
@@ -193,10 +177,10 @@ def _fetch_stats_sync(pro) -> dict | None:
    if sh_m is None or sz_m is None:
        return None
    # 两边各自取最新，日期不一致时以较旧一天为准凑齐口径（罕见，通常同日）
-    d = min(_d(sh_m["trade_date"]), _d(sz_m["trade_date"]))
+    d = min(d8_iso(sh_m["trade_date"]), d8_iso(sz_m["trade_date"]))

    def _sum(col: str) -> float | None:
-        a, b = _f(sh_m.get(col)), _f(sz_m.get(col))
+        a, b = f_clean(sh_m.get(col)), f_clean(sz_m.get(col))
        return None if a is None or b is None else round(a + b, 2)

    return {
@@ -204,7 +188,7 @@ def _fetch_stats_sync(pro) -> dict | None:
        "total_mv": _sum("total_mv"),
        "float_mv": _sum("float_mv"),
        "amount": _sum("amount"),
-        "turnover": _f(sh_m.get("tr")),  # 换手率仅沪市有，展示口径注明沪市
+        "turnover": f_clean(sh_m.get("tr")),  # 换手率仅沪市有，展示口径注明沪市
    }


@@ -222,7 +206,7 @@ def _fetch_amount_history_sync(pro) -> list[dict]:
    if len(common) == 0:
        return []
    total = (sh_m[common] + sz_m[common]).sort_index()
-    return [{"date": _d(d), "amount": round(float(v), 2)} for d, v in total.tail(_AMOUNT_HIST_BARS).items()]
+    return [{"date": d8_iso(d), "amount": round(float(v), 2)} for d, v in total.tail(_AMOUNT_HIST_BARS).items()]


 # ---- EOD 的 SWR（stale-while-revalidate）：新鲜期内直返；过期先返旧值后台刷新 ----
--- a/backend/app/data/reference.py
+++ b/backend/app/data/reference.py
@@ -16,6 +16,7 @@ from __future__ import annotations
 import asyncio
 import calendar
 import json
+import logging
 import time
 from dataclasses import dataclass
 from datetime import date
@@ -29,6 +30,8 @@ from ..db import async_session
 from ..models import StockReference
 from .sync_utils import call_retry, f_clean, fresh, get_pro_lazy, read_sync_state, s_clean, upsert_sync_state, utcnow

+log = logging.getLogger(__name__)
+
 _REFRESH_DAYS = 7


@@ -394,12 +397,12 @@ async def _sync_repurchase_locked(only_current: bool) -> dict[str, list[dict]]:


 async def _repurchase_backfill() -> None:
-    """后台全量回填（近 24 个月）；失败静默——下次触发重试。"""
+    """后台全量回填（近 24 个月）；失败记录日志——下次触发重试。"""
    try:
        async with _repurchase_lock:
            await _sync_repurchase_locked(only_current=False)
-    except Exception:  # noqa: BLE001  后台任务无人接异常
-        pass
+    except Exception:  # noqa: BLE001  后台任务无人接异常，至少留痕
+        log.warning("回购数据后台回填失败（下次触发重试）", exc_info=True)


 def _spawn_repurchase_backfill() -> None:
--- a/backend/app/data/sync_utils.py
+++ b/backend/app/data/sync_utils.py
@@ -68,6 +68,11 @@ def f_clean(v) -> float | None:
    return None if f != f else f  # NaN -> None


+def d8_iso(v) -> str | None:
+    """tushare YYYYMMDD -> 'YYYY-MM-DD'（字符串便于 JSON 缓存；pydantic 自动 coerce）。"""
+    return datetime.strptime(str(v), "%Y%m%d").date().isoformat() if v else None
+
+
 async def read_sync_state(session: AsyncSession, ts_code: str, kind: str) -> StockSyncState | None:
    return (await session.execute(
        select(StockSyncState).where(
--- a/backend/app/main.py
+++ b/backend/app/main.py
@@ -1,11 +1,12 @@
 """FastAPI 入口。数据库结构统一由 Alembic 管理。"""
-from contextlib import asynccontextmanager
+import asyncio
+from contextlib import asynccontextmanager, suppress

 from fastapi import FastAPI
 from fastapi.middleware.cors import CORSMiddleware
 from sqlalchemy import text

-from . import cache
+from . import cache, scheduler
 from .api import router
 from .auth_api import router as auth_router
 from .config import settings
@@ -16,7 +17,11 @@ from .db import engine
 async def lifespan(app: FastAPI):
    async with engine.connect() as conn:
        await conn.execute(text("SELECT 1"))
+    nightly = asyncio.create_task(scheduler.run_nightly_loop())
    yield
+    nightly.cancel()
+    with suppress(asyncio.CancelledError):
+        await nightly
    await engine.dispose()
    await cache.aclose()  # 释放 Redis 连接池（未启用时是 no-op）

--- a/backend/app/models.py
+++ b/backend/app/models.py
@@ -4,8 +4,8 @@ Candle 表设计与 TimescaleDB hypertable 完全兼容：将来在目标 PG 库
  SELECT create_hypertable('candles', 'ts');
 即可升级为时序表 + Continuous Aggregates 多周期预聚合，无需改表结构。

-智能选股三表（stock_basic / market_daily / daily_snapshot）与回测 candles(qfq)
-完全隔离：选股用未复权日线按 trade_date 全市场批量落地，避免污染回测复权缓存。
+智能选股直接读 candles 不复权底座（market_daily 已退役）；
+daily_snapshot 存每日指标快照（估值/市值，选股过滤用）。
 """
 from datetime import date, datetime

@@ -236,29 +236,8 @@ class StockReference(Base):
    )


-class MarketDaily(Base):
-    """全市场未复权日线（选股专用，与回测 candles(qfq) 隔离）。
-
-    单位沿用 Tushare 原始：vol 手、amount 千元。
-    """
-    __tablename__ = "market_daily"
-
-    id: Mapped[int] = mapped_column(Integer, primary_key=True, autoincrement=True)
-    trade_date: Mapped[datetime] = mapped_column(DateTime, index=True)
-    ts_code: Mapped[str] = mapped_column(String(12), index=True)
-    open: Mapped[float] = mapped_column(Float)
-    high: Mapped[float] = mapped_column(Float)
-    low: Mapped[float] = mapped_column(Float)
-    close: Mapped[float] = mapped_column(Float)
-    pre_close: Mapped[float] = mapped_column(Float)
-    change: Mapped[float | None] = mapped_column(Float)
-    pct_chg: Mapped[float | None] = mapped_column(Float)   # 日涨跌幅 %
-    vol: Mapped[float] = mapped_column(Float)              # 手
-    amount: Mapped[float] = mapped_column(Float)           # 千元
-
-    __table_args__ = (
-        UniqueConstraint("ts_code", "trade_date", name="uq_mkt_code_date"),
-    )
+# market_daily（全市场未复权日线）已于选股改读 candles 后退役：
+# ORM 模型已删，物理表暂留库中作冷备，确认无用后可手动 DROP TABLE market_daily。


 class DailySnapshot(Base):
--- a/backend/app/schemas.py
+++ b/backend/app/schemas.py
@@ -352,6 +352,93 @@ class StockReferenceOut(BaseModel):
    records: list[dict[str, str | float | None]] = Field(default_factory=list)


+# ---------- 打板专题（主页，同花顺口径） ----------
+class LimitStockOut(BaseModel):
+    """涨跌停榜单行：三池共用，涨停池字段最全（炸板/跌停池仅价格类字段）。"""
+    ts_code: str
+    name: str | None = None
+    price: float | None = None             # 收盘价（元）
+    pct_chg: float | None = None           # 涨跌幅 %
+    tag: str | None = None                 # 涨停标签：首板 / 2天2板（仅涨停池）
+    status: str | None = None              # 涨停状态：一字板 / 换手板 / N连板（仅涨停池）
+    lu_desc: str | None = None             # 涨停原因（仅涨停池）
+    open_num: float | None = None          # 打开次数
+    limit_amount_yi: float | None = None   # 封单额（亿元，仅涨停池）
+    turnover_yi: float | None = None       # 成交额（亿元，仅涨停池）
+    first_lu_time: str | None = None       # 首次涨停时间
+    last_lu_time: str | None = None        # 最后涨停时间（仅炸板池）
+    limit_up_suc_rate: float | None = None # 近一年封板率 %（仅涨停池）
+
+
+class LimitLadderOut(BaseModel):
+    ts_code: str
+    name: str | None = None
+    nums: int                              # 连板数
+
+
+class LimitBlockOut(BaseModel):
+    name: str | None = None                # 同花顺概念板块名
+    days: float | None = None              # 板块连涨天数
+    up_stat: str | None = None             # 如「6天3板」
+    cons_nums: float | None = None         # 连板家数
+    up_nums: float | None = None           # 涨停家数
+    pct_chg: float | None = None           # 板块涨跌 %
+
+
+class LimitSummaryOut(BaseModel):
+    up_count: int = 0
+    broken_count: int = 0
+    down_count: int = 0
+    first_board_count: int = 0
+    max_ladder: LimitLadderOut | None = None
+    ladder_dist: list[dict[str, int]] = Field(default_factory=list)  # [{nums, count}] 升序（2板起）
+
+
+class LimitBoardResponse(BaseModel):
+    trade_date: str                        # YYYY-MM-DD
+    updated_at: str
+    summary: LimitSummaryOut
+    up: list[LimitStockOut] = Field(default_factory=list)        # 涨停池（按封单额降序）
+    broken: list[LimitStockOut] = Field(default_factory=list)    # 炸板池
+    down: list[LimitStockOut] = Field(default_factory=list)      # 跌停池
+    ladder: list[LimitLadderOut] = Field(default_factory=list)   # 连板天梯（连板数降序）
+    blocks: list[LimitBlockOut] = Field(default_factory=list)    # 涨停最强板块
+    errors: list[str] = Field(default_factory=list)
+
+
+# ---------- 概念板块（THS：ths_index 列表 + ths_daily 行情 + ths_member 成分） ----------
+class ThsBoardOut(BaseModel):
+    ts_code: str                           # 885835.TI / 700001.TI
+    name: str | None = None
+    type: str | None = None                # N概念 I行业 TH主题 S特色 R地域 BB宽基 ST风格
+    count: float | None = None             # 成分个数
+    list_date: str | None = None           # YYYYMMDD
+    close: float | None = None             # 板块指数收盘（当日快照）
+    pct_change: float | None = None        # 涨跌幅 %
+    vol: float | None = None               # 成交量（手）
+    turnover_rate: float | None = None     # 换手率 %
+
+
+class ThsBoardListResponse(BaseModel):
+    trade_date: str | None = None
+    updated_at: str | None = None
+    boards: list[ThsBoardOut] = Field(default_factory=list)
+    errors: list[str] = Field(default_factory=list)
+
+
+class ThsMemberOut(BaseModel):
+    con_code: str                          # 成分股代码 000016.SZ
+    con_name: str | None = None
+    close: float | None = None             # 现价（candles 最新，北交所等无底座为空）
+    pct_chg: float | None = None           # 涨跌幅 %（最新收盘 / 前收 - 1）
+
+
+class ThsBoardMembersResponse(BaseModel):
+    code: str
+    name: str | None = None
+    members: list[ThsMemberOut] = Field(default_factory=list)
+
+
 # ---------- Auth ----------
 class LoginRequest(BaseModel):
    username: str = Field(min_length=1, max_length=64)
--- a/backend/app/screener/market_sync.py
+++ b/backend/app/screener/market_sync.py
@@ -10,6 +10,7 @@ daily 与 daily_basic 分步独立落库：daily_basic 积分不足时快照仍
 from __future__ import annotations

 import asyncio
+import logging
 import time
 from datetime import datetime, timedelta

@@ -20,9 +21,12 @@ from sqlalchemy.ext.asyncio import AsyncSession
 from .. import cache
 from ..config import settings
 from ..data.symbols import plain_code
+from ..data.sync_utils import call_retry, get_pro_lazy
 from ..models import AdjFactor, Candle, DailySnapshot, StockBasic, TradeCalendar
 from .llm import ScreenerError

+log = logging.getLogger(__name__)
+
 # 进程内单例任务状态（uvicorn --reload 单进程场景够用）
 _sync_state: dict = {
    "running": False,
@@ -40,32 +44,6 @@ _BATCH = 5000  # executemany 分批行数

 # Tushare 积分/权限不足的特征文案（daily_basic 常见门槛）
 _PERM_MARKS = ("抱歉，您没有访问该项目权限", "积分", "权限")
-# 频率超限特征（等待 62s 重试一次）
-_RATE_MARKS = ("频率超限", "每分钟")
-
-
-def _call_retry(fn, *args, **kwargs):
-    """同步调用 tushare 接口；「每分钟」级频率超限等 62s 重试一次（小时级限频直接抛）。"""
-    try:
-        return fn(*args, **kwargs)
-    except Exception as e:  # noqa: BLE001
-        msg = str(e)
-        if any(m in msg for m in _RATE_MARKS) and "小时" not in msg:
-            time.sleep(62)
-            return fn(*args, **kwargs)
-        raise
-
-
-def _get_pro():
-    """token 检查 + 返回 pro api 客户端（同步对象，调用需 to_thread 包裹）。
-
-    经 tushare_provider.get_pro 统一走镜像补丁（15000 积分档 token 只认 quicksync）。
-    """
-    if not settings.tushare_token:
-        raise ScreenerError("未配置 TUSHARE_TOKEN，无法同步全市场数据（backend/.env）")
-    from ..data.tushare_provider import get_pro
-
-    return get_pro()


 def _parse_d(s: str) -> datetime:
@@ -77,7 +55,7 @@ def _fetch_calendar_sync(pro) -> list[str]:
    time.sleep(settings.screener_sync_interval)
    end = (datetime.now() + timedelta(days=90)).strftime("%Y%m%d")
    start = (datetime.now() - timedelta(days=550)).strftime("%Y%m%d")
-    cal = _call_retry(pro.trade_cal, exchange="SSE", start_date=start, end_date=end, is_open="1")
+    cal = call_retry(pro.trade_cal, exchange="SSE", start_date=start, end_date=end, is_open="1")
    return sorted(cal["cal_date"].tolist())


@@ -112,7 +90,7 @@ async def _recent_trade_dates(session: AsyncSession, pro, days: int) -> list[str
 def _fetch_daily(pro, d: str) -> list[dict]:
    """拉取某交易日全市场日线（未复权）。当日数据未生成（盘前/盘中）返回空。"""
    time.sleep(settings.screener_sync_interval)
-    df = _call_retry(pro.daily, trade_date=d)
+    df = call_retry(pro.daily, trade_date=d)
    if df is None or df.empty:
        return []
    rows = []
@@ -138,7 +116,7 @@ def _fetch_basic(pro, d: str) -> list[dict]:
    """
    time.sleep(settings.screener_sync_interval)
    try:
-        df = _call_retry(pro.daily_basic, trade_date=d)
+        df = call_retry(pro.daily_basic, trade_date=d)
    except Exception as e:  # noqa: BLE001
        msg = str(e)
        if any(m in msg for m in _PERM_MARKS):
@@ -169,7 +147,7 @@ def _fetch_basic(pro, d: str) -> list[dict]:
 def _fetch_adj_factor(pro, d: str) -> list[dict]:
    """拉取某交易日全市场复权因子（K线 bfq->qfq/hfq 本地换算的底座）。"""
    time.sleep(settings.screener_sync_interval)
-    df = _call_retry(pro.adj_factor, trade_date=d)
+    df = call_retry(pro.adj_factor, trade_date=d)
    if df is None or df.empty:
        return []
    return [
@@ -181,7 +159,7 @@ def _fetch_adj_factor(pro, d: str) -> list[dict]:
 def _sync_stock_list_sync(pro) -> list[dict]:
    """拉取在市股票列表。"""
    time.sleep(settings.screener_sync_interval)
-    df = _call_retry(pro.stock_basic, exchange="", list_status="L",
+    df = call_retry(pro.stock_basic, exchange="", list_status="L",
                     fields="ts_code,symbol,name,area,industry,market,exchange,list_status,list_date,delist_date")
    rows = []
    for _, r in df.iterrows():
@@ -232,6 +210,27 @@ async def _existing_candle_dates(session: AsyncSession) -> set[str]:
    return {r[0].strftime("%Y%m%d") for r in res if r[0] is not None}


+_UPSERT_CHUNK = 3000  # 单语句行数（asyncpg 参数上限拆批）
+
+
+async def _recent_day_counts(session: AsyncSession, dates: list[str]) -> dict[str, int]:
+    """指定交易日在市股票的 candles 行数（半日数据自愈用）。
+
+    单条 GROUP BY 走 ts 索引范围扫，窗口 ≤15 日、代价可忽略。
+    """
+    if not dates:
+        return {}
+    lo = _parse_d(min(dates))
+    hi = _parse_d(max(dates)) + timedelta(days=1)
+    rows = (await session.execute(
+        select(func.date(Candle.ts), func.count())
+        .where(Candle.timeframe == "1d", Candle.ts >= lo, Candle.ts < hi,
+               Candle.symbol.in_(select(StockBasic.symbol).where(StockBasic.list_status == "L")))
+        .group_by(func.date(Candle.ts))
+    )).all()
+    return {r[0].strftime("%Y%m%d"): int(r[1]) for r in rows if r[0] is not None}
+
+
 async def _upsert_candle_day(session: AsyncSession, rows: list[dict], listed: set[str], d_str: str) -> None:
    """把某交易日全市场日线 upsert 进 candles（不复权底座，幂等）。

@@ -253,9 +252,10 @@ async def _upsert_candle_day(session: AsyncSession, rows: list[dict], listed: se
    if not batch:
        return
    # on_conflict 语句整批渲染为占位符（非 executemany），asyncpg 单语句参数上限 32766，
-    # 10 列 x 3000 行 = 30000 参数留出余量
-    for i in range(0, len(batch), 3000):
-        stmt = pg_insert(Candle).values(batch[i : i + 3000])
+    # 10 列 x 3000 行 = 30000 参数留出余量。分批只拆语句，commit 在循环外 ——
+    # 单日一事务：写一半崩溃整日回滚，该日期语义上「未同步」，下次自然重拉（不留半日数据）
+    for i in range(0, len(batch), _UPSERT_CHUNK):
+        stmt = pg_insert(Candle).values(batch[i : i + _UPSERT_CHUNK])
        stmt = stmt.on_conflict_do_update(
            index_elements=["symbol", "timeframe", "ts"],
            set_={
@@ -266,7 +266,7 @@ async def _upsert_candle_day(session: AsyncSession, rows: list[dict], listed: se
            },
        )
        await session.execute(stmt)
-        await session.commit()
+    await session.commit()


 async def _run_sync(days: int, force: bool) -> None:
@@ -277,7 +277,7 @@ async def _run_sync(days: int, force: bool) -> None:
    from ..db import async_session  # 延迟导入避免循环

    try:
-        pro = await asyncio.to_thread(_get_pro)
+        pro = await asyncio.to_thread(get_pro_lazy)

        # 1) 股票列表（已有数据则跳过——stock_basic 低积分版限频 1 次/小时）
        async with async_session() as session:
@@ -307,6 +307,15 @@ async def _run_sync(days: int, force: bool) -> None:
                    select(StockBasic.symbol).where(StockBasic.list_status == "L")
                )).scalars()
            )
+            if not force:
+                # 半日数据自愈（修单日原子化之前的历史残留）：写入中途崩溃的日期
+                # 行数 ≈ 1 批（3000），显著低于完整日（~5300）。只查最近 15 个交易日
+                # ——实际风险区且窗口内上市数变化 <2%，0.7 阈值安全；更老的日期不查
+                # （上市数变化会误判，历史缺口本就由 TDX 底座兜底）。
+                counts = await _recent_day_counts(session, [d for d in dates[:15] if d in have_daily])
+                if counts:
+                    floor = max(_UPSERT_CHUNK + 1, int(max(counts.values()) * 0.7))
+                    have_daily -= {d for d, n in counts.items() if n < floor}
        todo = [d for d in dates if d not in have_daily]
        _sync_state["total_days"] = len(todo)
        _sync_state["done_days"] = 0
@@ -352,7 +361,7 @@ async def _run_sync(days: int, force: bool) -> None:
        try:
            await _refresh_stats(await cache.get_version("candles"))
        except Exception:  # noqa: BLE001 —— 预热失败只影响统计数字的新鲜度
-            pass
+            log.warning("统计缓存预热失败（下轮轮询会 SWR 重算）", exc_info=True)
        _sync_state["step"] = "同步完成"
    except Exception as e:  # noqa: BLE001
        _sync_state["error"] = f"同步失败：{str(e)[:300]}"
@@ -414,16 +423,9 @@ async def _heavy_stats(session: AsyncSession) -> dict:

 async def _store_stats(ver: int, data: dict) -> None:
    _status_stats_cache.update(at=time.time(), ver=ver, data=data)
-    # 写 Redis 后台执行，失败由 cache 层静默降级，不拖慢调用方
-    tasks = [
-        asyncio.create_task(cache.cache_set(
-            f"syncstats:v{ver}", data, ttl=settings.sync_stats_redis_ttl)),
-        asyncio.create_task(cache.cache_set(
-            _STATS_LAST_KEY, data, ttl=settings.sync_stats_redis_ttl)),
-    ]
-    _stats_bg_tasks.update(tasks)
-    for t in tasks:
-        t.add_done_callback(_stats_bg_tasks.discard)
+    # 写 Redis 后台执行（cache.set_bg 挂全局集合防 GC），失败由 cache 层静默降级
+    cache.set_bg(f"syncstats:v{ver}", data, ttl=settings.sync_stats_redis_ttl)
+    cache.set_bg(_STATS_LAST_KEY, data, ttl=settings.sync_stats_redis_ttl)


 async def _refresh_stats(ver: int) -> None:
--- a/backend/pyproject.toml
+++ b/backend/pyproject.toml
@@ -21,6 +21,12 @@ dependencies = [
    "openpyxl>=3.1.5",
 ]

+[dependency-groups]
+dev = ["pytest>=8"]
+
+[tool.pytest.ini_options]
+testpaths = ["tests"]
+
 [tool.uv]
 # 应用型项目（非库）：不把自身打包安装，只管理依赖到 .venv
 package = false
--- a/backend/scripts/test_trades_parser.py
+++ b/backend/scripts/test_trades_parser.py
@@ -1,113 +0,0 @@
-"""交割单解析器离线自测：不碰数据库，直接调 app.trades.parse_statement。
-
-覆盖四类真实导出格式 + 边界行（转账/配号/利息跳过、费用合计列去重、日期多格式）。
-运行：uv run python scripts/test_trades_parser.py
-"""
-from __future__ import annotations
-
-import sys
-from pathlib import Path
-
-sys.path.insert(0, str(Path(__file__).resolve().parent.parent))
-
-from app.trades import parse_statement  # noqa: E402
-
-FAIL: list[str] = []
-
-
-def check(name: str, cond: bool, detail: str = "") -> None:
-    mark = "ok " if cond else "FAIL"
-    print(f"[{mark}] {name}{(' — ' + detail) if detail and not cond else ''}")
-    if not cond:
-        FAIL.append(name)
-
-
-# ---------- 1) 通达信式：GBK + 制表符 + 标题行在前 ----------
-tdx = (
-    "交割单\n"
-    "股东账号: A123456789    起始日期: 20240102    终止日期: 20240105    币种: 人民币\n"
-    "\t交割日期\t业务名称\t证券代码\t证券名称\t成交价格\t成交数量\t成交金额\t手续费\t印花税\t过户费\t发生金额\t资金余额\t合同号\n"
-    "\t20240102\t证券买入\t600519\t贵州茅台\t1680.00\t100\t168000.00\t5.00\t0.00\t1.68\t-168006.68\t200000.00\t1000001\n"
-    "\t20240102\t银行转存\t\t\t\t\t\t\t\t\t50000.00\t250000.00\t\n"
-    "\t20240103\t证券卖出\t600519\t贵州茅台\t1700.50\t100\t170050.00\t5.00\t170.05\t1.70\t169873.25\t419873.25\t1000002\n"
-    "\t20240105\t利息归本\t\t\t\t\t\t\t\t\t1.25\t419874.50\t\n"
-)
-r = parse_statement(tdx.encode("gbk"), "交割单.txt")
-check("tdx: 2 笔成交", len(r.trades) == 2, f"got {len(r.trades)}")
-check("tdx: 跳过 2 行非交易", r.skipped_other == 2, f"got {r.skipped_other}")
-t0, t1 = r.trades[0], r.trades[1]
-check("tdx: 日期/代码/后缀", (t0.trade_date.isoformat(), t0.ts_code) == ("2024-01-02", "600519.SH"), f"{t0.trade_date} {t0.ts_code}")
-check("tdx: 买入方向+费用合计", t0.direction == "buy" and abs(t0.fee - 6.68) < 1e-9, f"{t0.direction} fee={t0.fee}")
-check("tdx: 卖出费用含印花税", t1.direction == "sell" and abs(t1.fee - 176.75) < 1e-9, f"fee={t1.fee}")
-check("tdx: 金额取绝对值", t0.amount == 168000.0, f"amount={t0.amount}")
-
-# ---------- 2) 恒生柜台式：UTF-8 CSV，交收日期/交易类别/费用合计 ----------
-hs = (
-    "序号,交收日期,证券代码,证券名称,交易类别,成交价格,成交数量,证券余额,成交金额,资金发生数,资金余额,流水序号,业务标志,业务名称,发生金额,后资金额,货币类别,费用合计,净佣金,规费,印花税,过户费,合同号\n"
-    "1,2024-06-07,000858,五粮液,证券买入,132.50,200,200,26500.00,-26505.80,73494.20,1,0101,证券买入,-26505.80,73494.20,人民币,5.80,4.20,1.60,0.00,0.00,66778001\n"
-    "2,2024-06-07,,,\t,,,,5120.00,78614.20,2,2041,银行转存,5120.00,78614.20,人民币,0,0,0,0,0,\n"
-    "3,2024-06-10,000858,五粮液,证券卖出,135.00,200,0,27000.00,26975.30,105589.50,3,0102,证券卖出,26975.30,105589.50,人民币,24.70,4.20,1.60,18.90,0.00,66779001\n"
-)
-r2 = parse_statement(hs.encode("utf-8"), "hsi.csv")
-check("hs: 2 笔成交", len(r2.trades) == 2, f"got {len(r2.trades)}")
-check("hs: 费用合计不重复累加", abs(r2.trades[1].fee - 24.70) < 1e-9, f"fee={r2.trades[1].fee}")
-check("hs: 深市后缀", r2.trades[0].ts_code == "000858.SZ", r2.trades[0].ts_code)
-check("hs: 日期 YYYY-MM-DD", r2.trades[0].trade_date.isoformat() == "2024-06-07")
-
-# ---------- 3) HTML 伪 .xls（同花顺导出常见真身） ----------
-html = """<html><head><meta charset="gbk"></head><body>
-<table>
-<tr><td>客户姓名</td><td>测试</td></tr>
-<tr><td>成交日期</td><td>业务名称</td><td>证券代码</td><td>证券名称</td><td>成交价格</td><td>成交数量</td><td>成交金额</td><td>手续费</td></tr>
-<tr><td>2024/03/15</td><td>证券买入</td><td>300750</td><td>宁德时代</td><td>182.30</td><td>300</td><td>54,690.00</td><td>16.41</td></tr>
-<tr><td>2024/03/18</td><td>证券卖出</td><td>300750</td><td>宁德时代</td><td>185.00</td><td>300</td><td>55,500.00</td><td>5.55</td></tr>
-</table></body></html>"""
-r3 = parse_statement(html.encode("gbk"), "jiaogedan.xls")
-check("html: 2 笔成交", len(r3.trades) == 2, f"got {len(r3.trades)}")
-check("html: 千分位金额", r3.trades[0].amount == 54690.0, f"{r3.trades[0].amount}")
-check("html: 创业板后缀", r3.trades[0].ts_code == "300750.SZ", r3.trades[0].ts_code)
-check("html: 斜杠日期", r3.trades[1].trade_date.isoformat() == "2024-03-18")
-
-# ---------- 4) 无业务名称列：发生金额正负判方向（招商式） ----------
-zh = (
-    "证券名称,成交日期,成交价格,成交数量,发生金额,资金余额,合同编号\n"
-    "贵州茅台,20240102,1680.00,100,-168005.00,200000.00,SZ1000001\n"
-    "贵州茅台,20240103,1700.50,100,170049.50,370049.50,SZ1000002\n"
-)
-r4 = parse_statement(zh.encode("utf-8"), "zszs.csv")
-check("sign: 2 笔成交", len(r4.trades) == 2, f"got {len(r4.trades)}")
-check("sign: 负金额=买入", (r4.trades[0].direction, r4.trades[1].direction) == ("buy", "sell"),
-      f"{r4.trades[0].direction}/{r4.trades[1].direction}")
-
-# ---------- 5) xlsx（openpyxl 内存构造） ----------
-import io  # noqa: E402
-from openpyxl import Workbook  # noqa: E402
-
-wb = Workbook()
-ws = wb.active
-ws.append(["对账单", None, None])
-ws.append(["成交日期", "业务名称", "证券代码", "证券名称", "成交均价", "成交股数", "成交金额", "佣金", "过户费"])
-from datetime import datetime as dt  # noqa: E402
-ws.append([dt(2024, 2, 28, 14, 35, 0), "证券买入", "688981", "中芯国际", 52.80, 200, 10560.00, 2.50, 1.06])
-ws.append([dt(2024, 3, 1, 9, 31, 0), "证券卖出", "688981", "中芯国际", 54.10, 200, 10820.00, 2.50, 1.06])
-buf = io.BytesIO()
-wb.save(buf)
-r5 = parse_statement(buf.getvalue(), "sm.xlsx")
-check("xlsx: 2 笔成交", len(r5.trades) == 2, f"got {len(r5.trades)}")
-check("xlsx: datetime 日期", r5.trades[0].trade_date.isoformat() == "2024-02-28")
-check("xlsx: 科创板后缀", r5.trades[0].ts_code == "688981.SH", r5.trades[0].ts_code)
-check("xlsx: 佣金+过户费", abs(r5.trades[0].fee - 3.56) < 1e-9, f"fee={r5.trades[0].fee}")
-
-# ---------- 6) 错误分支 ----------
-from fastapi import HTTPException  # noqa: E402
-try:
-    parse_statement("随便一串不是交割单的文字,1,2,3".encode("utf-8"), "x.csv")
-    check("garbage: 应 422", False)
-except HTTPException as e:
-    check("garbage: 422", e.status_code == 422)
-
-print()
-if FAIL:
-    print(f"FAIL {len(FAIL)}: {FAIL}")
-    sys.exit(1)
-print("PASS: 交割单解析器全部用例通过")
--- a/backend/uv.lock
+++ b/backend/uv.lock
@@ -501,6 +501,15 @@ wheels = [
    { url = "https://files.pythonhosted.org/packages/1e/5e/d4e9f1a599fb8e573b7b87160658329fbf28d19eac2718f51fc3def3aa5a/idna-3.18-py3-none-any.whl", hash = "sha256:7f952cbe720b688055e3f87de14f5c3e5fdaa8bc3928985c4077ca689de849a2", size = 65455, upload-time = "2026-06-02T14:34:06.319Z" },
 ]

+[[package]]
+name = "iniconfig"
+version = "2.3.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/72/34/14ca021ce8e5dfedc35312d08ba8bf51fdd999c576889fc2c24cb97f4f10/iniconfig-2.3.0.tar.gz", hash = "sha256:c76315c77db068650d49c5b56314774a7804df16fee4402c1f19d6d15d8c4730", size = 20503, upload-time = "2025-10-18T21:55:43.219Z" }
+wheels = [
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