fix: 修复 6 个 issue (#225/#226/#201/#188/#200/#196)

后端:
- #225 自定义源分钟K字符串 datetime 不再被 cast 成 null:
  _normalize_minute 对 Utf8 列按常见格式链式解析 (参照 kline_sync 口径)
- #226 自定义源日K/除权因子单批失败只隔离该批 (重试 1 次 + 跳过 +
  warning 汇总), 不再丢弃已成功批次的全部进度
- #201 旧信号回测 _load_panel 加指标 warmup 窗口 (120 交易日保守日历日),
  计算后裁回 [start,end]; 数据不足时自然退化

前端:
- #188 因子回测单标的不再整面板空白: 外层条件改 !error, IC 卡片
  单独守卫并给出需 >=2 只的提示
- #200 因子回测支持调仓频率 (日/周/月) 与滑点 (bp) 配置
- #196 自选页板块筛选新增 ETF 分类, 旧偏好加载时补 ETF 键保持默认可见

backend/app/data_providers/custom/provider.py

@@ -3,6 +3,7 @@ from __future__ import annotations
 
 import logging
 import os
+import time
 from collections.abc import Callable
 from datetime import datetime, timedelta
 from pathlib import Path
@@ -132,6 +133,23 @@ class GenericHTTPProvider:
                     )
         return errors
 
+    def _request_rows_retry(
+        self, cfg, symbols: list[str], *, start_time=None, end_time=None, retries: int = 1
+    ) -> list[dict]:
+        """单批请求 + 短退避重试。仍失败抛出, 由调用方决定隔离粒度 (#226)。"""
+        last: Exception | None = None
+        for attempt in range(retries + 1):
+            try:
+                return self._request_rows(
+                    cfg, symbols=symbols, start_time=start_time, end_time=end_time
+                )
+            except Exception as e:  # noqa: BLE001
+                last = e
+                if attempt < retries:
+                    time.sleep(1.0 * (attempt + 1))
+        assert last is not None
+        raise last
+
     def get_daily(
         self,
         symbols: list[str],
@@ -143,15 +161,35 @@ class GenericHTTPProvider:
         cfg = self._dataset("daily")
         frames: list[pl.DataFrame] = []
         chunks = chunked(symbols, cfg.batch)
+        failed: list[str] = []
         for i, chunk in enumerate(chunks):
             sleep_between_batches(i, cfg.rpm)
-            rows = self._request_rows(cfg, symbols=chunk, start_time=start_time, end_time=end_time)
+            try:
+                rows = self._request_rows_retry(
+                    cfg, chunk, start_time=start_time, end_time=end_time
+                )
+            except Exception as e:  # noqa: BLE001
+                # 单批失败只隔离该批 (#226): 之前任一批 502 会让整个 stage
+                # 抛异常, 已成功批次的结果留在内存里全部丢弃
+                failed.extend(chunk)
+                logger.warning(
+                    "custom daily: batch %d/%d failed (%d symbols), skipped: %s",
+                    i + 1, len(chunks), len(chunk), e,
+                )
+                if on_chunk_done:
+                    on_chunk_done(i + 1, len(chunks))
+                continue
             df = self._mapped_frame(cfg, rows)
             df = normalize_daily(df, source=self.name)
             if not df.is_empty():
                 frames.append(df)
             if on_chunk_done:
                 on_chunk_done(i + 1, len(chunks))
+        if failed:
+            logger.warning(
+                "custom daily: %d/%d symbols missing due to batch failures: %s",
+                len(failed), len(symbols), ", ".join(failed[:20]),
+            )
         return pl.concat(frames, how="diagonal_relaxed") if frames else pl.DataFrame()
 
     def get_adj_factors(
@@ -165,15 +203,33 @@ class GenericHTTPProvider:
         cfg = self._dataset("adj_factor")
         frames: list[pl.DataFrame] = []
         chunks = chunked(symbols, cfg.batch)
+        failed: list[str] = []
         for i, chunk in enumerate(chunks):
             sleep_between_batches(i, cfg.rpm)
-            rows = self._request_rows(cfg, symbols=chunk, start_time=start_time, end_time=end_time)
+            try:
+                rows = self._request_rows_retry(
+                    cfg, chunk, start_time=start_time, end_time=end_time
+                )
+            except Exception as e:  # noqa: BLE001
+                failed.extend(chunk)
+                logger.warning(
+                    "custom adj_factor: batch %d/%d failed (%d symbols), skipped: %s",
+                    i + 1, len(chunks), len(chunk), e,
+                )
+                if on_chunk_done:
+                    on_chunk_done(i + 1, len(chunks))
+                continue
             df = self._mapped_frame(cfg, rows)
             df = normalize_adj_factors(df, source=self.name)
             if not df.is_empty():
                 frames.append(df)
             if on_chunk_done:
                 on_chunk_done(i + 1, len(chunks))
+        if failed:
+            logger.warning(
+                "custom adj_factor: %d/%d symbols missing due to batch failures: %s",
+                len(failed), len(symbols), ", ".join(failed[:20]),
+            )
         return pl.concat(frames, how="diagonal_relaxed") if frames else pl.DataFrame()
 
     def get_realtime(self) -> list[dict]:
@@ -293,12 +349,18 @@ class GenericHTTPProvider:
             return pl.DataFrame()
         return pl.concat(frames, how="diagonal_relaxed")
 
-    @staticmethod
-    def _normalize_minute(df: pl.DataFrame) -> pl.DataFrame:
+    @classmethod
+    def _normalize_minute(cls, df: pl.DataFrame) -> pl.DataFrame:
         """把映射后的 df 规范成 minute canonical 列。"""
         if df.is_empty():
             return df
         if "datetime" in df.columns and df.schema["datetime"] != pl.Datetime("us"):
+            if df.schema["datetime"] == pl.Utf8:
+                # 字符串 datetime 直接 cast 会整体置 null (polars 不做字符串解析);
+                # 先解析再对齐微秒精度 (#225, 参照
+                # kline_sync._enforce_minute_beijing_wallclock 的处理)。
+                # Series 级立即解析: 表达式错误要到 collect 才抛, 无法按格式回退
+                df = df.with_columns(cls._parse_datetime_series(df["datetime"]))
             df = df.with_columns(pl.col("datetime").cast(pl.Datetime("us"), strict=False))
         for col in ("open", "high", "low", "close", "volume", "amount"):
             if col in df.columns:
@@ -306,6 +368,27 @@ class GenericHTTPProvider:
         keep = [c for c in ("symbol", "datetime", "open", "high", "low", "close", "volume", "amount") if c in df.columns]
         return df.select(keep) if keep else pl.DataFrame()
 
+    _DATETIME_STR_FORMATS = (
+        None,  # 自动推断
+        "%Y-%m-%d %H:%M:%S",
+        "%Y-%m-%dT%H:%M:%S",
+        "%Y/%m/%d %H:%M:%S",
+        "%Y-%m-%d %H:%M",
+    )
+
+    @classmethod
+    def _parse_datetime_series(cls, s: pl.Series) -> pl.Series:
+        """逐格式尝试解析字符串 datetime; 均失败返回全 null (宽松语义)。"""
+        for fmt in cls._DATETIME_STR_FORMATS:
+            try:
+                return (
+                    s.str.to_datetime(strict=False, format=fmt)
+                    if fmt else s.str.to_datetime(strict=False)
+                )
+            except Exception:  # noqa: BLE001 — 该格式不适用, 换下一个
+                continue
+        return pl.Series("datetime", [None] * s.len(), dtype=pl.Datetime("us"))
+
     def test_dataset(self, dataset: str, symbols: list[str] | None = None) -> dict:
         cfg = self._dataset(dataset)
         test_symbols = symbols or ["000001.SZ"]

backend/app/services/backtest.py

@@ -7,7 +7,7 @@ from __future__ import annotations
 import logging
 import uuid
 from dataclasses import dataclass, field
-from datetime import date
+from datetime import date, timedelta
 from typing import Literal
 
 import numpy as np
@@ -20,6 +20,10 @@ from app.tickflow.repository import KlineRepository
 
 logger = logging.getLogger(__name__)
 
+# 旧信号回测的指标 warmup 日历窗口 (#201): 与 backtest.factor.FACTOR_WARMUP_DAYS
+# 同源 (120 交易日 → 保守取日历日), 覆盖 MA60/MACD/BOLL 等最长回看
+_WARMUP_CALENDAR_DAYS = 120 * 1.6
+
 # vectorbt 是 optional extras(见 pyproject.toml).未装时只有 backtest 不可用,其他功能正常.
 _vbt = None
 _vbt_unavailable_reason: str | None = None
@@ -162,11 +166,17 @@ class BacktestService:
         try:
             from app.tickflow.repository import enriched_dirname
             enriched_glob = str(self.repo.store.data_dir / enriched_dirname(asset_type) / "**" / "*.parquet")
+            # 指标 warmup (#201): MA/MACD/RSI/BOLL 需要区间前的历史窗口,
+            # 直接按 [start,end] 过滤后 compute_all 会让区间头部的指标失真。
+            # 与挖掘侧同款公式 (mining_runtime: warmup = max(120, bars*1.6)),
+            # 此处指标最长回看约 120 交易日, 取保守日历日窗口; 数据不足时
+            # 自然退化 (有多少算多少)。计算完成后裁回 [start,end]。
+            warmup_start = start - timedelta(days=_WARMUP_CALENDAR_DAYS)
             df = (
                 scan_enriched_parquet(enriched_glob)
                 .filter(
                     (pl.col("symbol").is_in(symbols))
-                    & (pl.col("date") >= start)
+                    & (pl.col("date") >= warmup_start)
                     & (pl.col("date") <= end)
                 )
                 .sort(["date", "symbol"])
@@ -182,6 +192,7 @@ class BacktestService:
         # 即时计算指标 + 信号
         from app.indicators.pipeline import compute_all
         df = compute_all(df)
+        df = df.filter(pl.col("date") >= start)
 
         # 选择需要的列
         needed_cols = [

backend/tests/test_backtest_warmup.py

@@ -0,0 +1,66 @@
+"""#201 回归: 旧信号回测的 _load_panel 必须带指标 warmup 窗口。
+
+直接按 [start,end] 过滤后 compute_all, 区间头部的 MA/MACD/RSI 会因缺
+历史窗口而失真 (回测起始段信号不可信)。
+"""
+from __future__ import annotations
+
+from datetime import date, timedelta
+from unittest.mock import MagicMock
+
+import polars as pl
+
+from app.services.backtest import BacktestService
+
+
+def _synthetic_enriched(n_days: int) -> pl.DataFrame:
+    base = date(2026, 1, 1)
+    days = [base + timedelta(days=i) for i in range(n_days)]
+    n = len(days)
+    closes = [10.0 + (i % 7) * 0.3 + i * 0.01 for i in range(n)]
+    return pl.DataFrame(
+        {
+            "symbol": ["600000.SH"] * n,
+            "date": days,
+            "open": [c - 0.05 for c in closes],
+            "high": [c + 0.1 for c in closes],
+            "low": [c - 0.1 for c in closes],
+            "close": closes,
+            "volume": [10000.0] * n,
+            "amount": [c * 10000.0 for c in closes],
+            "raw_close": closes,
+            "raw_high": [c + 0.1 for c in closes],
+            "raw_low": [c - 0.1 for c in closes],
+        }
+    )
+
+
+def test_load_panel_warms_up_indicators(monkeypatch) -> None:
+    df = _synthetic_enriched(250)
+    monkeypatch.setattr(
+        "app.services.backtest.scan_enriched_parquet", lambda glob: df.lazy()
+    )
+    svc = BacktestService(repo=MagicMock())
+
+    start = df["date"][-30]
+    end = df["date"][-1]
+    panel = svc._load_panel(["600000.SH"], start, end)
+
+    # warmup 行不进入结果面板 (pandas datetime64 与 date 直接比较会类型不符)
+    assert str(panel["date"].min())[:10] == start.isoformat()
+    assert str(panel["date"].max())[:10] == end.isoformat()
+    # 区间首日的指标已有历史窗口可用, 不再是 NaN
+    first = panel.iloc[0]
+    assert first["rsi_14"] == first["rsi_14"]  # NaN != NaN
+
+
+def test_load_panel_insufficient_history_degrades_gracefully(monkeypatch) -> None:
+    # 数据起点晚于 warmup 起点时自然退化: 有多少算多少, 不抛异常
+    df = _synthetic_enriched(20)
+    monkeypatch.setattr(
+        "app.services.backtest.scan_enriched_parquet", lambda glob: df.lazy()
+    )
+    svc = BacktestService(repo=MagicMock())
+
+    panel = svc._load_panel(["600000.SH"], df["date"][0], df["date"][-1])
+    assert len(panel) == 20

backend/tests/test_custom_batch_isolation.py

@@ -0,0 +1,140 @@
+"""#225/#226 回归: 自定义源分钟K字符串日期解析 + 日K分批失败隔离。"""
+from __future__ import annotations
+
+from datetime import date, datetime
+
+import polars as pl
+
+from app.data_providers.custom.config import CustomSourceConfig, DatasetConfig
+from app.data_providers.custom.loader import GenericHTTPProvider
+
+
+def _provider(datasets: dict[str, DatasetConfig]) -> GenericHTTPProvider:
+    return GenericHTTPProvider(CustomSourceConfig(
+        name="test_source",
+        display_name="Test Source",
+        datasets=datasets,
+    ))
+
+
+def _daily_config(batch: int = 2) -> DatasetConfig:
+    return DatasetConfig(
+        url="https://example.test/daily",
+        field_map={
+            "symbol": "symbol", "date": "date", "open": "open", "high": "high",
+            "low": "low", "close": "close", "volume": "volume", "amount": "amount",
+        },
+        batch=batch,
+    )
+
+
+# ── #225: 分钟K字符串 datetime 不得被 cast 成 null ────────────────
+
+def test_normalize_minute_parses_string_datetime() -> None:
+    df = pl.DataFrame(
+        {
+            "symbol": ["600000.SH"] * 2,
+            # 上游 YAML 映射后仍是字符串; 旧代码直接 cast → 全 null (#225)
+            "datetime": ["2026-09-01 09:35:00", "2026-09-01 09:40:00"],
+            "close": [10.0, 10.5],
+        }
+    )
+    out = GenericHTTPProvider._normalize_minute(df)
+    assert out.schema["datetime"] == pl.Datetime("us")
+    assert out["datetime"].null_count() == 0
+    assert out["datetime"][0] == datetime(2026, 9, 1, 9, 35)
+
+    # 非法字符串维持 strict=False 宽松行为 (null, 不抛异常)
+    bad = pl.DataFrame(
+        {"symbol": ["600000.SH"], "datetime": ["not-a-date"], "close": [1.0]}
+    )
+    out_bad = GenericHTTPProvider._normalize_minute(bad)
+    assert out_bad["datetime"].null_count() == 1
+
+
+def test_normalize_minute_datetime_already_typed_unchanged() -> None:
+    df = pl.DataFrame(
+        {
+            "symbol": ["600000.SH"],
+            "datetime": [datetime(2026, 9, 1, 9, 35)],
+            "close": [10.0],
+        }
+    )
+    out = GenericHTTPProvider._normalize_minute(df)
+    assert out["datetime"][0] == datetime(2026, 9, 1, 9, 35)
+
+
+# ── #226: get_daily 单批失败只隔离该批 ────────────────────────
+
+def _canonical_frame(symbols: list[str], day: str) -> pl.DataFrame:
+    return pl.DataFrame(
+        {
+            "symbol": symbols,
+            "date": [date.fromisoformat(day)] * len(symbols),
+            "open": [10.0] * len(symbols),
+            "high": [11.0] * len(symbols),
+            "low": [9.0] * len(symbols),
+            "close": [10.5] * len(symbols),
+            "volume": [100.0] * len(symbols),
+            "amount": [1050.0] * len(symbols),
+        }
+    )
+
+
+def test_get_daily_isolates_failed_batch() -> None:
+    provider = _provider({"daily": _daily_config(batch=2)})
+    calls: list[list[str]] = []
+
+    def request_rows(cfg, symbols=None, **kwargs):
+        calls.append(list(symbols))
+        if symbols == ["s3", "s4"]:
+            raise RuntimeError("502 Bad Gateway")
+        return [{"_rows": list(symbols)}]
+
+    provider._request_rows = request_rows
+    provider._mapped_frame = lambda cfg, rows: _canonical_frame(
+        rows[0]["_rows"], "2026-09-01"
+    )
+
+    df = provider.get_daily(
+        ["s1", "s2", "s3", "s4", "s5", "s6"],
+        datetime(2026, 8, 1), datetime(2026, 9, 1),
+    )
+
+    # 3 批都请求过 (失败批重试 1 次后跳过、流程继续), 返回第 1、3 批共 4 行
+    assert calls == [["s1", "s2"], ["s3", "s4"], ["s3", "s4"], ["s5", "s6"]]
+    assert df.height == 4
+    assert set(df["symbol"]) == {"s1", "s2", "s5", "s6"}
+
+
+def test_get_daily_progress_callback_fires_for_failed_batch() -> None:
+    provider = _provider({"daily": _daily_config(batch=2)})
+
+    def request_rows(cfg, symbols=None, **kwargs):
+        if symbols == ["s3", "s4"]:
+            raise RuntimeError("timeout")
+        return [{"_rows": list(symbols)}]
+
+    progress: list[tuple[int, int]] = []
+    provider._request_rows = request_rows
+    provider._mapped_frame = lambda cfg, rows: _canonical_frame(
+        rows[0]["_rows"], "2026-09-01"
+    )
+
+    provider.get_daily(
+        ["s1", "s2", "s3", "s4"], datetime(2026, 8, 1), datetime(2026, 9, 1),
+        on_chunk_done=lambda cur, tot: progress.append((cur, tot)),
+    )
+    # 失败批也推进进度, 前端进度条不会卡死
+    assert progress == [(1, 2), (2, 2)]
+
+
+def test_get_daily_all_batches_fail_returns_empty() -> None:
+    provider = _provider({"daily": _daily_config(batch=2)})
+
+    def request_rows(cfg, symbols=None, **kwargs):
+        raise RuntimeError("down")
+
+    provider._request_rows = request_rows
+    df = provider.get_daily(["s1", "s2"], datetime(2026, 8, 1), datetime(2026, 9, 1))
+    assert df.is_empty()

frontend/src/pages/Watchlist.tsx

@@ -57,6 +57,10 @@ import {
 const BOARDS = ['沪主板', '深主板', '创业板', '科创板', '北交所'] as const
 type BoardType = typeof BOARDS[number]
 
+// 板块筛选选项 = 股票板块 + ETF（ETF 无 symbol 板块语义，按 asset_type 匹配）
+const ETF_BOARD = 'ETF'
+const BOARD_OPTIONS = [...BOARDS, ETF_BOARD]
+
 function getBoardType(symbol: string): BoardType | null {
   if (/^(300|301)/.test(symbol)) return '创业板'
   if (/^688/.test(symbol))       return '科创板'
@@ -1112,9 +1116,10 @@ export function Watchlist() {
   const [filters, setFilters] = useState<Record<string, { min?: string; max?: string; text?: string }>>({})
 
   // 板块筛选（持久化）
+  // 兼容: 旧存储不含 ETF 键 → 加载时补上，保持 ETF 行默认可见
   const [boardFilter, setBoardFilter] = useState<Set<string>>(() => {
     const saved = storage.watchlistBoardFilter.get([])
-    return saved.length > 0 ? new Set(saved) : new Set(BOARDS) // 默认全选
+    return saved.length > 0 ? new Set([...saved, ETF_BOARD]) : new Set(BOARD_OPTIONS) // 默认全选
   })
   const persistBoardFilter = useCallback((next: Set<string>) => {
     setBoardFilter(next)
@@ -1156,7 +1161,7 @@ export function Watchlist() {
 
   const resetAllFilters = useCallback(() => {
     setFilters({})
-    persistBoardFilter(new Set(BOARDS))
+    persistBoardFilter(new Set(BOARD_OPTIONS))
     setExcludeST(false)
     storage.watchlistExcludeST.set(false)
   }, [persistBoardFilter])
@@ -1184,9 +1189,10 @@ export function Watchlist() {
   const filteredRows = useMemo(() => {
     // 板块筛选（全选时跳过）
     let result = rowsInSelectedGroup
-    if (boardFilter.size > 0 && boardFilter.size < BOARDS.length) {
+    if (boardFilter.size > 0 && boardFilter.size < BOARD_OPTIONS.length) {
       result = result.filter(r => {
-        // 非股票 (指数/ETF) 无板块语义, 不受板块筛选影响 (顺带修复 ETF 行被误过滤)
+        if (r.asset_type === 'etf') return boardFilter.has(ETF_BOARD)
+        // 其他非股票 (指数等) 无板块语义, 不受板块筛选影响
         if (r.asset_type && r.asset_type !== 'stock') return true
         const board = getBoardType(r.symbol)
         return board != null && boardFilter.has(board)
@@ -1219,7 +1225,7 @@ export function Watchlist() {
   }, [rowsInSelectedGroup, filters, columns, boardFilter, excludeST])
 
   const activeFilterCount = Object.values(filters).filter(v => v.min || v.max || v.text).length
-  const hasBoardFilter = boardFilter.size > 0 && boardFilter.size < BOARDS.length
+  const hasBoardFilter = boardFilter.size > 0 && boardFilter.size < BOARD_OPTIONS.length
   const hasActiveFilters = activeFilterCount > 0 || hasBoardFilter || excludeST
 
   // 排序（复用共享三态排序 hook）。分时列按「最新分钟收盘 vs 昨收」排序（分时图最后一点同口径），
@@ -1527,7 +1533,7 @@ export function Watchlist() {
           <div className="mb-2">
             <div className="text-[10px] text-muted uppercase tracking-wider mb-0.5">板块</div>
             <div className="flex flex-wrap gap-1">
-              {BOARDS.map(board => {
+              {BOARD_OPTIONS.map(board => {
                 const active = boardFilter.has(board)
                 return (
                   <button

frontend/src/pages/backtest/FactorBacktest.tsx

@@ -92,7 +92,9 @@ export function FactorBacktest({ initialFactorName = 'momentum_20d' }: { initial
   const [end, setEnd] = useState(TODAY)
   const [nGroups, setNGroups] = useState(5)
   const [weight, setWeight] = useState<'equal' | 'factor_weight'>('equal')
+  const [rebalance, setRebalance] = useState<'daily' | 'weekly' | 'monthly'>('daily')
   const [fees, setFees] = useState('2')
+  const [slippage, setSlippage] = useState('5')
   const [result, setResult] = useState<FactorBacktestResult | null>(null)
 
   const columns = useQuery({
@@ -129,9 +131,10 @@ export function FactorBacktest({ initialFactorName = 'momentum_20d' }: { initial
         start: start || null,
         end: end || undefined,
         n_groups: nGroups,
-        rebalance: 'daily',
+        rebalance,
         weight,
         fees_pct: Number(fees) / 10000,
+        slippage_bps: Number(slippage),
       }),
     onSuccess: (data) => {
       if (data.error) {
@@ -302,8 +305,21 @@ export function FactorBacktest({ initialFactorName = 'momentum_20d' }: { initial
             </select>
           </div>
           <div>
-            <label className="text-xs font-medium text-secondary block mb-1.5">佣金(万分之)</label>
-            <input type="number" value={fees} onChange={e => setFees(e.target.value)}
+            <label className="text-[11px] text-secondary block mb-1">调仓频率</label>
+            <select value={rebalance} onChange={e => setRebalance(e.target.value as any)} className={INPUT_CLS}>
+              <option value="daily">日度</option>
+              <option value="weekly">周度</option>
+              <option value="monthly">月度</option>
+            </select>
+          </div>
+          <div>
+            <label className="text-[11px] text-secondary block mb-1">佣金(万分之)</label>
+            <input type="number" min="0" value={fees} onChange={e => setFees(e.target.value)}
+              className={INPUT_CLS} />
+          </div>
+          <div>
+            <label className="text-[11px] text-secondary block mb-1">滑点(bp)</label>
+            <input type="number" min="0" value={slippage} onChange={e => setSlippage(e.target.value)}
               className={INPUT_CLS} />
           </div>
         </div>
@@ -322,7 +338,7 @@ export function FactorBacktest({ initialFactorName = 'momentum_20d' }: { initial
 
       {/* 结果面板 */}
       <section className="min-w-0 space-y-3 bg-base/15 px-3 py-3 xl:overflow-y-auto">
-        {result?.error && !result.ic_mean && (
+        {result?.error && (
           <div className="text-sm text-danger bg-danger/10 border border-danger/30 rounded-btn px-3 py-2">
             {result.error}
           </div>
@@ -352,7 +368,7 @@ export function FactorBacktest({ initialFactorName = 'momentum_20d' }: { initial
           <LoadingPanel symbolsText={symbols ? `${symbols.split(',').length} 只标的` : '全市场 · 当前区间'} />
         )}
 
-        {result && result.ic_mean != null && (
+        {result && !result.error && (
           <motion.div
             initial={{ opacity: 0, y: 8 }}
             animate={{ opacity: 1, y: 0 }}
@@ -374,7 +390,7 @@ export function FactorBacktest({ initialFactorName = 'momentum_20d' }: { initial
                     {saveCandidate.isPending ? '保存中' : '保存候选'}
                   </button>
                   <span className="text-[11px] text-muted">
-                    Rank IC · 日度调仓
+                    Rank IC · {rebalance === 'daily' ? '日度' : rebalance === 'weekly' ? '周度' : '月度'}调仓
                   </span>
                   {result.elapsed_ms > 0 && (
                     <span className="flex items-center gap-1 text-[11px] text-muted">
@@ -384,13 +400,12 @@ export function FactorBacktest({ initialFactorName = 'momentum_20d' }: { initial
                   )}
                 </div>
               </div>
+              {result.ic_mean != null ? (
                 <div className="grid grid-cols-4 gap-4">
                   <StatCard
                     label="IC 均值"
-                  value={result.ic_mean != null ? fmtPct(result.ic_mean) : null}
-                  highlight={result.ic_mean != null
-                    ? result.ic_mean > 0.03 ? 'bull' : result.ic_mean < -0.03 ? 'bear' : 'neutral'
-                    : undefined}
+                    value={fmtPct(result.ic_mean)}
+                    highlight={result.ic_mean > 0.03 ? 'bull' : result.ic_mean < -0.03 ? 'bear' : 'neutral'}
                   />
                   <StatCard label="IC 标准差" value={result.ic_std != null ? fmtPct(result.ic_std) : null} />
                   <StatCard
@@ -402,6 +417,11 @@ export function FactorBacktest({ initialFactorName = 'momentum_20d' }: { initial
                   />
                   <StatCard label="IC 胜率" value={result.ic_win_rate != null ? fmtPct(result.ic_win_rate) : null} />
                 </div>
+              ) : (
+                <div className="rounded-btn border border-border bg-base/40 px-3 py-3 text-xs text-muted">
+                  标的数量过少，无法计算 IC/IR（需 ≥2 只）。可清空标的使用全市场，或补充更多标的。
+                </div>
+              )}
             </div>
 
             {/* IC 时序图 */}