feat(minute): 分钟红7新增「N日内涨停过」条件 + minute_filter 日线窗口契约

分钟策略此前只能访问当日分钟窗口, 无法叠加日线维度条件。本次为
minute_filter 后端扩展可选日线历史契约:

- 契约: 策略声明 META["daily_history_bars"] (0-250) + filter_minute_history
  接受 daily 关键字 (加载期校验, 纯分钟策略零改动); 引擎聚合各策略声明
  (minute_daily_history_bars) 后由 ScreenerService 1m 分支装配
  context.daily_history (enriched 日线窗口), run() 以 daily= 注入
- 分钟红7: require_limit_up (默认开) + limit_up_days (5-60, 默认20) 参数;
  涨停判定复用 enriched 预计算信号 signal_limit_up (收盘封板) 或
  signal_broken_limit_up (炸板盘中触及), 任一命中即算涨停过, 输出
  recent_limit_ups 次数列; 日线窗口缺失时失败闭合 (宁可漏过不可错报)
- 测试 25 项: 涨停信号过滤/炸板计数/回看窗口边界(第20日含第21日不含)/
  失败闭合/开关旁路/加载校验(缺 daily 关键字与超范围)/引擎注入/服务装配

实盘验证: 用户参数(bars=6)基线 24 只 → 要求涨停过后 6 只, 全部带
recent_limit_ups; 全量 1112 项后端测试通过。

backend/app/services/screener.py

@@ -390,12 +390,20 @@ class ScreenerService:
             # 分钟策略数据源是本地当日分钟K分区 (单分区文件直读), 与日线
             # enriched 历史窗口无关, 不走 required_history_bars 日线路径。
             history = self._load_minute_history(as_of, current)
+            # 策略声明 META["daily_history_bars"] 时额外装配日线 enriched 窗口,
+            # 供分钟策略叠加日线维度条件 (如 N 日内涨停过)。
+            daily_history = None
+            if engine is not None:
+                daily_bars = engine.minute_daily_history_bars(strategy_ids)
+                if daily_bars > 0:
+                    daily_history = self._load_enriched_history(as_of, daily_bars)
             return StrategyDataContext(
                 asset_type=self.asset_type,
                 timeframe=timeframe,
                 as_of=as_of,
                 current=current,
                 history=history,
+                daily_history=daily_history,
                 market=None,
                 cache_key=cache_key,
             )

backend/app/strategy/builtin/minute_red_streak.py

@@ -4,6 +4,11 @@
 (symbol, datetime, open, high, low, close, volume, amount),
 由 ScreenerService.build_strategy_context 的 1m 分支从本地 kline_minute
 分区注入; 策略本身不感知数据来源 (本地同步 / 盘中增量刷新对它透明)。
+
+META["daily_history_bars"] 声明叠加日线维度的条件 (N 日内涨停过):
+引擎会以 daily= 关键字注入日线 enriched 窗口, 涨停判定直接复用
+enriched 预计算信号 — signal_limit_up (收盘封板) 或 signal_broken_limit_up
+(炸板: 盘中触及涨停未封住), 任一命中即算"盘中涨停过"。
 """
 
 import polars as pl
@@ -11,10 +16,12 @@ import polars as pl
 META = {
     "id": "minute_red_streak",
     "name": "分钟红7",
-    "description": "开盘前7根1分钟K至少5根收红, 且最高的2根(按最高价)都是红K",
+    "description": "开盘前7根1分钟K至少5根收红, 最高的2根(按最高价)都是红K, 且近20日盘中触及过涨停",
     "tags": ["分钟", "形态", "短线"],
     "asset_types": ["stock"],
     "timeframes": ["1m"],
+    # 日线 enriched 窗口 (交易日语义, 含 as_of): 覆盖 limit_up_days 参数上限
+    "daily_history_bars": 60,
     "params": [
         {
             "id": "bars",
@@ -49,6 +56,21 @@ META = {
             "type": "bool",
             "default": False,
         },
+        {
+            "id": "require_limit_up",
+            "label": "要求N日内涨停过",
+            "type": "bool",
+            "default": True,
+        },
+        {
+            "id": "limit_up_days",
+            "label": "涨停回看天数",
+            "type": "int",
+            "default": 20,
+            "min": 5,
+            "max": 60,
+            "step": 1,
+        },
     ],
     "order_by": "red_count",
     "descending": True,
@@ -60,12 +82,41 @@ ENTRY_SIGNALS: list[str] = []
 EXIT_SIGNALS: list[str] = []
 
 
-def filter_minute_history(df: pl.DataFrame, params: dict) -> pl.DataFrame:
+def _recent_limit_ups(daily: pl.DataFrame | None, lookback: int) -> pl.DataFrame:
+    """日线窗口 → (symbol, recent_limit_ups) 近 lookback 个交易日的涨停次数。
+
+    涨停过 = signal_limit_up (收盘封板) 或 signal_broken_limit_up (炸板触及)。
+    日线窗口缺失 / 无涨停信号列 → 返回空表 (调用方 inner join 即失败闭合,
+    宁可漏过不可错报)。
+    """
+    empty = pl.DataFrame(schema={"symbol": pl.Utf8, "recent_limit_ups": pl.UInt32})
+    if daily is None or daily.is_empty():
+        return empty
+    if not {"signal_limit_up", "signal_broken_limit_up"}.issubset(daily.columns):
+        return empty
+    return (
+        daily.select("symbol", "date", "signal_limit_up", "signal_broken_limit_up")
+        .sort(["symbol", "date"])
+        .filter(pl.int_range(pl.len()).over("symbol") >= pl.len().over("symbol") - lookback)
+        .group_by("symbol")
+        .agg(
+            recent_limit_ups=(
+                pl.col("signal_limit_up").fill_null(False)
+                | pl.col("signal_broken_limit_up").fill_null(False)
+            ).sum()
+        )
+        .filter(pl.col("recent_limit_ups") > 0)
+    )
+
+
+def filter_minute_history(df: pl.DataFrame, params: dict, *, daily: pl.DataFrame | None = None) -> pl.DataFrame:
     """红K形态过滤: 全向量化, 无逐行 Python 循环。
 
     - 每标的按时间取当日最早 bars 根 (开盘窗口); 不足 bars 根不触发
     - 红 = close > open; 窗口内红K数 >= min_red
     - 按 rank_by (high / close) 降序取前 top_red 根, 同值取时间更晚者, 需全红
+    - require_limit_up: 近 limit_up_days 个交易日盘中触及过涨停 (日线维度,
+      由 daily 窗口的预计算涨停信号判定; 窗口缺失时失败闭合不触发)
     """
     bars = int(params.get("bars") or 7)
     min_red = min(int(params.get("min_red") or 5), bars)
@@ -99,7 +150,7 @@ def filter_minute_history(df: pl.DataFrame, params: dict) -> pl.DataFrame:
         .agg(top_red_count=pl.col("_red").sum())
     )
 
-    return (
+    result = (
         window.join(top, on="symbol", how="inner")
         .filter(
             (pl.col("bars_checked") >= bars)
@@ -108,3 +159,7 @@ def filter_minute_history(df: pl.DataFrame, params: dict) -> pl.DataFrame:
         )
         .drop("bars_checked")
     )
+    if params.get("require_limit_up", True):
+        lookback = max(5, min(int(params.get("limit_up_days") or 20), 60))
+        result = result.join(_recent_limit_ups(daily, lookback), on="symbol", how="inner")
+    return result

backend/app/strategy/engine.py

@@ -155,6 +155,9 @@ class StrategyDataContext:
     as_of: date
     current: pl.DataFrame | None = None
     history: pl.DataFrame | None = None
+    # 仅 1m 分支: 策略声明 META["daily_history_bars"] 时注入的日线 enriched 窗口,
+    # 供分钟策略叠加日线维度条件 (如 N 日内涨停过); 未声明时为 None。
+    daily_history: pl.DataFrame | None = None
     market: Any | None = None
     cache_key: str | None = None
 
@@ -201,6 +204,9 @@ class StrategyDef:
     composite: CompositeSpec | None = None  # 仅 backend=="composite" 时非空
     # 仅 backend=="minute_filter" 时非空: 输入为当日分钟K窗口, 输出为命中标的行
     filter_minute_history_fn: Callable[[pl.DataFrame, dict], pl.DataFrame] | None = None
+    # 仅 minute_filter: META["daily_history_bars"] 声明需要的日线历史窗口 (0=不需要;
+    # >0 时 filter_minute_history 必须接受 daily 关键字, 引擎注入 context.daily_history)
+    minute_daily_bars: int = 0
 
 
 @dataclass
@@ -493,6 +499,7 @@ class StrategyEngine:
 
         matrix_strategy = getattr(mod, "MATRIX_STRATEGY", None)
         composite_spec: CompositeSpec | None = None
+        minute_daily_bars = 0
         if execution_backend == "matrix_native":
             from app.backtest.matrix import MatrixStrategy
 
@@ -535,6 +542,22 @@ class StrategyEngine:
                 raise ValueError(
                     "minute_filter strategy must declare timeframes == ['1m']"
                 )
+            # 可选日线历史窗口: 声明 daily_history_bars 时 fn 必须接受 daily 关键字,
+            # 引擎会把 context.daily_history (enriched 日线窗口) 注入进来。
+            minute_daily_bars = int(meta.get("daily_history_bars") or 0)
+            if minute_daily_bars < 0 or minute_daily_bars > 250:
+                raise ValueError(
+                    "minute_filter daily_history_bars must be within [0, 250]"
+                )
+            if minute_daily_bars > 0:
+                import inspect
+
+                sig = inspect.signature(filter_minute_history_fn)
+                if "daily" not in sig.parameters:
+                    raise ValueError(
+                        "minute_filter daily_history_bars requires "
+                        "filter_minute_history to accept a 'daily' keyword"
+                    )
         elif filter_history_fn is None or filter_fn is not None:
             raise ValueError("python_history_legacy strategy must declare only filter_history")
 
@@ -559,6 +582,7 @@ class StrategyEngine:
             matrix_strategy=matrix_strategy,
             composite=composite_spec,
             filter_minute_history_fn=filter_minute_history_fn,
+            minute_daily_bars=minute_daily_bars,
         )
 
     def reload(self) -> None:
@@ -673,6 +697,16 @@ class StrategyEngine:
             return None
         return max(0, int(value))
 
+    def minute_daily_history_bars(self, strategy_ids: list[str]) -> int:
+        """1m 分支需要的日线 enriched 窗口大小: 各 minute_filter 策略声明的
+        META["daily_history_bars"] 取 max, 未声明 (纯分钟策略) 为 0。"""
+        required = 0
+        for strategy_id in strategy_ids:
+            strategy = self.get(strategy_id)
+            if strategy.execution_backend == "minute_filter":
+                required = max(required, strategy.minute_daily_bars)
+        return required
+
     def required_history_bars(
         self,
         strategy_ids: list[str],
@@ -917,7 +951,10 @@ class StrategyEngine:
                     strategy_id=strategy_id,
                     exit_signal_hits=exit_signal_hits,
                 )
-            df = s.filter_minute_history_fn(history, params)
+            if s.minute_daily_bars > 0:
+                df = s.filter_minute_history_fn(history, params, daily=context.daily_history)
+            else:
+                df = s.filter_minute_history_fn(history, params)
             # 基础过滤/展示列 (name/total_shares/change_pct 等) 来自 enriched 快照,
             # 在命中结果上事后联表, 避免把 enriched 列铺到全市场分钟行上。
             if current is not None and not current.is_empty():

backend/tests/test_minute_strategy.py

@@ -52,7 +52,7 @@ def test_pattern_hits_five_red_of_seven_with_red_top_two():
         (10.5, 10.7, 10.80),  # 红
         (10.7, 10.8, 10.90),  # 红 (最高)
     ]
-    out = minute_red_streak.filter_minute_history(_bars("600000.SH", candles), {})
+    out = minute_red_streak.filter_minute_history(_bars("600000.SH", candles), {"require_limit_up": False})
     assert out["symbol"].to_list() == ["600000.SH"]
     row = out.row(0, named=True)
     assert row["red_count"] == 5
@@ -61,7 +61,7 @@ def test_pattern_hits_five_red_of_seven_with_red_top_two():
 
 
 def test_pattern_insufficient_bars_never_triggers():
-    out = minute_red_streak.filter_minute_history(_bars("600000.SH", [(10.0, 10.2, 10.3)] * 6), {})
+    out = minute_red_streak.filter_minute_history(_bars("600000.SH", [(10.0, 10.2, 10.3)] * 6), {"require_limit_up": False})
     assert out.is_empty()
 
 
@@ -76,7 +76,7 @@ def test_pattern_green_at_top_blocks_hit():
         (11.5, 11.0, 12.00),  # 绿 (最高)
         (11.0, 11.4, 11.90),  # 红 (次高)
     ]
-    out = minute_red_streak.filter_minute_history(_bars("600000.SH", candles), {})
+    out = minute_red_streak.filter_minute_history(_bars("600000.SH", candles), {"require_limit_up": False})
     assert out.is_empty()
 
 
@@ -91,9 +91,9 @@ def test_pattern_rank_by_close_uses_close_not_high():
         (11.4, 11.1, 11.50),  # 绿 (high 最高, 并列)
         (11.1, 11.5, 11.55),  # 红 (close 最高)
     ]
-    by_high = minute_red_streak.filter_minute_history(_bars("600000.SH", candles), {})
+    by_high = minute_red_streak.filter_minute_history(_bars("600000.SH", candles), {"require_limit_up": False})
     by_close = minute_red_streak.filter_minute_history(
-        _bars("600000.SH", candles), {"rank_by_close": True}
+        _bars("600000.SH", candles), {"rank_by_close": True, "require_limit_up": False}
     )
     assert by_high.is_empty()
     assert by_close["symbol"].to_list() == ["600000.SH"]
@@ -107,7 +107,7 @@ def test_pattern_sorts_unordered_input_by_datetime():
             (10.6, 10.5, 10.65), (10.5, 10.7, 10.80), (10.7, 10.8, 10.90),
         ]),
     ]).sample(fraction=1.0, shuffle=True, seed=7)
-    out = minute_red_streak.filter_minute_history(bars, {})
+    out = minute_red_streak.filter_minute_history(bars, {"require_limit_up": False})
     assert out["symbol"].to_list() == ["600000.SH"]
     assert out.row(0, named=True)["close"] == 10.8  # 最后一根(时间最大)的收盘
 
@@ -124,7 +124,7 @@ def test_pattern_three_way_high_tie_prefers_later_bars():
         (10.5, 10.6, 10.90),  # 红 (并列最高, 中间)
         (10.6, 10.8, 10.90),  # 红 (并列最高, 最晚)
     ]
-    out = minute_red_streak.filter_minute_history(_bars("600000.SH", candles), {})
+    out = minute_red_streak.filter_minute_history(_bars("600000.SH", candles), {"require_limit_up": False})
     assert out["symbol"].to_list() == ["600000.SH"]
     assert out.row(0, named=True)["top_red_count"] == 2
 
@@ -141,8 +141,8 @@ def test_pattern_min_red_threshold_respected():
         (10.5, 10.8, 10.90),  # 红
     ]
     bars = _bars("600000.SH", candles)
-    assert minute_red_streak.filter_minute_history(bars, {"min_red": 5}).is_empty()
-    assert not minute_red_streak.filter_minute_history(bars, {"min_red": 4}).is_empty()
+    assert minute_red_streak.filter_minute_history(bars, {"min_red": 5, "require_limit_up": False}).is_empty()
+    assert not minute_red_streak.filter_minute_history(bars, {"min_red": 4, "require_limit_up": False}).is_empty()
 
 
 def test_pattern_uses_opening_bars_even_if_day_turns_green():
@@ -159,7 +159,7 @@ def test_pattern_uses_opening_bars_even_if_day_turns_green():
         (10.8, 10.0, 10.85),  # 开盘窗口外的绿
         (10.0, 9.5, 10.05),   # 开盘窗口外的绿
     ]
-    out = minute_red_streak.filter_minute_history(_bars("600000.SH", candles), {})
+    out = minute_red_streak.filter_minute_history(_bars("600000.SH", candles), {"require_limit_up": False})
     assert out["symbol"].to_list() == ["600000.SH"]
     row = out.row(0, named=True)
     assert row["red_count"] == 5
@@ -180,10 +180,84 @@ def test_pattern_opening_window_miss_not_rescued_by_late_reds():
         (10.8, 10.9, 11.00),  # 红 (窗口外)
         (10.9, 11.0, 11.10),  # 红 (窗口外)
     ]
-    out = minute_red_streak.filter_minute_history(_bars("600000.SH", candles), {})
+    out = minute_red_streak.filter_minute_history(_bars("600000.SH", candles), {"require_limit_up": False})
     assert out.is_empty()
 
 
+# ── 涨停条件 (日线维度) ─────────────────────────────────────────────
+
+
+_HIT_CANDLES = [
+    (10.0, 10.2, 10.30),  # 红
+    (10.2, 10.1, 10.25),  # 绿 (低高点)
+    (10.1, 10.4, 10.50),  # 红
+    (10.4, 10.6, 10.70),  # 红 (次高)
+    (10.6, 10.5, 10.65),  # 绿 (低高点)
+    (10.5, 10.7, 10.80),  # 红
+    (10.7, 10.8, 10.90),  # 红 (最高)
+]
+
+
+def _daily(
+    symbol: str,
+    days: int,
+    flag_on: set[int] | None = None,
+    *,
+    broken: bool = False,
+) -> pl.DataFrame:
+    """days 个交易日的日线帧; flag_on 指定第几天 (0=最早) 触发涨停信号。"""
+    flag_on = flag_on or set()
+    base = date(2026, 8, 25)
+    return pl.DataFrame({
+        "symbol": [symbol] * days,
+        "date": [base - _dt.timedelta(days=days - i) for i in range(days)],
+        "signal_limit_up": [i in flag_on and not broken for i in range(days)],
+        "signal_broken_limit_up": [i in flag_on and broken for i in range(days)],
+    })
+
+
+def test_pattern_limit_up_condition_filters_by_daily_signals():
+    bars = pl.concat([
+        _bars("600001.SH", _HIT_CANDLES),
+        _bars("600002.SH", _HIT_CANDLES),
+        _bars("600003.SH", _HIT_CANDLES),
+    ])
+    daily = pl.concat([
+        _daily("600001.SH", 20, {3}),                 # 收盘涨停 → 过
+        _daily("600002.SH", 20, {15}, broken=True),   # 炸板触及 → 过
+        _daily("600003.SH", 20),                      # 无涨停 → 剔除
+    ])
+    out = minute_red_streak.filter_minute_history(bars, {}, daily=daily)
+    assert sorted(out["symbol"].to_list()) == ["600001.SH", "600002.SH"]
+    assert sorted(out["recent_limit_ups"].to_list()) == [1, 1]
+
+
+def test_pattern_limit_up_lookback_window_boundary():
+    # 25 个交易日, 涨停仅发生在第 5 天 (0=最早): 回看 20 日窗口 = 最后 20 根
+    # (索引 5..24), 第 5 天在窗外 → 不命中; 回看放宽到 25 → 命中
+    bars = _bars("600000.SH", _HIT_CANDLES)
+    daily = _daily("600000.SH", 25, {4})
+    assert minute_red_streak.filter_minute_history(bars, {}, daily=daily).is_empty()
+    out = minute_red_streak.filter_minute_history(
+        bars, {"limit_up_days": 25}, daily=daily
+    )
+    assert out["symbol"].to_list() == ["600000.SH"]
+
+
+def test_pattern_limit_up_fails_closed_without_daily():
+    # 日线窗口缺失时失败闭合 (宁可漏过不可错报)
+    out = minute_red_streak.filter_minute_history(_bars("600000.SH", _HIT_CANDLES), {})
+    assert out.is_empty()
+
+
+def test_pattern_limit_up_disabled_ignores_daily():
+    out = minute_red_streak.filter_minute_history(
+        _bars("600000.SH", _HIT_CANDLES), {"require_limit_up": False}
+    )
+    assert out["symbol"].to_list() == ["600000.SH"]
+    assert "recent_limit_ups" not in out.columns
+
+
 # ── 引擎加载与运行 ──────────────────────────────────────────────────
 
 
@@ -225,6 +299,75 @@ def test_minute_filter_backend_validation(tmp_path):
     assert any("timeframes" in e["error"] for e in engine.load_errors())
 
 
+def test_minute_filter_daily_history_validation(tmp_path):
+    # 声明 daily_history_bars: fn 必须接受 daily 关键字, 且范围 [0, 250]
+    (tmp_path / "m_daily_ok.py").write_text(
+        'import polars as pl\n'
+        'META = {"id": "m_daily_ok", "name": "x", "asset_types": ["stock"], '
+        '"timeframes": ["1m"], "daily_history_bars": 20}\n'
+        'EXECUTION_BACKEND = "minute_filter"\n'
+        'def filter_minute_history(df, params, *, daily=None):\n'
+        '    return df.group_by("symbol").agg(close=pl.col("close").max())\n'
+    )
+    (tmp_path / "m_daily_kw.py").write_text(
+        'import polars as pl\n'
+        'META = {"id": "m_daily_kw", "name": "x", "asset_types": ["stock"], '
+        '"timeframes": ["1m"], "daily_history_bars": 20}\n'
+        'EXECUTION_BACKEND = "minute_filter"\n'
+        'def filter_minute_history(df, params):\n'
+        '    return df.group_by("symbol").agg(close=pl.col("close").max())\n'
+    )
+    (tmp_path / "m_daily_range.py").write_text(
+        'import polars as pl\n'
+        'META = {"id": "m_daily_range", "name": "x", "asset_types": ["stock"], '
+        '"timeframes": ["1m"], "daily_history_bars": 300}\n'
+        'EXECUTION_BACKEND = "minute_filter"\n'
+        'def filter_minute_history(df, params, *, daily=None):\n'
+        '    return df.group_by("symbol").agg(close=pl.col("close").max())\n'
+    )
+    engine = StrategyEngine(strategy_dirs=[tmp_path])
+    assert engine.has("m_daily_ok")
+    assert engine.get("m_daily_ok").minute_daily_bars == 20
+    assert not engine.has("m_daily_kw")
+    assert not engine.has("m_daily_range")
+    assert any("'daily' keyword" in e["error"] for e in engine.load_errors())
+    assert any("[0, 250]" in e["error"] for e in engine.load_errors())
+
+
+def test_minute_run_injects_daily_history(tmp_path):
+    # fn 直接消费 daily (对涨停信号求和), 验证引擎把 context.daily_history 注入
+    (tmp_path / "m_use_daily.py").write_text(
+        'import polars as pl\n'
+        'META = {"id": "m_use_daily", "name": "x", "asset_types": ["stock"], '
+        '"timeframes": ["1m"], "daily_history_bars": 10}\n'
+        'EXECUTION_BACKEND = "minute_filter"\n'
+        'def filter_minute_history(df, params, *, daily=None):\n'
+        '    if daily is None:\n'
+        '        return pl.DataFrame(schema={"symbol": pl.Utf8})\n'
+        '    return daily.group_by("symbol").agg(\n'
+        '        close=pl.col("signal_limit_up").sum() + 10.0)\n'
+    )
+    engine = StrategyEngine(strategy_dirs=[tmp_path])
+    context = StrategyDataContext(
+        asset_type="stock",
+        timeframe="1m",
+        as_of=date(2026, 8, 25),
+        current=pl.DataFrame({
+            "symbol": ["600001.SH"],
+            "name": ["正常股"],
+            "total_shares": [1e8],
+            "float_shares": [5e7],
+            "amount": [3e8],
+            "change_pct": [0.01],
+        }),
+        history=_bars("600001.SH", [(10.0, 10.2, 10.3)] * 7),
+        daily_history=_daily("600001.SH", 10, {2}),
+    )
+    result = engine.run("m_use_daily", context)
+    assert result.total == 1
+    assert result.rows[0]["close"] == 11  # 10 + 窗口内 1 次收盘涨停
+
+
 def test_minute_context_run_applies_enriched_basic_filter(tmp_path):
     (tmp_path / "m_basic.py").write_text(_minute_code("m_basic"))
     engine = StrategyEngine(strategy_dirs=[tmp_path])
@@ -345,3 +488,30 @@ def test_minute_context_rejects_non_stock_asset():
         raise AssertionError("expected ValueError")
     except ValueError as e:
         assert "A 股" in str(e)
+
+
+def test_minute_context_loads_daily_history_for_declared_strategies():
+    class _FakeEngine:
+        def minute_daily_history_bars(self, strategy_ids):
+            return 5
+
+    daily = _daily("600001.SH", 6, {1})
+    repo = _FakeMinuteRepo({date(2026, 8, 25): _bars("600001.SH", [(10.0, 10.2, 10.3)] * 3)})
+    repo.get_enriched_history = lambda target_date, lookback_days: daily  # type: ignore[method-assign]
+    repo.get_instruments_asset = lambda asset_type: None  # type: ignore[method-assign]
+    svc = ScreenerService(repo, asset_type="stock")  # type: ignore[arg-type]
+    ctx = svc.build_strategy_context(
+        _FakeEngine(), date(2026, 8, 25), ["m_x"], timeframe="1m",
+        current=pl.DataFrame({"symbol": ["600001.SH"], "name": ["x"]}),
+    )
+    assert ctx.daily_history is not None
+    assert ctx.daily_history.height == 6  # 引擎声明 5 → 装配日线窗口
+
+
+def test_minute_context_without_engine_skips_daily_history():
+    svc = _svc({date(2026, 8, 25): _bars("600001.SH", [(10.0, 10.2, 10.3)] * 3)})
+    ctx = svc.build_strategy_context(
+        None, date(2026, 8, 25), [], timeframe="1m",
+        current=pl.DataFrame({"symbol": ["600001.SH"]}),
+    )
+    assert ctx.daily_history is None  # 无引擎声明 → 不装配日线