feat: improve strategy scoring and backtest execution

backend/app/api/backtest.py

@@ -194,7 +194,7 @@ class StrategyBacktestRequest(BaseModel):
     # matching 向后兼容; 显式传 entry_fill/exit_fill 时以二者为准。
     matching: Literal["close_t", "open_t+1"] = "open_t+1"
     entry_fill: Literal["close_t", "open_t+1"] | None = None
-    exit_fill: Literal["close_t", "open_t+1"] | None = None
+    exit_fill: Literal["close_t", "open_t+1", "signal_next_minute"] | None = None
     fees_pct: float = 0.0002
     commission_pct: float | None = None
     stamp_tax_pct: float | None = None
@@ -206,6 +206,7 @@ class StrategyBacktestRequest(BaseModel):
     mode: Literal["position", "full"] = "position"
     holding_days: int = 5
     asset_type: str = "stock"
+    minute_fill: bool = False
 
 
 @router.post("/strategy/run")
@@ -239,6 +240,7 @@ def strategy_run(req: StrategyBacktestRequest, request: Request):
         mode=req.mode,
         holding_days=req.holding_days,
         asset_type=req.asset_type,
+        minute_fill=req.minute_fill,
     )
     task = make_worker_task("backtest", settings.data_dir, cfg)
     return run_worker_task(task)
@@ -1006,4 +1008,3 @@ async def walkforward_cancel(request: Request):
         job.cancel_event.set()
         return {"ok": True}
     return {"ok": False, "message": "任务不存在或已完成"}
-

backend/app/api/strategy.py

@@ -17,6 +17,7 @@ from fastapi import APIRouter, HTTPException, Request
 from fastapi.responses import StreamingResponse
 from pydantic import BaseModel
 
+from app.backtest.minute_trigger import MINUTE_EXIT_TRIGGER_SIGNALS
 from app.strategy import config as strategy_config
 from app.strategy.ai_generator import AIStrategyGenerator, find_meta_assignment
 from app.strategy.engine import StrategyDef, StrategyEngine
@@ -99,6 +100,7 @@ def _strategy_detail(s: StrategyDef, overrides: dict | None = None) -> dict:
         "scoring": scoring,
         "entry_signals": overrides.get("entry_signals", s.entry_signals) if overrides else s.entry_signals,
         "exit_signals": overrides.get("exit_signals", s.exit_signals) if overrides else s.exit_signals,
+        "minute_exit_trigger_supported_signals": sorted(MINUTE_EXIT_TRIGGER_SIGNALS),
         "stop_loss": overrides.get("stop_loss", s.stop_loss) if overrides else s.stop_loss,
         "take_profit": getattr(s, "take_profit", None),
         "trailing_stop": getattr(s, "trailing_stop", None),
@@ -504,6 +506,7 @@ def _prepare_strategy_code(req: StrategyCodeValidateRequest | StrategyCodeSaveRe
     # 安全校验始终执行 (此前 strict 字段可被客户端设 false 绕过, 已移除)
     AIStrategyGenerator._validate_safety(code)
     meta = AIStrategyGenerator._extract_meta(code)
+    AIStrategyGenerator._validate_meta_semantics(code, meta)
     return {"code": code, "meta": meta}
 
 

backend/app/backtest/engine.py

@@ -49,7 +49,7 @@ class MatcherConfig:
     # 显式传入 entry_fill/exit_fill 时以二者为准 (允许建仓/清仓口径不同)。
     matching: Literal["close_t", "open_t+1"] = "close_t"
     entry_fill: Literal["close_t", "open_t+1"] | None = None
-    exit_fill: Literal["close_t", "open_t+1"] | None = None
+    exit_fill: Literal["close_t", "open_t+1", "signal_next_minute"] | None = None
     # 成本模型: 优先使用拆分口径 (佣金双边 + 印花税仅卖出 + 滑点双边)。
     # 未设 commission_pct 时回退到 fees_pct 作为双边佣金 (向后兼容, 无印花税)。
     fees_pct: float = 0.0002
@@ -694,6 +694,7 @@ class BacktestEngine:
             exit_delay_bars=1 if config.exit_fill == "open_t+1" else 0,
             entry_signal_ids=entry_signal_ids,
             exit_signal_ids=exit_signal_ids,
+            minute_exit_trigger=config.exit_fill == "signal_next_minute",
         )
         return self._simulate_independent_matrix(
             matrix, raw_candidates, config, progress_cb, cancel_event,
@@ -766,6 +767,18 @@ class BacktestEngine:
             )
             return precise if precise is not None else daily_price
 
+        def _minute_trigger_price(time_id: int, asset_id: int) -> float | None:
+            if not config.minute_fill or not minute_cache:
+                return None
+            rows = minute_cache.get((matrix.symbols[asset_id], matrix.timestamp_labels[time_id][:10]))
+            if rows is None:
+                return None
+            reference = float(matrix.reference_price[time_id, asset_id])
+            return self._resolve_minute_exit_trigger(
+                rows,
+                reference if _valid_price(reference) else None,
+            )
+
         def _one_price_limit(time_id: int, asset_id: int, direction: str) -> bool:
             if not matrix.tradable[time_id, asset_id]:
                 return False
@@ -839,12 +852,36 @@ class BacktestEngine:
             signal_date: str,
             override: float | None = None,
         ) -> bool:
+            signal_id = (
+                _signal_id(int(matrix.exit_signal_code[time_id, asset_id]), matrix.exit_signal_ids)
+                if reason == "signal" else None
+            )
+            minute_trigger = config.exit_fill == "signal_next_minute" and reason == "signal"
+            if minute_trigger and override is None:
+                if pos.get("pending_exit_next_open"):
+                    open_price = float(matrix.open[time_id, asset_id])
+                    override = open_price if _valid_price(open_price) else None
+                else:
+                    override = _minute_trigger_price(time_id, asset_id)
+                    if override is None:
+                        if not pos.get("pending_exit_reason"):
+                            pos["pending_exit_reason"] = reason
+                            pos["pending_exit_signal_date"] = signal_date
+                            pos["pending_exit_signal_id"] = signal_id
+                            pos["pending_exit_next_open"] = True
+                            _count("pending_exit")
+                        pos["blocked_exit_days"] += 1
+                        _count("sell_minute_trigger_fallback")
+                        return False
             ok, blocked = _can_sell(time_id, asset_id, override)
             if not ok:
                 if not pos.get("pending_exit_reason"):
                     pos["pending_exit_reason"] = reason
                     pos["pending_exit_signal_date"] = signal_date
+                    pos["pending_exit_signal_id"] = signal_id
                     _count("pending_exit")
+                if minute_trigger:
+                    pos["pending_exit_next_open"] = True
                 pos["blocked_exit_days"] += 1
                 _count(blocked)
                 return False
@@ -875,9 +912,7 @@ class BacktestEngine:
                 exit_signal_date=signal_date,
                 blocked_exit_days=int(pos["blocked_exit_days"]),
                 entry_signal_id=pos["entry_signal_id"],
-                exit_signal_id=_signal_id(
-                    int(matrix.exit_signal_code[time_id, asset_id]), matrix.exit_signal_ids
-                ) if reason == "signal" else None,
+                exit_signal_id=(pos.get("pending_exit_signal_id") or signal_id) if reason == "signal" else None,
             ))
             return True
 
@@ -933,6 +968,8 @@ class BacktestEngine:
                 "max_high": max(entry_price, float(matrix.high[time_id, asset_id])),
                 "pending_exit_reason": None,
                 "pending_exit_signal_date": None,
+                "pending_exit_signal_id": None,
+                "pending_exit_next_open": False,
                 "blocked_exit_days": 0,
             }
             closed = False
@@ -1437,6 +1474,34 @@ class BacktestEngine:
 
         return float(closes[-1]) if np.isfinite(closes[-1]) else None
 
+    @staticmethod
+    def _resolve_minute_exit_trigger(
+        minute_arr: np.ndarray,
+        ref_price: float | None,
+    ) -> float | None:
+        """分钟收盘确认向下穿越后，返回下一分钟开盘价。"""
+        if minute_arr is None or len(minute_arr) < 2:
+            return None
+        if ref_price is None or not np.isfinite(ref_price) or ref_price <= 0:
+            return None
+
+        ncols = minute_arr.shape[1] if minute_arr.ndim == 2 else 1
+        if ncols < 4:
+            return None
+        opens = minute_arr[:, 0]
+        closes = minute_arr[:, 3]
+        below = np.isfinite(closes) & (closes < ref_price)
+        previous_above = np.empty(len(closes), dtype=bool)
+        previous_above[0] = True
+        previous_above[1:] = np.isfinite(closes[:-1]) & (closes[:-1] >= ref_price)
+        crossings = np.flatnonzero(below & previous_above)
+        if crossings.size == 0:
+            return None
+        next_idx = int(crossings[0]) + 1
+        if next_idx >= len(opens) or not np.isfinite(opens[next_idx]) or opens[next_idx] <= 0:
+            return None
+        return float(opens[next_idx])
+
     # 分钟K cache 存储的数值列及固定顺序 (_resolve_minute_fill 按此顺序整数索引)。
     _MINUTE_NUMERIC_COLS = ["open", "high", "low", "close", "volume", "amount"]
 
@@ -1478,7 +1543,7 @@ class BacktestEngine:
             if df.is_empty():
                 continue
             # 按 (symbol, 日期) 分组, 每组转紧凑 float64 数组存入 cache
-            df = df.with_columns(
+            df = df.sort(["symbol", "datetime"]).with_columns(
                 pl.col("datetime").dt.strftime("%Y-%m-%d").alias("_d_str")
             )
             for sub in df.partition_by(["symbol", "_d_str"], as_dict=False):
@@ -1515,6 +1580,7 @@ class BacktestEngine:
             exit_delay_bars=1 if config.exit_fill == "open_t+1" else 0,
             entry_signal_ids=entry_signal_ids,
             exit_signal_ids=exit_signal_ids,
+            minute_exit_trigger=config.exit_fill == "signal_next_minute",
         )
         if not matrix.entry.any():
             return self._empty_result()
@@ -1625,6 +1691,19 @@ class BacktestEngine:
             )
             return precise if precise is not None else daily_price
 
+        def _minute_trigger_price(time_id: int, asset_id: int) -> float | None:
+            if not config.minute_fill or not minute_cache:
+                return None
+            key = (matrix.symbols[asset_id], matrix.timestamp_labels[time_id][:10])
+            minute_rows = minute_cache.get(key)
+            if minute_rows is None:
+                return None
+            reference = float(matrix.reference_price[time_id, asset_id])
+            return self._resolve_minute_exit_trigger(
+                minute_rows,
+                reference if _valid_price(reference) else None,
+            )
+
         def _one_price_limit(time_id: int, asset_id: int, direction: str) -> bool:
             if not matrix.tradable[time_id, asset_id]:
                 return False
@@ -1659,12 +1738,21 @@ class BacktestEngine:
                 return False, "sell_limit_down"
             return True, ""
 
-        def _mark_pending(asset_id: int, reason: str, signal_date: str) -> None:
+        def _mark_pending(
+            asset_id: int,
+            reason: str,
+            signal_date: str,
+            signal_id: str | None = None,
+            next_open: bool = False,
+        ) -> None:
             pos = positions[asset_id]
             if not pos.get("pending_exit_reason"):
                 pos["pending_exit_reason"] = reason
                 pos["pending_exit_signal_date"] = signal_date
+                pos["pending_exit_signal_id"] = signal_id
                 _count("pending_exit")
+            if next_open:
+                pos["pending_exit_next_open"] = True
             pos["blocked_exit_days"] += 1
 
         def _sell(
@@ -1706,8 +1794,9 @@ class BacktestEngine:
                 exit_signal_date=signal_date,
                 blocked_exit_days=int(pos["blocked_exit_days"]),
                 entry_signal_id=pos["entry_signal_id"],
-                exit_signal_id=_signal_id(
-                    int(matrix.exit_signal_code[time_id, asset_id]), matrix.exit_signal_ids
+                exit_signal_id=(
+                    pos.get("pending_exit_signal_id")
+                    or _signal_id(int(matrix.exit_signal_code[time_id, asset_id]), matrix.exit_signal_ids)
                 ) if reason == "signal" else None,
             ))
 
@@ -1719,9 +1808,30 @@ class BacktestEngine:
             sold_today: set[int],
             override: float | None = None,
         ) -> bool:
+            signal_id = (
+                _signal_id(int(matrix.exit_signal_code[time_id, asset_id]), matrix.exit_signal_ids)
+                if reason == "signal" else None
+            )
+            minute_trigger = config.exit_fill == "signal_next_minute" and reason == "signal"
+            if minute_trigger and override is None:
+                pos = positions[asset_id]
+                if pos.get("pending_exit_next_open"):
+                    override = float(matrix.open[time_id, asset_id])
+                else:
+                    override = _minute_trigger_price(time_id, asset_id)
+                    if override is None:
+                        _mark_pending(asset_id, reason, signal_date, signal_id, next_open=True)
+                        _count("sell_minute_trigger_fallback")
+                        return False
             ok, blocked = _can_sell(time_id, asset_id, override)
             if not ok:
-                _mark_pending(asset_id, reason, signal_date)
+                _mark_pending(
+                    asset_id,
+                    reason,
+                    signal_date,
+                    signal_id,
+                    next_open=minute_trigger,
+                )
                 _count(blocked)
                 return False
             _sell(time_id, asset_id, reason, signal_date, sold_today, override)
@@ -1890,6 +2000,8 @@ class BacktestEngine:
                                 "hold_days": 0,
                                 "pending_exit_reason": None,
                                 "pending_exit_signal_date": None,
+                                "pending_exit_signal_id": None,
+                                "pending_exit_next_open": False,
                                 "blocked_exit_days": 0,
                             }
 

backend/app/backtest/matrix.py

@@ -26,6 +26,8 @@ import pyarrow as pa
 import pyarrow.compute as pc
 import pyarrow.dataset as pads
 
+from app.backtest.minute_trigger import build_minute_exit_reference
+
 try:
     from numba import njit, prange
 except ImportError:
@@ -2233,6 +2235,7 @@ def build_market_matrix_from_signals(
     entry_delay_bars: int = 0,
     exit_delay_bars: int = 0,
     reference_price: np.ndarray | None = None,
+    minute_exit_trigger: bool = False,
 ) -> MarketMatrix:
     """Combine base data and strategy signals into the matcher input matrix."""
     if entry_delay_bars not in (0, 1) or exit_delay_bars not in (0, 1):
@@ -2266,6 +2269,16 @@ def build_market_matrix_from_signals(
             use = ~np.isfinite(resolved_reference_price) & np.isfinite(values) & (values > 0)
             resolved_reference_price[use] = values[use]
 
+    if minute_exit_trigger:
+        trigger_reference = build_minute_exit_reference(
+            market.close,
+            market.fields,
+            signals.exit_signal_code,
+            signals.exit_signal_ids,
+        )
+        trigger_mask = signals.exit != 0
+        resolved_reference_price[trigger_mask] = trigger_reference[trigger_mask]
+
     _make_read_only(
         entry,
         exit_,
@@ -2312,6 +2325,7 @@ def build_market_matrix(
     exit_delay_bars: int = 0,
     entry_signal_ids: list[str] | None = None,
     exit_signal_ids: list[str] | None = None,
+    minute_exit_trigger: bool = False,
 ) -> MarketMatrix:
     """Backward-compatible long-panel boundary used by legacy/Polars strategies."""
     if panel.is_empty():
@@ -2355,6 +2369,7 @@ def build_market_matrix(
         signals,
         entry_delay_bars=entry_delay_bars,
         exit_delay_bars=exit_delay_bars,
+        minute_exit_trigger=minute_exit_trigger,
     )
 
 
@@ -3458,6 +3473,8 @@ def _estimate_pipeline_cache_bytes(
             continue
         if name == "vol_ratio_5d":
             estimated += 2 * float_bytes
+        elif name == "ma20_bias":
+            estimated += 2 * float_bytes
         elif name == "change_pct" or (
             name.startswith("momentum_") and name.endswith("d")
         ):
@@ -3631,6 +3648,7 @@ def matrix_feature(market: MarketDataMatrix, name: str) -> np.ndarray:
             "high_60d",
             "low_60d",
             "annual_vol_20d",
+            "ma20_bias",
         }
         or (name.startswith("ma") and name[2:].isdigit())
         or (name.startswith("rsi_") and name[4:].isdigit())
@@ -3696,6 +3714,17 @@ def _compute_matrix_feature(market: MarketDataMatrix, name: str) -> np.ndarray:
             where=volume_valid & np.isfinite(previous_mean) & (previous_mean != 0),
         )
         return out
+    if name == "ma20_bias":
+        ma20 = valid_rolling_mean(market.close, close_valid, 20)
+        out = np.full(market.shape, np.nan, dtype=np.float32)
+        np.divide(
+            market.close,
+            ma20,
+            out=out,
+            where=close_valid & np.isfinite(ma20) & (ma20 != 0),
+        )
+        out -= np.float32(1.0)
+        return out
     if name.startswith("ma") and name[2:].isdigit():
         return valid_rolling_mean(market.close, close_valid, int(name[2:]))
     if name == "boll_upper" or name == "boll_lower":

backend/app/backtest/minute_trigger.py

@@ -0,0 +1,49 @@
+"""分钟级卖出信号回放的支持范围与参考价计算。"""
+from __future__ import annotations
+
+import numpy as np
+
+MINUTE_EXIT_TRIGGER_SIGNALS = frozenset({
+    "signal_ma5_breakdown",
+    "signal_ma10_breakdown",
+    "signal_ma20_breakdown",
+    "signal_ma_dead_5_20",
+})
+
+
+def unsupported_minute_exit_signals(signals: list[str] | tuple[str, ...]) -> list[str]:
+    return sorted(set(signals) - MINUTE_EXIT_TRIGGER_SIGNALS)
+
+
+def build_minute_exit_reference(
+    close: np.ndarray,
+    fields: dict[str, np.ndarray],
+    exit_signal_code: np.ndarray,
+    exit_signal_ids: tuple[str, ...],
+) -> np.ndarray:
+    """为可回放的卖出信号计算当日已知的价格触发线。"""
+    result = np.full(close.shape, np.nan, dtype=np.float32)
+
+    def _apply(code: int, value: np.ndarray) -> None:
+        mask = (exit_signal_code == code) & np.isfinite(value) & (value > 0)
+        result[mask] = value[mask].astype(np.float32)
+
+    with np.errstate(divide="ignore", invalid="ignore"):
+        for code, signal_id in enumerate(exit_signal_ids):
+            if signal_id == "signal_ma5_breakdown" and "ma5" in fields:
+                _apply(code, (5.0 * fields["ma5"] - close) / 4.0)
+            elif signal_id == "signal_ma10_breakdown" and "ma10" in fields:
+                _apply(code, (10.0 * fields["ma10"] - close) / 9.0)
+            elif signal_id == "signal_ma20_breakdown" and "ma20" in fields:
+                _apply(code, (20.0 * fields["ma20"] - close) / 19.0)
+            elif (
+                signal_id == "signal_ma_dead_5_20"
+                and "ma5" in fields
+                and "ma20" in fields
+            ):
+                sum4 = 5.0 * fields["ma5"] - close
+                sum19 = 20.0 * fields["ma20"] - close
+                _apply(code, (sum19 - 4.0 * sum4) / 3.0)
+
+    result.setflags(write=False)
+    return result

backend/app/backtest/strategy.py

@@ -32,6 +32,7 @@ from app.backtest.matrix import (
     slice_market_data_matrix,
     slice_signal_matrix,
 )
+from app.backtest.minute_trigger import unsupported_minute_exit_signals
 from app.config import settings
 from app.indicators.pipeline import (
     ENRICHED_STORAGE_COLS,
@@ -40,6 +41,7 @@ from app.indicators.pipeline import (
     get_signal_dependencies,
 )
 from app.strategy.engine import StrategyDataContext, StrategyDef, StrategyEngine
+from app.strategy.scoring import scoring_dependencies, scoring_value_expr
 
 logger = logging.getLogger(__name__)
 
@@ -133,7 +135,7 @@ class StrategyDependencyResolver:
 
         scoring = dict(strategy.meta.get("scoring", {}) or {})
         scoring.update(overrides.get("scoring") or {})
-        required_features.update(str(column) for column, weight in scoring.items() if weight)
+        required_features.update(scoring_dependencies(scoring))
         order_by = strategy.meta.get("order_by")
         if order_by and order_by != "score":
             required_features.add(str(order_by))
@@ -217,7 +219,7 @@ class StrategyDependencyResolver:
         required_features.update(_basic_filter_dependencies(basic_filter))
         scoring = dict(strategy.meta.get("scoring", {}) or {})
         scoring.update(overrides.get("scoring") or {})
-        required_features.update(str(name) for name, weight in scoring.items() if weight)
+        required_features.update(scoring_dependencies(scoring))
         order_by = strategy.meta.get("order_by")
         if order_by and order_by != "score":
             required_features.add(str(order_by))
@@ -457,7 +459,7 @@ class StrategyBacktestConfig:
     # matching 为向后兼容入口; 显式传 entry_fill/exit_fill 时以二者为准。
     matching: Literal["close_t", "open_t+1"] = "open_t+1"
     entry_fill: Literal["close_t", "open_t+1"] | None = None
-    exit_fill: Literal["close_t", "open_t+1"] | None = None
+    exit_fill: Literal["close_t", "open_t+1", "signal_next_minute"] | None = None
     fees_pct: float = 0.0002
     commission_pct: float | None = None
     stamp_tax_pct: float | None = None
@@ -536,6 +538,7 @@ class BacktestResultPolicy:
             "error",
             "timing_ms",
             "execution",
+            "selection",
             "execution_backend",
             "shared_market_data",
             "shared_market_data_bytes",
@@ -798,6 +801,14 @@ class StrategyBacktestService:
         basic_filter = self._effective_basic_filter(s, overrides)
         entry_signals = self._effective_signals(overrides, "entry_signals", s.entry_signals)
         exit_signals = self._effective_signals(overrides, "exit_signals", s.exit_signals)
+        if config.exit_fill == "signal_next_minute":
+            if not config.minute_fill:
+                return _err("触发后下一分钟成交需要先开启分钟成交")
+            if not exit_signals:
+                return _err("当前策略没有卖出信号，无法使用触发后下一分钟成交")
+            unsupported = unsupported_minute_exit_signals(exit_signals)
+            if unsupported:
+                return _err(f"以下卖出信号暂不支持分钟触发回放: {', '.join(unsupported)}")
         stop_loss = self._override_value(overrides, "stop_loss", s.stop_loss)
         take_profit = self._normalize_pct(
             self._override_value(overrides, "take_profit", getattr(s, "take_profit", None)),
@@ -972,6 +983,7 @@ class StrategyBacktestService:
             minute_fill=config.minute_fill,
         )
         t_signal = time.perf_counter()
+        selection_stats: dict[str, int | bool]
 
         if s.execution_backend == "matrix_native":
             if s.matrix_strategy is None:
@@ -1061,6 +1073,12 @@ class StrategyBacktestService:
                 return _err("在指定区间内未产生买入信号")
 
             raw_candidates = int(sim_signal_matrix.entry.sum())
+            selection_stats = {
+                "strategy_matches": raw_candidates,
+                "entry_candidates": raw_candidates,
+                "entry_trigger_filtered": 0,
+                "entry_trigger_enabled": False,
+            }
             del market_data, signal_matrix
 
             t_matrix = time.perf_counter()
@@ -1070,6 +1088,7 @@ class StrategyBacktestService:
                 entry_delay_bars=1 if matcher_config.entry_fill == "open_t+1" else 0,
                 exit_delay_bars=1 if matcher_config.exit_fill == "open_t+1" else 0,
                 reference_price=reference_price,
+                minute_exit_trigger=matcher_config.exit_fill == "signal_next_minute",
             )
             timing_ms["matrix_build"] = round((time.perf_counter() - t_matrix) * 1000, 1)
             del sim_market_data, sim_signal_matrix
@@ -1090,6 +1109,7 @@ class StrategyBacktestService:
             candidate_filter_mask = self._build_candidate_filter_mask(panel, s, params)
             candidate_mask = basic_mask & candidate_filter_mask
             panel = self._apply_score(panel, s, overrides, universe_mask=candidate_mask)
+            formal_candidate_mask = candidate_mask & formal_range
             entry_mask = self._build_entry_mask_from_candidate(panel, candidate_mask, s, entry_signals)
             entry_mask = entry_mask & formal_range
             raw_exit_mask = self._build_signal_mask(panel, exit_signals, "_exit")
@@ -1109,6 +1129,13 @@ class StrategyBacktestService:
             panel_rows = int(sim_panel.height)
             panel_columns = int(sim_panel.width)
             raw_candidates = int(sim_entry_mask.sum())
+            strategy_matches = int(formal_candidate_mask.sum())
+            selection_stats = {
+                "strategy_matches": strategy_matches,
+                "entry_candidates": raw_candidates,
+                "entry_trigger_filtered": max(strategy_matches - raw_candidates, 0),
+                "entry_trigger_enabled": bool(entry_signals),
+            }
 
             t_matrix = time.perf_counter()
             market_matrix = build_market_matrix(
@@ -1119,6 +1146,7 @@ class StrategyBacktestService:
                 exit_delay_bars=1 if matcher_config.exit_fill == "open_t+1" else 0,
                 entry_signal_ids=entry_signals,
                 exit_signal_ids=exit_signals,
+                minute_exit_trigger=matcher_config.exit_fill == "signal_next_minute",
             )
             timing_ms["matrix_build"] = round((time.perf_counter() - t_matrix) * 1000, 1)
             del panel, sim_panel, sim_entry_mask, sim_exit_mask
@@ -1165,6 +1193,7 @@ class StrategyBacktestService:
         result.stats["feature_columns"] = feature_width
         result.stats["full_feature_fallback"] = feature_plan.full_feature_fallback
         result.stats["execution_backend"] = s.execution_backend
+        result.stats["selection"] = selection_stats
         result.stats["shared_market_data"] = prepared is not None
         result.stats["matrix_data_cache_hit"] = matrix_data_cache_hit
         result.stats["matrix_data_cache_status"] = matrix_data_cache_status
@@ -1631,6 +1660,11 @@ class StrategyBacktestService:
             "matching": c.matching,
             "entry_fill": c.entry_fill,
             "exit_fill": c.exit_fill,
+            "timing_mode": (
+                "strict"
+                if c.entry_fill == "open_t+1" and c.exit_fill == "open_t+1"
+                else "custom"
+            ),
             "fees_pct": c.fees_pct,
             "commission_pct": c.commission_pct,
             "stamp_tax_pct": c.stamp_tax_pct,
@@ -1641,6 +1675,7 @@ class StrategyBacktestService:
             "position_sizing": c.position_sizing,
             "mode": c.mode,
             "holding_days": c.holding_days,
+            "minute_fill": c.minute_fill,
         }
 
     @staticmethod
@@ -1660,28 +1695,31 @@ class StrategyBacktestService:
         if has_universe:
             work = work.with_columns(universe_mask.rename("_score_universe"))
 
-        def _value_in_universe(col: str) -> pl.Expr:
+        def _value_in_universe(value: pl.Expr) -> pl.Expr:
             if has_universe:
-                return pl.when(pl.col("_score_universe")).then(pl.col(col)).otherwise(None)
-            return pl.col(col)
+                return pl.when(pl.col("_score_universe")).then(value).otherwise(None)
+            return value
 
         def _finish(df: pl.DataFrame) -> pl.DataFrame:
             return df.drop("_score_universe") if "_score_universe" in df.columns else df
 
         if scoring:
-            total_weight = sum(scoring.values())
+            executable = [
+                (value, weight)
+                for col, weight in scoring.items()
+                if weight and (value := scoring_value_expr(work.columns, str(col))) is not None
+            ]
+            total_weight = sum(weight for _, weight in executable)
             if total_weight > 0:
                 score_parts: list[pl.Expr] = []
-                for col, weight in scoring.items():
-                    if col not in work.columns:
-                        continue
+                for score_value, weight in executable:
                     w = weight / total_weight
-                    value = _value_in_universe(col)
+                    value = _value_in_universe(score_value)
                     col_min = value.min().over("date")
                     col_max = value.max().over("date")
                     col_range = col_max - col_min
                     normalized = pl.when(col_range > 0).then(
-                        (pl.col(col) - col_min) / col_range
+                        (score_value - col_min) / col_range
                     ).otherwise(pl.lit(0.5))
                     if has_universe:
                         normalized = pl.when(pl.col("_score_universe")).then(normalized).otherwise(0.0)

backend/app/jobs/daily_pipeline.py

@@ -786,7 +786,7 @@ def _maybe_push_review(content: str, meta: dict) -> None:
                     continue
                 secret = preferences.get_feishu_webhook_secret()
                 ok = webhook_adapter.send_feishu_card(
-                    url, "TickFlow · 每日复盘", subtitle, content, secret
+                    url, "每日复盘", subtitle, content, secret
                 )
                 logger.info("review push(feishu) %s", "sent" if ok else "failed")
             elif ch == "wecom":
@@ -797,7 +797,7 @@ def _maybe_push_review(content: str, meta: dict) -> None:
                 # 企业微信 markdown 标题已含一级标题, subtitle 拼到正文首行
                 full_body = (f"**{subtitle}**\n\n{content}" if subtitle else content)
                 ok = webhook_adapter.send_wecom_markdown(
-                    url, "TickFlow · 每日复盘", full_body
+                    url, "每日复盘", full_body
                 )
                 logger.info("review push(wecom) %s", "sent" if ok else "failed")
             # 未来更多渠道在此追加分支

backend/app/services/quote_service.py

@@ -1225,7 +1225,7 @@ class QuoteService:
             # 反查规则, 过滤出启用推送的事件
             source_labels = {
                 "strategy": "策略", "signal": "信号",
-                "price": "价格", "market": "异动",
+                "price": "价格", "market": "异动", "ladder": "连板梯队",
             }
             rules = engine.rules if engine is not None else {}
             enqueued = 0
@@ -1241,7 +1241,7 @@ class QuoteService:
                 symbol = ev.get("symbol") or ""
                 name = ev.get("name") or ""
                 message = ev.get("message") or ""
-                title = f"TickFlow · {source_label}"
+                title = source_label
                 body = f"{symbol} {name} {message}".strip() if symbol else (message or name)
                 # 提交到独立线程池, 不阻塞行情轮询线程 (webhook 慢/重试不拖累实时行情+告警)。
                 # 按渠道独立投递: 飞书 / 企业微信谁被勾选且已配置就推谁。

backend/app/strategy/ai_generator.py

@@ -7,9 +7,13 @@ from __future__ import annotations
 
 import ast
 import logging
+import math
 import re
 from pathlib import Path
 
+from app.indicators.pipeline import ENRICHED_COLUMNS
+from app.strategy.scoring import VIRTUAL_SCORING_DEPENDENCIES
+
 logger = logging.getLogger(__name__)
 
 # 策略开发精简指南路径 (随 backend/app 打包进 Docker, 避免 .dockerignore 排除 docs/ 导致运行时缺失)
@@ -24,10 +28,10 @@ _SYSTEM_PREFIX = """你是A股量化策略设计专家。根据用户描述的
 4. polars 策略只 import polars 和 datetime；matrix_native 策略只允许 import numpy 以及 from app.backtest.matrix import 所需矩阵协议和算子
 
 要求:
-1. 用户可能调整的策略阈值通过 META["params"] 暴露；公式常数、固定窗口边界、布尔开关不必强行参数化
+1. 用户可能调整的策略阈值通过 META["params"] 暴露，每项使用 id/label/type/default/min/max/step；公式常数、固定窗口边界、布尔开关不必强行参数化
 2. 遵循指南中的文件结构，但优先贴合用户规则，不要为了套模板歪曲策略含义
 3. ENTRY_SIGNALS/EXIT_SIGNALS 根据策略逻辑自行选择匹配的信号列，不要照搬示例
-4. scoring 权重根据策略核心逻辑定制，总和 = 1.0
+4. scoring 权重根据策略核心逻辑定制，总和 = 1.0；键只能使用指南中的真实数值字段或受控虚拟评分字段 ma20_bias，不得创造条件名称作为评分列
 5. 优先使用 Polars 表达式、窗口函数、聚合和 with_columns/filter 实现，避免逐行/逐股 Python 循环；只有表达式难以描述的复杂状态机才使用 partition_by/to_dicts
 6. 直接输出Python代码，不要输出其他内容
 7. 元数据必须使用模块顶层的 META = {...} 或 META: dict = {...}，不得省略或改名；并且必须定义所选执行后端要求的策略入口
@@ -44,6 +48,21 @@ _FENCED_CODE_RE = re.compile(
 _POLARS_ENTRYPOINT_ERROR = "找不到策略入口函数 filter() 或 filter_history()"
 _MATRIX_ENTRYPOINT_ERROR = "找不到 Matrix 策略入口 MATRIX_STRATEGY"
 
+_POLARS_SCORING_FIELDS = frozenset(
+    name
+    for name in ENRICHED_COLUMNS
+    if name not in {"symbol", "date", "name"} and not name.startswith("signal_")
+) | frozenset(VIRTUAL_SCORING_DEPENDENCIES)
+_MATRIX_SCORING_FIELDS = frozenset({
+    "open", "high", "low", "close", "volume", "amount", "turnover_rate",
+    "total_shares", "float_shares", "consecutive_limit_ups",
+    "consecutive_limit_downs", "prev_close", "change_pct", "change_amount",
+    "amplitude", "ma5", "ma10", "ma20", "ma30", "ma60", "boll_upper",
+    "boll_lower", "high_60d", "low_60d", "momentum_5d", "momentum_10d",
+    "momentum_20d", "momentum_30d", "momentum_60d", "annual_vol_20d",
+    "rsi_6", "rsi_14", "rsi_24", "vol_ratio_5d", "ma20_bias",
+})
+
 
 def _top_level_assignment(
     tree: ast.Module,
@@ -195,6 +214,16 @@ class AIStrategyGenerator:
                 "error": entrypoint_error,
             }
 
+        try:
+            self._validate_meta_semantics(code, meta)
+        except ValueError as e:
+            return {
+                "code": code,
+                "meta": meta,
+                "valid": False,
+                "error": str(e),
+            }
+
         return {
             "code": code,
             "meta": meta,
@@ -208,7 +237,42 @@ class AIStrategyGenerator:
         return error.startswith("解析META失败:") or error in {
             _POLARS_ENTRYPOINT_ERROR,
             _MATRIX_ENTRYPOINT_ERROR,
-        }
+        } or error.startswith(("META.params", "META.scoring"))
+
+    @staticmethod
+    def _validate_meta_semantics(code: str, meta: dict) -> None:
+        params = meta.get("params", [])
+        if isinstance(params, (list, tuple)):
+            for index, item in enumerate(params):
+                if isinstance(item, dict) and not str(item.get("id") or "").strip():
+                    raise ValueError(f"META.params[{index}] 缺少非空 id")
+
+        scoring = meta.get("scoring", {})
+        if not isinstance(scoring, dict):
+            raise ValueError("META.scoring 必须是字典")
+        if not scoring:
+            return
+
+        for name, weight in scoring.items():
+            if not isinstance(name, str) or not name:
+                raise ValueError("META.scoring 字段名必须是非空字符串")
+            if isinstance(weight, bool) or not isinstance(weight, (int, float)) \
+                    or not math.isfinite(float(weight)) or weight < 0:
+                raise ValueError(f"META.scoring[{name!r}] 权重必须是非负有限数值")
+        total_weight = sum(float(weight) for weight in scoring.values())
+        if not math.isclose(total_weight, 1.0, rel_tol=0.0, abs_tol=1e-6):
+            raise ValueError("META.scoring 权重总和必须为 1.0")
+
+        backend = _strategy_execution_backend(ast.parse(code), meta)
+        if backend == "python_history_legacy":
+            return
+        allowed = _MATRIX_SCORING_FIELDS if backend == "matrix_native" else _POLARS_SCORING_FIELDS
+        unknown = sorted(set(scoring) - set(allowed))
+        if unknown:
+            raise ValueError(
+                f"META.scoring 引用了不可用字段: {unknown}; "
+                "请使用真实数值字段或受控虚拟字段 ma20_bias"
+            )
 
     async def repair_code(self, code: str, error: str) -> dict:
         """Ask the model once for a complete replacement after a structural error."""

backend/app/strategy/engine.py

@@ -19,6 +19,8 @@ from typing import Any, Callable
 import numpy as np
 import polars as pl
 
+from app.strategy.scoring import scoring_dependencies, scoring_value_expr
+
 logger = logging.getLogger(__name__)
 
 # 引擎级默认基础过滤 — 策略未定义 BASIC_FILTER 时兜底
@@ -792,7 +794,7 @@ class StrategyEngine:
                 fields.add(field_name)
         scoring = dict(strategy.meta.get("scoring", {}) or {})
         scoring.update((overrides or {}).get("scoring") or {})
-        fields.update(scoring)
+        fields.update(scoring_dependencies(scoring))
         order_by = strategy.meta.get("order_by")
         if order_by and order_by != "score":
             fields.add(str(order_by))
@@ -1003,19 +1005,23 @@ class StrategyEngine:
         """通用评分: min-max 归一化 → 加权求和 → 0~100 分"""
         if not weights:
             return df
-        total_weight = sum(weights.values())
+
+        executable = [
+            (value, weight)
+            for col, weight in weights.items()
+            if weight and (value := scoring_value_expr(df.columns, str(col))) is not None
+        ]
+        total_weight = sum(weight for _, weight in executable)
         if total_weight <= 0:
             return df
 
         score_parts: list[pl.Expr] = []
-        for col, weight in weights.items():
-            if col not in df.columns:
-                continue
+        for value, weight in executable:
             w = weight / total_weight
-            col_min = pl.col(col).min()
-            col_range = pl.col(col).max() - col_min
+            col_min = value.min()
+            col_range = value.max() - col_min
             normalized = pl.when(col_range > 0).then(
-                (pl.col(col) - col_min) / col_range
+                (value - col_min) / col_range
             ).otherwise(pl.lit(0.5))
             score_parts.append(normalized * w)
 

backend/app/strategy/prompts/strategy-builder-step1.md

@@ -69,7 +69,7 @@ META = {
         # 只把用户可能调节的阈值放这里；每个参数含 id/label/type/default/min/max/step
     ],
     "scoring": {
-        # 根据策略核心逻辑定制权重，总和 = 1.0
+        # 只使用真实数值字段或 ma20_bias，总和 = 1.0
     },
     "order_by": "score",
     "descending": True,
@@ -199,7 +199,7 @@ def filter_history(df: pl.DataFrame, params: dict) -> pl.DataFrame:
 1. 用户可能调节的阈值才放 `params`；公式常数、固定窗口边界不必参数化
 2. 信号列使用 `.fill_null(False)` 处理空值
 3. `filter()` 只返回 `pl.Expr`，`filter_history()` 返回筛选后的 `DataFrame`
-4. scoring 权重总和 = 1.0
+4. scoring 权重总和 = 1.0，键只能使用可用数值列或临时评分字段 `ma20_bias`，不要使用 `close_above_ma20` 等条件名称
 5. **必须生成 RULES**：用中文逐条列出核心逻辑（至少 3 条），准确完整
 6. **贴合用户需求**：不为了用已有字段而改变用户本意。用户说"前高"就自己算前高
 7. **输出前自我检查**：确认 RULES 完整、语法正确、括号匹配、引号闭合

backend/app/strategy/prompts/strategy-guide-compact.md

@@ -7,8 +7,8 @@
 1. Polars/历史策略允许 `polars`、`datetime`；矩阵策略允许 `numpy`、`app.backtest.matrix`。
 2. AI 策略只属于 `data/strategies/ai/`，`META.id` 使用用户给定的 `ai_` ID。
 3. 不要读写文件，不要使用 `open/exec/eval/compile/__import__/globals/locals/vars/dir/getattr/setattr/delattr/type/input`。
-4. `META.params` 只放用户可能调整的阈值；公式常数和固定窗口边界不必参数化。
-5. `META.scoring` 权重总和必须为 1.0。
+4. `META.params` 每项使用 `id/label/type/default/min/max/step`，只放可调阈值。
+5. `META.scoring` 仅使用真实数值字段或 `ma20_bias`，权重和为 1.0。
 6. `ENTRY_SIGNALS` / `EXIT_SIGNALS` 只选和策略逻辑直接相关的信号，不要凑数。
 7. `RULES` 用中文逐条列出核心逻辑，至少 3 条。
 8. 优先 Polars 表达式、`with_columns`、`over("symbol")`、`group_by`、`join`、`filter`，避免逐行循环。
@@ -154,6 +154,8 @@ anchor_date = _date.fromisoformat(anchor_raw) if isinstance(anchor_raw, str) els
 
 动量与波动：`momentum_5d`, `momentum_10d`, `momentum_20d`, `momentum_30d`, `momentum_60d`, `annual_vol_20d`, `high_60d`, `low_60d`
 
+虚拟评分：`ma20_bias = close / ma20 - 1`（仅内存计算）。
+
 涨跌停：`consecutive_limit_ups`, `consecutive_limit_downs`
 
 市值相关：`total_shares`, `float_shares`，可用 `close * total_shares` 估算总市值。

backend/app/strategy/scoring.py

@@ -0,0 +1,37 @@
+"""策略评分字段解析。"""
+from __future__ import annotations
+
+from collections.abc import Collection, Mapping
+from typing import Any
+
+import polars as pl
+
+
+VIRTUAL_SCORING_DEPENDENCIES: dict[str, frozenset[str]] = {
+    "ma20_bias": frozenset({"close", "ma20"}),
+}
+
+
+def scoring_dependencies(scoring: Mapping[str, Any]) -> set[str]:
+    """把受控虚拟评分字段展开为实际数据依赖。"""
+    dependencies: set[str] = set()
+    for name, weight in scoring.items():
+        if not weight:
+            continue
+        dependencies.update(VIRTUAL_SCORING_DEPENDENCIES.get(str(name), {str(name)}))
+    return dependencies
+
+
+def scoring_value_expr(columns: Collection[str], name: str) -> pl.Expr | None:
+    """返回评分值表达式；依赖不完整时返回 None。"""
+    available = set(columns)
+    if name in available:
+        return pl.col(name)
+    dependencies = VIRTUAL_SCORING_DEPENDENCIES.get(name)
+    if dependencies is None or not dependencies.issubset(available):
+        return None
+    if name == "ma20_bias":
+        return pl.when(pl.col("ma20") != 0).then(
+            pl.col("close") / pl.col("ma20") - 1.0
+        ).otherwise(None)
+    return None

backend/tests/backtest/test_dependencies.py

@@ -44,6 +44,27 @@ def test_resolver_merges_signals_scoring_filter_and_execution_columns():
     assert plan.full_feature_fallback is False
 
 
+def test_resolver_expands_virtual_scoring_dependencies():
+    strategy = _strategy(meta={
+        "id": "deps",
+        "scoring": {"ma20_bias": 0.6, "vol_ratio_5d": 0.4},
+        "order_by": "score",
+    })
+
+    plan = StrategyDependencyResolver().resolve(
+        strategy,
+        params={"rsi_max": 30},
+        basic_filter={"enabled": False},
+        entry_signals=[],
+        exit_signals=[],
+    )
+
+    assert {"ma20", "vol_ratio_5d"} <= set(plan.indicator_columns)
+    assert "close" in plan.base_columns
+    assert "ma20_bias" not in plan.base_columns
+    assert "ma20_bias" not in plan.indicator_columns
+
+
 def test_history_strategy_without_required_features_falls_back_to_full(caplog):
     strategy = _strategy(
         filter_fn=None,

backend/tests/backtest/test_engine_portfolio.py

@@ -1,6 +1,6 @@
 from __future__ import annotations
 
-from datetime import date, timedelta
+from datetime import date, datetime, timedelta
 
 import polars as pl
 
@@ -421,3 +421,100 @@ def test_default_fill_is_buy_open_sell_close():
     assert trade.entry_price == 10.0   # 次日开盘
     assert trade.exit_price == 10.8    # 到期日收盘
     assert trade.exit_reason == "max_hold"
+
+
+class _MinuteRepo:
+    def __init__(self, rows: pl.DataFrame) -> None:
+        self.rows = rows
+
+    def get_minute_by_dates(self, symbols, dates, asset_type="stock"):
+        return self.rows.filter(pl.col("symbol").is_in(symbols))
+
+
+def _minute_trigger_panel() -> tuple[pl.DataFrame, pl.Series, pl.Series]:
+    panel = _panel(
+        ["A"],
+        days=4,
+        overrides={
+            ("A", 2): {"open": 10.1, "high": 10.3, "low": 8.9, "close": 9.0},
+            ("A", 3): {"open": 8.8, "high": 9.0, "low": 8.7, "close": 8.9},
+        },
+    ).with_columns([
+        pl.Series("ma20", [10.0, 10.0, 9.95, 9.9]),
+        pl.Series("signal_ma20_breakdown", [False, False, True, False]),
+    ])
+    return panel, _mask(panel, {("A", 0)}), _mask(panel, {("A", 2)})
+
+
+def test_minute_signal_exit_fills_at_next_minute_open():
+    panel, entries, exits = _minute_trigger_panel()
+    minute = pl.DataFrame({
+        "symbol": ["A", "A", "A"],
+        "datetime": [
+            datetime(2024, 1, 3, 9, 31),
+            datetime(2024, 1, 3, 9, 32),
+            datetime(2024, 1, 3, 9, 33),
+        ],
+        "open": [10.2, 10.1, 9.7],
+        "high": [10.3, 10.2, 9.8],
+        "low": [10.1, 9.8, 9.6],
+        "close": [10.2, 9.9, 9.7],
+        "volume": [100.0, 100.0, 100.0],
+        "amount": [1020.0, 990.0, 970.0],
+    })
+
+    result = BacktestEngine(repo=_MinuteRepo(minute)).simulate_portfolio(
+        panel,
+        entries,
+        exits,
+        MatcherConfig(
+            entry_fill="open_t+1",
+            exit_fill="signal_next_minute",
+            minute_fill=True,
+            fees_pct=0,
+            slippage_bps=0,
+            max_positions=1,
+            initial_capital=100_000,
+        ),
+        exit_signal_ids=["signal_ma20_breakdown"],
+    )
+
+    assert len(result.trades) == 1
+    assert result.trades[0].exit_date == "2024-01-03"
+    assert result.trades[0].exit_price == 9.7
+    assert result.trades[0].exit_signal_id == "signal_ma20_breakdown"
+
+
+def test_minute_signal_exit_without_next_bar_falls_back_to_next_open():
+    panel, entries, exits = _minute_trigger_panel()
+    minute = pl.DataFrame({
+        "symbol": ["A"],
+        "datetime": [datetime(2024, 1, 3, 15, 0)],
+        "open": [9.9],
+        "high": [10.0],
+        "low": [8.9],
+        "close": [9.0],
+        "volume": [100.0],
+        "amount": [900.0],
+    })
+
+    result = BacktestEngine(repo=_MinuteRepo(minute)).simulate_portfolio(
+        panel,
+        entries,
+        exits,
+        MatcherConfig(
+            entry_fill="open_t+1",
+            exit_fill="signal_next_minute",
+            minute_fill=True,
+            fees_pct=0,
+            slippage_bps=0,
+            max_positions=1,
+            initial_capital=100_000,
+        ),
+        exit_signal_ids=["signal_ma20_breakdown"],
+    )
+
+    assert len(result.trades) == 1
+    assert result.trades[0].exit_date == "2024-01-04"
+    assert result.trades[0].exit_price == 8.8
+    assert result.stats["execution"]["sell_minute_trigger_fallback"] == 1

backend/tests/backtest/test_matrix_strategy.py

@@ -66,6 +66,19 @@ def test_common_matrix_features_match_polars_indicator_pipeline():
         actual = matrix_feature(market, name)[:, 0]
         np.testing.assert_allclose(actual, expected, rtol=2e-5, atol=2e-5, equal_nan=True)
 
+    expected_bias = (
+        enriched.sort(["date", "symbol"])["close"].to_numpy()
+        / enriched.sort(["date", "symbol"])["ma20"].to_numpy()
+        - 1.0
+    )
+    np.testing.assert_allclose(
+        matrix_feature(market, "ma20_bias")[:, 0],
+        expected_bias,
+        rtol=2e-5,
+        atol=2e-5,
+        equal_nan=True,
+    )
+
 
 def _panel_with_missing_asset_bar() -> pl.DataFrame:
     rows = []

backend/tests/backtest/test_minute_fill.py

@@ -19,6 +19,7 @@ import numpy as np
 import polars as pl
 
 from app.backtest.engine import BacktestEngine
+from app.backtest.minute_trigger import build_minute_exit_reference
 
 NUMERIC_COLS = BacktestEngine._MINUTE_NUMERIC_COLS  # open/high/low/close/volume/amount
 
@@ -86,6 +87,40 @@ def test_resolve_minute_fill_empty_returns_none():
     assert BacktestEngine._resolve_minute_fill(None, None, "buy") is None
 
 
+def test_resolve_minute_exit_trigger_uses_next_minute_open():
+    arr = np.array([
+        [10.2, 10.3, 10.1, 10.2, 100, 1020],
+        [10.1, 10.2, 9.8, 9.9, 100, 990],
+        [9.7, 9.8, 9.6, 9.7, 100, 970],
+    ], dtype=np.float64)
+
+    assert BacktestEngine._resolve_minute_exit_trigger(arr, 10.0) == 9.7
+
+
+def test_resolve_minute_exit_trigger_without_next_bar_returns_none():
+    arr = np.array([
+        [10.2, 10.3, 10.1, 10.2, 100, 1020],
+        [10.1, 10.2, 9.8, 9.9, 100, 990],
+    ], dtype=np.float64)
+
+    assert BacktestEngine._resolve_minute_exit_trigger(arr, 10.0) is None
+
+
+def test_minute_exit_reference_removes_current_close_from_ma20():
+    close = np.array([[9.0]], dtype=np.float32)
+    fields = {"ma20": np.array([[9.95]], dtype=np.float32)}
+    codes = np.array([[0]], dtype=np.int16)
+
+    result = build_minute_exit_reference(
+        close,
+        fields,
+        codes,
+        ("signal_ma20_breakdown",),
+    )
+
+    assert result[0, 0] == 10.0
+
+
 class _FakeRepo:
     """最小 repo 桩: get_minute_by_dates 直接返回预构造的混合列 DataFrame。"""
 

backend/tests/backtest/test_strategy_backtest_correctness.py

@@ -154,6 +154,56 @@ def test_basic_filter_only_limits_entries_not_panel_rows():
     assert engine.sim_matrix.entry[:, 0].tolist() == [1, 0, 1]
     assert engine.load_args is not None
     assert engine.load_args[1] < start  # warmup 只用于计算, 不参与正式交易
+    assert result.stats["selection"] == {
+        "strategy_matches": 2,
+        "entry_candidates": 2,
+        "entry_trigger_filtered": 0,
+        "entry_trigger_enabled": False,
+    }
+
+
+def test_selection_stats_explain_entry_trigger_filtering():
+    start = date(2024, 1, 1)
+    panel = pl.DataFrame([
+        {
+            "symbol": symbol,
+            "name": symbol,
+            "date": start,
+            "open": 10.0,
+            "high": 10.0,
+            "low": 10.0,
+            "close": 10.0,
+            "volume": 1000.0,
+            "amount": 1000.0,
+            "signal_limit_up": symbol == "A",
+            "signal_limit_down": False,
+        }
+        for symbol in ("A", "B")
+    ]).sort(["symbol", "date"])
+    engine = _EngineStub(panel)
+    service = StrategyBacktestService(
+        engine=engine,
+        strategy_engine=_StrategyEngineStub(
+            _strategy(entry_signals=["signal_limit_up"]),
+        ),
+    )
+
+    result = service.run(StrategyBacktestConfig(
+        strategy_id="test",
+        symbols=None,
+        start=start,
+        end=start,
+        matching="close_t",
+        mode="position",
+    ))
+
+    assert result.error is None
+    assert result.stats["selection"] == {
+        "strategy_matches": 2,
+        "entry_candidates": 1,
+        "entry_trigger_filtered": 1,
+        "entry_trigger_enabled": True,
+    }
 
 
 def test_score_normalizes_inside_strategy_candidate_universe():
@@ -254,6 +304,12 @@ def test_matrix_native_strategy_uses_shared_orchestrator_path():
     assert engine.sim_matrix is not None
     assert engine.sim_matrix.entry[:, 0].tolist() == [1, 1]
     assert result.stats["execution_backend"] == "matrix_native"
+    assert result.stats["selection"] == {
+        "strategy_matches": 2,
+        "entry_candidates": 2,
+        "entry_trigger_filtered": 0,
+        "entry_trigger_enabled": False,
+    }
 
 
 def test_matrix_native_accepts_legacy_default_signal_overrides_but_rejects_replacements():

backend/tests/test_ai_strategy_meta_normalize.py

@@ -4,6 +4,7 @@ from __future__ import annotations
 import pytest
 
 from app.api.strategy import _normalize_build_result, _normalize_strategy_meta
+from app.strategy.ai_generator import AIStrategyGenerator
 
 RAW_CODE = '''"""模型返回的策略"""
 import polars as pl
@@ -210,6 +211,41 @@ MATRIX_STRATEGY = object()
     assert result["error"] is None
 
 
+def test_validate_code_accepts_controlled_virtual_scoring_field():
+    code = RAW_CODE.replace(
+        '"scoring": {},',
+        '"scoring": {"ma20_bias": 0.6, "vol_ratio_5d": 0.4},',
+    )
+
+    result = AIStrategyGenerator().validate_code(code)
+
+    assert result["valid"] is True
+
+
+def test_validate_code_rejects_unknown_scoring_field():
+    code = RAW_CODE.replace(
+        '"scoring": {},',
+        '"scoring": {"close_above_ma20": 1.0},',
+    )
+
+    result = AIStrategyGenerator().validate_code(code)
+
+    assert result["valid"] is False
+    assert "close_above_ma20" in result["error"]
+
+
+def test_validate_code_rejects_param_without_id():
+    code = RAW_CODE.replace(
+        '"params": [],',
+        '"params": [{"name": "volume_ratio", "default": 1.5}],',
+    )
+
+    result = AIStrategyGenerator().validate_code(code)
+
+    assert result["valid"] is False
+    assert result["error"] == "META.params[0] 缺少非空 id"
+
+
 def test_validate_code_rejects_missing_matrix_strategy_entrypoint():
     from app.strategy.ai_generator import AIStrategyGenerator
 

backend/tests/test_pipeline_and_monitor_fixes.py

@@ -7,12 +7,13 @@ from __future__ import annotations
 import polars as pl
 import pytest
 
-from app.services import pipeline_jobs
+from app.jobs import daily_pipeline
+from app.services import pipeline_jobs, quote_service
 from app.services.pipeline_jobs import JobStore
+from app.services.quote_service import QuoteService
 from app.strategy import monitor_rules
 from app.strategy.monitor import MonitorRuleEngine
 
-
 # ── JobStore 单飞 ────────────────────────────────────────────────────────
 
 def test_create_singleflight_dedupes_pending_window(tmp_path):
@@ -97,3 +98,55 @@ def test_apply_scope_sector_fails_closed():
         df, {"scope": "symbols", "symbols": ["600000.SH"]}
     )
     assert picked.height == 1
+
+
+def test_ladder_webhook_uses_chinese_title_without_brand(monkeypatch):
+    calls = []
+
+    class CaptureExecutor:
+        def submit(self, fn, *args):
+            calls.append((fn, args))
+
+    monkeypatch.setattr(quote_service, "_WEBHOOK_EXECUTOR", CaptureExecutor())
+    monkeypatch.setattr("app.services.preferences.get_feishu_webhook_url", lambda: "https://open.feishu.cn/open-apis/bot/v2/hook/test")
+    monkeypatch.setattr("app.services.preferences.get_feishu_webhook_secret", lambda: "secret")
+    monkeypatch.setattr("app.services.preferences.get_wecom_webhook_url", lambda: "wecom-key")
+
+    engine = type("Engine", (), {
+        "rules": {"r_ladder": {"webhook_channels": ["feishu", "wecom"]}},
+    })()
+    QuoteService._maybe_send_webhook(
+        object.__new__(QuoteService),
+        [{
+            "rule_id": "r_ladder",
+            "source": "ladder",
+            "symbol": "600000.SH",
+            "name": "浦发银行",
+            "message": "炸板预警",
+        }],
+        engine,
+    )
+
+    assert [args[1] for _, args in calls] == ["连板梯队", "连板梯队"]
+    assert all("TickFlow" not in args[1] for _, args in calls)
+
+
+def test_review_webhooks_use_title_without_brand(monkeypatch):
+    calls = []
+    monkeypatch.setattr("app.services.preferences.get_review_push_channels", lambda: ["feishu", "wecom"])
+    monkeypatch.setattr("app.services.preferences.get_feishu_webhook_url", lambda: "feishu-url")
+    monkeypatch.setattr("app.services.preferences.get_feishu_webhook_secret", lambda: "secret")
+    monkeypatch.setattr("app.services.preferences.get_wecom_webhook_url", lambda: "wecom-url")
+    monkeypatch.setattr(
+        "app.services.webhook_adapter.send_feishu_card",
+        lambda *args: calls.append(("feishu", args)) or True,
+    )
+    monkeypatch.setattr(
+        "app.services.webhook_adapter.send_wecom_markdown",
+        lambda *args: calls.append(("wecom", args)) or True,
+    )
+
+    daily_pipeline._maybe_push_review("复盘正文", {"as_of": "2026-07-18"})
+
+    assert [args[1] for _, args in calls] == ["每日复盘", "每日复盘"]
+    assert all("TickFlow" not in args[1] for _, args in calls)

backend/tests/test_strategy_code_save.py

@@ -61,6 +61,19 @@ def test_prepare_strategy_code_rejects_forbidden_import():
         _prepare_strategy_code(req)
 
 
+def test_prepare_strategy_code_rejects_unknown_scoring_field():
+    req = StrategyCodeValidateRequest(
+        strategy_id="custom_bad_score",
+        code=_code("custom_bad_score").replace(
+            '"scoring": {},',
+            '"scoring": {"volume_surge": 1.0},',
+        ),
+    )
+
+    with pytest.raises(ValueError, match="volume_surge"):
+        _prepare_strategy_code(req)
+
+
 def test_save_strategy_code_creates_ai_strategy_in_ai_dir(tmp_path):
     request = _request(tmp_path)
     req = StrategyCodeSaveRequest(

backend/tests/test_strategy_scoring.py

@@ -0,0 +1,39 @@
+from datetime import date
+from types import SimpleNamespace
+
+import polars as pl
+import pytest
+
+from app.backtest.strategy import StrategyBacktestService
+from app.strategy.engine import StrategyEngine
+
+
+def _candidates() -> pl.DataFrame:
+    return pl.DataFrame({
+        "symbol": ["A", "B"],
+        "date": [date(2024, 1, 2)] * 2,
+        "close": [11.0, 12.0],
+        "ma20": [10.0, 10.0],
+        "vol_ratio_5d": [2.0, 1.0],
+    })
+
+
+def test_virtual_scoring_is_shared_and_does_not_add_virtual_column():
+    weights = {"ma20_bias": 0.6, "vol_ratio_5d": 0.4}
+    realtime = StrategyEngine._apply_scoring(_candidates(), weights)
+    strategy = SimpleNamespace(meta={"scoring": weights, "order_by": "score"})
+    backtest = StrategyBacktestService._apply_score(_candidates(), strategy, None)
+
+    assert realtime["score"].to_list() == pytest.approx([40.0, 60.0])
+    assert backtest["score"].to_list() == pytest.approx([40.0, 60.0])
+    assert "ma20_bias" not in realtime.columns
+    assert "ma20_bias" not in backtest.columns
+
+
+def test_scoring_reweights_only_available_fields():
+    scored = StrategyEngine._apply_scoring(
+        _candidates().drop("ma20"),
+        {"ma20_bias": 0.6, "vol_ratio_5d": 0.4},
+    )
+
+    assert scored["score"].to_list() == pytest.approx([100.0, 0.0])

frontend/src/components/screener/StrategyBuilderDialog.tsx

@@ -1,4 +1,4 @@
-import { useState, useEffect, useCallback } from 'react'
+import { useState, useEffect, useCallback, useRef } from 'react'
 import { Modal } from '@/components/Modal'
 import { X, Sparkles, Save, Loader2, ChevronLeft, ChevronRight, AlertTriangle, Settings2, FileText, Copy, Check, Terminal } from 'lucide-react'
 import { api } from '@/lib/api'
@@ -166,9 +166,15 @@ class CustomMatrixStrategy:
 MATRIX_STRATEGY = CustomMatrixStrategy()
 `
 
-interface Props { open: boolean; onClose: () => void; onSavedId?: (id: string) => void | Promise<void>; mode?: 'create' | 'modify' }
+interface Props {
+  open: boolean
+  onClose: () => void
+  onSavedId?: (id: string) => void | Promise<void>
+  mode?: 'create' | 'modify'
+  existingStrategyIds?: ReadonlySet<string>
+}
 
-export function StrategyBuilderDialog({ open, onClose, onSavedId, mode = 'create' }: Props) {
+export function StrategyBuilderDialog({ open, onClose, onSavedId, mode = 'create', existingStrategyIds }: Props) {
   // 根据 mode 选择存储 key
   const draftStore = mode === 'modify' ? storage.strategyModify : storage.strategyDraft
   const [step, setStep] = useState(1)
@@ -192,6 +198,7 @@ export function StrategyBuilderDialog({ open, onClose, onSavedId, mode = 'create
   const [aiStatus, setAiStatus] = useState<{ configured: boolean } | null>(null)
   const [checkedAi, setCheckedAi] = useState(false)
   const [loaded, setLoaded] = useState(false)
+  const suppressPersistRef = useRef(false)
 
   const resetDraftState = useCallback(() => {
     setStep(1); setTab('ai'); setName(''); setDescription(''); setDirection('long')
@@ -203,25 +210,31 @@ export function StrategyBuilderDialog({ open, onClose, onSavedId, mode = 'create
   useEffect(() => {
     if (!open) { setLoaded(false); return }
     const d = draftStore.get(null)
-    if (d) {
+    const draftCodeId = d ? parseMetaField(d.code ?? '', 'id') : ''
+    const completedDraft = mode === 'create' && !!d && (
+      (!!d.strategyId && existingStrategyIds?.has(d.strategyId))
+      || (!!draftCodeId && existingStrategyIds?.has(draftCodeId))
+    )
+    if (completedDraft) {
+      draftStore.set(null)
+      resetDraftState()
+    } else if (d) {
       const restoredSource = d.source ?? (d.strategyId?.startsWith('custom_') ? 'custom' : 'ai')
-      const restoredId = mode === 'create' && d.strategyId
-        ? slugId(restoredSource)
-        : (d.strategyId ?? '')
       setStep(d.step ?? 1); setName(d.name ?? ''); setDescription(d.description ?? '')
       setDirection(d.direction ?? 'long')
       setExecutionBackend(
         (d as any).executionBackend
         ?? (String(d.code ?? '').includes('matrix_native') ? 'matrix_native' : 'polars_expr'),
       )
-      setRules(d.rules ?? ''); setCode(d.code ?? ''); setStrategyId(restoredId)
+      setRules(d.rules ?? ''); setCode(d.code ?? ''); setStrategyId(d.strategyId ?? '')
       setSource(restoredSource)
       setTab(mode === 'modify' || restoredSource === 'custom' ? 'custom' : 'ai')
     } else {
       resetDraftState()
     }
+    suppressPersistRef.current = false
     setLoaded(true)
-  }, [open, mode, draftStore, resetDraftState])
+  }, [open, mode, draftStore, existingStrategyIds, resetDraftState])
 
   // 打开时检查 AI 状态
   useEffect(() => {
@@ -236,15 +249,20 @@ export function StrategyBuilderDialog({ open, onClose, onSavedId, mode = 'create
     } else {
       draftStore.set({ name, description, direction, executionBackend, rules, code, step, strategyId, source } as any)
     }
-  }, [name, description, direction, executionBackend, rules, code, step, strategyId, source])
-  useEffect(() => { if (loaded) persist() }, [loaded, persist])
+  }, [draftStore, name, description, direction, executionBackend, rules, code, step, strategyId, source])
+  useEffect(() => {
+    if (loaded && !suppressPersistRef.current) persist()
+  }, [loaded, persist])
 
   const clearDraft = () => {
     draftStore.set(null)
     resetDraftState()
   }
 
-  const handleClose = () => { if (name || rules || code) persist(); onClose() }
+  const handleClose = () => {
+    if (!suppressPersistRef.current && (name || rules || code)) persist()
+    onClose()
+  }
 
   const resolveStrategyId = (target: 'ai' | 'custom' = source) => {
     if (mode === 'modify' && strategyId) return strategyId
@@ -353,6 +371,7 @@ export function StrategyBuilderDialog({ open, onClose, onSavedId, mode = 'create
         name: name.trim(),
         description: description.trim(),
       })
+      suppressPersistRef.current = true
       clearDraft()
       const genRules = parseRules(draftCode)
       const finalRules = (genRules || rules).trim()
@@ -396,7 +415,7 @@ export function StrategyBuilderDialog({ open, onClose, onSavedId, mode = 'create
             </div>
             {/* 中间：标题 */}
             <span id="strategy-builder-title" className="text-sm font-semibold text-foreground">
-              {strategyId ? '修改策略' : '创建策略'}
+              {mode === 'modify' ? '修改策略' : '创建策略'}
             </span>
             {/* 右侧：步骤 + 关闭 */}
             <div className="flex items-center justify-end gap-2">

frontend/src/components/screener/StrategySettingsDialog.tsx

@@ -21,6 +21,7 @@ Object.assign(FIELD_LABEL, {
   vol_ratio_5d: '量比', vol_ratio_20d: '20日量比',
   macd_dif: 'MACD-DIF', macd_dea: 'MACD-DEA', macd_hist: 'MACD柱',
   boll_upper: '布林上轨', boll_lower: '布林下轨',
+  ma20_bias: 'MA20乖离率',
 })
 
 interface Props {

frontend/src/lib/api.ts

@@ -401,6 +401,7 @@ export interface StrategyDetail {
   scoring: Record<string, number>
   entry_signals: string[]
   exit_signals: string[]
+  minute_exit_trigger_supported_signals: string[]
   stop_loss: number | null
   take_profit: number | null
   trailing_stop: number | null
@@ -1455,7 +1456,7 @@ export const api = {
     overrides?: Record<string, any> | null
     matching?: 'close_t' | 'open_t+1'
     entry_fill?: 'close_t' | 'open_t+1' | null
-    exit_fill?: 'close_t' | 'open_t+1' | null
+    exit_fill?: 'close_t' | 'open_t+1' | 'signal_next_minute' | null
     fees_pct?: number
     commission_pct?: number
     stamp_tax_pct?: number
@@ -1464,6 +1465,7 @@ export const api = {
     initial_capital?: number
     position_sizing?: 'equal' | 'score_weight'
     asset_type?: 'stock' | 'etf'
+    minute_fill?: boolean
   }) =>
     request<StrategyBacktestResult>('/api/backtest/strategy/run', {
       method: 'POST',

frontend/src/lib/storage.ts

@@ -96,7 +96,7 @@ export const storage = {
     end: string
     matching: 'close_t' | 'open_t+1'
     entryFill: 'close_t' | 'open_t+1'
-    exitFill: 'close_t' | 'open_t+1'
+    exitFill: 'close_t' | 'open_t+1' | 'signal_next_minute'
     fees: string
     stampTax?: string
     slippage: string
@@ -106,6 +106,7 @@ export const storage = {
     positionSizing: 'equal' | 'score_weight'
     mode: 'position' | 'full'
     holdingDays: string
+    minuteFill?: boolean
     params?: Record<string, any>
     overrides?: Record<string, any>
     result: any

frontend/src/pages/Screener.tsx

@@ -900,7 +900,7 @@ export function Screener() {
                 <ScanSearch className="h-7 w-7 text-accent/40" />
               </div>
               <div className="flex flex-col items-center gap-1.5">
-                <span className="text-sm text-secondary">可先在右上角切换日期，再点击策略卡片查看选股结果</span>
+                <span className="text-sm text-secondary">点击策略卡片查看选股结果</span>
                 <span className="text-[11px] text-muted">若提示 enriched 表无数据，请先运行盘后管道</span>
               </div>
             </div>
@@ -981,6 +981,7 @@ export function Screener() {
         open={showBuilder}
         onClose={() => setShowBuilder(false)}
         mode={builderMode}
+        existingStrategyIds={availableStrategyIds}
         onSavedId={async id => {
           const data = await qc.fetchQuery({ queryKey: QK.screenerStrategies('stock'), queryFn: () => api.screenerStrategies('stock'), staleTime: 0 })
           if (!data.presets.some(s => s.id === id)) {

frontend/src/pages/backtest/StrategyBacktest.tsx

@@ -1,7 +1,7 @@
 import { useState, useMemo, useEffect, useRef, type ReactNode } from 'react'
 import { useQuery } from '@tanstack/react-query'
 import { motion, AnimatePresence } from 'framer-motion'
-import { Play, FlaskConical, Clock, Loader2, Square, Search, Plus, X, SlidersHorizontal, BarChart3, Gauge, Zap, ListPlus, HelpCircle } from 'lucide-react'
+import { Play, FlaskConical, Clock, Loader2, Square, Search, Plus, X, SlidersHorizontal, BarChart3, Gauge, Zap, ListPlus, HelpCircle, ChevronRight, AlertTriangle } from 'lucide-react'
 import {
   api,
   type StrategyBacktestResult,
@@ -89,12 +89,12 @@ const quickRangeTitle = (range: QuickRangeConfig) => range.unit === 'all'
 const INPUT_CLS = `w-full px-2.5 py-1.5 rounded-input bg-surface border border-border text-xs
   focus:outline-none focus:border-accent transition-colors duration-150 ease-smooth`
 
-/** 建仓/清仓口径说明 — 黄色问号图标, 点击弹出气泡。
+/** 成交时序说明 — 黄色问号图标, 点击弹出气泡。
  * 用 fixed 定位脱离父容器 overflow 裁剪(左侧表单是 overflow-y-auto, absolute 气泡会被裁)。 */
 function FillRuleHint() {
   const [open, setOpen] = useState(false)
   const [pos, setPos] = useState<{ top: number; left: number } | null>(null)
-  const iconRef = useRef<HTMLDivElement>(null)
+  const iconRef = useRef<HTMLButtonElement>(null)
 
   const handleOpen = () => {
     if (!open && iconRef.current) {
@@ -108,11 +108,18 @@ function FillRuleHint() {
   const bubbleLeft = pos ? Math.min(pos.left, window.innerWidth - 256 - 8) : 0
 
   return (
-    <div ref={iconRef} className="relative inline-flex items-center">
-      <HelpCircle
-        className="h-3.5 w-3.5 text-yellow-500/80 hover:text-yellow-500 cursor-help transition-colors"
+    <div className="relative inline-flex items-center">
+      <button
+        ref={iconRef}
+        type="button"
         onClick={handleOpen}
-      />
+        aria-label="查看成交时序说明"
+        aria-expanded={open}
+        title="查看成交时序说明"
+        className="inline-flex h-3.5 w-3.5 items-center justify-center text-yellow-500/80 transition-colors hover:text-yellow-500"
+      >
+        <HelpCircle className="h-3.5 w-3.5" />
+      </button>
       <AnimatePresence>
         {open && pos && (
           <>
@@ -126,11 +133,12 @@ function FillRuleHint() {
               className="fixed z-50 w-64 bg-surface border border-border rounded-md shadow-xl p-3 text-[11px] text-secondary leading-relaxed"
               onClick={e => e.stopPropagation()}
             >
-              <div className="font-medium text-foreground mb-1.5">成交口径说明</div>
+              <div className="font-medium text-foreground mb-1.5">成交时序说明</div>
               <div className="space-y-1">
-                <div><b className="text-foreground">建仓</b>默认<b className="text-foreground">次日开盘</b>(避免未来函数)</div>
-                <div><b className="text-foreground">清仓</b>默认<b className="text-foreground">当日收盘</b>(持仓中可盘中/收盘卖)</div>
-                <div>买卖点由<b className="text-foreground">策略触发器</b>决定,这里只决定成交价。</div>
+                <div><b className="text-foreground">建仓口径</b>和<b className="text-foreground">清仓口径</b>分别控制买卖信号出现后的成交时点。</div>
+                <div><b className="text-foreground">信号日收盘</b>仅适用于收盘前可确认的信号；收盘后确认的信号应选择<b className="text-foreground">次日开盘</b>。</div>
+                <div><b className="text-foreground">信号触发卖出</b>仅在分钟成交开启且卖出信号支持分钟回放时可用；分钟收盘确认后按下一分钟开盘成交。</div>
+                <div>买卖信号由<b className="text-foreground">策略触发器</b>决定，这里只控制信号出现后的成交时点。</div>
               </div>
             </motion.div>
           </>
@@ -158,6 +166,7 @@ Object.assign(FIELD_LABEL, {
   vol_ratio_5d: '量比', vol_ratio_20d: '20日量比',
   macd_dif: 'MACD-DIF', macd_dea: 'MACD-DEA', macd_hist: 'MACD柱',
   boll_upper: '布林上轨', boll_lower: '布林下轨',
+  ma20_bias: 'MA20乖离率',
 })
 const BOARD_OPTIONS = ['沪主板', '深主板', '创业板', '科创板', '北交所']
 const BASIC_FILTER_FIELDS = [
@@ -387,11 +396,12 @@ function TradeLegCell({ trade, side, signalNames }: { trade: StrategyBacktestTra
   const amount = isBuy ? trade.entry_value : trade.exit_value
   const signalId = isBuy ? trade.entry_signal_id : trade.exit_signal_id
   const signalLabel = signalId ? cnSignal(signalId, signalNames) : null
+  const signalDateLabel = isBuy || trade.exit_reason === 'signal' ? '信号' : '触发'
 
   return (
     <div className="min-w-[8.25rem] rounded-btn border border-border/60 bg-base/35 px-2 py-1 text-xs leading-4">
       <div className="flex items-center justify-between gap-2">
-        <span className="font-mono text-secondary">{date}</span>
+        <span className="font-mono text-secondary">成交 {date}</span>
         <span className={`rounded px-1.5 py-px text-[10px] font-medium ${
           isBuy ? 'bg-accent/15 text-accent' : 'bg-elevated text-secondary'
         }`}>
@@ -405,8 +415,8 @@ function TradeLegCell({ trade, side, signalNames }: { trade: StrategyBacktestTra
       {signalLabel && (
         <div className="mt-0.5 text-[10px] text-accent/80 truncate" title={signalLabel}>{signalLabel}</div>
       )}
-      {!signalLabel && signalDate && signalDate !== date && (
-        <div className="mt-0.5 text-[10px] text-muted">信号 {signalDate}</div>
+      {signalDate && (
+        <div className="mt-0.5 text-[10px] text-muted">{signalDateLabel} {signalDate}</div>
       )}
     </div>
   )
@@ -421,10 +431,96 @@ function fmtDuration(ms: number): string {
   return `${m}分${rest}秒`
 }
 
-function SharpeLabel() {
+const METRIC_HELP = {
+  avgReturn: {
+    title: '平均收益',
+    description: '所有已执行候选交易收益率的算术平均值。',
+    note: '容易受极端盈亏影响，建议与中位数一起看。',
+  },
+  medianReturn: {
+    title: '中位数收益',
+    description: '将每笔收益排序后位于中间的值。',
+    note: '比平均收益更不容易被少数极端样本扭曲。',
+  },
+  winRate: {
+    title: '胜率',
+    description: '盈利交易数占已完成交易数的比例。',
+    note: '胜率高不代表总收益一定高，还需结合盈亏比。',
+  },
+  profitFactor: {
+    title: '盈亏比',
+    description: '平均盈利幅度 ÷ 平均亏损幅度的绝对值。',
+    note: '大于 1 表示平均单笔盈利大于平均单笔亏损。',
+  },
+  totalReturn: {
+    title: '总收益',
+    description: '回测期末权益相对初始资金的累计收益率。',
+    note: '已反映回测中的仓位、费用、滑点和成交约束。',
+  },
+  annualReturn: {
+    title: '年化收益',
+    description: '将回测期总收益按复利折算为一年的收益率。',
+    note: '短周期回测的年化结果可能被明显放大。',
+  },
+  benchmarkReturn: {
+    title: '同期上证',
+    description: '同一回测区间内上证指数的累计收益率。',
+    note: '用于判断策略表现是否主要来自市场整体涨跌。',
+  },
+  excessReturn: {
+    title: '超额收益',
+    description: '策略总收益率减去同期上证指数收益率。',
+    note: '正值表示跑赢基准，负值表示跑输基准。',
+  },
+  sharpe: {
+    title: '夏普比率 (Sharpe Ratio)',
+    description: '收益序列的平均收益 ÷ 总波动，并按 252 期年化。',
+    note: '数值越高，单位波动获得的收益越多；小样本时仅供参考。',
+  },
+  sortino: {
+    title: '索提诺比率 (Sortino Ratio)',
+    description: '收益序列的平均收益 ÷ 下行偏差，并按 252 期年化。',
+    note: '只惩罚负收益波动，不将向上波动视为风险。',
+  },
+  maxDrawdown: {
+    title: '最大回撤',
+    description: '回测权益从历史高点到随后最低点的最大跌幅。',
+    note: '越接近 0 通常代表历史资金回撤越小。',
+  },
+  mcDrawdownMedian: {
+    title: '蒙卡回撤中位数',
+    description: '对交易收益有放回重抽样，各自计算最大回撤后取中位数。',
+    note: '表示交易顺序变化时较典型的最大回撤场景。',
+  },
+  mcDrawdown95: {
+    title: '蒙卡回撤 95% 边界',
+    description: '交易收益重抽样结果中偏悲观的最大回撤边界。',
+    note: '约有 95% 的模拟顺序回撤不劣于此值，但不是未来承诺。',
+  },
+  tradeCount: {
+    title: '交易数',
+    description: '回测期内已完成建仓和清仓的交易笔数。',
+    note: '样本越少，胜率和风险指标的稳定性越低。',
+  },
+  avgDuration: {
+    title: '平均持仓',
+    description: '所有已完成交易的平均持仓天数。',
+    note: '全量模式下每个候选独立执行后再汇总。',
+  },
+  finalEquity: {
+    title: '最终权益',
+    description: '回测结束时账户现金与持仓市值的合计。',
+    note: '已反映成交费用、滑点和仓位约束。',
+  },
+} as const
+
+type MetricHelpKey = keyof typeof METRIC_HELP
+
+function MetricLabel({ label, metric }: { label: string; metric: MetricHelpKey }) {
   const [open, setOpen] = useState(false)
   const [alignRight, setAlignRight] = useState(false)
   const ref = useRef<HTMLSpanElement>(null)
+  const help = METRIC_HELP[metric]
   useEffect(() => {
     if (!open) return
     const onClick = (e: MouseEvent) => {
@@ -442,20 +538,22 @@ function SharpeLabel() {
   }
   return (
     <span className="relative inline-flex items-center gap-1" ref={ref}>
-      夏普
+      {label}
       <button
         type="button"
         onClick={toggle}
-        className="inline-flex h-3.5 w-3.5 items-center justify-center rounded-full border border-border bg-base text-[10px] text-muted transition-colors hover:border-accent/50 hover:text-accent"
+        aria-label={`查看${label}说明`}
+        aria-expanded={open}
+        title={`查看${label}说明`}
+        className="inline-flex h-3.5 w-3.5 items-center justify-center text-muted transition-colors hover:text-accent"
       >
-        ?
+        <HelpCircle className="h-3.5 w-3.5" />
       </button>
       {open && (
         <span className={`absolute top-full z-50 mt-1.5 w-60 max-w-[calc(100vw-1.5rem)] rounded-lg border border-border bg-elevated px-3 py-2.5 text-[11px] leading-relaxed text-secondary shadow-xl ${alignRight ? 'right-0' : 'left-0'}`}>
-          <span className="block font-medium text-foreground">夏普比率 (Sharpe Ratio)</span>
-          <span className="mt-1 block">衡量<b className="text-foreground">单位波动风险</b>换来的超额收益。</span>
-          <span className="mt-0.5 block">数值越高，收益相对波动越优秀；</span>
-          <span className="mt-0.5 block text-warning">短周期或交易次数少时容易偏高，仅供参考。</span>
+          <span className="block font-medium text-foreground">{help.title}</span>
+          <span className="mt-1 block">{help.description}</span>
+          <span className="mt-0.5 block text-warning">{help.note}</span>
         </span>
       )}
     </span>
@@ -747,7 +845,9 @@ export function StrategyBacktest() {
   // 成交口径: 建仓/清仓可独立配置。向后兼容老 matching (派生为 entry=exit=matching)。
   const [matching] = useState<'close_t' | 'open_t+1'>(saved?.matching ?? 'open_t+1')
   const [entryFill, setEntryFill] = useState<'close_t' | 'open_t+1'>(saved?.entryFill ?? saved?.matching ?? 'open_t+1')
-  const [exitFill, setExitFill] = useState<'close_t' | 'open_t+1'>(saved?.exitFill ?? saved?.matching ?? 'close_t')
+  const [exitFill, setExitFill] = useState<'close_t' | 'open_t+1' | 'signal_next_minute'>(
+    saved ? (saved.exitFill ?? saved.matching ?? 'close_t') : 'open_t+1',
+  )
   const [fees, setFees] = useState(saved?.fees ?? '2')
   const [stampTax, setStampTax] = useState(saved?.stampTax ?? '1')
   const [slippage, setSlippage] = useState(saved?.slippage ?? '5')
@@ -757,11 +857,18 @@ export function StrategyBacktest() {
   const [positionSizing, setPositionSizing] = useState<'equal' | 'score_weight'>(saved?.positionSizing ?? 'equal')
   const [simMode, setSimMode] = useState<'position' | 'full'>(saved?.mode ?? 'position')
   const [holdingDays, setHoldingDays] = useState(saved?.holdingDays ?? '5')
+  const [highGranularity, setHighGranularity] = useState(saved?.minuteFill ?? false)
   const [settingsOpen, setSettingsOpen] = useState(false)
-  // 分钟K精确回测: 用当日分钟K确定精确成交价 (穿越价/VWAP), 需 Pro+ 分钟K能力
-  const [highGranularity, setHighGranularity] = useState(false)
+  // 分钟K成交价细化: 不改变信号日或成交日, 需 Pro+ 分钟K能力
   const { data: caps } = useCapabilities()
   const hasMinuteBatch = !!caps?.capabilities?.['kline.minute.batch']
+  const toggleMinuteFill = () => {
+    if (!hasMinuteBatch) return
+    if (highGranularity) {
+      if (exitFill === 'signal_next_minute') setExitFill('close_t')
+    }
+    setHighGranularity(value => !value)
+  }
   const [rangeSettingsOpen, setRangeSettingsOpen] = useState(false)
   const [quickRanges, setQuickRanges] = useState(loadQuickRanges)
   const [settingsTab, setSettingsTab] = useState<AdvancedSettingsTab>('params')
@@ -865,6 +972,7 @@ export function StrategyBacktest() {
         positionSizing,
         mode: simMode,
         holdingDays,
+        minuteFill: highGranularity,
         params: strategyParams,
         overrides,
         result: backtestTask.result,
@@ -1051,6 +1159,15 @@ export function StrategyBacktest() {
   const basicFilter = (overrides.basic_filter ?? {}) as Record<string, any>
   const entrySignals = (overrides.entry_signals ?? []) as string[]
   const exitSignals = (overrides.exit_signals ?? []) as string[]
+  const effectiveExitSignals = (overrides.exit_signals ?? detail?.exit_signals ?? []) as string[]
+  const minuteTriggerSignals = detail?.minute_exit_trigger_supported_signals ?? []
+  const unsupportedMinuteExitSignals = effectiveExitSignals.filter(signal => !minuteTriggerSignals.includes(signal))
+  const minuteExitTriggerSupported = effectiveExitSignals.length > 0 && unsupportedMinuteExitSignals.length === 0
+
+  useEffect(() => {
+    if (highGranularity && minuteExitTriggerSupported) return
+    if (exitFill === 'signal_next_minute') setExitFill('close_t')
+  }, [exitFill, highGranularity, minuteExitTriggerSupported])
 
   const scoring = useMemo(() => (overrides.scoring ?? {}) as Record<string, number>, [overrides.scoring])
   const scoreMinValue = overrides.score_min == null ? '' : String(overrides.score_min)
@@ -1119,6 +1236,20 @@ export function StrategyBacktest() {
   const resultStartDate = result?.config?.start ?? result?.equity_curve?.[0]?.date ?? start
   const resultEndDate = result?.config?.end ?? result?.equity_curve?.[result.equity_curve.length - 1]?.date ?? end
   const resultTradeDays = result?.equity_curve?.length ?? 0
+  const selectionStats = result?.stats?.selection as Record<string, number | boolean> | undefined
+  const selectionStages = selectionStats
+    ? [
+        {
+          key: 'strategy',
+          label: result?.stats?.execution_backend === 'matrix_native' ? '策略信号' : '策略命中',
+          value: Number(selectionStats.strategy_matches ?? 0),
+        },
+        ...(selectionStats.entry_trigger_enabled === true
+          ? [{ key: 'entry', label: '入场候选', value: Number(selectionStats.entry_candidates ?? 0) }]
+          : []),
+        { key: 'trades', label: '完成交易', value: Number(result?.stats?.n_trades ?? result?.trades.length ?? 0) },
+      ]
+    : []
   const executionStats = (result?.stats?.execution ?? {}) as Record<string, number>
   const executionSummary = [
     ['buy_no_slot', '满仓未买'],
@@ -1129,6 +1260,7 @@ export function StrategyBacktest() {
     ['sell_limit_down', '跌停阻塞'],
     ['sell_suspended', '停牌阻塞'],
     ['pending_exit', '待卖阻塞'],
+    ['sell_minute_trigger_fallback', '分钟信号顺延'],
   ]
     .map(([key, label]) => ({ key, label, value: Number(executionStats[key] ?? 0) }))
     .filter(item => item.value > 0)
@@ -1140,15 +1272,15 @@ export function StrategyBacktest() {
         <div>
           <div className="flex items-center justify-between mb-1.5">
             <label className="text-xs font-medium text-secondary">选择策略</label>
-            {/* 分钟K精确回测 */}
+            {/* 分钟K成交 */}
             <div className="flex items-center gap-1">
               <Gauge className={`h-3 w-3 ${highGranularity ? 'text-amber-400' : 'text-muted/50'}`} />
               <button
-                onClick={() => { if (!hasMinuteBatch) return; setHighGranularity(v => !v) }}
+                onClick={toggleMinuteFill}
                 disabled={!hasMinuteBatch}
                 title={!hasMinuteBatch
-                  ? '分钟K精确回测：需 Pro+ 权限 (分钟K批量)'
-                  : '分钟K精确回测：用当日分钟K确定精确成交价（穿越价/VWAP），比收盘价更真实。⚠️ 回测速度会变慢。'
+                  ? '分钟K成交价：需 Pro+ 权限 (分钟K批量)'
+                  : '分钟K成交：细化成交价，并为兼容的卖出信号提供下一分钟成交。'
                 }
                 className={`group relative inline-flex h-3.5 w-6 items-center rounded-full shrink-0 transition-colors duration-200 ${
                   !hasMinuteBatch ? 'bg-elevated opacity-50 cursor-not-allowed'
@@ -1160,7 +1292,7 @@ export function StrategyBacktest() {
                   highGranularity ? 'translate-x-[13px]' : 'translate-x-0.5'
                 }`} />
               </button>
-              <span className={`text-[9px] font-medium ${highGranularity ? 'text-amber-400' : 'text-muted/50'}`}>分钟K</span>
+              <span className={`text-[9px] font-medium ${highGranularity ? 'text-amber-400' : 'text-muted/50'}`}>分钟成交</span>
               {!hasMinuteBatch && (
                 <span className="text-[8px] text-accent/70 font-medium bg-accent/10 px-1 py-px rounded">Pro+</span>
               )}
@@ -1171,8 +1303,8 @@ export function StrategyBacktest() {
             <div className="mb-2 flex items-start gap-1.5 rounded-btn border border-amber-400/30 bg-amber-400/5 px-2 py-1.5">
               <Zap className="h-3 w-3 text-amber-400 shrink-0 mt-px" />
               <div className="text-[10px] leading-snug text-amber-400/90">
-                <span className="font-medium">分钟K精确回测</span>
-                ：信号触发日用当日分钟K确定成交价（均线类信号按穿越价, 其他按 VWAP 均价）。需本地有足够的分钟K历史, 回测速度会变慢。
+                <span className="font-medium">分钟K成交价</span>
+                ：默认在成交日细化穿越价/VWAP；选择“信号触发卖出”时，会对兼容的卖出信号做分钟回放。需本地有足够的分钟K历史。
               </div>
             </div>
           )}
@@ -1360,22 +1492,46 @@ export function StrategyBacktest() {
 
         <div className="grid grid-cols-2 gap-2">
           <div>
-            <div className="flex items-center gap-1 mb-1.5">
+            <div className="mb-1.5 flex items-center gap-1">
               <label className="text-xs font-medium text-secondary">建仓口径</label>
               <FillRuleHint />
             </div>
-            <select value={entryFill} onChange={e => setEntryFill(e.target.value as any)} className={INPUT_CLS}>
-              <option value="open_t+1">次日开盘成交（推荐）</option>
-              <option value="close_t">信号日收盘成交</option>
+            <select value={entryFill} onChange={e => setEntryFill(e.target.value as 'close_t' | 'open_t+1')} className={INPUT_CLS}>
+              <option value="open_t+1">次日开盘（推荐）</option>
+              <option value="close_t">信号日收盘</option>
             </select>
           </div>
           <div>
-            <label className="text-xs font-medium text-secondary block mb-1.5">清仓口径</label>
-            <select value={exitFill} onChange={e => setExitFill(e.target.value as any)} className={INPUT_CLS}>
-              <option value="close_t">到期/信号日收盘成交（推荐）</option>
-              <option value="open_t+1">次日开盘成交</option>
+            <label className="mb-1.5 block text-xs font-medium text-secondary">清仓口径</label>
+            <select
+              value={exitFill}
+              onChange={e => setExitFill(e.target.value as 'close_t' | 'open_t+1' | 'signal_next_minute')}
+              className={INPUT_CLS}
+            >
+              <option value="close_t">信号日收盘（推荐）</option>
+              <option value="open_t+1">次日开盘</option>
+              {highGranularity && minuteExitTriggerSupported && (
+                <option value="signal_next_minute">信号触发卖出 BETA</option>
+              )}
             </select>
           </div>
+          {(entryFill === 'close_t' || exitFill === 'close_t') && (
+            <div className="col-span-2 flex items-start gap-1 text-[10px] leading-4 text-warning">
+              <AlertTriangle className="mt-0.5 h-3 w-3 shrink-0" />
+              <span>信号日收盘仅适合收盘前已确认的信号</span>
+            </div>
+          )}
+          {exitFill === 'signal_next_minute' && (
+            <div className="col-span-2 text-[10px] leading-4 text-accent">
+              分钟收盘确认卖出信号后，按下一分钟开盘成交；尾盘或分钟数据缺失时顺延到下一交易日开盘
+            </div>
+          )}
+          {highGranularity && effectiveExitSignals.length > 0 && !minuteExitTriggerSupported && (
+            <div className="col-span-2 flex items-start gap-1 text-[10px] leading-4 text-muted">
+              <AlertTriangle className="mt-0.5 h-3 w-3 shrink-0" />
+              <span>当前卖出信号暂不支持分钟触发回放</span>
+            </div>
+          )}
         </div>
 
         {simMode === 'position' && (
@@ -1596,14 +1752,14 @@ export function StrategyBacktest() {
 
             {/* 统计卡片 */}
             <div className="grid grid-cols-2 md:grid-cols-4 gap-2">
-              <Stat label="平均收益" value={fmtPct(result.stats.avg_return)} color={statValueColor(result.stats.avg_return)} />
-              <Stat label="中位数" value={fmtPct(result.stats.median_return)} color={statValueColor(result.stats.median_return)} />
-              <Stat label="胜率" value={fmtPct(result.stats.win_rate)} color={statValueColor(result.stats.win_rate)} />
-              <Stat label="盈亏比" value={result.stats.profit_factor != null ? Number(result.stats.profit_factor).toFixed(2) : '—'} />
-              <Stat label="超额(vs基准)" value={fmtPct(result.stats.excess)} color={statValueColor(result.stats.excess)} />
-              <Stat label="夏普" value={result.stats.sharpe != null ? Number(result.stats.sharpe).toFixed(2) : '—'} />
-              <Stat label="最大回撤" value={fmtPct(result.stats.max_drawdown)} color={statValueColor(result.stats.max_drawdown)} />
-              <Stat label="累计收益" value={fmtPct(result.stats.total_return)} color={statValueColor(result.stats.total_return)} />
+              <Stat label={<MetricLabel label="平均收益" metric="avgReturn" />} value={fmtPct(result.stats.avg_return)} color={statValueColor(result.stats.avg_return)} />
+              <Stat label={<MetricLabel label="中位数" metric="medianReturn" />} value={fmtPct(result.stats.median_return)} color={statValueColor(result.stats.median_return)} />
+              <Stat label={<MetricLabel label="胜率" metric="winRate" />} value={fmtPct(result.stats.win_rate)} color={statValueColor(result.stats.win_rate)} />
+              <Stat label={<MetricLabel label="盈亏比" metric="profitFactor" />} value={result.stats.profit_factor != null ? Number(result.stats.profit_factor).toFixed(2) : '—'} />
+              <Stat label={<MetricLabel label="超额(vs基准)" metric="excessReturn" />} value={fmtPct(result.stats.excess)} color={statValueColor(result.stats.excess)} />
+              <Stat label={<MetricLabel label="夏普" metric="sharpe" />} value={result.stats.sharpe != null ? Number(result.stats.sharpe).toFixed(2) : '—'} />
+              <Stat label={<MetricLabel label="最大回撤" metric="maxDrawdown" />} value={fmtPct(result.stats.max_drawdown)} color={statValueColor(result.stats.max_drawdown)} />
+              <Stat label={<MetricLabel label="累计收益" metric="totalReturn" />} value={fmtPct(result.stats.total_return)} color={statValueColor(result.stats.total_return)} />
             </div>
 
             <div className="flex flex-wrap items-center gap-x-4 gap-y-1 text-[11px] text-muted">
@@ -1693,32 +1849,47 @@ export function StrategyBacktest() {
             {/* 统计卡片 */}
             <div className="rounded-card border border-border bg-surface p-4">
               <div className="grid grid-cols-[repeat(auto-fit,minmax(9rem,1fr))] gap-3">
-                <Stat label="总收益" value={strategyReturn != null ? fmtPct(strategyReturn) : '—'}
+                <Stat label={<MetricLabel label="总收益" metric="totalReturn" />} value={strategyReturn != null ? fmtPct(strategyReturn) : '—'}
                   color={statValueColor(strategyReturn)} />
-                <Stat label="年化" value={pick('annual_return') != null ? fmtPct(pick('annual_return') as number) : '—'}
+                <Stat label={<MetricLabel label="年化" metric="annualReturn" />} value={pick('annual_return') != null ? fmtPct(pick('annual_return') as number) : '—'}
                   color={statValueColor(pick('annual_return') as number)} />
-                <Stat label="同期上证" value={benchmarkReturn != null ? fmtPct(benchmarkReturn) : '—'}
+                <Stat label={<MetricLabel label="同期上证" metric="benchmarkReturn" />} value={benchmarkReturn != null ? fmtPct(benchmarkReturn) : '—'}
                   color={statValueColor(benchmarkReturn)} />
-                <Stat label="超额收益" value={excessReturn != null ? fmtPct(excessReturn) : '—'}
+                <Stat label={<MetricLabel label="超额收益" metric="excessReturn" />} value={excessReturn != null ? fmtPct(excessReturn) : '—'}
                   color={statValueColor(excessReturn)} />
-                <Stat label={<SharpeLabel />} value={pick('sharpe') != null ? Number(pick('sharpe')).toFixed(2) : '—'} />
-                <Stat label="索提诺" value={pick('sortino') != null ? Number(pick('sortino')).toFixed(2) : '—'} />
-                <Stat label="最大回撤" value={pick('max_drawdown') != null ? fmtPct(pick('max_drawdown') as number) : '—'}
+                <Stat label={<MetricLabel label="夏普" metric="sharpe" />} value={pick('sharpe') != null ? Number(pick('sharpe')).toFixed(2) : '—'} />
+                <Stat label={<MetricLabel label="索提诺" metric="sortino" />} value={pick('sortino') != null ? Number(pick('sortino')).toFixed(2) : '—'} />
+                <Stat label={<MetricLabel label="最大回撤" metric="maxDrawdown" />} value={pick('max_drawdown') != null ? fmtPct(pick('max_drawdown') as number) : '—'}
                   color="#34d399" />
-                <Stat label="蒙卡回撤(中位)" value={pick('mc_maxdd_p50') != null ? fmtPct(pick('mc_maxdd_p50') as number) : '—'}
+                <Stat label={<MetricLabel label="蒙卡回撤(中位)" metric="mcDrawdownMedian" />} value={pick('mc_maxdd_p50') != null ? fmtPct(pick('mc_maxdd_p50') as number) : '—'}
                   color="#34d399" />
-                <Stat label="蒙卡回撤(95%置信不差于此)" value={pick('mc_maxdd_p95') != null ? fmtPct(pick('mc_maxdd_p95') as number) : '—'}
+                <Stat label={<MetricLabel label="蒙卡回撤(95%边界)" metric="mcDrawdown95" />} value={pick('mc_maxdd_p95') != null ? fmtPct(pick('mc_maxdd_p95') as number) : '—'}
                   color="#34d399" />
-                <Stat label="胜率" value={pick('win_rate') != null ? fmtPct(pick('win_rate') as number) : '—'} />
-                <Stat label="交易数" value={pick('n_trades') != null ? String(pick('n_trades')) : '—'} />
+                <Stat label={<MetricLabel label="胜率" metric="winRate" />} value={pick('win_rate') != null ? fmtPct(pick('win_rate') as number) : '—'} />
+                <Stat label={<MetricLabel label="交易数" metric="tradeCount" />} value={pick('n_trades') != null ? String(pick('n_trades')) : '—'} />
                 {result.stats.full_kind === 'candidate_execution' ? (
-                  <Stat label="平均持仓" value={pick('avg_duration') != null ? `${Number(pick('avg_duration')).toFixed(1)}天` : '—'} />
+                  <Stat label={<MetricLabel label="平均持仓" metric="avgDuration" />} value={pick('avg_duration') != null ? `${Number(pick('avg_duration')).toFixed(1)}天` : '—'} />
                 ) : (
-                  <Stat label="最终权益" value={pick('final_equity') != null ? fmtPrice(pick('final_equity') as number) : '—'} />
+                  <Stat label={<MetricLabel label="最终权益" metric="finalEquity" />} value={pick('final_equity') != null ? fmtPrice(pick('final_equity') as number) : '—'} />
                 )}
               </div>
             </div>
 
+            {selectionStages.length > 0 && (
+              <div className="flex flex-wrap items-center gap-y-2 rounded-card border border-border bg-base/35 px-3 py-2 text-[11px] text-secondary">
+                <span className="mr-2 font-medium text-foreground">选择漏斗</span>
+                {selectionStages.map((stage, index) => (
+                  <div key={stage.key} className="flex items-center">
+                    {index > 0 && <ChevronRight className="mx-1.5 h-3 w-3 text-muted/60" />}
+                    <span>{stage.label} <b className="font-mono text-foreground">{stage.value}</b></span>
+                  </div>
+                ))}
+                {Number(selectionStats?.entry_trigger_filtered ?? 0) > 0 && (
+                  <span className="ml-auto text-amber-400">入场触发器过滤 {Number(selectionStats?.entry_trigger_filtered)} 个</span>
+                )}
+              </div>
+            )}
+
             {executionSummary.length > 0 && (
               <div className="rounded-card border border-amber-400/25 bg-amber-400/5 px-3 py-2 text-[11px] leading-5 text-secondary">
                 <span className="font-medium text-amber-300">成交约束：</span>