diff --git a/zhixing-server/src/zhixing_server/modules/selection/domain/gold_brick.py b/zhixing-server/src/zhixing_server/modules/selection/domain/gold_brick.py
index f8c22d1..6547bb4 100644
--- a/zhixing-server/src/zhixing_server/modules/selection/domain/gold_brick.py
+++ b/zhixing-server/src/zhixing_server/modules/selection/domain/gold_brick.py
@@ -19,9 +19,7 @@ from .zhixing_b1 import compute_signal_masks, prepare_zhixing_b1_indicators
 
 GOLD_BRICK_MINIMUM_HISTORY = 200
 GOLD_BRICK_TURNOVER_RATE_THRESHOLD = 0.99
-GOLD_BRICK_SIGNAL_ORDER: tuple[GoldBrickCategory, ...] = (
-    GoldBrickCategory.RESONANCE,
-)
+GOLD_BRICK_SIGNAL_ORDER: tuple[GoldBrickCategory, ...] = (GoldBrickCategory.RESONANCE,)
 
 
 def _safe_ratio(numerator: pd.Series, denominator: pd.Series) -> pd.Series:
@@ -90,9 +88,7 @@ def prepare_gold_brick_indicators(frame: pd.DataFrame, code: str) -> pd.DataFram
     )
     multiple_volume_bonus = pd.Series(
         np.where(
-            (close > open_price)
-            & (close > previous_close)
-            & (volume > previous_volume * 1.8),
+            (close > open_price) & (close > previous_close) & (volume > previous_volume * 1.8),
             multiple_volume_coefficient,
             1.0,
         ),
@@ -115,13 +111,9 @@ def prepare_gold_brick_indicators(frame: pd.DataFrame, code: str) -> pd.DataFram
     )
     result["j_momentum"] = j_momentum
     result["rsi_momentum"] = rsi_momentum
-    result["yellow_column"] = (
-        momentum_sum.div(2).mul(shadow_coefficient).mul(multiple_volume_bonus)
-    )
+    result["yellow_column"] = momentum_sum.div(2).mul(shadow_coefficient).mul(multiple_volume_bonus)
     x_condition = (
-        (close > open_price)
-        & (close > previous_close)
-        & (momentum_sum > previous_momentum_sum)
+        (close > open_price) & (close > previous_close) & (momentum_sum > previous_momentum_sum)
     )
     result["x_momentum"] = (
         momentum_sum.sub(previous_momentum_sum)
@@ -163,9 +155,8 @@ def prepare_gold_brick_indicators(frame: pd.DataFrame, code: str) -> pd.DataFram
         high - close,
         high - upper_shadow_floor,
     )
-    result["upper_shadow_condition"] = (
-        ((close >= open_price) | (close > previous_close))
-        & (result["upper_shadow_strength"] > 0.618)
+    result["upper_shadow_condition"] = ((close >= open_price) | (close > previous_close)) & (
+        result["upper_shadow_strength"] > 0.618
     )
 
     long = result["long_oscillator"]