diff --git a/Zeus_8_3_2_B_4_2.py b/Zeus_8_3_2_B_4_2.py
index 62ebc25..f156145 100644
--- a/Zeus_8_3_2_B_4_2.py
+++ b/Zeus_8_3_2_B_4_2.py
@@ -494,8 +494,8 @@ class Zeus_8_3_2_B_4_2(IStrategy):
         def tag_by_derivatives(row):
             d1 = row['mid_smooth_deriv1']
             d2 = row['mid_smooth_deriv2']
-            d1_lim_inf = -5
-            d1_lim_sup = 5
+            d1_lim_inf = -0.01
+            d1_lim_sup = 0.01
             if d1 >= d1_lim_inf and d1 <= d1_lim_sup: # and d2 >= d2_lim_inf and d2 <= d2_lim_sup:
                 return 'P' # Palier
             if d1 == 0.0:
@@ -797,12 +797,12 @@ class Zeus_8_3_2_B_4_2(IStrategy):
     def calculateTendency(self, dataframe, window=12):
         dataframe['mid'] = dataframe['open'] + (dataframe['close'] - dataframe['open']) / 2
         # 2. Calcul du lissage sur 200 bougies par moyenne mobile médiane
-        dataframe['mid_smooth'] = dataframe['mid'].rolling(window=window, center=True, min_periods=1).median().rolling(
+        dataframe['mid_smooth'] = dataframe['close'].rolling(window=window, center=True, min_periods=1).median().rolling(
             3).mean()
         # 2. Dérivée première = différence entre deux bougies successives
-        dataframe['mid_smooth_deriv1'] = round(100000 * dataframe['mid_smooth'].pct_change(), 2)
+        dataframe['mid_smooth_deriv1'] = round(100 * dataframe['mid_smooth'].diff() / dataframe['mid_smooth'], 4)
         # 3. Dérivée seconde = différence de la dérivée première
-        dataframe['mid_smooth_deriv2'] = round(100 * dataframe['mid_smooth_deriv1'].pct_change().rolling(3).mean(), 2)
+        dataframe['mid_smooth_deriv2'] = round(100 * dataframe['mid_smooth_deriv1'].diff().rolling(3).mean(), 4)
         dataframe = self.add_tendency_column(dataframe)
         return dataframe
 
@@ -898,6 +898,7 @@ class Zeus_8_3_2_B_4_2(IStrategy):
                 # (dataframe["bb_width"] > 0.01)
                 (dataframe['down_count'].shift(1) < - 1)
                 & (dataframe['down_count'] == 0)
+                & (dataframe['mid_smooth_deriv1'] >= -0.01)
                 # & (dataframe['tendency'] != "B--")
                 # & (dataframe['tendency'] != "B-")
             ), ['enter_long', 'enter_tag']] = (1, 'down')
@@ -906,15 +907,10 @@ class Zeus_8_3_2_B_4_2(IStrategy):
             (
                 (dataframe['low'] < dataframe['min200'])
                 & (dataframe['min50'] == dataframe['min50'].shift(3))
-                # & (dataframe['tendency'] != "B--")
-                # & (dataframe['tendency'] != "B-")
+                #
+                & (dataframe['tendency'] != "B-")
             ), ['enter_long', 'enter_tag']] = (1, 'low')
-        # dataframe.loc[
-        #     (
-        #         (dataframe['mid_smooth_deriv1'] > 0)
-        #         & (dataframe['rsi'] < 50)
-        #         & (dataframe['mid_smooth_deriv1'] > dataframe['mid_smooth_deriv1'].shift(1) * 1.5)
-        #     ), ['enter_long', 'enter_tag']] = (1, 'mid')
+
 
         dataframe['test'] = np.where(dataframe['enter_long'] == 1, dataframe['close'] * 1.01, np.nan)
 
@@ -1079,12 +1075,12 @@ class Zeus_8_3_2_B_4_2(IStrategy):
 
     def expectedProfit(self, pair: str, last_candle: DataFrame):
 
-        first_price = last_candle['first_price']
-        first_max = 0.01
-        if first_price < last_candle['max200']:
-            first_max = (last_candle['max200'] - first_price) / first_price
+        # first_price = last_candle['first_price']
+        # first_max = 0.01
+        # if first_price < last_candle['max200']:
+        #     first_max = (last_candle['max200'] - first_price) / first_price
 
-        expected_profit = min(0.01, first_max)
+        expected_profit = 0.004 #min(0.01, first_max)
 
         # print(
         #     f"Expected profit price={current_price:.4f} min_max={min_max:.4f} min_14={min_14_days:.4f} max_14={max_14_days:.4f} percent={percent:.4f} expected_profit={expected_profit:.4f}")