diff --git a/Zeus_8_3_2_B_4_2.py b/Zeus_8_3_2_B_4_2.py
index 45e4482..49907ff 100644
--- a/Zeus_8_3_2_B_4_2.py
+++ b/Zeus_8_3_2_B_4_2.py
@@ -773,7 +773,7 @@ class Zeus_8_3_2_B_4_2(IStrategy):
         dataframe.loc[
             (
                 (dataframe['low'] < dataframe['min200'])
-                & (dataframe['min50'] == dataframe['min200'].shift(3))
+                & (dataframe['min50'] == dataframe['min50'].shift(3))
                 # & (dataframe['tendency'] != "B--")
                 # & (dataframe['tendency'] != "B-")
             ), ['enter_long', 'enter_tag']] = (1, 'low')
@@ -965,21 +965,12 @@ class Zeus_8_3_2_B_4_2(IStrategy):
     #     # Retourner le stoploss dynamique en pourcentage du prix actuel
     #     return (atr_stoploss / current_rate) - 1
 
-    def expectedProfit(self, pair: str, dataframe: DataFrame):
+    def expectedProfit(self, pair: str, last_candle: DataFrame):
 
-        current_price = dataframe['last_price']  # dataframe['close']
+        first_price = last_candle['first_price']
+        first_max = (last_candle['max200'] - first_price) / first_price
 
-        # trade = self.getTrade(pair)
-        # if trade:
-        #     current_price = trade.open_rate
-
-        # Calculer le max des 14 derniers jours
-        min_14_days = dataframe['lowest_1d']
-        max_14_days = dataframe['highest_1d']
-        percent = (max_14_days - current_price) / (min_14_days)
-
-        min_max = dataframe['pct_min_max_1d']  # (max_14_days - min_14_days) / min_14_days
-        expected_profit = min(0.01, max(0.01, dataframe['min_max200'] * 0.5))
+        expected_profit = min(0.01, first_max * 0.5)
 
         # 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}")