diff --git a/FrictradeLearning.py b/FrictradeLearning.py
index 774c42d..d363769 100644
--- a/FrictradeLearning.py
+++ b/FrictradeLearning.py
@@ -180,7 +180,7 @@ class FrictradeLearning(IStrategy):
          "note": "pic oct. 2025 (source agrégée, à vérifier selon l'exchange)"}
     ]
 
-    def dynamic_trailing_offset(self, pair, stake, price, ath, count_of_buys, max_dca=5):
+    def dynamic_trailing_offset(self, pair, stake, last_candle, price, ath, count_of_buys, max_dca=5):
         # dd_ath = (ath - price) / ath
         # dd_ath = max(0.0, min(dd_ath, 0.5))
         #
@@ -192,6 +192,9 @@ class FrictradeLearning(IStrategy):
         # OFFSET_MIN = self.offset_min.value
         # OFFSET_MAX = self.offset_min.value + self.offset_max.value
 
+        if last_candle['sma180_deriv1'] < 0.005:
+            return stake / 200
+
         return stake / 100 # OFFSET_MIN + breathing_score * (OFFSET_MAX - OFFSET_MIN)
 
     def cooldown_from_heat(self, score):
@@ -468,7 +471,8 @@ class FrictradeLearning(IStrategy):
             self.printLineLog()
             df = pd.DataFrame.from_dict(self.pairs, orient='index')
             colonnes_a_exclure = ['last_candle',
-                                  'trade_info', 'last_date', 'last_count_of_buys', 'base_stake_amount', 'stop_buy']
+                                  'trade_info', 'last_date', 'last_count_of_buys',
+                                  'base_stake_amount', 'stop_buy', 'mises', 'dca_thresholds']
             df_filtered = df[df['count_of_buys'] > 0].drop(columns=colonnes_a_exclure)
             # df_filtered = df_filtered["first_price", "last_max", "max_touch", "last_sell","last_price", 'count_of_buys', 'current_profit']
 
@@ -890,6 +894,12 @@ class FrictradeLearning(IStrategy):
         self.calculeDerivees(dataframe, 'sma12', ema_period=6)
         self.calculeDerivees(dataframe, 'sma5', ema_period=3)
 
+        dataframe['sma60'] = dataframe['mid'].ewm(span=60, adjust=False).mean()
+        self.calculeDerivees(dataframe, 'sma60', ema_period=20)
+
+        dataframe['sma180'] = dataframe['mid'].ewm(span=180, adjust=False).mean()
+        self.calculeDerivees(dataframe, 'sma180', ema_period=60)
+
         horizon = 180
         self.calculateScores(dataframe, horizon)
 
@@ -979,7 +989,7 @@ class FrictradeLearning(IStrategy):
                         total_stake += stake
                         pct += dca
                         loss_amount += total_stake * dca_previous
-                        offset = self.dynamic_trailing_offset(pair, total_stake, price=val, ath=ath, count_of_buys=count)
+                        offset = self.dynamic_trailing_offset(pair, total_stake, last_candle, price=val, ath=ath, count_of_buys=count)
 
                         if count == self.pairs[pair]['count_of_buys'] - self.pairs[pair]['has_gain'] - 1:
                             print(f"next_buy={round(val * (1 - pct),1)} count={count} pct={round(pct, 4)}")
@@ -1059,6 +1069,8 @@ class FrictradeLearning(IStrategy):
             & (dataframe['sma5_deriv1'] > 0)
             & (dataframe['sma5_deriv2'] > 0)
             & (dataframe['rsi'] < 77)
+            & (dataframe['heat_score_1h'] < 0.5)
+            & (dataframe['sma180_deriv1'] > 0)
             # & (dataframe['open'] < dataframe['max180'] * 0.997)
             # & (dataframe['min180'].shift(3) == dataframe['min180'])
             , ['enter_long', 'enter_tag']
@@ -1454,7 +1466,7 @@ class FrictradeLearning(IStrategy):
         current_trailing_only_offset_is_reached = self.trailing_only_offset_is_reached
         current_trailing_stop_positive_offset = self.trailing_stop_positive_offset
 
-        current_trailing_stop_positive_offset = self.dynamic_trailing_offset(pair, self.pairs[pair]['total_amount'], price=current_rate,
+        current_trailing_stop_positive_offset = self.dynamic_trailing_offset(pair, self.pairs[pair]['total_amount'], last_candle, price=current_rate,
                                                                              ath=self.pairs[pair]['last_ath'],
                                                                              count_of_buys=count_of_buys)