diff --git a/Zeus_8_3_2_B_4_2.py b/Zeus_8_3_2_B_4_2.py
index 25206ef..e04408f 100644
--- a/Zeus_8_3_2_B_4_2.py
+++ b/Zeus_8_3_2_B_4_2.py
@@ -254,8 +254,7 @@ class Zeus_8_3_2_B_4_2(IStrategy):
         # val = self.getProbaHausse144(last_candle)
 
         # allow_to_buy = True #(not self.stop_all) #& (not self.all_down)
-        allow_to_buy = not self.pairs[pair][
-            'stop']  # and val > self.buy_val.value #not last_candle['tendency'] in ('B-', 'B--')  # (rate <= float(limit)) | (entry_tag == 'force_entry')
+        allow_to_buy = not self.pairs[pair]['stop']  # and val > self.buy_val.value #not last_candle['tendency'] in ('B-', 'B--')  # (rate <= float(limit)) | (entry_tag == 'force_entry')
 
         # if allow_to_buy:
         #     poly_func, x_future, y_future, count = self.polynomial_forecast(
@@ -455,9 +454,16 @@ class Zeus_8_3_2_B_4_2(IStrategy):
         if self.columns_logged % 30 == 0:
             self.printLog(
                 f"| {'Date':<16} | {'Action':<10} |{'Pair':<5}| {'Trade Type':<18} |{'Rate':>8} | {'Dispo':>6} | {'Profit':>8} | {'Pct':>6} | {'max_touch':>11} | {'last_lost':>12} | {'last_max':>7}|{'Buys':>4}| {'Stake':>5} |"
-                f"Tdc|{'val':>6}| sma5  |smooth |"
+                f"Tdc|{'val':>6}| sma5  |smooth |Distmax"
             )
             self.printLineLog()
+            df = pd.DataFrame.from_dict(self.pairs, orient='index')
+            colonnes_a_exclure = ['last_candle', 'last_trade', 'last_palier_index', 'stop',
+                                  'trade_info', 'last_date', 'expected_profit', 'last_count_of_buys', 'base_stake_amount', 'stop_buy']
+            df_filtered = df[df['count_of_buys'] > 0].drop(columns=colonnes_a_exclure)
+            # df_filtered = df_filtered["first_buy", "last_max", "max_touch", "last_sell","last_buy", 'count_of_buys', 'current_profit']
+
+            print(df_filtered)
 
         self.columns_logged += 1
         date = str(date)[:16] if date else "-"
@@ -492,6 +498,8 @@ class Zeus_8_3_2_B_4_2(IStrategy):
 
         total_counts = str(buys) + '/' + str(sum(pair_data['count_of_buys'] for pair_data in self.pairs.values()))
 
+        dist_max = round(100 * (last_candle['max12_1d'] - last_candle['min12_1d']) / last_candle['min12_1d'], 0)
+
         # if trade_type is not None:
         #     if np.isnan(last_candle['rsi_1d']):
         #         string = '  '
@@ -514,7 +522,7 @@ class Zeus_8_3_2_B_4_2(IStrategy):
             # f"|{round(last_candle['mid_smooth_24_deriv1'],3) or '-':>6}|{round(last_candle['mid_smooth_1h_deriv1'],3) or '-':>6}|{round(last_candle['mid_smooth_deriv1_1d'],3) or '-' :>6}|"
             # f"{round(last_candle['mid_smooth_24_deriv2'],3) or '-' :>6}|{round(last_candle['mid_smooth_1h_deriv2'],3) or '-':>6}|{round(last_candle['mid_smooth_deriv2_1d'],3) or '-':>6}|"
             f"{round(val, 1) or '-' :>6}|"
-            f"{round(last_candle['sma5_deriv1_1h'], 4) or '-' :>7}|{round(last_candle['mid_smooth_1h_deriv1'], 4) or '-' :>7}|"
+            f"{round(last_candle['sma5_deriv1_1h'], 4) or '-' :>7}|{round(last_candle['mid_smooth_1h_deriv1'], 4) or '-' :>7}|{dist_max:>7}"
         )
 
     def printLineLog(self):
@@ -523,7 +531,7 @@ class Zeus_8_3_2_B_4_2(IStrategy):
             f"+{'-' * 18}+{'-' * 12}+{'-' * 5}+{'-' * 20}+{'-' * 9}+{'-' * 8}+{'-' * 10}+{'-' * 8}+{'-' * 13}+{'-' * 14}+{'-' * 9}+{'-' * 4}+{'-' * 7}+"
             f"{'-' * 3}"
             # "+{'-' * 3}+{'-' * 3}
-            f"+{'-' * 6}+{'-' * 7}+{'-' * 7}+"
+            f"+{'-' * 6}+{'-' * 7}+{'-' * 7}+{'-' * 7}+"
         )
 
     def printLog(self, str):
@@ -595,9 +603,7 @@ class Zeus_8_3_2_B_4_2(IStrategy):
                 (dataframe["close"] - dataframe["bb_lowerband"]) /
                 (dataframe["bb_upperband"] - dataframe["bb_lowerband"])
         )
-        dataframe["bb_width"] = (
-                (dataframe["bb_upperband"] - dataframe["bb_lowerband"]) / dataframe["bb_upperband"]
-        )
+        dataframe["bb_width"] = ((dataframe["bb_upperband"] - dataframe["bb_lowerband"]) / dataframe["bb_upperband"])
         # Normalization
 
         # dataframe = self.calculateRegression(dataframe, column='mid_smooth', window=24, degree=4, future_offset=12)
@@ -859,10 +865,10 @@ class Zeus_8_3_2_B_4_2(IStrategy):
 
         # self.paliers = self.get_dca_stakes()
 
-        if self.dp.runmode.value in ('backtest'):
-            today = datetime.now().strftime("%Y-%m-%d-%H:%M:%S")
-            dataframe.to_feather(f"user_data/data/binance/{today}-{metadata['pair'].replace('/', '_')}_df.feather")
-            dataframe.to_csv(f"user_data/data/binance/{today}-{metadata['pair'].replace('/', '_')}_df.csv")
+        # if self.dp.runmode.value in ('backtest'):
+        #     today = datetime.now().strftime("%Y-%m-%d-%H:%M:%S")
+        #     dataframe.to_feather(f"user_data/data/binance/{today}-{metadata['pair'].replace('/', '_')}_df.feather")
+        #     dataframe.to_csv(f"user_data/data/binance/{today}-{metadata['pair'].replace('/', '_')}_df.csv")
         #
         #     df = dataframe
         #