diff --git a/Zeus_8_3_2_B_4_2.py b/Zeus_8_3_2_B_4_2.py
index b026c32..fad4235 100644
--- a/Zeus_8_3_2_B_4_2.py
+++ b/Zeus_8_3_2_B_4_2.py
@@ -190,8 +190,11 @@ class Zeus_8_3_2_B_4_2(IStrategy):
     # Somme Mises	50	100	150	250	350	500	700	950	1300	1750	2350	3150	4200
     # baisse	1	2	3	5	7	10	14	19	26	35	47	63	84
 
-    factors = [1, 1.1, 1.25, 1.5, 2.0, 3]
-    thresholds = [2, 5, 10, 20, 30, 50]
+    # factors = [1, 1.1, 1.25, 1.5, 2.0, 3]
+    # thresholds = [2, 5, 10, 20, 30, 50]
+
+    factors = [1, 1.25, 1.5, 2.0]
+    thresholds = [2, 5, 10, 20]
 
     trades = list()
     max_profit_pairs = {}
@@ -523,6 +526,8 @@ class Zeus_8_3_2_B_4_2(IStrategy):
         # val1h = self.getProbaHausse1h(last_candle)
         val = self.getProbaHausse(last_candle)
 
+        pct60 = round(100 * self.getPct60D(pair, last_candle), 2)
+
         self.printLog(
             f"| {date:<16} | {action:<10} | {pair[0:3]:<3} | {trade_type or '-':<18} |{rate or '-':>9}| {dispo or '-':>6} "
             f"| {profit or '-':>8} | {pct_max or '-':>6} | {round(self.pairs[pair]['max_touch'], 2) or '-':>11} | {last_lost or '-':>12} "
@@ -531,7 +536,7 @@ 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['mid_smooth_12'], 4) or '-' :>7}|{round(last_candle['mid_smooth_12_deriv1'], 4) or '-' :>7}|{dist_max:>7}"
+            f"{round(last_candle['mid_smooth_12'], 4) or '-' :>7}|{round(last_candle['mid_smooth_12_deriv1'], 4) or '-' :>7}|{dist_max:>7}|{pct60:>7}"
         )
 
     def printLineLog(self):
@@ -726,8 +731,8 @@ class Zeus_8_3_2_B_4_2(IStrategy):
         # ===============================
         # lissage des valeurs horaires
         dataframe['mid_smooth_1h'] = dataframe['mid'].rolling(window=6).mean()
-        dataframe["mid_smooth_1h_deriv1"] = 100 * dataframe["mid_smooth_1h"].diff() / dataframe['mid_smooth_1h']
-        dataframe["mid_smooth_1h_deriv2"] = 10 * dataframe["mid_smooth_1h_deriv1"].diff()
+        dataframe["mid_smooth_1h_deriv1"] = 100 * dataframe["mid_smooth_1h"].diff().rolling(window=6).mean() / dataframe['mid_smooth_1h']
+        dataframe["mid_smooth_1h_deriv2"] = 10 * dataframe["mid_smooth_1h_deriv1"].diff().rolling(window=6).mean()
 
         # Compter les baisses / hausses consécutives
         self.calculateDownAndUp(dataframe, limit=0.0001)
@@ -855,12 +860,18 @@ class Zeus_8_3_2_B_4_2(IStrategy):
 
         dataframe.loc[
             (
-                # (dataframe['mid_smooth_1h_deriv1'].shift(2) >= dataframe['mid_smooth_1h_deriv1'].shift(1))
-                # & (dataframe['mid_smooth_1h_deriv1'].shift(1) <= dataframe['mid_smooth_1h_deriv1'])
-
-                (dataframe['mid_smooth_1h_deriv1'].shift(2) >= dataframe['mid_smooth_1h_deriv1'].shift(1))
-                & (dataframe['mid_smooth_1h_deriv1'].shift(1) <= dataframe['mid_smooth_1h_deriv1'])
-                & (dataframe['close'] < dataframe['bb_middleband'])
+                # (dataframe['deriv2_1h'].shift(2) >= dataframe['deriv2_1h'].shift(1))
+                # & (dataframe['deriv2_1h'].shift(1) <= dataframe['deriv2_1h'])
+                # (dataframe['deriv1_1h'] >= -0.01)
+                # & (dataframe['deriv2_1h'] >= -0.00)
+                    (dataframe['mid_smooth_3_deriv1'].shift(2) >= dataframe['mid_smooth_3_deriv1'].shift(1))
+                    & (dataframe['mid_smooth_3_deriv1'].shift(1) <= dataframe['mid_smooth_3_deriv1'])
+                #
+                #
+                # (dataframe['mid_smooth_1h_deriv1'] >= 0)
+                # & (dataframe['mid_smooth_1h_deriv1'] >= 0)
+                # & (dataframe['mid_smooth_1h_deriv1'].shift(1) <= 0)
+                # & (dataframe['mid_smooth_1h_deriv1'] >= dataframe['mid_smooth_1h_deriv1'].shift(1))
                 ), ['enter_long', 'enter_tag']] = (1, 'smth')
 
         dataframe['can_sell'] = np.where(((dataframe['mid_smooth_1h_deriv1'].shift(2) <= dataframe['mid_smooth_1h_deriv1'].shift(1))\
@@ -872,6 +883,9 @@ class Zeus_8_3_2_B_4_2(IStrategy):
         dataframe['test'] = np.where(dataframe['enter_long'] == 1, dataframe['close'] * 1.01, np.nan)
         dataframe['perte_02'] = np.where((dataframe['hapercent3'] * 100 < -0.2), dataframe['close'], np.nan)
 
+        dataframe['mid_smooth_1h_deriv2_inv'] = np.where((dataframe['mid_smooth_1h_deriv2'].shift(2) >= dataframe['mid_smooth_1h_deriv2'].shift(1))
+        & (dataframe['mid_smooth_1h_deriv2'].shift(1) <= dataframe['mid_smooth_1h_deriv2']), dataframe['close'], np.nan)
+
         # self.paliers = self.get_dca_stakes()
 
         # if self.dp.runmode.value in ('backtest'):
@@ -1017,12 +1031,14 @@ class Zeus_8_3_2_B_4_2(IStrategy):
 
         if pair in ('BTC/USDT', 'BTC/USDC') or count_of_buys <= 2:
             lim = - pct - (count_of_buys * 0.001)
+            #lim = self.getLimitBuy(pair, last_candle, pct)
             # lim = - (0.012 * (1 + round(count_of_buys / 5)) + 0.001 * (count_of_buys - 1))
             # lim = - (0.012 + 0.001 * (count_of_buys - 1) + (0.002 * count_of_buys if count_of_buys > 10 else 0.001 * count_of_buys if count_of_buys > 5 else 0))
 
         else:
             pct = 0.05
             lim = - pct - (count_of_buys * 0.001)
+            #lim = self.getLimitBuy(pair, last_candle, pct)
 
         if (len(dataframe) < 1):
             print("skip dataframe")
@@ -1255,6 +1271,23 @@ class Zeus_8_3_2_B_4_2(IStrategy):
     def getPctLastBuy(self, pair, last_candle):
         return round((last_candle['close'] - self.pairs[pair]['last_buy']) / self.pairs[pair]['last_buy'], 4)
 
+    def getPct60D(self, pair, last_candle):
+        return round((last_candle['max60_1d'] - last_candle['min60_1d']) / last_candle['max60_1d'], 4)
+
+    def getLimitBuy(self, pair, last_candle, first_pct):
+        count_of_buys = self.pairs[pair]['count_of_buys']
+        pct60 = self.getPct60D(pair, last_candle) # exemple 0.3 pour 30%
+        if (pct60 < 0.05):
+            lim = - first_pct - (count_of_buys * 0.001 * 0.05 / 0.05)
+        else:
+            # 0.1
+            # 0.4
+
+            lim = - first_pct - (count_of_buys * 0.001 * pct60 / 0.05)
+
+        return lim
+
+
     def getProbaHausse(self, last_candle):
         value_1 = self.getValuesFromTable(self.ema_volume, last_candle['ema_volume'])
         value_2 = self.getValuesFromTable(self.mid_smooth_1h_deriv1, last_candle['mid_smooth_1h_deriv1'])
@@ -1298,8 +1331,7 @@ class Zeus_8_3_2_B_4_2(IStrategy):
         count_of_buys = self.pairs[pair]['count_of_buys']
         pct_first = self.getPctFirstBuy(pair, last_candle)
 
-        expected_profit = max(0.004, abs(
-            pct_first / 3))  # 0.004 + 0.002 * self.pairs[pair]['count_of_buys'] #min(0.01, first_max)
+        expected_profit = max(0.004, abs(pct_first / 3))  # 0.004 + 0.002 * self.pairs[pair]['count_of_buys'] #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}")
@@ -1833,7 +1865,7 @@ class Zeus_8_3_2_B_4_2(IStrategy):
                 max_pair = p
             total_non_btc += self.pairs[p]['count_of_buys']
 
-        pct_max = self.getPctLastBuy(pair, last_candle)
+        pct_max = self.getPctFirstBuy(pair, last_candle) #self.getPctLastBuy(pair, last_candle)
 
         # val = self.getProbaHausse(last_candle)
         # if (val < 40):