Test nouvelle stratégie max 24h

Zeus_8_3_2_B_4_2.py

@@ -124,21 +124,18 @@ class Zeus_8_3_2_B_4_2(IStrategy):
      ]
 
     levels = [1, 2, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20]
-    # startup_candle_count = 12 * 24 * 5
+    startup_candle_count = 12 * 24 * 5
 
     # ROI table:
     minimal_roi = {
-        "0": 0.564,
+        "1440": 0
-        "567": 0.273,
-        "2814": 0.12,
-        "7675": 0
     }
     stakes = 40
 
     # Stoploss:
     stoploss = -1  # 0.256
     # Custom stoploss
-    use_custom_stoploss = False
+    use_custom_stoploss = True
 
     trailing_stop = True
     trailing_stop_positive = 0.15
@@ -257,6 +254,7 @@ class Zeus_8_3_2_B_4_2(IStrategy):
     columns_logged = False
     pairs = {
         pair: {
+            'first_amount': 0,
             "first_buy": 0,
             "last_buy": 0.0,
             "last_min": 999999999999999.5,
@@ -329,52 +327,11 @@ class Zeus_8_3_2_B_4_2(IStrategy):
     pct = DecimalParameter(0.005, 0.05, default=0.012, decimals=3, space='buy', optimize=True, load=True)
     pct_inc = DecimalParameter(0.0001, 0.003, default=0.0022, decimals=4, space='buy', optimize=True, load=True)
 
-    indic_5m_slope_sup_buy = CategoricalParameter(indicators, default="sma60", space='buy')
-    # indic_deriv_5m_slop_sup_buy = CategoricalParameter(indicators, default="sma12", space='buy', optimize=True, load=True)
-    # deriv_5m_slope_sup_buy = DecimalParameter(-0.1, 0.5, default=0, decimals=2, space='buy', optimize=True, load=True)
-
-    indic_5m_slope_inf_buy = CategoricalParameter(indicators, default="sma60", space='buy')
-    # indic_deriv_5m_slop_inf_buy = CategoricalParameter(indicators, default="sma12", space='buy', optimize=True, load=True)
-    # deriv_5m_slope_inf_buy = DecimalParameter(-0.1, 0.5, default=0, decimals=2, space='buy', optimize=True, load=True)
-
-
-    # indic_deriv1_5m = DecimalParameter(-2, 2, default=0, decimals=2, space='buy', optimize=True, load=True)
-    # indic_deriv2_5m = DecimalParameter(-2, 2, default=0, decimals=2, space='buy', optimize=True, load=True)
-
-    # indic_1h = CategoricalParameter(indicators, default="sma60", space='buy')
-    # indic_deriv1_1h = DecimalParameter(-5, 5, default=0, decimals=1, space='buy', optimize=True, load=True)
-    # indic_deriv2_1h = DecimalParameter(-10, 10, default=0, decimals=1, space='buy', optimize=True, load=True)
-
-    # indic_1d_p = CategoricalParameter(indicators, default="sma60", space='protection')
-    # indic_deriv1_1d_p_stop = DecimalParameter(-2, 2, default=0, decimals=1, space='protection', optimize=True, load=True)
-    # indic_deriv2_1d_p_stop = DecimalParameter(-4, 4, default=0, decimals=1, space='protection', optimize=True, load=True)
-    # indic_deriv1_1d_p_start = DecimalParameter(-2, 2, default=0, decimals=1, space='protection', optimize=True, load=True)
-    # indic_deriv2_1d_p_start = DecimalParameter(-4, 4, default=0, decimals=1, space='protection', optimize=True, load=True)
-
-
-    indic_5m_slope_sup_sell = CategoricalParameter(indicators, default="sma60", space='sell')
-    indic_deriv_5m_slope_sup_sell = CategoricalParameter(indicators, default="sma60", space='sell')
-    deriv_5m_slope_sup_sell = DecimalParameter(-0.1, 0.5, default=0, decimals=2, space='sell', optimize=True, load=True)
-
-    indic_5m_slope_inf_sell = CategoricalParameter(indicators, default="sma60", space='sell')
-    indic_deriv_5m_slope_inf_sell = CategoricalParameter(indicators, default="sma60", space='sell')
-    deriv_5m_slope_inf_sell = DecimalParameter(-0.1, 0.5, default=0, decimals=2, space='sell', optimize=True, load=True)
-
     deriv1_buy_protect = DecimalParameter(-0.3, 0.1, default=-0.1, decimals=2, space='protection', optimize=True, load=True)
     rsi_buy_protect = IntParameter(50, 90, default=70, space='protection', optimize=True, load=True)
     indic_5m_slope_sup = CategoricalParameter(indicators, default="sma60", space='protection')
     indic_1h_slope_sup = CategoricalParameter(indicators, default="sma5", space='protection')
 
-    # indic_percent_sell = CategoricalParameter(indicators_percent, default="sma60", space='sell')
-
-    # percent_5m_sell = DecimalParameter(-0.1, -0.0, default=0, decimals=2, space='sell', optimize=True, load=True)
-
-    # indic_deriv1_5m_sell = DecimalParameter(-2, 2, default=0, decimals=2, space='sell', optimize=True, load=True)
-    # indic_deriv2_5m_sell = DecimalParameter(-2, 2, default=0, decimals=2, space='sell', optimize=True, load=True)
-
-    # indic_deriv1_1h_sell = DecimalParameter(-5, 5, default=0, decimals=1, space='sell', optimize=True, load=True)
-    # indic_deriv2_1h_sell = DecimalParameter(-10, 10, default=0, decimals=1, space='sell', optimize=True, load=True)
-
     labels = ['B3', 'B2', 'B1', 'N0', 'H1', 'H2', 'H3']
     index_labels = ['B3', 'B2', 'B1', 'N0', 'H1', 'H2', 'H3']
     ordered_labels = ['B3', 'B2', 'B1', 'N0', 'H1', 'H2', 'H3']
@@ -456,6 +413,18 @@ class Zeus_8_3_2_B_4_2(IStrategy):
             if not self.should_enter_trade(pair, last_candle, current_time):
                 allow_to_buy = False
 
+        # On récupère le dernier trade ouvert (toutes paires)
+        last_date = self.pairs[pair]['last_date']
+
+        if not last_date:
+            last_date = dataframe.iloc[-1]["date"]
+            self.pairs[pair]['last_date'] = last_date
+
+        now = dataframe.iloc[-1]["date"]
+
+        if now - last_date >= timedelta(hours=24):
+            allow_to_buy = True
+
         if allow_to_buy:
             self.trades = list()
             self.pairs[pair]['first_buy'] = rate
@@ -466,7 +435,7 @@ class Zeus_8_3_2_B_4_2(IStrategy):
             self.pairs[pair]['current_profit'] = 0
             self.pairs[pair]['last_max'] = max(last_candle['close'], self.pairs[pair]['last_max'])
             self.pairs[pair]['last_min'] = min(last_candle['close'], self.pairs[pair]['last_min'])
-
+            self.pairs[pair]['last_date'] = current_time
             dispo = round(self.wallets.get_available_stake_amount())
             self.printLineLog()
 
@@ -499,7 +468,7 @@ class Zeus_8_3_2_B_4_2(IStrategy):
 
         profit =trade.calc_profit(rate)
         force = self.pairs[pair]['force_sell']
-        allow_to_sell = (last_candle['hapercent'] < 0 and profit > 0) or force or (exit_reason == 'force_exit') or (exit_reason == 'stop_loss')
+        allow_to_sell = (last_candle['hapercent'] < 0 and profit > 0) or force or (exit_reason == 'sma48') or (exit_reason == 'force_exit') or (exit_reason == 'stop_loss')
 
         minutes = int(round((current_time - trade.date_last_filled_utc).total_seconds() / 60, 0))
 
@@ -534,7 +503,7 @@ class Zeus_8_3_2_B_4_2(IStrategy):
             self.pairs[pair]['current_trade'] = None
         # else:
         #     print(f"STOP triggered for {pair} ({exit_reason}) but condition blocked", "warning")
-        return (allow_to_sell) | (exit_reason == 'force_exit') | (exit_reason == 'stop_loss')
+        return (allow_to_sell) | (exit_reason == 'force_exit') | (exit_reason == 'stop_loss') | force
 
     def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: float,
                             proposed_stake: float, min_stake: float, max_stake: float,
@@ -580,21 +549,15 @@ class Zeus_8_3_2_B_4_2(IStrategy):
         self.pairs[pair]['current_profit'] = profit
 
         dispo = round(self.wallets.get_available_stake_amount())
-        hours_since_first_buy = (current_time - trade.open_date_utc).seconds / 3600.0
+        hours_since_first_buy = round((current_time - trade.open_date_utc).seconds / 3600.0, 1)
         days_since_first_buy = (current_time - trade.open_date_utc).days
         hours = (current_time - trade.date_last_filled_utc).total_seconds() / 3600.0
 
-        # trend = last_candle['trend_class']
-        #
-        # indic_5m_sell = self.getParamValue( pair, trend, 'sell', 'indic_5m_sell')
-        # indic_deriv1_5m_sell = self.getParamValue( pair, trend, 'sell', 'indic_deriv1_5m_sell')
-        # indic_deriv2_5m_sell = self.getParamValue( pair, trend, 'sell', 'indic_deriv2_5m_sell')
-
         if hours % 4 == 0:
             self.log_trade(
                 last_candle=last_candle,
                 date=current_time,
-                action="🔴 CURRENT" if self.pairs[pair]['stop'] else "🟢 CURRENT",
+                action="🔴 CURRENT" if self.pairs[pair]['stop'] else "🟢 CURRENT " + str(hours_since_first_buy),
                 dispo=dispo,
                 pair=pair,
                 rate=last_candle['close'],
@@ -612,32 +575,16 @@ class Zeus_8_3_2_B_4_2(IStrategy):
         # if (last_candle['sma24_deriv1'] < 0 and before_last_candle['sma24_deriv1'] >= 0) and (current_profit > expected_profit):
         #     return 'Drv_' + str(count_of_buys)
         pair_name = self.getShortName(pair)
-        # if (current_profit > expected_profit) and last_candle['can_sell']:
-        #     return 'Can_' + pair_name + '_' + str(count_of_buys)
-        # trend = last_candle['trend_class_1d']
-        # if (trend == "B-" or trend == "B--") and self.pairs[pair]['has_gain'] == 0: # and (last_candle[f"{indic_5m_sell}_deriv1"] <= indic_deriv1_5m_sell and last_candle[f"{indic_5m_sell}_deriv2"] <= indic_deriv2_5m_sell):
-        #
-        #     if (last_candle['max_rsi_12_1h'] > 75) and last_candle['trend_class_1h'] == 1 and profit > max(5, expected_profit) and (last_candle['hapercent'] < 0):
-        #         self.pairs[pair]['stop'] = True
-        #         self.log_trade(
-        #             last_candle=last_candle,
-        #             date=current_time,
-        #             action="🔴STOP",
-        #             dispo=dispo,
-        #             pair=pair,
-        #             rate=last_candle['close'],
-        #             trade_type='',
-        #             profit=self.pairs[pair]['current_profit'],
-        #             buys=self.pairs[pair]['count_of_buys'],
-        #             stake=0
-        #         )
-        #         return "MAX_RSI"
-        #
-        #     return None
 
-        # if (trend == "B-" or trend == "B--") and last_candle[f"{self.indic_5m_sell.value}_deriv1"] <= self.indic_deriv1_5m_sell.value \
+        if current_profit < - 0.02 and last_candle['sma48'] < before_last_candle['sma48'] - 10:
-        #         and last_candle[f"{self.indic_5m_sell.value}_deriv2"] <= self.indic_deriv2_5m_sell.value:
+            self.pairs[pair]['force_sell'] = True
-        #     return None
+            self.pairs[pair]['force_buy'] = False #(self.pairs[pair]['count_of_buys'] - self.pairs[pair]['has_gain'] > 3)
+            return str(count_of_buys) + '_' + 'stop48_' + pair_name + '_' + str(self.pairs[pair]['has_gain'])
+
+        if hours_since_first_buy >= 23.9    :
+            self.pairs[pair]['force_sell'] = True
+            self.pairs[pair]['force_buy'] = False #(self.pairs[pair]['count_of_buys'] - self.pairs[pair]['has_gain'] > 3)
+            return str(count_of_buys) + '_' + 'hours_' + pair_name + '_' + str(self.pairs[pair]['has_gain'])
 
         if last_candle['max_rsi_24'] > 85 and profit > max(5, expected_profit) and (last_candle['hapercent'] < 0) and last_candle['sma60_deriv1'] < 0.05:
             self.pairs[pair]['force_sell'] = False
@@ -650,12 +597,12 @@ class Zeus_8_3_2_B_4_2(IStrategy):
             return str(count_of_buys) + '_' +  'Frc_' + pair_name + '_' + str(self.pairs[pair]['has_gain'])
 
         if profit > max(5, expected_profit) and baisse > 0.30:
-            self.pairs[pair]['force_sell'] = False
+            self.pairs[pair]['force_sell'] = True
             self.pairs[pair]['force_buy'] = (self.pairs[pair]['count_of_buys'] - self.pairs[pair]['has_gain'] > 3)
             return str(count_of_buys) + '_' + 'B30_' + pair_name + '_' + str(self.pairs[pair]['has_gain'])
 
         if max_profit > 0.5 * count_of_buys and baisse > 0.15 and last_candle['sma12_state'] <= 0 and last_candle['sma60_state'] <= - 1:
-            self.pairs[pair]['force_sell'] = False
+            self.pairs[pair]['force_sell'] = True
             self.pairs[pair]['force_buy'] = (self.pairs[pair]['count_of_buys'] - self.pairs[pair]['has_gain'] > 3)
             return str(count_of_buys) + '_' + 'B15_' + pair_name + '_' + str(self.pairs[pair]['has_gain'])
 
@@ -831,16 +778,6 @@ class Zeus_8_3_2_B_4_2(IStrategy):
         #     🟡 Dérivée 1 > 0 et dérivée 2 < 0: tendance haussière qui ralentit → essoufflement potentiel.
         #     🔴 Dérivée 1 < 0 et dérivée 2 < 0: tendance baissière qui s’accélère.
         #     🟠 Dérivée 1 < 0 et dérivée 2 > 0: tendance baissière qui ralentit → possible bottom.
-        # trend = last_candle['trend_class_1d']
-        #
-        # indic_5m = self.getParamValue(pair, trend, 'buy', 'indic_5m')
-        # indic_deriv1_5m = self.getParamValue(pair, trend, 'buy', 'indic_deriv1_5m')
-        # indic_deriv2_5m = self.getParamValue(pair, trend, 'buy', 'indic_deriv2_5m')
-        #
-        # indic_5m_sell = self.getParamValue(pair, trend, 'sell', 'indic_5m_sell')
-        # indic_deriv1_5m_sell = self.getParamValue(pair, trend, 'sell', 'indic_deriv1_5m_sell')
-        # indic_deriv2_5m_sell = self.getParamValue(pair, trend, 'sell', 'indic_deriv2_5m_sell')
-
 
         self.printLog(
             f"| {date:<16} |{action:<10} | {pair[0:3]:<3} | {trade_type or '-':<18} |{rate or '-':>9}| {dispo or '-':>6} "
@@ -856,7 +793,7 @@ class Zeus_8_3_2_B_4_2(IStrategy):
             # f"|{last_candle['min60_1d']}|{last_candle['max60_1d']}"
             # f"|{last_candle['mid_smooth_tdc_5_1d'] or '-':>3}|{last_candle['mid_smooth_tdc_5_1h'] or '-':>3}|{last_candle['mid_smooth_tdc_5'] or '-':>3}"
             f"|{last_candle['mid_smooth_5_state_1d'] or '-':>3}|{last_candle['mid_smooth_24_state_1h'] or '-':>3}|{last_candle['mid_smooth_5_state_1h'] or '-':>3}|{last_candle['mid_smooth_5_state'] or '-':>3}"
-            f"|{last_candle['trend_class_1d']:>5} {last_candle['trend_class_1h']:>5}" # {indic_5m} {indic_deriv1_5m} {indic_deriv2_5m} {indic_5m_sell} {indic_deriv1_5m_sell} {indic_deriv2_5m_sell}"
+            f"|{last_candle['trend_class_1d']:>5} {last_candle['trend_class_1h']:>5}"
         )
 
     def getLastLost(self, last_candle, pair):
@@ -1921,14 +1858,14 @@ class Zeus_8_3_2_B_4_2(IStrategy):
 
 
         # dérivée relative simple
-        dataframe[d1_col] = (dataframe[name] - dataframe[name].shift(1)) / dataframe[name].shift(1)
+        dataframe[d1_col] = factor1 * ((dataframe[name] - dataframe[name].shift(3)) / dataframe[name].shift(3))
         # lissage EMA
-        dataframe[d1_col] = factor1 * dataframe[d1_col].ewm(span=ema_period, adjust=False).mean()
+        # dataframe[d1_col] = factor1 * dataframe[d1_col].ewm(span=ema_period, adjust=False).mean()
 
         # dataframe[d1_col] = dataframe[d1_col].rolling(window=ema_period, center=True).median()
 
-        dataframe[d2_col] = dataframe[d1_col] - dataframe[d1_col].shift(1)
+        dataframe[d2_col] = factor2 * (dataframe[d1_col] - dataframe[d1_col].shift(1))
-        dataframe[d2_col] = factor2 * dataframe[d2_col].ewm(span=ema_period, adjust=False).mean()
+        # dataframe[d2_col] = factor2 * dataframe[d2_col].ewm(span=ema_period, adjust=False).mean()
 
         # epsilon adaptatif via rolling percentile
         p_low_d1 = dataframe[d1_col].rolling(window=window, min_periods=1).quantile(0.05)
@@ -2006,116 +1943,13 @@ class Zeus_8_3_2_B_4_2(IStrategy):
 
     def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
         pair = metadata['pair']
-        # trend = self.getTrend(dataframe)
-        # # params = self.loadParamsFor(pair, trend)
-        #
-        # indic_5m = self.getParamValue(pair, trend, 'buy', 'indic_5m')
-        # indic_deriv1_5m = self.getParamValue( pair, trend, 'buy', 'indic_deriv1_5m')
-        # indic_deriv2_5m = self.getParamValue( pair, trend, 'buy', 'indic_deriv2_5m')
-
-        # dataframe.loc[
-        #     (
-        #             (dataframe['mid_smooth_3'].shift(1) < dataframe['mid_smooth_3'])
-        #             & (dataframe['hapercent'] > 0)
-        #             & ((dataframe['max_rsi_24_1h'] < 70) | (dataframe['close'] < dataframe['close_1d']))
-        #             & (dataframe['open'] <= dataframe['bb_middleband'])
-        #             & (dataframe[f"{self.indic_5m.value}_deriv1"] >= self.indic_deriv1_5m.value)
-        #             & (dataframe[f"{self.indic_5m.value}_deriv2"] >= self.indic_deriv2_5m.value)
-        #             # & (dataframe[f"{indic_1h}_deriv1"] >= self.indic_deriv1_1h.value)
-        #             # & (dataframe[f"{indic_1h}_deriv2"] >= self.indic_deriv2_1h.value)
-        #     ), ['enter_long', 'enter_tag']] = (1, 'smth')
-
-        # dataframe.loc[
-        #     (
-        #             (dataframe['sma24_deriv2'].shift(1) < 0)
-        #             & (dataframe['sma24_deriv2'] > 0)
-        #             & ((dataframe['max_rsi_24_1h'] < 70) | (dataframe['close'] < dataframe['close_1d']))
-        #             & (dataframe[f"{self.indic_5m.value}_deriv1"] >= self.indic_deriv1_5m.value)
-        #             & (dataframe[f"{self.indic_5m.value}_deriv2"] >= self.indic_deriv2_5m.value)
-        #             # & (dataframe[f"{indic_1h}_deriv1"] >= self.indic_deriv1_1h.value)
-        #             # & (dataframe[f"{indic_1h}_deriv2"] >= self.indic_deriv2_1h.value)
-        #             & (dataframe[f"sma60_deriv1"] >= -0.2)
-        #     ), ['enter_long', 'enter_tag']] = (1, 'invert')
-        #
-        # dataframe.loc[
-        #     (
-        #             (dataframe['sma24_deriv1'] > 0)
-        #             & (dataframe['sma60_deriv1'].shift(1) < 0)
-        #             & (dataframe['sma60_deriv1'] > 0)
-        #             & ((dataframe['max_rsi_24_1h'] < 70) | (dataframe['close'] < dataframe['close_1d']))
-        #             & (dataframe[f"{self.indic_5m.value}_deriv1"] >= self.indic_deriv1_5m.value)
-        #             & (dataframe[f"{self.indic_5m.value}_deriv2"] >= self.indic_deriv2_5m.value)
-        #             # & (dataframe[f"{indic_1h}_deriv1"] >= self.indic_deriv1_1h.value)
-        #             # & (dataframe[f"{indic_1h}_deriv2"] >= self.indic_deriv2_1h.value)
-        #             & (dataframe[f"sma60_deriv1"] >= -0.2)
-        #     ), ['enter_long', 'enter_tag']] = (1, 'raise')
-        #
-        # dataframe.loc[
-        #     (
-        #             (dataframe['sma60_deriv1'].shift(1) < 0)
-        #             & (dataframe['sma24_deriv2'] > 0)
-        #             & ((dataframe['max_rsi_24_1h'] < 70) | (dataframe['close'] < dataframe['close_1d']))
-        #             & (dataframe[f"{self.indic_5m.value}_deriv1"] >= self.indic_deriv1_5m.value)
-        #             & (dataframe[f"{self.indic_5m.value}_deriv2"] >= self.indic_deriv2_5m.value)
-        #             # & (dataframe[f"{indic_1h}_deriv1"] >= self.indic_deriv1_1h.value)
-        #             # & (dataframe[f"{indic_1h}_deriv2"] >= self.indic_deriv2_1h.value)
-        #             & (dataframe[f"sma60_deriv1"] >= -0.2)
-        #     ), ['enter_long', 'enter_tag']] = (1, 'stg_inv')
-        #
-        # dataframe.loc[
-        #     (
-        #             (dataframe['mid_smooth_24'].shift(24) >= dataframe['mid_smooth_24'].shift(12))
-        #             & (dataframe['mid_smooth_24'].shift(12) <= dataframe['mid_smooth_24'])
-        #             & ((dataframe['max_rsi_24_1h'] < 70) | (dataframe['close'] < dataframe['close_1d']))
-        #             & (dataframe[f"{self.indic_5m.value}_deriv1"] >= self.indic_deriv1_5m.value)
-        #             & (dataframe[f"{self.indic_5m.value}_deriv2"] >= self.indic_deriv2_5m.value)
-        #             # & (dataframe[f"{indic_1h}_deriv1"] >= self.indic_deriv1_1h.value)
-        #             & (dataframe[f"sma60_deriv1"] >= -0.2)
-        #     ), ['enter_long', 'enter_tag']] = (1, 'smth3_inv')
 
         dataframe.loc[
             (
-                (dataframe['percent3'] < -0.03)
+               # (dataframe["mid_smooth_5_deriv1_1d"] > 0)
-                & (dataframe['percent'] > 0)
+               #  (dataframe["sma48_deriv2"] > 0)
-            ), ['enter_long', 'enter_tag']] = (1, 'pct3')
+               (dataframe['sma48'] > dataframe['sma48'].shift(1) + 20)
-
+            ), ['enter_long', 'enter_tag']] = (1, 'sma48')
-        dataframe.loc[
-            (
-               (dataframe["sma24"].shift(2) >= dataframe["sma24"].shift(1))
-                & (dataframe["sma24"].shift(1) <= dataframe["sma24"])
-                & (dataframe['percent3_1h'] < -0.03)
-            ), ['enter_long', 'enter_tag']] = (1, 'pct3_1h')
-
-        dataframe.loc[
-            (
-                (dataframe[f"{self.indic_5m_slope_sup_buy.value}"].shift(2) >= dataframe[f"{self.indic_5m_slope_sup_buy.value}"].shift(1))
-                & (dataframe[f"{self.indic_5m_slope_sup_buy.value}"].shift(1) <= dataframe[f"{self.indic_5m_slope_sup_buy.value}"])
-                & (dataframe['slope_norm_1d'] < dataframe['slope_norm_1h'])
-                & (dataframe['stop_buying'] == False)
-                # & (dataframe[f"{self.indic_deriv_5m_buy.value}_deriv1"] > self.deriv_5m_buy.value)
-                # & (dataframe[f"sma60_deriv1"] >= -0.2)
-                # & (dataframe[f"hapercent"] >= -0.001)
-            ), ['enter_long', 'enter_tag']] = (1, f"{self.indic_5m_slope_sup.value}_sup")
-
-        dataframe.loc[
-            (
-                (dataframe[f"{self.indic_5m_slope_inf_buy.value}"].shift(2) >= dataframe[f"{self.indic_5m_slope_inf_buy.value}"].shift(1))
-                & (dataframe[f"{self.indic_5m_slope_inf_buy.value}"].shift(1) <= dataframe[f"{self.indic_5m_slope_inf_buy.value}"])
-                & (dataframe['slope_norm_1d'] > dataframe['slope_norm_1h'])
-                & (dataframe['stop_buying'] == False)
-                # & (dataframe[f"{self.indic_deriv_5m_buy.value}_deriv1"] > self.deriv_5m_buy.value)
-                # & (dataframe[f"sma60_deriv1"] >= -0.2)
-                # & (dataframe[f"hapercent"] >= -0.001)
-            ), ['enter_long', 'enter_tag']] = (1, f"{self.indic_5m_slope_inf_buy.value}_inf")
-
-        dataframe.loc[
-            (
-                (dataframe['stop_buying'] == False)
-                & (dataframe['stop_buying'].shift(1) == True)
-                # & (dataframe[f"{self.indic_deriv_5m_buy.value}_deriv1"] > self.deriv_5m_buy.value)
-                # & (dataframe[f"sma60_deriv1"] >= -0.2)
-                # & (dataframe[f"hapercent"] >= -0.001)
-            ), ['enter_long', 'enter_tag']] = (1, f"end")
 
         dataframe['test'] = np.where(dataframe['enter_long'] == 1, dataframe['close'] * 1.01, np.nan)
 
@@ -2270,40 +2104,8 @@ class Zeus_8_3_2_B_4_2(IStrategy):
     def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
         # dataframe.loc[
         #     (
-        #         (dataframe['mid_smooth_deriv1'] == 0)
+        #         (dataframe['sma48'] < dataframe['sma48'].shift(1) - 10)
-        #         & (dataframe['mid_smooth_deriv1'].shift(1) > 0)
+        #     ), ['exit_long', 'exit_tag']] = (1, 'sma48')
-        #     ), ['sell', 'exit_long']] = (1, 'sell_sma5_pct_1h')
-
-        # {indic_5m_sell} {indic_deriv1_5m_sell} {indic_deriv2_5m_sell}
-        dataframe.loc[
-            (
-                (dataframe[f"{self.indic_5m_slope_sup_sell.value}"].shift(2) < dataframe[f"{self.indic_5m_slope_sup_sell.value}"].shift(1))
-                & (dataframe[f"{self.indic_5m_slope_sup_sell.value}"].shift(1) > dataframe[f"{self.indic_5m_slope_sup_sell.value}"])
-                & (dataframe[f"{self.indic_deriv_5m_slope_sup_sell.value}_deriv1"] < self.deriv_5m_slope_sup_sell.value)
-                & (dataframe['slope_norm_1d'] > dataframe['slope_norm_1h'])
-            ), ['exit_long', 'exit_tag']] = (1, f"{self.indic_5m_slope_sup_sell.value}_sup")
-
-        dataframe.loc[
-            (
-                (dataframe[f"{self.indic_5m_slope_inf_sell.value}"].shift(2) < dataframe[f"{self.indic_5m_slope_inf_sell.value}"].shift(1))
-                & (dataframe[f"{self.indic_5m_slope_inf_sell.value}"].shift(1) > dataframe[f"{self.indic_5m_slope_inf_sell.value}"])
-                & (dataframe[f"{self.indic_deriv_5m_slope_inf_sell.value}_deriv1"] < self.deriv_5m_slope_inf_sell.value)
-                & (dataframe['slope_norm_1d'] < dataframe['slope_norm_1h'])
-            ), ['exit_long', 'exit_tag']] = (1, f"{self.indic_5m_slope_inf_sell.value}_inf")
-
-        dataframe.loc[
-            (
-                (dataframe['stop_buying'] == True)
-                & (dataframe['stop_buying'].shift(1) == False)
-                # & (dataframe[f"{self.indic_deriv_5m_buy.value}_deriv1"] > self.deriv_5m_buy.value)
-                # & (dataframe[f"sma60_deriv1"] >= -0.2)
-                # & (dataframe[f"hapercent"] >= -0.001)
-            ), ['enter_long', 'enter_tag']] = (1, f"start")
-
-        # dataframe.loc[
-        #     (
-        #         (dataframe[f"{self.indic_percent_sell.value}"] < self.percent_5m_sell.value)
-        #     ), ['exit_long', 'exit_tag']] = (1, f"{self.indic_percent_sell.value}")
 
         return dataframe
 
@@ -2443,7 +2245,7 @@ class Zeus_8_3_2_B_4_2(IStrategy):
                 print(exception)
                 return None
 
-        if (profit > self.pairs[pair]['previous_profit'] and profit > self.pairs[pair]['expected_profit']  and hours > 6
+        if (profit > self.pairs[pair]['previous_profit'] and profit > self.pairs[pair]['expected_profit'] and hours > 6
                 # and last_candle['sma60_deriv1'] > 0
                 and last_candle['max_rsi_12_1h'] < 75
                 and last_candle['rsi_1d'] < 58
@@ -2542,6 +2344,7 @@ class Zeus_8_3_2_B_4_2(IStrategy):
         # Calculer le minimum des 14 derniers jours
         nb_pairs = len(self.dp.current_whitelist())
 
+        return self.config.get('stake_amount')
         base_stake_amount = self.config.get('stake_amount') / (self.mises.value) # * nb_pairs) # Montant de base configuré
         # pct60 = round(100 * self.getPctClose60D(pair, last_candle), 2)