20250101-20250714 1.61 440.619

Zeus_8_3_2_B_4_2.py

@@ -90,6 +90,9 @@ class Zeus_8_3_2_B_4_2(IStrategy):
             "sma20": {
                 "color": "yellow"
             },
+            "sma60": {
+                "color": "green"
+            },
             "bb_lowerband": {
                 "color": "#da59a6"},
             "bb_upperband": {
@@ -125,7 +128,8 @@ class Zeus_8_3_2_B_4_2(IStrategy):
                     "color": "blue"
                 }
             },
-            "Rsi_deriv": {
+            },
+            "Rsi_deriv1": {
                 "rsi_deriv1_1h": {
                     "color": "red"
                 },
@@ -186,7 +190,8 @@ class Zeus_8_3_2_B_4_2(IStrategy):
             'last_palier_index': -1,
             'total_amount': 0,
             'has_gain': 0,
-            'force_sell': False
+            'force_sell': False,
+            'force_buy': False
         }
         for pair in ["BTC/USDC", "ETH/USDC", "DOGE/USDC", "XRP/USDC", "SOL/USDC",
                      "BTC/USDT", "ETH/USDT", "DOGE/USDT", "XRP/USDT", "SOL/USDT"]
@@ -330,7 +335,11 @@ class Zeus_8_3_2_B_4_2(IStrategy):
         #
         #     if count < 3:
         #         allow_to_buy = False
-
+        force = self.pairs[pair]['force_buy']
+        if self.pairs[pair]['force_buy']:
+            self.pairs[pair]['force_buy'] = False
+            allow_to_buy = True
+        else:
             if not self.should_enter_trade(pair, last_candle, current_time):
                 allow_to_buy = False
 
@@ -375,7 +384,8 @@ class Zeus_8_3_2_B_4_2(IStrategy):
         dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
         last_candle = dataframe.iloc[-1].squeeze()
 
-        allow_to_sell = (last_candle['percent'] < 0)
+        force = self.pairs[pair]['force_sell']
+        allow_to_sell = (last_candle['percent'] < 0) #or force
 
         minutes = int(round((current_time - trade.date_last_filled_utc).total_seconds() / 60, 0))
 
@@ -446,11 +456,15 @@ class Zeus_8_3_2_B_4_2(IStrategy):
 
         count_of_buys = trade.nr_of_successful_entries
 
-        # baisse = self.pairs[pair]['max_profit'] - current_profit
+        profit = round(current_profit * trade.stake_amount, 1)
-        # mx = self.pairs[pair]['max_profit'] / 5
+        self.pairs[pair]['max_profit'] = max(self.pairs[pair]['max_profit'], profit)
+        max_profit = self.pairs[pair]['max_profit']
+        baisse = 0
+        if profit > 0:
+            baisse = 100 * abs(max_profit - profit) / max_profit
+        mx = max_profit / 5
         self.pairs[pair]['count_of_buys'] = count_of_buys
-        self.pairs[pair]['current_profit'] = current_profit
+        self.pairs[pair]['current_profit'] = profit
-        self.pairs[pair]['max_profit'] = max(self.pairs[pair]['max_profit'], current_profit)
 
         dispo = round(self.wallets.get_available_stake_amount())
         hours_since_first_buy = (current_time - trade.open_date_utc).seconds / 3600.0
@@ -466,14 +480,14 @@ class Zeus_8_3_2_B_4_2(IStrategy):
                 pair=pair,
                 rate=last_candle['close'],
                 trade_type='',
-                profit=round(current_profit * trade.stake_amount, 1),
+                profit=profit,
                 buys='',
                 stake=0
             )
 
-        if last_candle['mid_smooth_1h_deriv1'] > self.sma5_deriv1_1h_stop_sell.value \
+        # if last_candle['mid_smooth_1h_deriv1'] > self.sma5_deriv1_1h_stop_sell.value \
-            and last_candle['mid_smooth_1h_deriv2'] > self.sma5_deriv2_1h_stop_sell.value:
+        #     and last_candle['mid_smooth_1h_deriv2'] > self.sma5_deriv2_1h_stop_sell.value:
-            return None
+        #     return None
 
         # if (last_candle['mid_smooth_deriv1'] >= 0):
         #     return None
@@ -486,9 +500,31 @@ class Zeus_8_3_2_B_4_2(IStrategy):
         # if (current_profit > expected_profit) and last_candle['can_sell']:
         #     return 'Can_' + pair_name + '_' + str(count_of_buys)
 
-        # if 1 <= count_of_buys <= 3:
+        # if self.pairs[pair]['force_sell']:
-        if last_candle['mid_smooth_1h_deriv1'] < 0 and current_profit > expected_profit:
+        #     self.pairs[pair]['force_sell'] = False
+        #     return 'Force' + pair_name + '_' + str(count_of_buys) + '_' + str(self.pairs[pair]['has_gain'])
+
+        # if profit > - 2 and last_candle['rsi_1h'] < 50 \
+        #         and (last_candle['sma20'] - before_last_candle['sma20'] < 0) \
+        #         and (last_candle['rsi_deriv1_1h'] < -4 and last_candle['rsi_deriv2_1h'] < -4) :
+        #     self.pairs[pair]['force_sell'] = False
+        #     return 'stoploss_' + pair_name + '_' + str(count_of_buys) + '_' + str(self.pairs[pair]['has_gain'])
+
+        if baisse > 15 and profit > expected_profit:
             self.pairs[pair]['force_sell'] = False
+            self.pairs[pair]['force_buy'] = (self.pairs[pair]['count_of_buys'] - self.pairs[pair]['has_gain'] > 5)
+            return 'Baisse_' + pair_name + '_' + str(count_of_buys) + '_' + str(self.pairs[pair]['has_gain'])
+        #
+        # # if 1 <= count_of_buys <= 3:
+        # if last_candle['rsi_1h'] < 50 \
+        #         and last_candle['mid_smooth_1h_deriv1'] < 0 and profit > expected_profit \
+        #         and (last_candle['sma20'] - before_last_candle['sma20'] < 0) :
+        #     self.pairs[pair]['force_sell'] = False
+        #     return 'Drv3_' + pair_name + '_' + str(count_of_buys) + '_' + str(self.pairs[pair]['has_gain'])
+
+        if last_candle['mid_smooth_1h_deriv1'] < 0 and last_candle['rsi_1h'] < 50 and profit > expected_profit:
+            self.pairs[pair]['force_sell'] = False
+            self.pairs[pair]['force_buy'] = (self.pairs[pair]['count_of_buys'] - self.pairs[pair]['has_gain'] > 5)
             return 'Drv3_' + pair_name + '_' + str(count_of_buys) + '_' + str(self.pairs[pair]['has_gain'])
 
         # if 4 <= count_of_buys <= 6:
@@ -556,7 +592,7 @@ class Zeus_8_3_2_B_4_2(IStrategy):
             return
         if self.columns_logged % 10 == 0:
             self.printLog(
-                f"| {'Date':<16} | {'Action':<10} |{'Pair':<5}| {'Trade Type':<18} |{'Rate':>8} | {'Dispo':>6} | {'Profit':>10} | {'Pct':>6} | {'max_touch':>11} | {'last_lost':>12} | {'last_max':>7}| {'last_max':>7}|{'Buys':>5}| {'Stake':>5} |"
+                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}| {'last_max':>7}|{'Buys':>5}| {'Stake':>5} |"
                 f"Tdc|{'val':>6}|Distmax|s201d|s5_1d|s5_2d|s51h|s52h|smt1h|smt2h|"
             )
             self.printLineLog()
@@ -623,15 +659,15 @@ class Zeus_8_3_2_B_4_2(IStrategy):
         color_sma20 = GREEN if last_candle['sma20_deriv1_1d'] > 0 else RED
         color_sma5 = GREEN if last_candle['mid_smooth_5_deriv1_1d'] > 0 else RED
         color_sma5_2 = GREEN if last_candle['mid_smooth_5_deriv2_1d'] > 0 else RED
-        color_sma5_1h = GREEN if last_candle['sma5_deriv1_1h'] > 0 else RED
+        color_sma5_1h = GREEN if last_candle['sma60_deriv1'] > 0 else RED
-        color_sma5_2h = GREEN if last_candle['sma5_deriv2_1h'] > 0 else RED
+        color_sma5_2h = GREEN if last_candle['sma60_deriv2'] > 0 else RED
         color_smooth_1h = GREEN if last_candle['mid_smooth_1h_deriv1'] > 0 else RED
         color_smooth2_1h = GREEN if last_candle['mid_smooth_1h_deriv2'] > 0 else RED
 
         last_max = int(self.pairs[pair]['last_max']) if self.pairs[pair]['last_max'] > 1 else round(self.pairs[pair]['last_max'],3)
         last_min = int(self.pairs[pair]['last_min']) if self.pairs[pair]['last_min'] > 1 else round(self.pairs[pair]['last_min'], 3)
 
-        profit = str(round(self.pairs[pair]['current_profit'], 2)) + '/' + str(profit)
+        profit = str(profit) + '/' + str(round(self.pairs[pair]['max_profit'], 2))
 
         #     🟢 Dérivée 1 > 0 et dérivée 2 > 0: tendance haussière qui s’accélère.
         #     🟡 Dérivée 1 > 0 et dérivée 2 < 0: tendance haussière qui ralentit → essoufflement potentiel.
@@ -649,7 +685,7 @@ class Zeus_8_3_2_B_4_2(IStrategy):
             f"{round(val, 1) or '-' :>6}|"
             f"{dist_max:>7}|{color_sma20}{round(last_candle['sma20_deriv1_1d'], 2):>5}{RESET}"
             f"|{color_sma5}{round(last_candle['mid_smooth_5_deriv1_1d'], 2):>5}{RESET}|{color_sma5_2}{round(last_candle['mid_smooth_5_deriv2_1d'], 2):>5}{RESET}"
-            f"|{color_sma5_1h}{round(last_candle['sma5_deriv1_1h'], 2):>5}{RESET}|{color_sma5_2h}{round(last_candle['sma5_deriv2_1h'], 2):>5}{RESET}"
+            f"|{color_sma5_1h}{round(last_candle['sma60_deriv1'], 2):>5}{RESET}|{color_sma5_2h}{round(last_candle['sma60_deriv2'], 2):>5}{RESET}"
             f"|{color_smooth_1h}{round(last_candle['mid_smooth_1h_deriv1'], 2):>5}{RESET}|{color_smooth2_1h}{round(last_candle['mid_smooth_1h_deriv2'], 2):>5}{RESET}"
             # f"|{last_candle['min60_1d']}|{last_candle['max60_1d']}"
         )
@@ -712,9 +748,11 @@ class Zeus_8_3_2_B_4_2(IStrategy):
 
         dataframe['sma5'] = talib.SMA(dataframe, timeperiod=5)
         dataframe['sma10'] = talib.SMA(dataframe, timeperiod=10)
-        self.calculeDerivees(dataframe, 'sma10')
+        self.calculeDerivees(dataframe, 'sma10', horizon=10)
         dataframe['sma20'] = talib.SMA(dataframe, timeperiod=20)
-        self.calculeDerivees(dataframe, 'sma20')
+        self.calculeDerivees(dataframe, 'sma20', horizon=20)
+        dataframe['sma60'] = talib.SMA(dataframe, timeperiod=60)
+        self.calculeDerivees(dataframe, 'sma60', horizon=60)
         dataframe['sma144'] = talib.SMA(dataframe, timeperiod=144)
         self.calculeDerivees(dataframe, 'sma144')
 
@@ -728,14 +766,14 @@ class Zeus_8_3_2_B_4_2(IStrategy):
 
         dataframe["mid_re_smooth_3"] = self.conditional_smoothing(dataframe['mid_smooth_3'].dropna(),
                                                                   threshold=0.0005).dropna()
-        self.calculeDerivees(dataframe, "mid_re_smooth_3")
+        self.calculeDerivees(dataframe, "mid_re_smooth_3", horizon=3)
         dataframe = self.calculateDerivation(dataframe, window=12, suffixe="_12")
         dataframe = self.calculateDerivation(dataframe, window=24, suffixe="_24", factor_1=1000, factor_2=10)
 
         # print(metadata['pair'])
         dataframe['rsi'] = talib.RSI(dataframe['close'], timeperiod=14)
         dataframe['max_rsi_12'] = talib.MAX(dataframe['rsi'], timeperiod=12)
-        self.calculeDerivees(dataframe, 'rsi')
+        self.calculeDerivees(dataframe, 'rsi', horizon=12)
 
         dataframe['max48'] = talib.MAX(dataframe['close'], timeperiod=48)
         dataframe['min36'] = talib.MIN(dataframe['close'], timeperiod=36)
@@ -780,8 +818,8 @@ class Zeus_8_3_2_B_4_2(IStrategy):
 
         informative['sma5'] = talib.SMA(informative, timeperiod=5)
         informative['sma24'] = talib.SMA(informative, timeperiod=24)
-        self.calculeDerivees(informative, 'sma5')
+        self.calculeDerivees(informative, 'sma5', horizon=5)
-        self.calculeDerivees(informative, 'sma24')
+        self.calculeDerivees(informative, 'sma24', horizon=24)
         # informative["mid_re_smooth"] = self.conditional_smoothing(informative['mid_smooth'].dropna(), threshold=0.0005).dropna()
         # self.calculeDerivees(informative, "mid_re_smooth")
         # self.calculateDownAndUp(informative, limit=0.0012)
@@ -823,18 +861,17 @@ class Zeus_8_3_2_B_4_2(IStrategy):
         informative['max60'] = talib.MAX(informative['close'], timeperiod=period_1d)
         informative['min12'] = talib.MIN(informative['close'], timeperiod=12)
         informative['min60'] = talib.MIN(informative['close'], timeperiod=period_1d)
-        informative['volatility'] = talib.STDDEV(informative['close'], timeperiod=14) / informative['close']
-        self.calculeDerivees(informative, 'volatility')
 
         informative["percent"] = (informative["close"] - informative["open"]) / informative["open"]
-        informative['rsi6'] = talib.RSI(informative['close'], timeperiod=6)
+        informative['rsi'] = talib.RSI(informative['close'], timeperiod=6)
-        # self.calculeDerivees(informative, 'rsi')
+        informative['rsi'] = informative['rsi'].rolling(6).mean()
+        self.calculeDerivees(informative, 'rsi', horizon=6)
         #
         informative['sma5'] = talib.SMA(informative, timeperiod=5)
         informative['sma20'] = talib.SMA(informative, timeperiod=20)
 
-        self.calculeDerivees(informative, 'sma5', factor_1=10, factor_2=1)
+        self.calculeDerivees(informative, 'sma5', factor_1=10, factor_2=1, horizon=5)
-        self.calculeDerivees(informative, 'sma20', factor_1=10, factor_2=1)
+        self.calculeDerivees(informative, 'sma20', factor_1=10, factor_2=1, horizon=20)
 
         informative = self.add_tendency_column(informative, "", "sma5")
         informative = self.add_tendency_column(informative, "", "sma20")
@@ -861,6 +898,14 @@ class Zeus_8_3_2_B_4_2(IStrategy):
         #     print("##################")
         #     self.calculateStats(informative, 'sma5_deriv1', 'futur_percent_3')
 
+        if self.dp.runmode.value in ('backtest'):
+            informative['futur_percent'] = 100 * (informative['close'].shift(-1) - informative['close']) / informative['close']
+            # informative['futur_percent_3d'] = 100 * (informative['close'].shift(-3) - informative['close']) / informative['close']
+        #
+        #     self.calculateProbabilite2Index(informative, ['futur_percent_1d'], 'rsi_deriv1', 'rsi')
+        #     # self.calculateProbabilite2Index(dataframe, ['futur_percent_3d'], 'rsi_deriv1', 'sma5')
+
+
         # informative['close_smooth'] = self.conditional_smoothing(informative['mid'].dropna(), threshold=0.0015).dropna()
         # informative['smooth'], informative['deriv1'], informative['deriv2'] = self.smooth_and_derivatives(informative['close_smooth'])
         # informative['deriv1'] = 100 * informative['deriv1'] / informative['mid']
@@ -937,7 +982,7 @@ class Zeus_8_3_2_B_4_2(IStrategy):
         # dataframe['deriv2_1h'] = 1000 * dataframe['deriv2_1h'] / dataframe['mid_smooth_1h']
 
         horizon_h = 12
-        dataframe['sma5_1h'] = dataframe['sma5_1h'].rolling(window=horizon_h).mean()
+        # dataframe['sma5_1h'] = dataframe['sma5_1h'].rolling(window=horizon_h).mean()
         # dataframe['ema_volume'] = dataframe['ema_volume'].rolling(window=horizon_h).mean()
         # dataframe['sma24_1h'] = dataframe['sma24_1h'].rolling(window=horizon_h).mean()
         # dataframe['sma24_deriv1_1h'] = dataframe['sma24_deriv1_1h'].rolling(window=horizon_h).mean()
@@ -961,6 +1006,10 @@ class Zeus_8_3_2_B_4_2(IStrategy):
         # ===============================
         # Lissage des valeurs Journalières
         horizon_d = 12 * 5 * 24
+        # dataframe['rsi_1h'] = dataframe['rsi_1h'].rolling(12).mean()
+        # dataframe['rsi_deriv1_1h'] = dataframe['rsi_deriv1_1h'].rolling(12).mean()
+        # dataframe['rsi_deriv2_1h'] = dataframe['rsi_deriv2_1h'].rolling(12).mean()
+
         # dataframe['mid_smooth_1d'] = dataframe['mid_smooth_1d'].rolling(window=horizon_d * 5).mean()
         # dataframe["mid_smooth_deriv1_1d"] = dataframe["mid_smooth_1d"].rolling(horizon_d).mean().diff() / horizon_d
         # dataframe["mid_smooth_deriv2_1d"] = horizon_d * dataframe["mid_smooth_deriv1_1d"].rolling(horizon_d).mean().diff()
@@ -988,13 +1037,13 @@ class Zeus_8_3_2_B_4_2(IStrategy):
         dataframe['ema_volume'] = 20 * (dataframe['volume'] * dataframe['percent']) / (
                 abs(dataframe['volume'].shift(1)) + abs(dataframe['volume'].shift(2)))
 
-        self.calculeDerivees(dataframe, 'ema_volume', factor_1=10, factor_2=1)
+        self.calculeDerivees(dataframe, 'ema_volume', factor_1=10, factor_2=1, horizon=14)
         # if self.dp.runmode.value in ('backtest'):
         #     print("##################")
         #     print("# STAT DAY vs HOUR")
         #     print("##################")
-        #     self.calculateProbabilite2Index(dataframe, futur_cols=['futur_percent_3h'], indic_1='ema_volume',
+        #     self.calculateProbabilite2Index(dataframe, futur_cols=['futur_percent_1d'], indic_1='sma5_deriv1_1d',
-        #                                     indic_2='mid_smooth_1h_deriv1')
+        #                                     indic_2='sma5_deriv2_1d')
 
         # dataframe['proba_hausse'] = dataframe.apply(lambda row: self.getProbaHausseEmaVolume(row), axis=1)
 
@@ -1018,9 +1067,9 @@ class Zeus_8_3_2_B_4_2(IStrategy):
 
         return dataframe
 
-    def calculeDerivees(self, dataframe, indic, factor_1=100, factor_2=10):
+    def calculeDerivees(self, dataframe, indic, factor_1=100, factor_2=10, horizon=5):
-        dataframe[f"{indic}_deriv1"] = factor_1 * dataframe[f"{indic}"].diff() / dataframe[f"{indic}"]
+        dataframe[f"{indic}_deriv1"] = (factor_1 * dataframe[f"{indic}"].diff() / dataframe[f"{indic}"]).rolling(horizon).mean()
-        dataframe[f"{indic}_deriv2"] = factor_2 * dataframe[f"{indic}_deriv1"].diff()
+        dataframe[f"{indic}_deriv2"] = (factor_2 * dataframe[f"{indic}_deriv1"].diff()).rolling(horizon).mean()
 
     def calculateDownAndUp(self, dataframe, limit=0.0001):
         dataframe['down'] = dataframe['mid_smooth_1h_deriv1'] < limit  # dataframe['hapercent'] <= limit
@@ -1418,7 +1467,7 @@ class Zeus_8_3_2_B_4_2(IStrategy):
             try:
 
                 if self.pairs[pair]['has_gain']:
-                    self.pairs[pair]['force_sell'] = True;
+                    self.pairs[pair]['force_sell'] = True
 
                 # if 6 <= count_of_buys:
                 #     if not ((before_last_candle_24['sma24_deriv1_1h'] > before_last_candle_12['sma24_deriv1_1h'])
@@ -2210,7 +2259,7 @@ class Zeus_8_3_2_B_4_2(IStrategy):
         # if not pair.startswith('BTC'):
         dispo = round(self.wallets.get_available_stake_amount())
 
-        if self.pairs[pair]['stop'] and last_candle['mid_smooth_5_deriv1_1d'] > -0.9 and last_candle['sma5_deriv1_1h'] > 0:
+        if self.pairs[pair]['stop'] and last_candle['mid_smooth_5_deriv1_1d'] > -0.9 and last_candle['sma5_deriv1_1d'] > 0 and last_candle['sma5_deriv2_1d'] > 0:
             self.pairs[pair]['stop'] = False
             self.log_trade(
                 last_candle=last_candle,
@@ -2225,8 +2274,10 @@ class Zeus_8_3_2_B_4_2(IStrategy):
                 stake=0
             )
         else:
-            if self.pairs[pair]['stop'] == False and last_candle['sma5_deriv1_1h'] < -0.2:
+            if self.pairs[pair]['stop'] == False and (last_candle['sma5_deriv1_1d'] < -0.2 or last_candle['sma5_deriv2_1d'] < -3):
                 self.pairs[pair]['stop'] = True
+                # if self.pairs[pair]['current_profit'] > 0:
+                #     self.pairs[pair]['force_sell'] = True
                 self.log_trade(
                     last_candle=last_candle,
                     date=current_time,
@@ -2235,7 +2286,7 @@ class Zeus_8_3_2_B_4_2(IStrategy):
                     pair=pair,
                     rate=last_candle['close'],
                     trade_type='',
-                    profit=0,
+                    profit=self.pairs[pair]['current_profit'],
                     buys=self.pairs[pair]['count_of_buys'],
                     stake=0
                 )
@@ -2243,7 +2294,6 @@ class Zeus_8_3_2_B_4_2(IStrategy):
         if self.pairs[pair]['stop']:
             return False
 
-
         if last_candle['sma5_deriv1_1h'] < -0.02:
             return False
 
@@ -2256,8 +2306,8 @@ class Zeus_8_3_2_B_4_2(IStrategy):
         if last_candle['mid_smooth_1h_deriv1'] < 0.0 and last_candle['mid_smooth_1h_deriv2'] < -0.0 and last_candle['sma5_deriv2_1h'] < 0:
             return False
 
-        if pair.startswith('BTC'):
+        # if pair.startswith('BTC'):
-            return True  # BTC toujours autorisé
+        #     return True  # BTC toujours autorisé
 
         #return True