Merge remote-tracking branch 'origin/Multiple_paires' into Multiple_paires

EmptyShort.py

@@ -151,7 +151,8 @@ class EmptyShort(IStrategy):
             'current_trade': None,
             'last_trade': None,
             'force_stop': False,
-            'count_of_lost': 0
+            'count_of_lost': 0,
+            'take_profit': 0.01
         }
         for pair in ["BTC/USDC", "BTC/USDT", 'BTC/USDT:USDT']
     }
@@ -261,7 +262,7 @@ class EmptyShort(IStrategy):
             # if last_candle['enter_tag'] in ['fall', 'bear', 'Force', 'Range-']:
             #     amount = self.wallets.get_available_stake_amount() / self.mises_bear.value
             # else:
-            amount = self.wallets.get_available_stake_amount() / self.mises_bull.value # / (2 * self.pairs[pair]['count_of_lost'] + 1)
+            amount = self.wallets.get_available_stake_amount() #/ self.mises_bull.value # / (2 * self.pairs[pair]['count_of_lost'] + 1)
 
             # factor = 1
             #
@@ -439,6 +440,21 @@ class EmptyShort(IStrategy):
             self.pairs[pair]['total_amount'] = stake_amount
             # print(f"Buy {pair} {current_time} {entry_tag}  dispo={dispo}  amount={stake_amount} rate={rate} rate={rate}")
 
+
+            # sl_price = rate * (1 - last_candle['up_pct_1h'] / 2)
+            #
+            # self.custom_trade_info[pair] = {
+            #     "custom_exit": last_candle['up_pct_1h'] / 200
+            # }
+
+            # trade = kwargs.get("trade", None)
+            # if trade:
+                # trade.set_custom_data({
+                #     # "stoploss": rate * 0.95,
+                #     "takeprofit": last_candle['up_pct_1h'] / 200
+                # })
+            self.pairs[pair]['take_profit'] = last_candle['up_pct_1h'] / 200
+
             self.log_trade(
                 last_candle=last_candle,
                 date=current_time,
@@ -477,7 +493,7 @@ class EmptyShort(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 (trade.enter_tag == 'short' and profit > 0 and last_candle['hapercent'] > 0) or (exit_reason == 'force_exit') or (exit_reason == 'stop_loss') or (exit_reason == 'trailing_stop_loss')
+                        or (trade.enter_tag == 'short' ) or (exit_reason == 'force_exit') or (exit_reason == 'stop_loss') or (exit_reason == 'trailing_stop_loss')
 
         minutes = int(round((current_time - trade.date_last_filled_utc).total_seconds() / 60, 0))
 
@@ -547,12 +563,13 @@ class EmptyShort(IStrategy):
 
         is_short = (trade.enter_tag == 'short')
         if trade.enter_tag == 'short':
-            if current_profit < self.sell_force_sell.value \
-                    and last_candle[f"close"] > last_candle[self.sell_indicator.value]:
-                self.pairs[pair]['force_sell'] = True
-                return 'sma'
-
-            if current_profit > 0.01 and \
+            tp_price = self.pairs[pair]['take_profit']
+            # if current_profit < self.sell_force_sell.value \
+            #         and last_candle[f"close"] > last_candle[self.sell_indicator.value]:
+            #     self.pairs[pair]['force_sell'] = True
+            #     return 'sma'
+            #
+            if current_profit > tp_price and \
                     (baisse > self.baisse.value and last_candle[f"close"] > last_candle[self.b30_indicateur.value]) \
                     and last_candle['hapercent'] > 0:
                 self.pairs[pair]['force_sell'] = True
@@ -572,23 +589,16 @@ class EmptyShort(IStrategy):
         #     stake=0
         # )
         else:
-            if self.pairs[pair]['current_trade'].enter_tag in ['bear', 'Force', 'Range-']:
-                if current_profit < - 0.02 and last_candle[f"close"] <= last_candle['sma60'] and self.wallets.get_available_stake_amount() < 50:
-                    self.pairs[pair]['force_sell'] = True
-                    return 'smaBF'
-            else:
-                if current_profit < self.sell_force_sell.value \
-                        and last_candle[f"close"] <= last_candle[self.sell_indicator.value]:
-                    self.pairs[pair]['force_sell'] = True
-                    return 'sma'
+            # if self.pairs[pair]['current_trade'].enter_tag in ['bear', 'Force', 'Range-']:
+            #     if current_profit < - 0.02 and last_candle[f"close"] <= last_candle['sma60'] and self.wallets.get_available_stake_amount() < 50:
+            #         self.pairs[pair]['force_sell'] = True
+            #         return 'smaBF'
+            # else:
+            #     if current_profit < self.sell_force_sell.value \
+            #             and last_candle[f"close"] <= last_candle[self.sell_indicator.value]:
+            #         self.pairs[pair]['force_sell'] = True
+            #         return 'sma'
 
-            if is_short:
-                if current_profit > 0.005 and \
-                        (baisse > self.baisse.value and last_candle[f"close"] > last_candle[self.b30_indicateur.value]) \
-                        and last_candle['hapercent'] > 0:
-                    self.pairs[pair]['force_sell'] = True
-                    return 'B30Sht'
-            else:
             if current_profit > 0.00 and \
                     (baisse > self.baisse.value and last_candle[f"close"] <= last_candle[self.b30_indicateur.value]) \
                     and last_candle['hapercent'] <0 :
@@ -699,12 +709,15 @@ class EmptyShort(IStrategy):
         dataframe['zero'] = 0
 
         dataframe[f"percent"] = dataframe['close'].pct_change()
-        for timeperiod in timeperiods:
+        for timeperiod in timeperiods + long_timeperiods:
             dataframe[f"mid_smooth{timeperiod}"] = dataframe['mid'].rolling(timeperiod).mean()
             dataframe[f'max{timeperiod}'] = talib.MAX(dataframe['close'], timeperiod=timeperiod)
             dataframe[f'min{timeperiod}'] = talib.MIN(dataframe['close'], timeperiod=timeperiod)
             dataframe[f"percent{timeperiod}"] = dataframe['close'].pct_change(timeperiod)
-            dataframe[f"sma{timeperiod}"] = dataframe['mid'].ewm(span=timeperiod, adjust=False).mean()
+            # dataframe[f"sma{timeperiod}"] = dataframe['mid'].ewm(span=timeperiod, adjust=False).mean()
+            ema = dataframe['mid'].ewm(span=timeperiod, adjust=False).mean()
+            dataframe[f"sma{timeperiod}"] = ema.ewm(span=int(timeperiod / 2), adjust=False).mean()
+
             # dataframe[f"high{timeperiod}"] = dataframe['high'].ewm(span=timeperiod, adjust=False).mean()
             # dataframe[f"low{timeperiod}"] = dataframe['low'].ewm(span=timeperiod, adjust=False).mean()
             # dataframe = self.calculateRegression(dataframe, column=f"high{timeperiod}", window=10, degree=1, future_offset=12)
@@ -712,9 +725,9 @@ class EmptyShort(IStrategy):
 
             self.calculeDerivees(dataframe, f"sma{timeperiod}", timeframe=self.timeframe, ema_period=timeperiod)
 
-        dataframe = self.calculateRegression(dataframe, column='mid', window=30, degree=1, future_offset=12)
-        dataframe = self.calculateRegression(dataframe, column='sma24', window=30, degree=1, future_offset=12)
-        self.calculeDerivees(dataframe, "sma24_regression", timeframe=self.timeframe, ema_period=12)
+        dataframe = self.calculateRegression(dataframe, column='mid', window=30, degree=1, future_offset=6)
+        dataframe = self.calculateRegression(dataframe, column='sma120', window=60, degree=2, future_offset=30)
+        # self.calculeDerivees(dataframe, "sma24_regression", timeframe=self.timeframe, ema_period=12)
 
         dataframe["percent"] = dataframe["mid"].pct_change(1)
         dataframe["percent3"] = dataframe["mid"].pct_change(3)
@@ -738,7 +751,7 @@ class EmptyShort(IStrategy):
         informative['haclose'] = heikinashi['close']
         informative['hapercent'] = (informative['haclose'] - informative['haopen']) / informative['haclose']
         informative['mid'] = informative['open'] + (informative['close'] - informative['open']) / 2
-        for timeperiod in timeperiods:
+        for timeperiod in [3, 5, 12]:
             informative[f'max{timeperiod}'] = talib.MAX(informative['close'], timeperiod=timeperiod)
             informative[f'min{timeperiod}'] = talib.MIN(informative['close'], timeperiod=timeperiod)
             # informative[f"range{timeperiod}"] = ((informative["close"] - informative[f'min{timeperiod}']) / (informative[f'max{timeperiod}'] - informative[f'min{timeperiod}']))
@@ -746,9 +759,9 @@ class EmptyShort(IStrategy):
             informative[f"sma{timeperiod}"] = informative['mid'].ewm(span=timeperiod, adjust=False).mean()
             self.calculeDerivees(informative, f"sma{timeperiod}", timeframe=self.timeframe, ema_period=timeperiod)
 
-        informative = self.calculateRegression(informative, column='mid', window=10, degree=1, future_offset=12)
-        informative = self.calculateRegression(informative, column='sma3', window=10, degree=1, future_offset=12)
-        informative = self.calculateRegression(informative, column='low', window=10, degree=1, future_offset=12)
+        informative = self.calculateRegression(informative, column='mid', window=10, degree=1, future_offset=2)
+        informative = self.calculateRegression(informative, column='sma3', window=10, degree=1, future_offset=2)
+        informative = self.calculateRegression(informative, column='low', window=10, degree=1, future_offset=2)
 
         for timeperiod in long_timeperiods:
             informative[f"sma{timeperiod}"] = informative['mid'].ewm(span=timeperiod, adjust=False).mean()
@@ -758,20 +771,22 @@ class EmptyShort(IStrategy):
         self.calculeDerivees(informative, f"rsi", timeframe=self.timeframe, ema_period=14)
         informative['max_rsi_12'] = talib.MAX(informative['rsi'], timeperiod=12)
         informative['max_rsi_24'] = talib.MAX(informative['rsi'], timeperiod=24)
+        informative['min_rsi_12'] = talib.MIN(informative['rsi'], timeperiod=12)
+        informative['min_rsi_24'] = talib.MIN(informative['rsi'], timeperiod=24)
 
-        informative[f'stop_buying_deb'] = qtpylib.crossed_below(informative[f"sma12"], informative['sma36']) & (informative['close'] < informative['sma100'])
-        informative[f'stop_buying_end'] = qtpylib.crossed_above(informative[f"sma12"], informative['sma36']) & (informative['close'] > informative['sma100'])
+        # informative[f'stop_buying_deb'] = qtpylib.crossed_below(informative[f"sma12"], informative['sma36']) & (informative['close'] < informative['sma100'])
+        # informative[f'stop_buying_end'] = qtpylib.crossed_above(informative[f"sma12"], informative['sma36']) & (informative['close'] > informative['sma100'])
 
-        latched = np.zeros(len(informative), dtype=bool)
-
-        for i in range(1, len(informative)):
-            if informative['stop_buying_deb'].iloc[i]:
-                latched[i] = True
-            elif informative['stop_buying_end'].iloc[i]:
-                latched[i] = False
-            else:
-                latched[i] = latched[i - 1]
-        informative['stop_buying'] = latched
+        # latched = np.zeros(len(informative), dtype=bool)
+        #
+        # for i in range(1, len(informative)):
+        #     if informative['stop_buying_deb'].iloc[i]:
+        #         latched[i] = True
+        #     elif informative['stop_buying_end'].iloc[i]:
+        #         latched[i] = False
+        #     else:
+        #         latched[i] = latched[i - 1]
+        # informative['stop_buying'] = latched
         informative = self.calculateDownAndUp(informative, limit=0.0001)
 
         dataframe = merge_informative_pair(dataframe, informative, self.timeframe, "1h", ffill=True)
@@ -787,7 +802,7 @@ class EmptyShort(IStrategy):
         # informative['haclose'] = heikinashi['close']
         # informative['hapercent'] = (informative['haclose'] - informative['haopen']) / informative['haclose']
         informative['mid'] = informative['open'] + (informative['close'] - informative['open']) / 2
-        for timeperiod in timeperiods:
+        for timeperiod in [3, 5, 12]:
             informative[f'max{timeperiod}'] = talib.MAX(informative['close'], timeperiod=timeperiod)
             informative[f'min{timeperiod}'] = talib.MIN(informative['close'], timeperiod=timeperiod)
             # informative[f"range{timeperiod}"] = ((informative["close"] - informative[f'min{timeperiod}']) / (informative[f'max{timeperiod}'] - informative[f'min{timeperiod}']))
@@ -804,19 +819,23 @@ class EmptyShort(IStrategy):
         informative['max_rsi_12'] = talib.MAX(informative['rsi'], timeperiod=12)
         informative['max_rsi_24'] = talib.MAX(informative['rsi'], timeperiod=24)
 
-        informative[f'stop_buying_deb'] = qtpylib.crossed_below(informative[f"sma12"], informative['sma36']) & (informative['close'] < informative['sma100'])
-        informative[f'stop_buying_end'] = qtpylib.crossed_above(informative[f"sma12"], informative['sma36']) & (informative['close'] > informative['sma100'])
+        # informative = self.calculateRegression(informative, column='mid', window=10, degree=1, future_offset=2)
+        informative = self.calculateRegression(informative, column='sma3', window=10, degree=1, future_offset=2)
+        # informative = self.calculateRegression(informative, column='low', window=10, degree=1, future_offset=2)
 
-        latched = np.zeros(len(informative), dtype=bool)
-
-        for i in range(1, len(informative)):
-            if informative['stop_buying_deb'].iloc[i]:
-                latched[i] = True
-            elif informative['stop_buying_end'].iloc[i]:
-                latched[i] = False
-            else:
-                latched[i] = latched[i - 1]
-        informative['stop_buying'] = latched
+        # informative[f'stop_buying_deb'] = qtpylib.crossed_below(informative[f"sma12"], informative['sma36']) & (informative['close'] < informative['sma100'])
+        # informative[f'stop_buying_end'] = qtpylib.crossed_above(informative[f"sma12"], informative['sma36']) & (informative['close'] > informative['sma100'])
+        #
+        # latched = np.zeros(len(informative), dtype=bool)
+        #
+        # for i in range(1, len(informative)):
+        #     if informative['stop_buying_deb'].iloc[i]:
+        #         latched[i] = True
+        #     elif informative['stop_buying_end'].iloc[i]:
+        #         latched[i] = False
+        #     else:
+        #         latched[i] = latched[i - 1]
+        # informative['stop_buying'] = latched
 
         dataframe = merge_informative_pair(dataframe, informative, self.timeframe, "1d", ffill=True)
         # ######################################################################################################
@@ -979,14 +998,14 @@ class EmptyShort(IStrategy):
         #     ['enter_long', 'enter_tag']
         # ] = (1, 'cross_min')
 
-        conditions = list()
-        conditions.append(dataframe['sma12_deriv1'] > 0.00)
-        conditions.append(dataframe['sma60_deriv1'] > 0.0)
-        conditions.append(dataframe['sma5_deriv1_1h'] > 0.0)
-        conditions.append(dataframe['sma12_deriv1_1h'] > 0.0)
-        conditions.append(dataframe['sma24_deriv1_1h'] > 0.0)
-        conditions.append(dataframe['sma100_deriv1_1h'] > 0.0)
-        conditions.append(dataframe[f"range_pos"] < 0.025)
+        # conditions = list()
+        # conditions.append(dataframe['sma12_deriv1'] > 0.00)
+        # conditions.append(dataframe['sma60_deriv1'] > 0.0)
+        # conditions.append(dataframe['sma5_deriv1_1h'] > 0.0)
+        # conditions.append(dataframe['sma12_deriv1_1h'] > 0.0)
+        # conditions.append(dataframe['sma24_deriv1_1h'] > 0.0)
+        # conditions.append(dataframe['sma100_deriv1_1h'] > 0.0)
+        # conditions.append(dataframe[f"range_pos"] < 0.025)
         # conditions.append(dataframe['sma12_deriv1_1h'] > 0.0)
         # # conditions.append(dataframe['close_1h'] < dataframe[f'sma{self.buy_longue.value}_1h'])
         # # conditions.append(dataframe['has_cross_min'].rolling(6).max() == 1)
@@ -1022,10 +1041,9 @@ class EmptyShort(IStrategy):
 
         # dataframe.loc[
         #     (
-        #         (qtpylib.crossed_above(
-        #             dataframe['sma60'],
-        #             dataframe['mid_regression_1h'])
-        #         )
+        #         # (qtpylib.crossed_above(dataframe['sma60'], dataframe['mid_regression_1h']))
+        #         (dataframe['rsi_1h'] <= 30)
+        #         & (dataframe['min_rsi_12_1h'] == dataframe['rsi_1h'])
         #     ),
         #     ['enter_long', 'enter_tag']
         # ] = (1, 'long')
@@ -1037,13 +1055,34 @@ class EmptyShort(IStrategy):
                 #     dataframe['mid_regression_1h'])
                 # ) &
                 (
-                    (dataframe['close'] > dataframe['max5_1h'])
-                    & (dataframe['sma12'] < dataframe['sma12'].shift(1))
+                    # (dataframe['close'] > dataframe['max5_1h'])
+                    # & (dataframe['sma12'] < dataframe['sma12'].shift(1))
+                    # # & (dataframe['sma120_inv'] == 1)
+                    # & (dataframe['close'] > dataframe['close_1h'])
+                    # & (dataframe['close'] > dataframe['close_1d'])
+                    # (dataframe['rsi_1h'] >= 65)
+                    # & (dataframe['max_rsi_12_1h'] == dataframe['rsi_1h'])
+                    # & (dataframe['hapercent'] < 0)
+                    # & (dataframe['sma60'] < dataframe['sma60'].shift(1))
+                    (dataframe['sma12_1h'] < dataframe['sma12_1h'].shift(61))
+                    & (dataframe['close'] > dataframe['close_1d'])
                 )
             ),
             ['enter_short', 'enter_tag']
         ] = (1, 'short')
 
+        # dataframe.loc[
+        #     (
+        #        (dataframe['close'] >= dataframe['max5_1h'])
+        #        & (dataframe['close'] >= dataframe['max12_1d'])
+        #        & (dataframe['sma12_1h'] <= dataframe['max12_1d'])
+        #        & (dataframe['mid_1h'] <= dataframe['mid_1h'].shift(61))
+        #        & (dataframe['hapercent'] < 0)
+        #         # & (dataframe['sma60'] < dataframe['sma60'].shift(1))
+        #     ),
+        #     ['enter_short', 'enter_tag']
+        # ] = (1, 'short')
+
         return dataframe
 
     def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
@@ -1102,15 +1141,31 @@ class EmptyShort(IStrategy):
         # if conditions:
         #     dataframe.loc[reduce(lambda x, y: x & y, conditions), ['exit_long', 'exit_tag']] = (1, 'god')
 
+        # dataframe.loc[
+        #     (
+        #         # (qtpylib.crossed_above(
+        #         #     dataframe['sma'],
+        #         #     dataframe['mid_regression_1h'])
+        #         # )
+        #         (dataframe['rsi_1h'] >= 70)
+        #         & (dataframe['max_rsi_12_1h'] == dataframe['rsi_1h'])
+        #     ),
+        #     ['exit_long', 'exit_tag']
+        # ] = (1, 'long')
+
         dataframe.loc[
             (
-                (qtpylib.crossed_above(
-                    dataframe['sma60'],
-                    dataframe['mid_regression_1h'])
-                )
+                # (qtpylib.crossed_above(
+                #     dataframe['sma'],
+                #     dataframe['mid_regression_1h'])
+                # )
+                (dataframe['rsi_1h'] <= 30)
+                & (dataframe['min_rsi_12_1h'] == dataframe['rsi_1h'])
+                & (dataframe['hapercent'] > 0)
+                & (dataframe['sma60'] >= dataframe['sma60'].shift(1))
             ),
             ['exit_short', 'exit_tag']
-        ] = (1, 'long')
+        ] = (1, 'short')
 
         return dataframe
 
@@ -1143,7 +1198,34 @@ class EmptyShort(IStrategy):
         dataframe[d1_col] = (dataframe[name] - dataframe[name].shift(3)) / dataframe[name].shift(3)
         dataframe[d2_col] = (dataframe[d1_col] - dataframe[d1_col].shift(1))
 
-        dataframe[f"{name}{suffixe}_inv"] = np.where(cond_bas, -1, np.where(cond_haut, 1, 0))
+        # dataframe[f"{name}{suffixe}_inv"] = np.where(cond_bas, -1, np.where(cond_haut, 1, 0))
+
+        # col = f"{name}{suffixe}"
+        # slope = dataframe[col].diff()
+        # threshold = 0.02 #slope.rolling(20).std() * 0.1
+        # sign = np.sign(slope.where(slope.abs() > threshold, 0))
+        # turn = sign.diff()
+        # dataframe[f"{name}{suffixe}_inv"] = np.where(turn == -2, 1, np.where(turn == 2, -1, 0))
+        # # dataframe[f"{name}{suffixe}_inv"] = (
+        # #         (dataframe[f"{name}{suffixe}_inv"] != 0) &
+        # #         (dataframe['slope'].abs() > dataframe['slope'].rolling(20).mean())
+        # # )
+
+        col = f"{name}{suffixe}"
+        # pente (variation)
+        dataframe['slope'] = dataframe[col].diff()
+        # signe de la pente
+        dataframe['sign'] = dataframe['slope'].apply(lambda x: 1 if x > 0 else (-1 if x < 0 else 0))
+        # changement de signe = inversion
+        dataframe[f"{name}{suffixe}_turn"] = dataframe['sign'].diff()
+        # mapping :
+        # +2  -> passage -1 → +1 => creux => inversion haussière
+        # -2  -> passage +1 → -1 => sommet => inversion baissière
+        col_inv = f"{name}{suffixe}_inv"
+        dataframe[col_inv] = 0
+        dataframe.loc[dataframe[f"{name}{suffixe}_turn"] == -2, col_inv] = 1  # sommet (max)
+        dataframe.loc[dataframe[f"{name}{suffixe}_turn"] == 2, col_inv] = -1  # creux (min)
+
 
         short = d1.rolling(pmin).mean()
         long = d1.rolling(ema_period).mean()
@@ -1446,7 +1528,7 @@ class EmptyShort(IStrategy):
         # df[f"{column}_regression_deriv2"] = round(
         #     10 * df[f"{column}_regression_deriv1"].rolling(int(window / 4)).mean().diff(), 4)
         #
-        # df[f"{column}_future_{future_offset}"] = regression_future_fit
+        df[f"{column}_future_{future_offset}"] = regression_future_fit
 
         # # 2. Dérivée première = différence entre deux bougies successives
         # df[f"{column}_future_{future_offset}_deriv1"] = round(100 * df[f"{column}_future_{future_offset}"].diff() / df[f"{column}_future_{future_offset}"], 4)