┏━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━┓
┃ Strategy ┃ Trades ┃ Avg Profit % ┃ Tot Profit USDT ┃ Tot Profit % ┃ Avg Duration ┃ Win Draw Loss Win% ┃ Drawdown ┃ ┡━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━┩ │ EmptyShort │ 34 │ 1.08 │ 379.432 │ 37.94 │ 1 day, 10:30:00 │ 18 0 16 52.9 │ 81.299 USDT 6.08% │ └────────────┴────────┴──────────────┴─────────────────┴──────────────┴─────────────────┴────────────────────────┴────────────────────┘
This commit is contained in:
Jérôme Delacotte
405
EmptyShort.py
405
EmptyShort.py
@@ -373,24 +373,25 @@ class EmptyShort(IStrategy):
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last_candle_2 = dataframe.iloc[-2].squeeze()
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last_candle_3 = dataframe.iloc[-3].squeeze()
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if entry_tag == 'Range-':
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self.pairs[pair]['count_of_lost'] = 0
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if entry_tag == 'Force':
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if self.pairs[pair]['count_of_lost'] >= 1:
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self.pairs[pair]['count_of_lost'] = 0
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condition = False
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else:
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condition = False if self.pairs[pair]['count_of_lost'] >= 1 \
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and (last_candle['sma12_deriv1_1h'] < 0.00) \
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and entry_tag != 'dist' else True
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reason = ''
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if not condition:
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reason = 'lost'
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if condition:
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if last_candle['range_pos'] > 0.05:# and self.pairs[pair]['force_stop']:
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condition = last_candle['sma5'] > last_candle['sma60']
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if not condition:
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reason = 'range'
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condition = True
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# if entry_tag == 'Range-':
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# self.pairs[pair]['count_of_lost'] = 0
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# if entry_tag == 'Force':
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# if self.pairs[pair]['count_of_lost'] >= 1:
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# self.pairs[pair]['count_of_lost'] = 0
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# condition = False
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# else:
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# condition = False if self.pairs[pair]['count_of_lost'] >= 1 \
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# and (last_candle['sma12_deriv1_1h'] < 0.00) \
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# and entry_tag != 'dist' else True
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# reason = ''
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# if not condition:
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# reason = 'lost'
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# if condition:
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# if last_candle['range_pos'] > 0.05:# and self.pairs[pair]['force_stop']:
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# condition = last_candle['sma5'] > last_candle['sma60']
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# if not condition:
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# reason = 'range'
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# if self.pairs[pair]['force_stop'] and last_candle['range_pos'] < 0.02:
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# self.pairs[pair]['force_stop'] = False
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@@ -563,17 +564,17 @@ class EmptyShort(IStrategy):
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is_short = (trade.enter_tag == 'short')
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if trade.enter_tag == 'short':
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tp_price = self.pairs[pair]['take_profit']
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tp_price = max(0.02, self.pairs[pair]['take_profit'])
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# if current_profit < self.sell_force_sell.value \
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# and last_candle[f"close"] > last_candle[self.sell_indicator.value]:
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# self.pairs[pair]['force_sell'] = True
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# return 'sma'
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#
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# if current_profit > tp_price and \
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# (baisse > self.baisse.value and last_candle[f"close"] > last_candle[self.b30_indicateur.value]) \
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# and last_candle['hapercent'] > 0:
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# self.pairs[pair]['force_sell'] = True
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# return 'B30Sht'
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if current_profit > tp_price and \
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(baisse > last_candle[f"range_pos"] and last_candle[f"close"] > last_candle[self.b30_indicateur.value]) \
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and last_candle['hapercent'] > 0 and last_candle['sma12_deriv1_1h'] > -0.004:
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self.pairs[pair]['force_sell'] = True
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return 'B30Sht'
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return None
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# self.log_trade(
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@@ -598,12 +599,12 @@ class EmptyShort(IStrategy):
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# and last_candle[f"close"] <= last_candle[self.sell_indicator.value]:
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# self.pairs[pair]['force_sell'] = True
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# return 'sma'
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if current_profit > 0.00 and \
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(baisse > self.baisse.value and last_candle[f"close"] <= last_candle[self.b30_indicateur.value]) \
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and last_candle['hapercent'] <0 :
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if current_profit > 0.01:
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self.pairs[pair]['force_buy'] = True
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tp_price = max(0.02, self.pairs[pair]['take_profit'])
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if current_profit > tp_price and \
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(baisse > min(last_candle[f"range_pos"], 0.5) and last_candle[f"close"] <= last_candle[self.b30_indicateur.value]) \
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and last_candle['hapercent'] <0 and last_candle['sma12_deriv1_1h'] < 0.004:
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# if current_profit > 0.01:
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# self.pairs[pair]['force_buy'] = True
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self.pairs[pair]['force_sell'] = True
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return 'B30'
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@@ -611,9 +612,9 @@ class EmptyShort(IStrategy):
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# self.pairs[pair]['force_sell'] = True
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# return 'B50'
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if current_profit > - self.sell_force_sell.value and last_candle['has_cross_sma3_1h'] == 1:
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self.pairs[pair]['force_sell'] = True
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return 'Cross'
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# if current_profit > - self.sell_force_sell.value and last_candle['has_cross_sma3_1h'] == 1:
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# self.pairs[pair]['force_sell'] = True
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# return 'Cross'
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# if profit > max(5, expected_profit) and last_candle['sma5_deriv1_1h'] < 0 and baisse > 0.15:
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# self.pairs[pair]['force_sell'] = True
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@@ -844,6 +845,8 @@ class EmptyShort(IStrategy):
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range_max = dataframe[f"max12_1d"]
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dataframe[f"range_pos"] = ((dataframe['mid'] - range_min) / (range_max - range_min)) #.rolling(5).mean()
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dataframe['min_max_12_1d'] = ((range_max - dataframe['close']) / range_min)
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dataframe['pct_min_max_12_1d'] = ((range_max - range_min) / range_min)
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# dataframe['cross_sma60'] = qtpylib.crossed_below(dataframe[self.sell_sma_indicators.value], dataframe[self.sell_crossed_sma_indicators.value])
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@@ -875,310 +878,82 @@ class EmptyShort(IStrategy):
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def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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pair = metadata['pair']
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conditions = list()
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# #####################################################################################
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# CA MONTE !!
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# #####################################################################################
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# conditions.append(dataframe[f"{self.start_bull_indicator.value}_deriv1_1h" ] > self.start_bull_deriv1.value)
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# conditions.append(dataframe[f"{self.start_bull_indicator.value}_deriv2_1h"] > self.start_bull_deriv2.value)
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# conditions.append(dataframe['sma12_deriv1'] > self.buy_deriv1_sma60.value)
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# conditions.append(dataframe['sma5_deriv1_1h'] > self.buy_deriv1_sma5d.value)
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# conditions.append(dataframe['sma12_deriv1_1h'] > self.buy_deriv1_sma12d.value)
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# conditions.append(dataframe['sma36_deriv2'] > self.buy_deriv2_sma60.value)
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# conditions.append(dataframe['sma5_deriv2_1h'] > self.buy_deriv2_sma5d.value)
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# conditions.append(dataframe['sma12_deriv2_1h'] > self.buy_deriv2_sma12d.value)
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conditions.append(dataframe['hapercent'] > 0)
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# conditions.append(dataframe['percent12'] < 0.01)
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# conditions.append(dataframe['percent5'] < 0.01)
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# conditions.append(dataframe['max_rsi_24'] < 80)
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# dynamic_rsi_threshold = 70 + 15 * np.tanh(dataframe["dist_sma200_1h"] * 5)
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# conditions.append((dataframe['max_rsi_12_1h'] < dynamic_rsi_threshold))
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# conditions.append(dataframe[f"close"] > dataframe['sma60'])
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# conditions.append(((dataframe[f"range_pos"] < 0.05) ) | ((dataframe['sma12_deriv1'] > 0) & (dataframe['sma12_deriv2'] > 0)))
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#
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# conditions.append(
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# (dataframe['close_1h'] > dataframe[f'sma{self.buy_longue.value}_1h'])
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# | (dataframe['sma60_inv_1h'] == -1)
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# )
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# if conditions:
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# dataframe.loc[
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# reduce(lambda x, y: x & y, conditions),
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# ['enter_long', 'enter_tag']
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# ] = (1, 'bull')
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# #####################################################################################
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# conditions = list()
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# conditions.append(dataframe['dist_sma200_1h'] < -0.05)
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# conditions.append(dataframe['sma12_inv'] == 1)
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# # conditions.append(dataframe['sma100_deriv1_1h'] > 0)
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# if conditions:
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# dataframe.loc[
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# reduce(lambda x, y: x & y, conditions),
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# ['enter_long', 'enter_tag']
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# ] = (1, 'dist')
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# #####################################################################################
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# conditions = list()
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# conditions.append(dataframe['close'] < dataframe['sma100_1h'])
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# conditions.append(dataframe['mid_smooth12'] > dataframe['mid_smooth12'].shift(1))
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# conditions.append(dataframe['sma100_deriv1_1h'] > 0)
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# conditions.append(dataframe[f"range_pos"] < 0.01)
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# if conditions:
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# dataframe.loc[
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# reduce(lambda x, y: x & y, conditions),
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# ['enter_long', 'enter_tag']
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# ] = (1, 'dist')
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# #####################################################################################
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# CA BAISSE !!
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# "buy": {
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# "buy_deriv1_sma12d": 0.0,
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# "buy_deriv1_sma5d": 0.0,
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# "buy_deriv1_sma60": 0.003,
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# "buy_deriv2_sma12d": -0.03,
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# "buy_deriv2_sma5d": 0.0,
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# "buy_deriv2_sma60": -0.002,
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# "buy_longue": 200,
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# "buy_longue_derive": "sma160_deriv1_1h"
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# },
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# "protection": {
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# "drop_from_last_entry": -0.03,
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# "b30_indicateur": "sma3",
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# "baisse": 0.31
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# },
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# #####################################################################################
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conditions = list()
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# conditions.append(dataframe[f"{self.start_bull_indicator.value}_deriv1_1h" ] < self.start_bull_deriv1.value)
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# conditions.append(dataframe[f"{self.start_bull_indicator.value}_deriv2_1h"] < self.start_bull_deriv2.value)
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# conditions.append(dataframe[f"{self.start_bear_indicator.value}_deriv1_1h" ] > self.start_bear_deriv1.value)
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# conditions.append(dataframe[f"{self.start_bear_indicator.value}_deriv2_1h"] > self.start_bear_deriv2.value)
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# conditions.append(dataframe['sma12_deriv1'] > self.buy_deriv1_sma60.value)
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# conditions.append(dataframe['sma5_deriv1_1h'] > self.buy_deriv1_sma5d.value)
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# conditions.append(dataframe['sma12_deriv1_1h'] > self.buy_deriv1_sma12d.value)
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#
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# # conditions.append(dataframe['sma12_deriv2'] > -0.002)
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# # conditions.append(dataframe['sma5_deriv2_1h'] > 0)
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# # conditions.append(dataframe['sma12_deriv2_1h'] > -0.03)
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#
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# conditions.append(dataframe['hapercent'] > 0)
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# # conditions.append(dataframe['percent12'] < 0.01)
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# # conditions.append(dataframe['percent5'] < 0.01)
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# conditions.append(dataframe['max_rsi_24'] < 80)
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#
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# dynamic_rsi_threshold = 70 + 15 * np.tanh(dataframe["dist_sma200_1h"] * 5)
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# conditions.append((dataframe['max_rsi_12_1h'] < dynamic_rsi_threshold))
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# conditions.append(dataframe[f"close"] > dataframe['sma60'])
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# conditions.append(((dataframe[f"range_pos"] < 0.05) ) | ((dataframe['sma12_deriv1'] > 0) & (dataframe['sma12_deriv2'] > 0)))
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#
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# conditions.append(
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# (dataframe['close_1h'] > dataframe[f'sma{self.buy_longue.value}_1h'])
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# | (dataframe['sma60_inv_1h'] == -1)
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# )
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# if conditions:
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# dataframe.loc[
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# reduce(lambda x, y: x & y, conditions),
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# ['enter_long', 'enter_tag']
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# ] = (1, 'bear')
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# conditions = list()
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# conditions.append(dataframe['close_1h'] < dataframe[f'sma{self.buy_longue.value}_1h'])
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# conditions.append(dataframe['has_cross_min'].rolling(6).max() == 1)
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# conditions.append(dataframe['mid_smooth5'] > dataframe['mid_smooth5'].shift(1))
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# conditions.append(dataframe['hapercent'] > 0)
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# conditions.append(dataframe['percent5'] < 0.01)
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# dataframe.loc[
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# reduce(lambda x, y: x & y, conditions),
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# ['enter_long', 'enter_tag']
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# ] = (1, 'cross_min')
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# conditions = list()
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# conditions.append(dataframe['sma12_deriv1'] > 0.00)
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# conditions.append(dataframe['sma60_deriv1'] > 0.0)
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# conditions.append(dataframe['sma5_deriv1_1h'] > 0.0)
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# conditions.append(dataframe['sma12_deriv1_1h'] > 0.0)
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# conditions.append(dataframe['sma24_deriv1_1h'] > 0.0)
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# conditions.append(dataframe['sma100_deriv1_1h'] > 0.0)
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# conditions.append(dataframe[f"range_pos"] < 0.025)
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# conditions.append(dataframe['sma12_deriv1_1h'] > 0.0)
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# # conditions.append(dataframe['close_1h'] < dataframe[f'sma{self.buy_longue.value}_1h'])
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# # conditions.append(dataframe['has_cross_min'].rolling(6).max() == 1)
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# # conditions.append(dataframe['mid_smooth5'] > dataframe['mid_smooth5'].shift(1))
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# conditions.append(dataframe['min12'] == dataframe['min12'].shift(3))
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# conditions.append((dataframe['percent24'] < -0.025) | (dataframe['percent12'] < -0.025))
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# dataframe.loc[
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# reduce(lambda x, y: x & y, conditions),
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# ['enter_long', 'enter_tag']
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# ] = (1, 'Rise')
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# conditions = list()
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# conditions.append(dataframe['has_cross_min_6'] == True)
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# conditions.append(dataframe['min36'] == dataframe['min36'].shift(3))
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# dataframe.loc[
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# reduce(lambda x, y: x & y, conditions),
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# ['enter_long', 'enter_tag']
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# ] = (1, 'Force')
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conditions = list()
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# conditions.append(dataframe['mid_regression'].shift(2) > dataframe['mid_regression'].shift(1))
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# conditions.append(dataframe['mid_regression'].shift(1) < dataframe['mid_regression'])
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conditions.append(dataframe['close'] <= dataframe['min12_1h'])
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conditions.append(dataframe['min60'] == dataframe['min60'].shift(5))
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conditions.append(dataframe['has_cross_min_6'] == 1)
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conditions.append(dataframe['down_count'] <= 5)
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conditions.append(dataframe['down_count'] <= 5)
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# conditions.append(dataframe['sma12_deriv1'] >= 0)
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# dataframe.loc[
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# reduce(lambda x, y: x & y, conditions),
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# ['enter_long', 'enter_tag']
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# ] = (1, 'Mid')
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dataframe.loc[
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(
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# (qtpylib.crossed_above(dataframe['sma60'], dataframe['mid_regression_1h']))
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(dataframe['sma12_1h'] > dataframe['sma12_1h'].shift(61))
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& (dataframe['range_pos'] < 0.05)
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& (dataframe['close'] < dataframe['close_1d'])
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& (dataframe['close'] < dataframe['close_1h'])
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(dataframe['sma12_deriv1_1h'] > 0)
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& (dataframe['max_rsi_12'] < 70)
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& (dataframe['max_rsi_12_1h'] < 70)
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& (dataframe['range_pos'] < 1)
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& (dataframe['hapercent'] > 0)
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& (dataframe['pct_min_max_12_1d'] < 0.04)
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),
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['enter_long', 'enter_tag']
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] = (1, 'long')
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dataframe.loc[
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(
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# (qtpylib.crossed_below(
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# dataframe['sma60'],
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# dataframe['mid_regression_1h'])
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# ) &
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(
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# (dataframe['close'] > dataframe['max5_1h'])
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# & (dataframe['sma12'] < dataframe['sma12'].shift(1))
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# # & (dataframe['sma120_inv'] == 1)
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# & (dataframe['close'] > dataframe['close_1h'])
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# & (dataframe['close'] > dataframe['close_1d'])
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# (dataframe['rsi_1h'] >= 65)
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# & (dataframe['max_rsi_12_1h'] == dataframe['rsi_1h'])
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# & (dataframe['hapercent'] < 0)
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#
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(dataframe['sma12_1h'] < dataframe['sma12_1h'].shift(61))
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& (dataframe['range_pos'] > 0.05)
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& (dataframe['close'] > dataframe['close_1d'])
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& (dataframe['close'] > dataframe['close_1h'])
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(dataframe['sma12_deriv1_1h'] < -0.0005)
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& (dataframe['max_rsi_12'] < 60)
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& (
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(
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(dataframe['max_rsi_24_1h'] > 60) & (dataframe['range_pos'] > 0.8)
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| (dataframe['sma12_deriv1_1d'] < -0.01)
|
||||
)
|
||||
)
|
||||
& (dataframe['hapercent'] < 0)
|
||||
# & (dataframe['pct_min_max_12_1d'] > 0.04)
|
||||
),
|
||||
['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')
|
||||
dataframe['short'] = np.where(dataframe['enter_short'] == 1, dataframe['close'] * 0.99, np.nan)
|
||||
dataframe['long'] = np.where(dataframe['enter_long'] == 1, dataframe['close'] * 1.01, np.nan)
|
||||
|
||||
return dataframe
|
||||
|
||||
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
conditions = list()
|
||||
# # TODO: Its not dry code!
|
||||
# sell_indicator = self.sell_indicator0.value
|
||||
# sell_crossed_indicator = self.sell_crossed_indicator0.value
|
||||
# sell_operator = self.sell_operator0.value
|
||||
# sell_real_num = self.sell_real_num0.value
|
||||
# condition, dataframe = condition_generator(
|
||||
# dataframe,
|
||||
# sell_operator,
|
||||
# sell_indicator,
|
||||
# sell_crossed_indicator,
|
||||
# sell_real_num
|
||||
# )
|
||||
# conditions.append(condition)
|
||||
# conditions = list()
|
||||
# dataframe.loc[
|
||||
# (
|
||||
# # (qtpylib.crossed_above(
|
||||
# # dataframe['sma'],
|
||||
# # dataframe['mid_regression_1h'])
|
||||
# # )
|
||||
# # (dataframe['sma12_1h'] < dataframe['sma12_1h'].shift(61))
|
||||
# # & (dataframe['range_pos'] > 0.5)
|
||||
# # & (dataframe['close'] > dataframe['close_1d'])
|
||||
# # & (dataframe['close'] > dataframe['close_1h'])
|
||||
#
|
||||
# sell_indicator = self.sell_indicator1.value
|
||||
# sell_crossed_indicator = self.sell_crossed_indicator1.value
|
||||
# sell_operator = self.sell_operator1.value
|
||||
# sell_real_num = self.sell_real_num1.value
|
||||
# condition, dataframe = condition_generator(
|
||||
# dataframe,
|
||||
# sell_operator,
|
||||
# sell_indicator,
|
||||
# sell_crossed_indicator,
|
||||
# sell_real_num
|
||||
# )
|
||||
# conditions.append(condition)
|
||||
# (dataframe['sma12_deriv1_1h'] < -0.0005)
|
||||
# # & (dataframe['max_rsi_12'] < 70)
|
||||
# # & (dataframe['max_rsi_12_1h'] < 70)
|
||||
# # & (dataframe['range_pos'] > 0.8)
|
||||
# & (dataframe['hapercent'] < 0)
|
||||
#
|
||||
# sell_indicator = self.sell_indicator2.value
|
||||
# sell_crossed_indicator = self.sell_crossed_indicator2.value
|
||||
# sell_operator = self.sell_operator2.value
|
||||
# sell_real_num = self.sell_real_num2.value
|
||||
# condition, dataframe = condition_generator(
|
||||
# dataframe,
|
||||
# sell_operator,
|
||||
# sell_indicator,
|
||||
# sell_crossed_indicator,
|
||||
# sell_real_num
|
||||
# )
|
||||
# conditions.append(condition)
|
||||
# ),
|
||||
# ['exit_long', 'exit_tag']
|
||||
# ] = (1, 'long')
|
||||
#
|
||||
# dataframe.loc[
|
||||
# (
|
||||
# # (qtpylib.crossed_above(
|
||||
# # dataframe['sma'],
|
||||
# # dataframe['mid_regression_1h'])
|
||||
# # )
|
||||
#
|
||||
# print(f"SELL indicators tested \n"
|
||||
# f"{self.sell_indicator0.value} {self.sell_crossed_indicator0.value} {self.sell_operator0.value} {self.sell_real_num0.value} \n"
|
||||
# f"{self.sell_indicator1.value} {self.sell_crossed_indicator1.value} {self.sell_operator1.value} {self.sell_real_num1.value} \n"
|
||||
# f"{self.sell_indicator2.value} {self.sell_crossed_indicator2.value} {self.sell_operator2.value} {self.sell_real_num2.value} \n"
|
||||
# )
|
||||
# # (dataframe['rsi_1h'] <= 30)
|
||||
# # & (dataframe['min_rsi_12_1h'] == dataframe['rsi_1h'])
|
||||
# # & (dataframe['hapercent'] > 0)
|
||||
# # & (dataframe['sma60'] >= dataframe['sma60'].shift(1))
|
||||
#
|
||||
#
|
||||
# conditions.append(qtpylib.crossed_below(dataframe['mid'], dataframe['sma24']))
|
||||
# conditions.append((dataframe['range_pos'] > 0.04))
|
||||
#
|
||||
# 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['sma12_1h'] < dataframe['sma12_1h'].shift(61))
|
||||
& (dataframe['range_pos'] > 0.01)
|
||||
& (dataframe['close'] > dataframe['close_1d'])
|
||||
& (dataframe['close'] > dataframe['close_1h'])
|
||||
),
|
||||
['exit_long', 'exit_tag']
|
||||
] = (1, 'long')
|
||||
|
||||
dataframe.loc[
|
||||
(
|
||||
# (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))
|
||||
|
||||
(dataframe['sma12_1h'] > dataframe['sma12_1h'].shift(61))
|
||||
& (dataframe['range_pos'] < 0.01)
|
||||
& (dataframe['close'] < dataframe['close_1d'])
|
||||
& (dataframe['close'] < dataframe['close_1h'])
|
||||
|
||||
),
|
||||
['exit_short', 'exit_tag']
|
||||
] = (1, 'short')
|
||||
# (dataframe['sma12_deriv1_1h'] > 0.0005)
|
||||
# # & (dataframe['max_rsi_12'] < 70)
|
||||
# # & (dataframe['max_rsi_12_1h'] < 70)
|
||||
# # & (dataframe['range_pos'] < 0.2)
|
||||
# & (dataframe['hapercent'] > 0)
|
||||
# ),
|
||||
# ['exit_short', 'exit_tag']
|
||||
# ] = (1, 'short')
|
||||
|
||||
return dataframe
|
||||
|
||||
|
||||
Reference in New Issue
Block a user