Test buy mid_smooth_1h_deriv1
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@@ -173,7 +173,9 @@ class Zeus_8_3_2_B_4_2(IStrategy):
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'last_date': 0,
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'stop': False,
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'max_profit': 0,
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'last_palier_index': -1
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'last_palier_index': -1,
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'baisse': 0,
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'mx': 0
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}
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for pair in ["BTC/USDC", "ETH/USDC", "DOGE/USDC", "XRP/USDC", "SOL/USDC",
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"BTC/USDT", "ETH/USDT", "DOGE/USDT", "XRP/USDT", "SOL/USDT"]
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@@ -240,6 +242,8 @@ class Zeus_8_3_2_B_4_2(IStrategy):
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# buy_level_predict_1h = IntParameter(2, 5, default=4, space='buy')
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should_enter_trade_count = 0
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def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float, time_in_force: str,
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current_time: datetime, entry_tag: Optional[str], **kwargs) -> bool:
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@@ -377,6 +381,9 @@ class Zeus_8_3_2_B_4_2(IStrategy):
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self.pairs[pair]['max_profit'] = max(self.pairs[pair]['max_profit'], current_profit)
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self.pairs[pair]['total_amount'] = 0
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self.pairs[pair]['baisse'] = baisse
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self.pairs[pair]['mx'] = mx
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# if (last_candle['mid_smooth_deriv1'] >= 0):
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# return None
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# if (last_candle['tendency'] in ('H++', 'H+')) and (last_candle['rsi'] < 80):
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@@ -384,24 +391,25 @@ class Zeus_8_3_2_B_4_2(IStrategy):
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#
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# if (last_candle['sma20_deriv1'] < 0 and before_last_candle['sma20_deriv1'] >= 0) and (current_profit > expected_profit):
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# return 'Drv_' + str(count_of_buys)
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pair_name = pair.replace("/USDT", '').replace("/USDC", '')
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if 1 <= count_of_buys <= 3:
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if ((before_last_candle_2['mid_smooth_3_deriv1'] <= before_last_candle['mid_smooth_3_deriv1'])
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& (before_last_candle['mid_smooth_3_deriv1'] >= last_candle['mid_smooth_3_deriv1'])) \
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and (current_profit > expected_profit):
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return 'Drv3_' + pair + '_' + str(count_of_buys)
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return 'Drv3_' + pair_name + '_' + str(count_of_buys)
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if 4 <= count_of_buys <= 6:
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if ((before_last_candle_2['mid_smooth_12_deriv1'] <= before_last_candle['mid_smooth_12_deriv1'])
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& (before_last_candle['mid_smooth_12_deriv1'] >= last_candle['mid_smooth_12_deriv1'])) \
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and (current_profit > expected_profit):
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return 'Drv13_' + pair + '_' + str(count_of_buys)
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return 'Drv13_' + pair_name + '_' + str(count_of_buys)
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if 7 <= count_of_buys:
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if ((before_last_candle_24['sma24_deriv1_1h'] <= before_last_candle_12['sma24_deriv1_1h'])
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& (before_last_candle_12['sma24_deriv1_1h'] >= last_candle['sma24_deriv1_1h'])) \
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and (current_profit > expected_profit):
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return 'Drv24_' + pair + '_' + str(count_of_buys)
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return 'Drv24_' + pair_name + '_' + str(count_of_buys)
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# if (baisse > mx) & (current_profit > expected_profit):
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# self.trades = list()
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@@ -454,7 +462,7 @@ class Zeus_8_3_2_B_4_2(IStrategy):
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if self.columns_logged % 30 == 0:
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self.printLog(
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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}|{'Buys':>4}| {'Stake':>5} |"
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f"Tdc|{'val':>6}| sma5 |smooth |Distmax"
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f"Tdc|{'val':>6}| smooth|smoodrv|Distmax|baisse| mx |"
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)
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self.printLineLog()
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df = pd.DataFrame.from_dict(self.pairs, orient='index')
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@@ -522,7 +530,7 @@ class Zeus_8_3_2_B_4_2(IStrategy):
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# f"|{round(last_candle['mid_smooth_24_deriv1'],3) or '-':>6}|{round(last_candle['mid_smooth_1h_deriv1'],3) or '-':>6}|{round(last_candle['mid_smooth_deriv1_1d'],3) or '-' :>6}|"
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# f"{round(last_candle['mid_smooth_24_deriv2'],3) or '-' :>6}|{round(last_candle['mid_smooth_1h_deriv2'],3) or '-':>6}|{round(last_candle['mid_smooth_deriv2_1d'],3) or '-':>6}|"
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f"{round(val, 1) or '-' :>6}|"
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f"{round(last_candle['sma5_deriv1_1h'], 4) or '-' :>7}|{round(last_candle['mid_smooth_1h_deriv1'], 4) or '-' :>7}|{dist_max:>7}"
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f"{round(last_candle['mid_smooth_12'], 4) or '-' :>7}|{round(last_candle['mid_smooth_12_deriv1'], 4) or '-' :>7}|{dist_max:>7}|{round(self.pairs[pair]['baisse'], 3):>7}|{round(self.pairs[pair]['mx'], 4):>7}"
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)
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def printLineLog(self):
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@@ -846,19 +854,19 @@ class Zeus_8_3_2_B_4_2(IStrategy):
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dataframe.loc[
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(
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# (dataframe['deriv2_1h'].shift(2) >= dataframe['deriv2_1h'].shift(1))
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# & (dataframe['deriv2_1h'].shift(1) <= dataframe['deriv2_1h'])
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# (dataframe['deriv1_1h'] >= -0.01)
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# & (dataframe['deriv2_1h'] >= -0.00)
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(dataframe['mid_smooth_3_deriv1'].shift(2) >= dataframe['mid_smooth_3_deriv1'].shift(1))
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& (dataframe['mid_smooth_3_deriv1'].shift(1) <= dataframe['mid_smooth_3_deriv1'])
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#
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#
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# (dataframe['mid_smooth_1h_deriv1'] >= 0)
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# & (dataframe['mid_smooth_1h_deriv1'] >= 0)
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# & (dataframe['mid_smooth_1h_deriv1'].shift(1) <= 0)
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# & (dataframe['mid_smooth_1h_deriv1'] >= dataframe['mid_smooth_1h_deriv1'].shift(1))
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), ['enter_long', 'enter_tag']] = (1, 'smth')
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# (dataframe['mid_smooth_1h_deriv1'].shift(2) >= dataframe['mid_smooth_1h_deriv1'].shift(1))
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# & (dataframe['mid_smooth_1h_deriv1'].shift(1) <= dataframe['mid_smooth_1h_deriv1'])
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(dataframe['mid_smooth_1h_deriv1'].shift(2) >= dataframe['mid_smooth_1h_deriv1'].shift(1))
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& (dataframe['mid_smooth_1h_deriv1'].shift(1) <= dataframe['mid_smooth_1h_deriv1'])
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& (dataframe['close'] < dataframe['bb_middleband'])
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), ['enter_long', 'enter_tag']] = (1, 'smth')
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dataframe['can_sell'] = np.where(((dataframe['mid_smooth_1h_deriv1'].shift(2) <= dataframe['mid_smooth_1h_deriv1'].shift(1))\
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& (dataframe['mid_smooth_1h_deriv1'].shift(1) >= dataframe['mid_smooth_1h_deriv1'])), dataframe['close'], np.nan)
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dataframe['can_buy'] = np.where(((dataframe['mid_smooth_1h_deriv1'].shift(2) >= dataframe['mid_smooth_1h_deriv1'].shift(1))\
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& (dataframe['mid_smooth_1h_deriv1'].shift(1) <= dataframe['mid_smooth_1h_deriv1'])), dataframe['close'], np.nan)
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dataframe['test'] = np.where(dataframe['enter_long'] == 1, dataframe['close'] * 1.01, np.nan)
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dataframe['perte_02'] = np.where((dataframe['hapercent3'] * 100 < -0.2), dataframe['close'], np.nan)
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@@ -1807,7 +1815,13 @@ class Zeus_8_3_2_B_4_2(IStrategy):
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max_nb_trades = 0
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total_non_btc = 0
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max_pair = ''
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count_decrease = 0
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for p in non_btc_pairs:
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# dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
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# last_candle = dataframe.iloc[-1].squeeze()
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# if last_candle['sma5_deriv1_1h'] < -0.5:
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# count_decrease += 1
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max_nb_trades = max(max_nb_trades, self.pairs[p]['count_of_buys'])
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if (max_nb_trades == self.pairs[p]['count_of_buys'] and max_nb_trades > limit):
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max_pair = p
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@@ -1815,6 +1829,18 @@ class Zeus_8_3_2_B_4_2(IStrategy):
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pct_max = round((last_candle['close'] - self.pairs[pair]['first_buy']) / self.pairs[pair]['first_buy'], 3)
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# val = self.getProbaHausse(last_candle)
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# if (val < 40):
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# return False
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# if count_decrease == len(non_btc_pairs):
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# self.should_enter_trade_count += 1
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# char="."
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# print(f"should_enter_trade canceled all pairs decreased {'':{char}>{self.should_enter_trade_count}}")
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# return False
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self.should_enter_trade_count = 0
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if max_pair != '':
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return max_pair == pair or pct_max < - 0.25
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else:
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