Synchronise
This commit is contained in:
Jérôme Delacotte
@@ -24,6 +24,7 @@ import ta
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import talib.abstract as talib
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import freqtrade.vendor.qtpylib.indicators as qtpylib
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import requests
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from datetime import timezone, timedelta
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logger = logging.getLogger(__name__)
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@@ -233,7 +234,7 @@ class Zeus_8_3_2_B_4_2(IStrategy):
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# allow_to_buy = rate <= dataframe['lbp_3']
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self.trades = list()
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dispo = round(self.wallets.get_available_stake_amount())
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logger.info(f"{pair} allow_to_buy {allow_to_buy} limit={limit} Buy {entry_tag} {current_time} dispo={dispo}")
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logger.info(f"BUY {pair} allow_to_buy {allow_to_buy} limit={limit} Buy {entry_tag} {current_time} dispo={dispo}")
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return allow_to_buy
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@@ -250,14 +251,14 @@ class Zeus_8_3_2_B_4_2(IStrategy):
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if allow_to_sell:
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self.trades = list()
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logger.info('Sell trade ' + exit_reason + ' ' + str(current_time) + ' ' + pair + " dispo=" + str(
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logger.info('Sell ' + exit_reason + ' ' + str(current_time) + ' ' + pair + " dispo=" + str(
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round(self.wallets.get_available_stake_amount())) # "+ str(amount) + ' ' + str(rate)
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+ " open_rate=" + str(trade.open_rate) + " rate=" + str(rate) + " profit=" + str(
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trade.calc_profit(rate, amount))
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+ " " + string)
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# del self.max_profit_pairs[pair]
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else:
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logger.info('Cancel Sell trade ' + exit_reason + ' ' + str(current_time) + ' ' + pair)
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logger.info('Cancel Sell ' + exit_reason + ' ' + str(current_time) + ' ' + pair)
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return (allow_to_sell) | (exit_reason == 'force_exit')
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def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: float,
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@@ -272,36 +273,35 @@ class Zeus_8_3_2_B_4_2(IStrategy):
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# Use default stake amount.
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return adjusted_stake_amount
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def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
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current_rate: float, current_profit: float, **kwargs) -> float:
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# # hard stoploss profit
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# HSL = self.pHSL.value
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# PF_1 = self.pPF_1.value
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# SL_1 = self.pSL_1.value
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# PF_2 = self.pPF_2.value
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# SL_2 = self.pSL_2.value
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#
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# # For profits between PF_1 and PF_2 the stoploss (sl_profit) used is linearly interpolated
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# # between the values of SL_1 and SL_2. For all profits above PL_2 the sl_profit value
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# # rises linearly with current profit, for profits below PF_1 the hard stoploss profit is used.
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#
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# if current_profit > PF_2:
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# sl_profit = SL_2 + (current_profit - PF_2)
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# elif current_profit > PF_1:
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# sl_profit = SL_1 + ((current_profit - PF_1) * (SL_2 - SL_1) / (PF_2 - PF_1))
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# else:
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# sl_profit = HSL
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#print(f"entry_tag={trade.entry_tag} max={trade.max_rate} min={trade.min_rate} ")
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if current_profit > 0.0125:
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sl_profit = 0.75 * current_profit # 75% du profit en cours
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else:
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sl_profit = self.pHSL.value # Hard stop-loss
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stoploss = stoploss_from_open(sl_profit, current_profit)
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logger.info(f"stoploss={stoploss}")
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return stoploss
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#
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# def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
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# current_rate: float, current_profit: float, **kwargs) -> float:
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#
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# # # hard stoploss profit
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# # HSL = self.pHSL.value
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# # PF_1 = self.pPF_1.value
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# # SL_1 = self.pSL_1.value
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# # PF_2 = self.pPF_2.value
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# # SL_2 = self.pSL_2.value
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# #
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# # # For profits between PF_1 and PF_2 the stoploss (sl_profit) used is linearly interpolated
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# # # between the values of SL_1 and SL_2. For all profits above PL_2 the sl_profit value
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# # # rises linearly with current profit, for profits below PF_1 the hard stoploss profit is used.
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# #
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# # if current_profit > PF_2:
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# # sl_profit = SL_2 + (current_profit - PF_2)
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# # elif current_profit > PF_1:
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# # sl_profit = SL_1 + ((current_profit - PF_1) * (SL_2 - SL_1) / (PF_2 - PF_1))
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# # else:
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# # sl_profit = HSL
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#
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# #print(f"entry_tag={trade.entry_tag} max={trade.max_rate} min={trade.min_rate} ")
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# if current_profit > 0.0125:
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# sl_profit = 0.75 * current_profit # 75% du profit en cours
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# else:
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# sl_profit = self.pHSL.value # Hard stop-loss
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# stoploss = stoploss_from_open(sl_profit, current_profit)
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# return stoploss
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#
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# dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
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@@ -347,33 +347,33 @@ class Zeus_8_3_2_B_4_2(IStrategy):
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# print('current_profit=' + str(current_profit) + ' stop from open=' + str(slfo))
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# return slfo
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# def custom_exit(self, pair: str, trade: Trade, current_time, current_rate, current_profit, **kwargs):
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#
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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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#
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# # self.analyze_conditions(pair, dataframe)
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#
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# print("---------------" + pair + "----------------")
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# expected_profit = self.expectedProfit(pair, last_candle)
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#
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# dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
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# last_candle = dataframe.iloc[-1]
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#
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# # Calcul du prix cible basé sur l'ATR
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# atr_take_profit = trade.open_rate + (last_candle['atr'] * 2) # Prendre profit à 2x l'ATR
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#
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# logger.info(f"{pair} Custom exit atr_take_profit={atr_take_profit:.4f}")
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# # if current_rate >= atr_take_profit:
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# # return 'sell_atr_take_profit'
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#
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# if (last_candle['percent3'] < -0.002) & (last_candle['percent12'] < 0) & (
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# current_profit > last_candle['min_max200'] / 2):
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# self.trades = list()
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# return 'min_max200'
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# if (last_candle['percent12'] <= -0.01) & (current_profit >= expected_profit):
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# self.trades = list()
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# return 'profit'
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def custom_exit(self, pair: str, trade: Trade, current_time, current_rate, current_profit, **kwargs):
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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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# self.analyze_conditions(pair, dataframe)
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# print("---------------" + pair + "----------------")
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expected_profit = self.expectedProfit(pair, last_candle)
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dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
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last_candle = dataframe.iloc[-1]
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# Calcul du prix cible basé sur l'ATR
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atr_take_profit = trade.open_rate + (last_candle['atr'] * 2) # Prendre profit à 2x l'ATR
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# logger.info(f"{pair} Custom exit atr_take_profit={atr_take_profit:.4f}")
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# if current_rate >= atr_take_profit:
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# return 'sell_atr_take_profit'
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if (last_candle['percent3'] < -0.002) & (last_candle['percent12'] < 0) & (
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current_profit > last_candle['min_max200'] / 2):
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self.trades = list()
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return 'min_max200'
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if (last_candle['percent12'] <= -0.01) & (current_profit >= expected_profit):
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self.trades = list()
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return 'profit'
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def informative_pairs(self):
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# get access to all pairs available in whitelist.
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@@ -548,6 +548,7 @@ class Zeus_8_3_2_B_4_2(IStrategy):
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dataframe['limit'] = dataframe['last_price'] * (1 - self.baisse[count] / 100)
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dataframe['amount'] = amount
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print(f"amount= {amount}")
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# trades = Trade.get_trades([Trade.is_open is False]).all()
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trades = Trade.get_trades_proxy(is_open=False, pair=metadata['pair'])
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if trades:
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@@ -627,19 +628,19 @@ class Zeus_8_3_2_B_4_2(IStrategy):
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self.trades = Trade.get_open_trades()
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return self.trades
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def getTrade(self, pair):
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trades = self.getOpenTrades()
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trade_for_pair = None
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for trade in trades:
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if trade.pair == pair:
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trade_for_pair = trade
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break
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return trade_for_pair
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# def getTrade(self, pair):
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# trades = self.getOpenTrades()
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# trade_for_pair = None
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# for trade in trades:
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# if trade.pair == pair:
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# trade_for_pair = trade
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# break
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# return trade_for_pair
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def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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pair = metadata['pair']
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self.getOpenTrades()
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# self.getOpenTrades()
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expected_profit = self.expectedProfit(pair, dataframe.iloc[-1])
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# self.getBinanceOrderBook(pair, dataframe)
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last_candle = dataframe.iloc[-1].squeeze()
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@@ -648,7 +649,7 @@ class Zeus_8_3_2_B_4_2(IStrategy):
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# self.updateLastValue(dataframe, 'expected_profit', expected_profit)
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print("---------------" + pair + "----------------")
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print('adjust stake amount ' + str(self.adjust_stake_amount(pair, dataframe.iloc[-1])))
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print('adjust exit price ' + str(self.adjust_exit_price(dataframe.iloc[-1])))
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# print('adjust exit price ' + str(self.adjust_exit_price(dataframe.iloc[-1])))
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print('calcul expected_profit ' + str(expected_profit))
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buy_level = dataframe['buy_level'] # self.get_buy_level(pair, dataframe)
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@@ -705,7 +706,7 @@ class Zeus_8_3_2_B_4_2(IStrategy):
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)
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& (dataframe['percent'] >= -0.0005)
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& (dataframe['min12'].shift(2) == dataframe['min12'])
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), ['buy', 'enter_tag']] = (1, 'buy_min_max200_2')
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), ['enter_long', 'enter_tag']] = (1, 'buy_min_max200_2')
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dataframe.loc[
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(
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((dataframe['count_buys'] > 0) & (dataframe['close'] <= dataframe['limit']))
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@@ -715,7 +716,7 @@ class Zeus_8_3_2_B_4_2(IStrategy):
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(dataframe['min12'].shift(2) == dataframe['min12']) |
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(dataframe['min200'].shift(60) >= dataframe['min200'] * 1.03)
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)
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), ['buy', 'enter_tag']] = (1, 'buy_count_buy')
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), ['enter_long', 'enter_tag']] = (1, 'buy_count_buy')
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dataframe.loc[
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(
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@@ -737,7 +738,7 @@ class Zeus_8_3_2_B_4_2(IStrategy):
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| (dataframe['percent12'] < -0.022)
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| (dataframe['percent24'] < -0.022)
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)
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), ['buy', 'enter_tag']] = (1, 'buy_0_percent12')
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), ['enter_long', 'enter_tag']] = (1, 'buy_0_percent12')
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dataframe.loc[
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(
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# (dataframe['percent12'] < -0.015)
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@@ -745,7 +746,7 @@ class Zeus_8_3_2_B_4_2(IStrategy):
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& (dataframe['open'] < dataframe['average_line_50'])
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& (dataframe['close'] < dataframe['min12'] * 1.002)
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& (dataframe['min12'].shift(2) == dataframe['min12'])
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), ['buy', 'enter_tag']] = (1, 'buy_percent12')
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), ['enter_long', 'enter_tag']] = (1, 'buy_percent12')
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dataframe.loc[
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(
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@@ -755,7 +756,7 @@ class Zeus_8_3_2_B_4_2(IStrategy):
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& (dataframe['min50'].shift(3) == dataframe['min50'])
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& (dataframe['close'] <= dataframe['min50'] * 1.002)
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& (dataframe['open'] < dataframe['average_line_288'])
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), ['buy', 'enter_tag']] = (1, 'buy_percent_max_144')
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), ['enter_long', 'enter_tag']] = (1, 'buy_percent_max_144')
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dataframe.loc[
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(
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(dataframe['close'] <= dataframe['min200'] * 1.002)
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@@ -763,7 +764,7 @@ class Zeus_8_3_2_B_4_2(IStrategy):
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& (dataframe['pct_change'] < 0)
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& (dataframe['haopen'] < buy_level)
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& (dataframe['open'] < dataframe['average_line_288'])
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), ['buy', 'enter_tag']] = (1, 'buy_min_max_200')
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), ['enter_long', 'enter_tag']] = (1, 'buy_min_max_200')
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dataframe.loc[
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(
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(dataframe['percent_max_144'] <= -0.02)
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@@ -773,21 +774,21 @@ class Zeus_8_3_2_B_4_2(IStrategy):
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& (dataframe['close'] <= dataframe['average_line_288_099'])
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& (dataframe['min50'].shift(3) == dataframe['min50'])
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& (dataframe['close'] <= dataframe['min50'] * 1.002)
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), ['buy', 'enter_tag']] = (1, 'buy_close_02')
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dataframe.loc[
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(
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(dataframe['close'] <= dataframe['lowest_4_average'] * 1.002)
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& (dataframe['haopen'] >= dataframe['lbp_3'])
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& (dataframe['haclose'] <= dataframe['lbp_3'])
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& (dataframe['haopen'] < buy_level)
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), ['buy', 'enter_tag']] = (1, 'buy_lbp_3')
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), ['enter_long', 'enter_tag']] = (1, 'buy_close_02')
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# dataframe.loc[
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# (
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# (dataframe['close'] <= dataframe['lowest_4_average'] * 1.002)
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# & (dataframe['haopen'] >= dataframe['lbp_3'])
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# & (dataframe['haclose'] <= dataframe['lbp_3'])
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# & (dataframe['haopen'] < buy_level)
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# ), ['enter_long', 'enter_tag']] = (1, 'buy_lbp_3')
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dataframe.loc[
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(
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(dataframe['close'] <= dataframe['lowest_4_average'] * 1.002)
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& (dataframe['haopen'] >= dataframe['average_line_288_098'])
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& (dataframe['haclose'] <= dataframe['average_line_288_098'])
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& (dataframe['haopen'] < buy_level)
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), ['buy', 'enter_tag']] = (1, 'buy_average_line_288_098')
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), ['enter_long', 'enter_tag']] = (1, 'buy_average_line_288_098')
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dataframe.loc[
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(
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(dataframe['close'].shift(2) <= dataframe['min200'])
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@@ -797,9 +798,9 @@ class Zeus_8_3_2_B_4_2(IStrategy):
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& (dataframe['count_buys'] == 0 |
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((dataframe['count_buys'] > 0) & (dataframe['close'] <= dataframe['limit']))
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)
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), ['buy', 'enter_tag']] = (1, 'buy_min200')
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), ['enter_long', 'enter_tag']] = (1, 'buy_min200')
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dataframe['test'] = np.where(dataframe['buy'] == 1, dataframe['close'] * 1.01, np.nan)
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dataframe['test'] = np.where(dataframe['enter_long'] == 1, dataframe['close'] * 1.01, np.nan)
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return dataframe
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@@ -810,7 +811,7 @@ class Zeus_8_3_2_B_4_2(IStrategy):
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# for trade in self.trades:
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# if trade.pair != pair:
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# continue
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# filled_buys = trade.select_filled_orders('buy')
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# filled_buys = trade.select_filled_orders('enter_long')
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# print('populate_buy_trend filled_buys : ' + str(len(filled_buys)))
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# # Affichez les valeurs
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# print(pair, limit)
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@@ -848,10 +849,10 @@ class Zeus_8_3_2_B_4_2(IStrategy):
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if count_of_buys >= max_buys:
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return None
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if 'buy' in last_candle:
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condition = (last_candle['buy'] == 1)
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else:
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condition = False
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# if 'buy' in last_candle:
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# condition = (last_candle['buy'] == 1)
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# else:
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# condition = False
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# self.protection_nb_buy_lost.value
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# limits = ['lbp_3', 'lbp_6', 'lbp_9', 'lbp_12', 'lbp_20']
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# limit = last_candle[limits[count_of_buys]]
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@@ -859,17 +860,20 @@ class Zeus_8_3_2_B_4_2(IStrategy):
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stake_amount = min(200, self.adjust_stake_amount(pair, last_candle) * self.fibo[count_of_buys])
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# print("Adjust " + trade.pair + " time=" + str(current_time) + ' rate=' + str(current_rate) + " buys=" + str(count_of_buys) + " limit=" + str(limit) + " stake=" + str(stake_amount))
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logger.info(
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f"Adjust price={trade.pair} buy={condition} rate={current_rate:.4f} buys={count_of_buys} limit={limit:.4f} stake={stake_amount:.4f}")
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# logger.info(
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# f"Adjust price={trade.pair} buy={condition} rate={current_rate:.4f} buys={count_of_buys} limit={limit:.4f} stake={stake_amount:.4f}")
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current_time_utc = current_time.astimezone(timezone.utc)
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open_date = trade.open_date.astimezone(timezone.utc)
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days_since_open = (current_time_utc - open_date).days
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if (0 < count_of_buys <= max_buys) & (current_rate <= limit) & (condition):
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if (days_since_open > count_of_buys) & (0 < count_of_buys <= max_buys) & (current_rate <= limit) & (last_candle['enter_long'] == 1):
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try:
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# This then calculates current safety order size
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# stake_amount = stake_amount * pow(1.5, count_of_buys)
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# print("Effective Adjust " + trade.pair + " time=" + str(current_time) + ' rate=' + str(current_rate) + " buys=" + str(count_of_buys) + " limit=" + str(limit) + " stake=" + str(stake_amount))
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logger.info(
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f"Effective Adjust price={trade.pair} rate={current_rate:.4f} buys={count_of_buys} limit={limit:.4f} stake={stake_amount:.4f}")
|
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f"Adjust {current_time} price={trade.pair} rate={current_rate:.4f} buys={count_of_buys} limit={limit:.4f} stake={stake_amount:.4f}")
|
||||
|
||||
return stake_amount
|
||||
except Exception as exception:
|
||||
@@ -907,24 +911,24 @@ class Zeus_8_3_2_B_4_2(IStrategy):
|
||||
|
||||
# adjusted_stake_amount_2 = max(base_stake_amount / 2.5, min(75, base_stake_amount * percent))
|
||||
|
||||
print(
|
||||
f"Stack amount ajusté price={current_price} max_min={max_min_4:.4f} min_14={min_14_days_4:.4f} max_14={max_14_days_4:.4f} factor={factor_4:.4f} percent={percent_4:.4f} amount={adjusted_stake_amount:.4f}")
|
||||
# print(
|
||||
# f"Stack amount ajusté price={current_price} max_min={max_min_4:.4f} min_14={min_14_days_4:.4f} max_14={max_14_days_4:.4f} factor={factor_4:.4f} percent={percent_4:.4f} amount={adjusted_stake_amount:.4f}")
|
||||
# print(f"Stack amount ajusté price={current_price} max_min={max_min:.4f} min_14={min_14_days:.4f} max_14={max_14_days:.4f} factor={factor:.4f} percent={percent:.4f} amount={adjusted_stake_amount_2:.4f}")
|
||||
|
||||
return adjusted_stake_amount
|
||||
|
||||
def adjust_exit_price(self, dataframe: DataFrame):
|
||||
# Calculer le max des 14 derniers jours
|
||||
min_14_days = dataframe['lowest_1d']
|
||||
max_14_days = dataframe['highest_1d']
|
||||
entry_price = dataframe['fbp']
|
||||
current_price = dataframe['close']
|
||||
percent = 0.5 * (max_14_days - min_14_days) / min_14_days
|
||||
exit_price = (1 + percent) * entry_price
|
||||
|
||||
print(f"Exit price ajusté price={current_price:.4f} max_14={max_14_days:.4f} exit_price={exit_price:.4f}")
|
||||
|
||||
return exit_price
|
||||
# def adjust_exit_price(self, dataframe: DataFrame):
|
||||
# # Calculer le max des 14 derniers jours
|
||||
# min_14_days = dataframe['lowest_1d']
|
||||
# max_14_days = dataframe['highest_1d']
|
||||
# entry_price = dataframe['fbp']
|
||||
# current_price = dataframe['close']
|
||||
# percent = 0.5 * (max_14_days - min_14_days) / min_14_days
|
||||
# exit_price = (1 + percent) * entry_price
|
||||
#
|
||||
# print(f"Exit price ajusté price={current_price:.4f} max_14={max_14_days:.4f} exit_price={exit_price:.4f}")
|
||||
#
|
||||
# return exit_price
|
||||
|
||||
def adjust_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
|
||||
current_rate: float, current_profit: float, **kwargs) -> float:
|
||||
@@ -954,8 +958,8 @@ class Zeus_8_3_2_B_4_2(IStrategy):
|
||||
min_max = dataframe['pct_min_max_1d'] # (max_14_days - min_14_days) / min_14_days
|
||||
expected_profit = min(0.1, max(0.01, dataframe['min_max200'] * 0.5))
|
||||
|
||||
print(
|
||||
f"Expected profit price={current_price:.4f} min_max={min_max:.4f} min_14={min_14_days:.4f} max_14={max_14_days:.4f} percent={percent:.4f} expected_profit={expected_profit:.4f}")
|
||||
# print(
|
||||
# f"Expected profit price={current_price:.4f} min_max={min_max:.4f} min_14={min_14_days:.4f} max_14={max_14_days:.4f} percent={percent:.4f} expected_profit={expected_profit:.4f}")
|
||||
|
||||
# self.analyze_conditions(pair, dataframe)
|
||||
return expected_profit
|
||||
|
||||
Reference in New Issue
Block a user