TEST SHORT
This commit is contained in:
Jérôme Delacotte
242
EmptyShort.py
242
EmptyShort.py
@@ -151,7 +151,8 @@ class EmptyShort(IStrategy):
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'current_trade': None,
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'last_trade': None,
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'force_stop': False,
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'count_of_lost': 0
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'count_of_lost': 0,
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'take_profit': 0.01
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}
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for pair in ["BTC/USDC", "BTC/USDT", 'BTC/USDT:USDT']
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}
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@@ -261,7 +262,7 @@ class EmptyShort(IStrategy):
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# if last_candle['enter_tag'] in ['fall', 'bear', 'Force', 'Range-']:
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# amount = self.wallets.get_available_stake_amount() / self.mises_bear.value
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# else:
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amount = self.wallets.get_available_stake_amount() / self.mises_bull.value # / (2 * self.pairs[pair]['count_of_lost'] + 1)
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amount = self.wallets.get_available_stake_amount() #/ self.mises_bull.value # / (2 * self.pairs[pair]['count_of_lost'] + 1)
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# factor = 1
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#
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@@ -439,6 +440,21 @@ class EmptyShort(IStrategy):
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self.pairs[pair]['total_amount'] = stake_amount
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# print(f"Buy {pair} {current_time} {entry_tag} dispo={dispo} amount={stake_amount} rate={rate} rate={rate}")
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# sl_price = rate * (1 - last_candle['up_pct_1h'] / 2)
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#
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# self.custom_trade_info[pair] = {
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# "custom_exit": last_candle['up_pct_1h'] / 200
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# }
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# trade = kwargs.get("trade", None)
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# if trade:
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# trade.set_custom_data({
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# # "stoploss": rate * 0.95,
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# "takeprofit": last_candle['up_pct_1h'] / 200
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# })
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self.pairs[pair]['take_profit'] = last_candle['up_pct_1h'] / 200
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self.log_trade(
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last_candle=last_candle,
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date=current_time,
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@@ -477,7 +493,7 @@ class EmptyShort(IStrategy):
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profit = trade.calc_profit(rate)
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force = self.pairs[pair]['force_sell']
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allow_to_sell = (last_candle['hapercent'] < 0 and profit > 0) or force \
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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')
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or (trade.enter_tag == 'short' ) or (exit_reason == 'force_exit') or (exit_reason == 'stop_loss') or (exit_reason == 'trailing_stop_loss')
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minutes = int(round((current_time - trade.date_last_filled_utc).total_seconds() / 60, 0))
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@@ -547,12 +563,13 @@ 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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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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if current_profit > 0.01 and \
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tp_price = 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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@@ -572,23 +589,16 @@ class EmptyShort(IStrategy):
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# stake=0
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# )
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else:
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if self.pairs[pair]['current_trade'].enter_tag in ['bear', 'Force', 'Range-']:
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if current_profit < - 0.02 and last_candle[f"close"] <= last_candle['sma60'] and self.wallets.get_available_stake_amount() < 50:
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self.pairs[pair]['force_sell'] = True
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return 'smaBF'
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else:
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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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# if self.pairs[pair]['current_trade'].enter_tag in ['bear', 'Force', 'Range-']:
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# if current_profit < - 0.02 and last_candle[f"close"] <= last_candle['sma60'] and self.wallets.get_available_stake_amount() < 50:
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# self.pairs[pair]['force_sell'] = True
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# return 'smaBF'
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# else:
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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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if is_short:
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if current_profit > 0.005 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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else:
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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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@@ -699,12 +709,15 @@ class EmptyShort(IStrategy):
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dataframe['zero'] = 0
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dataframe[f"percent"] = dataframe['close'].pct_change()
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for timeperiod in timeperiods:
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for timeperiod in timeperiods + long_timeperiods:
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dataframe[f"mid_smooth{timeperiod}"] = dataframe['mid'].rolling(timeperiod).mean()
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dataframe[f'max{timeperiod}'] = talib.MAX(dataframe['close'], timeperiod=timeperiod)
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dataframe[f'min{timeperiod}'] = talib.MIN(dataframe['close'], timeperiod=timeperiod)
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dataframe[f"percent{timeperiod}"] = dataframe['close'].pct_change(timeperiod)
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dataframe[f"sma{timeperiod}"] = dataframe['mid'].ewm(span=timeperiod, adjust=False).mean()
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# dataframe[f"sma{timeperiod}"] = dataframe['mid'].ewm(span=timeperiod, adjust=False).mean()
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ema = dataframe['mid'].ewm(span=timeperiod, adjust=False).mean()
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dataframe[f"sma{timeperiod}"] = ema.ewm(span=int(timeperiod / 2), adjust=False).mean()
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# dataframe[f"high{timeperiod}"] = dataframe['high'].ewm(span=timeperiod, adjust=False).mean()
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# dataframe[f"low{timeperiod}"] = dataframe['low'].ewm(span=timeperiod, adjust=False).mean()
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# dataframe = self.calculateRegression(dataframe, column=f"high{timeperiod}", window=10, degree=1, future_offset=12)
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@@ -712,9 +725,9 @@ class EmptyShort(IStrategy):
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self.calculeDerivees(dataframe, f"sma{timeperiod}", timeframe=self.timeframe, ema_period=timeperiod)
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dataframe = self.calculateRegression(dataframe, column='mid', window=30, degree=1, future_offset=12)
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dataframe = self.calculateRegression(dataframe, column='sma24', window=30, degree=1, future_offset=12)
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self.calculeDerivees(dataframe, "sma24_regression", timeframe=self.timeframe, ema_period=12)
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dataframe = self.calculateRegression(dataframe, column='mid', window=30, degree=1, future_offset=6)
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dataframe = self.calculateRegression(dataframe, column='sma120', window=60, degree=2, future_offset=30)
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# self.calculeDerivees(dataframe, "sma24_regression", timeframe=self.timeframe, ema_period=12)
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dataframe["percent"] = dataframe["mid"].pct_change(1)
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dataframe["percent3"] = dataframe["mid"].pct_change(3)
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@@ -738,7 +751,7 @@ class EmptyShort(IStrategy):
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informative['haclose'] = heikinashi['close']
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informative['hapercent'] = (informative['haclose'] - informative['haopen']) / informative['haclose']
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informative['mid'] = informative['open'] + (informative['close'] - informative['open']) / 2
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for timeperiod in timeperiods:
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for timeperiod in [3, 5, 12]:
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informative[f'max{timeperiod}'] = talib.MAX(informative['close'], timeperiod=timeperiod)
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informative[f'min{timeperiod}'] = talib.MIN(informative['close'], timeperiod=timeperiod)
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# informative[f"range{timeperiod}"] = ((informative["close"] - informative[f'min{timeperiod}']) / (informative[f'max{timeperiod}'] - informative[f'min{timeperiod}']))
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@@ -746,9 +759,9 @@ class EmptyShort(IStrategy):
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informative[f"sma{timeperiod}"] = informative['mid'].ewm(span=timeperiod, adjust=False).mean()
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self.calculeDerivees(informative, f"sma{timeperiod}", timeframe=self.timeframe, ema_period=timeperiod)
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informative = self.calculateRegression(informative, column='mid', window=10, degree=1, future_offset=12)
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informative = self.calculateRegression(informative, column='sma3', window=10, degree=1, future_offset=12)
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informative = self.calculateRegression(informative, column='low', window=10, degree=1, future_offset=12)
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informative = self.calculateRegression(informative, column='mid', window=10, degree=1, future_offset=2)
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informative = self.calculateRegression(informative, column='sma3', window=10, degree=1, future_offset=2)
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informative = self.calculateRegression(informative, column='low', window=10, degree=1, future_offset=2)
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for timeperiod in long_timeperiods:
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informative[f"sma{timeperiod}"] = informative['mid'].ewm(span=timeperiod, adjust=False).mean()
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@@ -758,20 +771,22 @@ class EmptyShort(IStrategy):
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self.calculeDerivees(informative, f"rsi", timeframe=self.timeframe, ema_period=14)
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informative['max_rsi_12'] = talib.MAX(informative['rsi'], timeperiod=12)
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informative['max_rsi_24'] = talib.MAX(informative['rsi'], timeperiod=24)
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informative['min_rsi_12'] = talib.MIN(informative['rsi'], timeperiod=12)
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informative['min_rsi_24'] = talib.MIN(informative['rsi'], timeperiod=24)
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informative[f'stop_buying_deb'] = qtpylib.crossed_below(informative[f"sma12"], informative['sma36']) & (informative['close'] < informative['sma100'])
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informative[f'stop_buying_end'] = qtpylib.crossed_above(informative[f"sma12"], informative['sma36']) & (informative['close'] > informative['sma100'])
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# informative[f'stop_buying_deb'] = qtpylib.crossed_below(informative[f"sma12"], informative['sma36']) & (informative['close'] < informative['sma100'])
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# informative[f'stop_buying_end'] = qtpylib.crossed_above(informative[f"sma12"], informative['sma36']) & (informative['close'] > informative['sma100'])
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latched = np.zeros(len(informative), dtype=bool)
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for i in range(1, len(informative)):
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if informative['stop_buying_deb'].iloc[i]:
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latched[i] = True
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elif informative['stop_buying_end'].iloc[i]:
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latched[i] = False
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else:
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latched[i] = latched[i - 1]
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informative['stop_buying'] = latched
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# latched = np.zeros(len(informative), dtype=bool)
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#
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# for i in range(1, len(informative)):
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# if informative['stop_buying_deb'].iloc[i]:
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# latched[i] = True
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# elif informative['stop_buying_end'].iloc[i]:
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# latched[i] = False
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# else:
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# latched[i] = latched[i - 1]
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# informative['stop_buying'] = latched
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informative = self.calculateDownAndUp(informative, limit=0.0001)
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dataframe = merge_informative_pair(dataframe, informative, self.timeframe, "1h", ffill=True)
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@@ -787,7 +802,7 @@ class EmptyShort(IStrategy):
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# informative['haclose'] = heikinashi['close']
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# informative['hapercent'] = (informative['haclose'] - informative['haopen']) / informative['haclose']
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informative['mid'] = informative['open'] + (informative['close'] - informative['open']) / 2
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for timeperiod in timeperiods:
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for timeperiod in [3, 5, 12]:
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informative[f'max{timeperiod}'] = talib.MAX(informative['close'], timeperiod=timeperiod)
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informative[f'min{timeperiod}'] = talib.MIN(informative['close'], timeperiod=timeperiod)
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# informative[f"range{timeperiod}"] = ((informative["close"] - informative[f'min{timeperiod}']) / (informative[f'max{timeperiod}'] - informative[f'min{timeperiod}']))
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@@ -804,19 +819,23 @@ class EmptyShort(IStrategy):
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informative['max_rsi_12'] = talib.MAX(informative['rsi'], timeperiod=12)
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informative['max_rsi_24'] = talib.MAX(informative['rsi'], timeperiod=24)
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informative[f'stop_buying_deb'] = qtpylib.crossed_below(informative[f"sma12"], informative['sma36']) & (informative['close'] < informative['sma100'])
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informative[f'stop_buying_end'] = qtpylib.crossed_above(informative[f"sma12"], informative['sma36']) & (informative['close'] > informative['sma100'])
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# informative = self.calculateRegression(informative, column='mid', window=10, degree=1, future_offset=2)
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informative = self.calculateRegression(informative, column='sma3', window=10, degree=1, future_offset=2)
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# informative = self.calculateRegression(informative, column='low', window=10, degree=1, future_offset=2)
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latched = np.zeros(len(informative), dtype=bool)
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for i in range(1, len(informative)):
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if informative['stop_buying_deb'].iloc[i]:
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latched[i] = True
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elif informative['stop_buying_end'].iloc[i]:
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latched[i] = False
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else:
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latched[i] = latched[i - 1]
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informative['stop_buying'] = latched
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# informative[f'stop_buying_deb'] = qtpylib.crossed_below(informative[f"sma12"], informative['sma36']) & (informative['close'] < informative['sma100'])
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# informative[f'stop_buying_end'] = qtpylib.crossed_above(informative[f"sma12"], informative['sma36']) & (informative['close'] > informative['sma100'])
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#
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# latched = np.zeros(len(informative), dtype=bool)
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#
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# for i in range(1, len(informative)):
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# if informative['stop_buying_deb'].iloc[i]:
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# latched[i] = True
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# elif informative['stop_buying_end'].iloc[i]:
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# latched[i] = False
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# else:
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# latched[i] = latched[i - 1]
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# informative['stop_buying'] = latched
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dataframe = merge_informative_pair(dataframe, informative, self.timeframe, "1d", ffill=True)
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# ######################################################################################################
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@@ -979,14 +998,14 @@ class EmptyShort(IStrategy):
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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 = 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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@@ -1022,10 +1041,9 @@ class EmptyShort(IStrategy):
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# dataframe.loc[
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# (
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# (qtpylib.crossed_above(
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# dataframe['sma60'],
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# dataframe['mid_regression_1h'])
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# )
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# # (qtpylib.crossed_above(dataframe['sma60'], dataframe['mid_regression_1h']))
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# (dataframe['rsi_1h'] <= 30)
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# & (dataframe['min_rsi_12_1h'] == dataframe['rsi_1h'])
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# ),
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# ['enter_long', 'enter_tag']
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# ] = (1, 'long')
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@@ -1037,13 +1055,34 @@ class EmptyShort(IStrategy):
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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['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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# & (dataframe['sma60'] < dataframe['sma60'].shift(1))
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(dataframe['sma12_1h'] < dataframe['sma12_1h'].shift(61))
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& (dataframe['close'] > dataframe['close_1d'])
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)
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),
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['enter_short', 'enter_tag']
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] = (1, 'short')
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# dataframe.loc[
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# (
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# (dataframe['close'] >= dataframe['max5_1h'])
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# & (dataframe['close'] >= dataframe['max12_1d'])
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# & (dataframe['sma12_1h'] <= dataframe['max12_1d'])
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# & (dataframe['mid_1h'] <= dataframe['mid_1h'].shift(61))
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# & (dataframe['hapercent'] < 0)
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# # & (dataframe['sma60'] < dataframe['sma60'].shift(1))
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# ),
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# ['enter_short', 'enter_tag']
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# ] = (1, 'short')
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return dataframe
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def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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@@ -1102,15 +1141,31 @@ class EmptyShort(IStrategy):
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# if conditions:
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# dataframe.loc[reduce(lambda x, y: x & y, conditions), ['exit_long', 'exit_tag']] = (1, 'god')
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# dataframe.loc[
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# (
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# # (qtpylib.crossed_above(
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# # dataframe['sma'],
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# # dataframe['mid_regression_1h'])
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# # )
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# (dataframe['rsi_1h'] >= 70)
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# & (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)
|
||||
|
||||
Reference in New Issue
Block a user