TEST SHORT
This commit is contained in:
Jérôme Delacotte
126
EmptyShort.py
126
EmptyShort.py
@@ -571,7 +571,7 @@ class EmptyShort(IStrategy):
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#
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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_deriv2_1h'] > 0.0:
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and last_candle['hapercent'] > 0: # and last_candle['sma12_deriv2_1h'] > 0.0:
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self.pairs[pair]['force_sell'] = True
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return 'B30Sht'
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@@ -609,7 +609,7 @@ class EmptyShort(IStrategy):
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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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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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@@ -759,7 +759,7 @@ 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 [3, 5, 12]:
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for timeperiod in [3, 5, 12, 50, 200]:
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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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@@ -771,9 +771,9 @@ class EmptyShort(IStrategy):
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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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self.calculeDerivees(informative, f"sma{timeperiod}", timeframe=self.timeframe, ema_period=timeperiod)
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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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# self.calculeDerivees(informative, f"sma{timeperiod}", timeframe=self.timeframe, ema_period=timeperiod)
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informative['rsi'] = talib.RSI(informative['close'], timeperiod=14)
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self.calculeDerivees(informative, f"rsi", timeframe=self.timeframe, ema_period=14)
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@@ -818,9 +818,9 @@ 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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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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self.calculeDerivees(informative, f"sma{timeperiod}", timeframe=self.timeframe, ema_period=timeperiod)
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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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# self.calculeDerivees(informative, f"sma{timeperiod}", timeframe=self.timeframe, ema_period=timeperiod)
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informative['rsi'] = talib.RSI(informative['close'], timeperiod=14)
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self.calculeDerivees(informative, f"rsi", timeframe=self.timeframe, ema_period=14)
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@@ -881,6 +881,8 @@ class EmptyShort(IStrategy):
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dataframe['volume_spike'] = (abs(dataframe['volume2']) > abs(dataframe['volume2'].rolling(window=20).mean() * 5)) \
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& (dataframe['volume'].rolling(window=5).max() > 1000)
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dataframe['sma5_1h'] = dataframe['sma5_1h'].rolling(window=60).mean()
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# récupérer le dernier trade fermé
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trades = Trade.get_trades_proxy(pair=pair,is_open=False)
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if trades:
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@@ -894,60 +896,92 @@ class EmptyShort(IStrategy):
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pair = metadata['pair']
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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_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['sma5_1h'] > dataframe['close'])
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# & (dataframe['sma12_deriv1_1h'] > 0)
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# & (dataframe['max_rsi_12'] > 45)
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# & (
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# (
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# (dataframe['max_rsi_24_1h'] < 45) & (dataframe['range_pos'] < 0.3)
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# | (dataframe['sma12_deriv1_1d'] > 0)
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# )
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# )
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(dataframe['sma60_deriv1'] > 0)
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& (dataframe['sma60_deriv2'] > 0)
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# & (dataframe['sma5_deriv1_1h'] > 0)
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& (dataframe['hapercent'] > 0)
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& (dataframe['pct_min_max_12_1d'] < 0.04)
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& (dataframe['up_count'] >= 3)
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& (dataframe['up_count'] <= 7)
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# & (dataframe['open'] < dataframe['sma5_1h'] * 1.01)
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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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(dataframe['volume_spike'] == 1)
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& (dataframe['down_count'] >= 3)
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& (dataframe['down_count'] < 15)
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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_long', 'enter_tag']
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] = (1, 'long_spike')
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# dataframe.loc[
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# (
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# # (dataframe['sma5_1h'] > dataframe['close'])
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# (dataframe['sma60_deriv1'] < 0)
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# & (dataframe['sma60_deriv2'] < 0)
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# & (dataframe['sma12_deriv1_1h'] > 0)
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# & (dataframe['volume_spike'] == 1)
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# & (dataframe['down_count'] >= 3)
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# & (dataframe['down_count'] < 15)
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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_long', 'enter_tag']
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# ] = (1, 'long_spike')
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dataframe.loc[
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(
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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)
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)
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)
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# (dataframe['sma5_1h'] < dataframe['close'])
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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)
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# )
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# )
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(dataframe['sma60_deriv1'] < 0)
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& (dataframe['sma60_deriv2'] < 0)
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# & (dataframe['sma5_deriv1_1h'] < 0)
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& (dataframe['hapercent'] < 0)
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# & (dataframe['pct_min_max_12_1d'] > 0.04)
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& (dataframe['down_count'] <= -3)
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& (dataframe['down_count'] > -15)
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& (dataframe['down_count'] >= -7)
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# & (dataframe['open'] > dataframe['sma5_1h'] * 1.01)
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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['volume_spike'] == 1)
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& (dataframe['down_count'] <= -3)
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& (dataframe['down_count'] > -15)
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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_spike')
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# dataframe.loc[
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# (
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# (dataframe['sma5_1h'] < dataframe['close'])
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# & (dataframe['volume_spike'] == 1)
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# & (dataframe['down_count'] <= -3)
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# & (dataframe['down_count'] > -15)
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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_spike')
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dataframe['short'] = np.where(dataframe['enter_short'] == 1, dataframe['close'] * 0.99, np.nan)
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dataframe['long'] = np.where(dataframe['enter_long'] == 1, dataframe['close'] * 1.01, np.nan)
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# no_trade = (
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# # range
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# (abs(dataframe['sma50_1h'] - dataframe['sma200_1h']) / dataframe['close'] < 0.002)
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# # momentum faible
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# | (abs(dataframe['sma12_deriv1']) < 0.0002)
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# # RSI neutre
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# | ((dataframe['rsi'] > 45) & (dataframe['rsi'] < 55))
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# # faible volatilité
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# | (dataframe['atr'] / dataframe['close'] < 0.003)
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# # milieu de range
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# | (dataframe['range_pos'].between(0.4, 0.6))
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# )
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return dataframe
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def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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@@ -1366,8 +1400,8 @@ class EmptyShort(IStrategy):
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return df
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def calculateDownAndUp(self, dataframe, limit=0.0001):
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dataframe['down'] = dataframe['mid_regression'] <= dataframe['mid_regression'].shift(1)
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dataframe['up'] = dataframe['mid_regression'] >= dataframe['mid_regression'].shift(1)
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dataframe['down'] = dataframe['hapercent'] < 0
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dataframe['up'] = dataframe['hapercent'] > 0
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dataframe['down_count'] = - dataframe['down'].astype(int) * (
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dataframe['down'].groupby((dataframe['down'] != dataframe['down'].shift()).cumsum()).cumcount() + 1)
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dataframe['up_count'] = dataframe['up'].astype(int) * (
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