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TEST SHORT

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This commit is contained in:
Jérôme Delacotte
2026-04-02 20:17:55 +02:00
parent 7954c518bd
commit 3302266b4a
1 changed files with 80 additions and 46 deletions
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126
EmptyShort.py
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@@ -571,7 +571,7 @@ class EmptyShort(IStrategy):
# #
if current_profit > tp_price and \ if current_profit > tp_price and \
(baisse > last_candle[f"range_pos"] and last_candle[f"close"] > last_candle[self.b30_indicateur.value]) \ (baisse > last_candle[f"range_pos"] and last_candle[f"close"] > last_candle[self.b30_indicateur.value]) \
and last_candle['hapercent'] > 0 and last_candle['sma12_deriv2_1h'] > 0.0: and last_candle['hapercent'] > 0: # and last_candle['sma12_deriv2_1h'] > 0.0:
self.pairs[pair]['force_sell'] = True self.pairs[pair]['force_sell'] = True
return 'B30Sht' return 'B30Sht'
@@ -609,7 +609,7 @@ class EmptyShort(IStrategy):
tp_price = max(0.02, self.pairs[pair]['take_profit']) tp_price = max(0.02, self.pairs[pair]['take_profit'])
if current_profit > tp_price and \ if current_profit > tp_price and \
(baisse > min(last_candle[f"range_pos"], 0.5) and last_candle[f"close"] <= last_candle[self.b30_indicateur.value]) \ (baisse > min(last_candle[f"range_pos"], 0.5) and last_candle[f"close"] <= last_candle[self.b30_indicateur.value]) \
and last_candle['hapercent'] <0 and last_candle['sma12_deriv1_1h'] < 0.004: and last_candle['hapercent'] <0 : #and last_candle['sma12_deriv1_1h'] < 0.004:
# if current_profit > 0.01: # if current_profit > 0.01:
# self.pairs[pair]['force_buy'] = True # self.pairs[pair]['force_buy'] = True
self.pairs[pair]['force_sell'] = True self.pairs[pair]['force_sell'] = True
@@ -759,7 +759,7 @@ class EmptyShort(IStrategy):
informative['haclose'] = heikinashi['close'] informative['haclose'] = heikinashi['close']
informative['hapercent'] = (informative['haclose'] - informative['haopen']) / informative['haclose'] informative['hapercent'] = (informative['haclose'] - informative['haopen']) / informative['haclose']
informative['mid'] = informative['open'] + (informative['close'] - informative['open']) / 2 informative['mid'] = informative['open'] + (informative['close'] - informative['open']) / 2
for timeperiod in [3, 5, 12]: for timeperiod in [3, 5, 12, 50, 200]:
informative[f'max{timeperiod}'] = talib.MAX(informative['close'], timeperiod=timeperiod) informative[f'max{timeperiod}'] = talib.MAX(informative['close'], timeperiod=timeperiod)
informative[f'min{timeperiod}'] = talib.MIN(informative['close'], timeperiod=timeperiod) informative[f'min{timeperiod}'] = talib.MIN(informative['close'], timeperiod=timeperiod)
# informative[f"range{timeperiod}"] = ((informative["close"] - informative[f'min{timeperiod}']) / (informative[f'max{timeperiod}'] - informative[f'min{timeperiod}'])) # informative[f"range{timeperiod}"] = ((informative["close"] - informative[f'min{timeperiod}']) / (informative[f'max{timeperiod}'] - informative[f'min{timeperiod}']))
@@ -771,9 +771,9 @@ class EmptyShort(IStrategy):
informative = self.calculateRegression(informative, column='sma3', window=10, degree=1, future_offset=2) informative = self.calculateRegression(informative, column='sma3', window=10, degree=1, future_offset=2)
informative = self.calculateRegression(informative, column='low', window=10, degree=1, future_offset=2) informative = self.calculateRegression(informative, column='low', window=10, degree=1, future_offset=2)
for timeperiod in long_timeperiods: # for timeperiod in long_timeperiods:
informative[f"sma{timeperiod}"] = informative['mid'].ewm(span=timeperiod, adjust=False).mean() # informative[f"sma{timeperiod}"] = informative['mid'].ewm(span=timeperiod, adjust=False).mean()
self.calculeDerivees(informative, f"sma{timeperiod}", timeframe=self.timeframe, ema_period=timeperiod) # self.calculeDerivees(informative, f"sma{timeperiod}", timeframe=self.timeframe, ema_period=timeperiod)
informative['rsi'] = talib.RSI(informative['close'], timeperiod=14) informative['rsi'] = talib.RSI(informative['close'], timeperiod=14)
self.calculeDerivees(informative, f"rsi", timeframe=self.timeframe, ema_period=14) self.calculeDerivees(informative, f"rsi", timeframe=self.timeframe, ema_period=14)
@@ -818,9 +818,9 @@ class EmptyShort(IStrategy):
informative[f"sma{timeperiod}"] = informative['mid'].ewm(span=timeperiod, adjust=False).mean() informative[f"sma{timeperiod}"] = informative['mid'].ewm(span=timeperiod, adjust=False).mean()
self.calculeDerivees(informative, f"sma{timeperiod}", timeframe=self.timeframe, ema_period=timeperiod) self.calculeDerivees(informative, f"sma{timeperiod}", timeframe=self.timeframe, ema_period=timeperiod)
for timeperiod in long_timeperiods: # for timeperiod in long_timeperiods:
informative[f"sma{timeperiod}"] = informative['mid'].ewm(span=timeperiod, adjust=False).mean() # informative[f"sma{timeperiod}"] = informative['mid'].ewm(span=timeperiod, adjust=False).mean()
self.calculeDerivees(informative, f"sma{timeperiod}", timeframe=self.timeframe, ema_period=timeperiod) # self.calculeDerivees(informative, f"sma{timeperiod}", timeframe=self.timeframe, ema_period=timeperiod)
informative['rsi'] = talib.RSI(informative['close'], timeperiod=14) informative['rsi'] = talib.RSI(informative['close'], timeperiod=14)
self.calculeDerivees(informative, f"rsi", timeframe=self.timeframe, ema_period=14) self.calculeDerivees(informative, f"rsi", timeframe=self.timeframe, ema_period=14)
@@ -881,6 +881,8 @@ class EmptyShort(IStrategy):
dataframe['volume_spike'] = (abs(dataframe['volume2']) > abs(dataframe['volume2'].rolling(window=20).mean() * 5)) \ dataframe['volume_spike'] = (abs(dataframe['volume2']) > abs(dataframe['volume2'].rolling(window=20).mean() * 5)) \
& (dataframe['volume'].rolling(window=5).max() > 1000) & (dataframe['volume'].rolling(window=5).max() > 1000)
dataframe['sma5_1h'] = dataframe['sma5_1h'].rolling(window=60).mean()
# récupérer le dernier trade fermé # récupérer le dernier trade fermé
trades = Trade.get_trades_proxy(pair=pair,is_open=False) trades = Trade.get_trades_proxy(pair=pair,is_open=False)
if trades: if trades:
@@ -894,60 +896,92 @@ class EmptyShort(IStrategy):
pair = metadata['pair'] pair = metadata['pair']
dataframe.loc[ dataframe.loc[
( (
# (qtpylib.crossed_above(dataframe['sma60'], dataframe['mid_regression_1h'])) # (dataframe['sma5_1h'] > dataframe['close'])
(dataframe['sma12_deriv1_1h'] > 0) # & (dataframe['sma12_deriv1_1h'] > 0)
& (dataframe['max_rsi_12'] < 70) # & (dataframe['max_rsi_12'] > 45)
& (dataframe['max_rsi_12_1h'] < 70) # & (
& (dataframe['range_pos'] < 1) # (
# (dataframe['max_rsi_24_1h'] < 45) & (dataframe['range_pos'] < 0.3)
# | (dataframe['sma12_deriv1_1d'] > 0)
# )
# )
(dataframe['sma60_deriv1'] > 0)
& (dataframe['sma60_deriv2'] > 0)
# & (dataframe['sma5_deriv1_1h'] > 0)
& (dataframe['hapercent'] > 0) & (dataframe['hapercent'] > 0)
& (dataframe['pct_min_max_12_1d'] < 0.04) & (dataframe['up_count'] >= 3)
& (dataframe['up_count'] <= 7)
# & (dataframe['open'] < dataframe['sma5_1h'] * 1.01)
), ),
['enter_long', 'enter_tag'] ['enter_long', 'enter_tag']
] = (1, 'long') ] = (1, 'long')
dataframe.loc[ # dataframe.loc[
( # (
(dataframe['volume_spike'] == 1) # # (dataframe['sma5_1h'] > dataframe['close'])
& (dataframe['down_count'] >= 3) # (dataframe['sma60_deriv1'] < 0)
& (dataframe['down_count'] < 15) # & (dataframe['sma60_deriv2'] < 0)
& (dataframe['hapercent'] > 0) # & (dataframe['sma12_deriv1_1h'] > 0)
& (dataframe['sma60'] > dataframe['sma60'].shift(1)) # & (dataframe['volume_spike'] == 1)
), # & (dataframe['down_count'] >= 3)
['enter_long', 'enter_tag'] # & (dataframe['down_count'] < 15)
] = (1, 'long_spike') # & (dataframe['hapercent'] > 0)
# & (dataframe['sma60'] > dataframe['sma60'].shift(1))
# ),
# ['enter_long', 'enter_tag']
# ] = (1, 'long_spike')
dataframe.loc[ dataframe.loc[
( (
(dataframe['sma12_deriv1_1h'] < -0.0005) # (dataframe['sma5_1h'] < dataframe['close'])
& (dataframe['max_rsi_12'] < 60) # & (dataframe['sma12_deriv1_1h'] < -0.0005)
& ( # & (dataframe['max_rsi_12'] < 60)
( # & (
(dataframe['max_rsi_24_1h'] > 60) & (dataframe['range_pos'] > 0.8) # (
| (dataframe['sma12_deriv1_1d'] < -0.01) # (dataframe['max_rsi_24_1h'] > 60) & (dataframe['range_pos'] > 0.8)
) # | (dataframe['sma12_deriv1_1d'] < -0.01)
) # )
# )
(dataframe['sma60_deriv1'] < 0)
& (dataframe['sma60_deriv2'] < 0)
# & (dataframe['sma5_deriv1_1h'] < 0)
& (dataframe['hapercent'] < 0) & (dataframe['hapercent'] < 0)
# & (dataframe['pct_min_max_12_1d'] > 0.04) # & (dataframe['pct_min_max_12_1d'] > 0.04)
& (dataframe['down_count'] <= -3) & (dataframe['down_count'] <= -3)
& (dataframe['down_count'] > -15) & (dataframe['down_count'] >= -7)
# & (dataframe['open'] > dataframe['sma5_1h'] * 1.01)
), ),
['enter_short', 'enter_tag'] ['enter_short', 'enter_tag']
] = (1, 'short') ] = (1, 'short')
dataframe.loc[ # dataframe.loc[
( # (
(dataframe['volume_spike'] == 1) # (dataframe['sma5_1h'] < dataframe['close'])
& (dataframe['down_count'] <= -3) # & (dataframe['volume_spike'] == 1)
& (dataframe['down_count'] > -15) # & (dataframe['down_count'] <= -3)
& (dataframe['hapercent'] < 0) # & (dataframe['down_count'] > -15)
& (dataframe['sma60'] < dataframe['sma60'].shift(1)) # & (dataframe['hapercent'] < 0)
), # & (dataframe['sma60'] < dataframe['sma60'].shift(1))
['enter_short', 'enter_tag'] # ),
] = (1, 'short_spike') # ['enter_short', 'enter_tag']
# ] = (1, 'short_spike')
dataframe['short'] = np.where(dataframe['enter_short'] == 1, dataframe['close'] * 0.99, np.nan) dataframe['short'] = np.where(dataframe['enter_short'] == 1, dataframe['close'] * 0.99, np.nan)
dataframe['long'] = np.where(dataframe['enter_long'] == 1, dataframe['close'] * 1.01, np.nan) dataframe['long'] = np.where(dataframe['enter_long'] == 1, dataframe['close'] * 1.01, np.nan)
# no_trade = (
# # range
# (abs(dataframe['sma50_1h'] - dataframe['sma200_1h']) / dataframe['close'] < 0.002)
# # momentum faible
# | (abs(dataframe['sma12_deriv1']) < 0.0002)
# # RSI neutre
# | ((dataframe['rsi'] > 45) & (dataframe['rsi'] < 55))
# # faible volatilité
# | (dataframe['atr'] / dataframe['close'] < 0.003)
# # milieu de range
# | (dataframe['range_pos'].between(0.4, 0.6))
# )
return dataframe return dataframe
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
@@ -1366,8 +1400,8 @@ class EmptyShort(IStrategy):
return df return df
def calculateDownAndUp(self, dataframe, limit=0.0001): def calculateDownAndUp(self, dataframe, limit=0.0001):
dataframe['down'] = dataframe['mid_regression'] <= dataframe['mid_regression'].shift(1) dataframe['down'] = dataframe['hapercent'] < 0
dataframe['up'] = dataframe['mid_regression'] >= dataframe['mid_regression'].shift(1) dataframe['up'] = dataframe['hapercent'] > 0
dataframe['down_count'] = - dataframe['down'].astype(int) * ( dataframe['down_count'] = - dataframe['down'].astype(int) * (
dataframe['down'].groupby((dataframe['down'] != dataframe['down'].shift()).cumsum()).cumcount() + 1) dataframe['down'].groupby((dataframe['down'] != dataframe['down'].shift()).cumsum()).cumcount() + 1)
dataframe['up_count'] = dataframe['up'].astype(int) * ( dataframe['up_count'] = dataframe['up'].astype(int) * (