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tendance sur 3 heures 3 jours

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This commit is contained in:
Jérôme Delacotte
2025-04-29 15:59:41 +02:00
parent bf034b1189
commit e0dfdb195e
1 changed files with 4 additions and 4 deletions
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Zeus_8_3_2_B_4_2.py
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@@ -491,7 +491,7 @@ class Zeus_8_3_2_B_4_2(IStrategy):
# normalized_close = self.min_max_scaling(dataframe['close']) # normalized_close = self.min_max_scaling(dataframe['close'])
################### INFORMATIVE 1h ################### INFORMATIVE 1h
informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe="1h") informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe="1h")
informative = self.calculateTendency(informative) informative = self.calculateTendency(informative, 3)
informative['volatility'] = talib.STDDEV(informative['close'], timeperiod=14) / informative['close'] informative['volatility'] = talib.STDDEV(informative['close'], timeperiod=14) / informative['close']
informative['atr'] = (talib.ATR(informative['high'], informative['low'], informative['close'], timeperiod=14)) / informative['close'] informative['atr'] = (talib.ATR(informative['high'], informative['low'], informative['close'], timeperiod=14)) / informative['close']
informative['rsi'] = talib.RSI(informative['close'], length=7) informative['rsi'] = talib.RSI(informative['close'], length=7)
@@ -504,7 +504,7 @@ class Zeus_8_3_2_B_4_2(IStrategy):
################### INFORMATIVE 1d ################### INFORMATIVE 1d
informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe="1d") informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe="1d")
informative = self.calculateTendency(informative) informative = self.calculateTendency(informative, 3)
informative['rsi'] = talib.RSI(informative['close'], length=7) informative['rsi'] = talib.RSI(informative['close'], length=7)
informative['rsi_diff'] = informative['rsi'].diff() informative['rsi_diff'] = informative['rsi'].diff()
@@ -645,10 +645,10 @@ class Zeus_8_3_2_B_4_2(IStrategy):
return dataframe return dataframe
def calculateTendency(self, dataframe): def calculateTendency(self, dataframe, window=12):
dataframe['mid'] = dataframe['open'] + (dataframe['close'] - dataframe['open']) / 2 dataframe['mid'] = dataframe['open'] + (dataframe['close'] - dataframe['open']) / 2
# 2. Calcul du lissage sur 200 bougies par moyenne mobile médiane # 2. Calcul du lissage sur 200 bougies par moyenne mobile médiane
dataframe['mid_smooth'] = dataframe['mid'].rolling(window=12, center=True, min_periods=1).median().rolling( dataframe['mid_smooth'] = dataframe['mid'].rolling(window=window, center=True, min_periods=1).median().rolling(
3).mean() 3).mean()
# 2. Dérivée première = différence entre deux bougies successives # 2. Dérivée première = différence entre deux bougies successives
dataframe['mid_smooth_deriv1'] = dataframe['mid_smooth'].diff() dataframe['mid_smooth_deriv1'] = dataframe['mid_smooth'].diff()