def strategy_two(self,df):
df["volati"] = df["close"].rolling(window=5).std() * np.sqrt(5)
# simple moving avarage
df['SMA_7'] = TA.SSMA(df,7)
df['SMA_14'] = TA.SSMA(df,14)
df['SMA_28'] = TA.SSMA(df,28)
df['SMA_56'] = TA.SSMA(df,56)
df['SMA_112'] = TA.SSMA(df,112)
# exponential moving average
df['EMA_3'] = TA.EMA(df,3, adjust=False)
df['EMA_6'] = TA.EMA(df,6, adjust=False)
df['EMA_12'] = TA.EMA(df,12, adjust=False)
df['EMA_24'] = TA.EMA(df,24, adjust=False)
df['EMA_48'] = TA.EMA(df,48, adjust=False)
#Stochastic Oscillator
df['%K'] = TA.STOCH(df, 7)
df['%D'] = TA.STOCHD(df, 7)
df['%K'] = TA.STOCH(df, 14)
df['%D'] = TA.STOCHD(df, 14)
#Relative Strenght Index 'RSI'
df['rsi'] = TA.RSI(df, 14)
df['cci'] = TA.CCI(df,period=14)
df['rsi'] = TA.RSI(df, 7)
df['cci'] = TA.CCI(df,period=7)
df['ROC1'] = TA.ROC(df,1)
df['ROC2'] = TA.ROC(df,2)
df['ROC3'] = TA.ROC(df,3)
df['ROC4'] = TA.ROC(df,4)
df['ROC5'] = TA.ROC(df,5)
# timestamp to date
# date = pd.to_datetime(df['date'],unit='s')
# df['HOUR'] = date.dt.hour
# df['MINUTE'] = date.dt.minute
# predict and train data manipulation
df['return_next'] = df['ROC1'].shift(-1)
df['return'] = df['ROC1']
# df['return'] = df['return'].apply(lambda x: 1 if x>0 else 0)
# df['return_next'] = df['return_next'].apply(lambda x: 1 if x>0 else 0)
df = df.drop(columns = {'date'})
return df
def strategy_three(self,df):
# DO Channels
doch = TA.DO(df)
df['DO_UPPER'] = doch['LOWER']
df['DO_MID'] = doch['MIDDLE']
df['DO_LOWER'] = doch['UPPER']
df['ROC'] = TA.ROC(df,1)
# # timestamp to date
df.index = pd.to_datetime(df.index)
#for get year
df['YEAR'] =df.index.dt.year
#for get month
df['MONTH'] =df.index.dt.month
#for get day
df['DAY'] =df.index.dt.day
#for get hour
df['HOUR'] = df.index.dt.hour
#for get minute
df['MINUTE'] = df.index.dt.minute
# predict and train data manipulation
df['return_next'] = df['ROC'].shift(-1)
df['return'] = df['ROC']
return df
def Indicators(df):
df = df.rename(columns={'min': 'low', 'max': 'high'}) # raname DF columns
df["volatilidade"] = df["close"].rolling(window=45).std() * np.sqrt(45)
#simple moving avarage
df['SMA_20'] = TA.SSMA(df, 20)
df['SMA_50'] = TA.SSMA(df, 50)
# exponential moving average
df['EMA_20'] = TA.EMA(df, 20, adjust=False)
df['EMA_50'] = TA.EMA(df, 50, adjust=False)
#Stochastic Oscillator
df['%K'] = TA.STOCH(df, 14)
df['%D'] = TA.STOCHD(df, 14)
#Relative Strenght Index 'RSI'
df['rsi'] = TA.RSI(df, 14)
df['cci'] = TA.CCI(df, period=14)
df['ROC'] = TA.ROC(df, 1)
# THIS IS THE TARGET "ALVO"
df['GOAL'] = TA.ROC(df, 1)
# # df['VPT'] = TA.VPT(df)
# df['VWAP'] = TA.VWAP(df)
# # Keltner Channels
# keltner = TA.KC(df)
# df['KC_UPPER'] = keltner['KC_UPPER']
# df['KC_LOWER'] = keltner['KC_LOWER']
# bolinger = TA.BBANDS(df)
# df['BB_UPPER'] = bolinger['BB_UPPER']
# df['BB_MIDDLE'] = bolinger['BB_MIDDLE']
# df['BB_LOWER'] = bolinger['BB_LOWER']
df = df.drop(columns={'open', 'high', 'low'})
# print(df)
return df
def Indicators(df):
df = df.rename(columns = {'min':'low', 'max':'high'}) # raname DF columns
# Keltner Channels
keltner = TA.KC(df)
df['KC_UPPER'] = keltner['KC_UPPER']
df['KC_LOWER'] = keltner['KC_LOWER']
bolinger = TA.BBANDS(df)
df['BB_UPPER'] = bolinger['BB_UPPER']
df['BB_MIDDLE'] = bolinger['BB_MIDDLE']
df['BB_LOWER'] = bolinger['BB_LOWER']
# # MACD
# MACD = TA.MACD(df)
# df['MACD'] = MACD['MACD']
# df['SIGNAL'] = MACD['SIGNAL']
# df['BearishHarami'] = cd.bearish_harami(df)
# df['BullishHarami'] = cd.bullish_harami(df)
# df['DarkCloudCover'] = cd.dark_cloud_cover(df)
# # df['MorningStarDoji'] = cd.morning_star_doji(df) # bug
# # df['ShootingStar'] = cd.shooting_star(df) # bug
# df['DragonflyDoji'] = cd.dragonfly_doji(df)
# df['BearishEngulfing'] = cd.bearish_engulfing(df)
# df['BullishEngulfing'] = cd.bullish_engulfing(df)
# df['HangingMan'] = cd.hanging_man(df)
# df['MorningStar'] = cd.morning_star(df)
# df['MorningStarDoji'] = cd.morning_star_doji(df)
# df['PiercingPattern'] = cd.piercing_pattern(df)
# df['RainDrop'] = cd.rain_drop(df)
# df['RainDropDoji'] = cd.rain_drop_doji(df)
# df['GravestoneDoji'] = cd.gravestone_doji(df)
# df['ShootingStar'] = cd.shooting_star(df)
# df['Hammer'] = cd.hammer(df)
# df['doji'] = cd.doji(df)
# df['Star'] = cd.star(df)
#simple moving avarage
df['SMA_20'] = TA.SSMA(df,20)
df['SMA_50'] = TA.SSMA(df,50)
# exponential moving average
df['EMA_20'] = TA.EMA(df,20, adjust=False)
df['EMA_50'] = TA.EMA(df,50, adjust=False)
#Stochastic Oscillator
df['%K'] = TA.STOCH(df, 14)
df['%D'] = TA.STOCHD(df, 14)
#Relative Strenght Index 'RSI'
df['rsi'] = TA.RSI(df, 14)
df['cci'] = TA.CCI(df,period=14)
ROC = TA.ROC(df,1)
df['close'] = ROC
# df['VPT'] = TA.VPT(df)
# df['VWAP'] = TA.VWAP(df)
# df = df.rename(columns = {'low':'min', 'high':'max'})
# df = df.drop(columns = {'volume'})
return df
def strategy_one(self,df, qout):
df["volati"] = df["close"].rolling(window=45).std() * np.sqrt(45)
# simple moving avarage
df['SMA_014'] = TA.SSMA(df,14)
df['SMA_020'] = TA.SSMA(df,20)
df['SMA_050'] = TA.SSMA(df,50)
df['SMA_100'] = TA.SSMA(df,100)
df['SMA_200'] = TA.SSMA(df,200)
# exponential moving average
df['EMA_014'] = TA.EMA(df,14, adjust=False)
df['EMA_020'] = TA.EMA(df,20, adjust=False)
df['EMA_050'] = TA.EMA(df,50, adjust=False)
df['EMA_100'] = TA.EMA(df,100, adjust=False)
df['EMA_200'] = TA.EMA(df,200, adjust=False)
#Stochastic Oscillator
df['%K'] = TA.STOCH(df, 14)
df['%D'] = TA.STOCHD(df, 14)
#Relative Strenght Index 'RSI'
df['rsi'] = TA.RSI(df, 14)
# CCI
df['cci'] = TA.CCI(df,period=14)
# Keltner Channels
keltner = TA.KC(df)
df['KC_UPPER'] = keltner['KC_UPPER']
df['KC_LOWER'] = keltner['KC_LOWER']
# DO Channels
doch = TA.DO(df)
df['DO_UPPER'] = doch['LOWER']
df['DO_MID'] = doch['MIDDLE']
df['DO_LOWER'] = doch['UPPER']
# MACD
MACD = TA.MACD(df)
df['MACD'] = MACD['MACD']
df['SIGNAL'] = MACD['SIGNAL']
df['ROC'] = TA.ROC(df,1)
df['ROC2'] = TA.ROC(df,2)
df['ROC3'] = TA.ROC(df,3)
df['ROC4'] = TA.ROC(df,4)
df['ROC5'] = TA.ROC(df,5)
# # timestamp to date
# date = pd.to_datetime(df['date'],unit='s')
# df['HOUR'] = date.dt.hour
# df['MINUTE'] = date.dt.minute
# predict and train data manipulation
df['return_next'] = df['ROC'].shift(-1)
df['return'] = df['ROC']
# df['return'] = df['return'].apply(lambda x: 1 if x>0 else 0)
# df['return_next'] = df['return_next'].apply(lambda x: 1 if x>0 else 0)
df = df.drop(columns = {'date'})
return df