def apply_indicators(df: pd.DataFrame):
# ADX
df['adx'] = ta.ADX(df)
# EMA
df['ema_5'] = ta.EMA(df, 5)
df['ema_10'] = ta.EMA(df, 10)
df['ema_20'] = ta.EMA(df, 20)
df['ema_50'] = ta.EMA(df, 50)
df['ema_100'] = ta.EMA(df, 100)
df['ema_200'] = ta.EMA(df, 200)
# MACD
macd = ta.MACD(df)
df['macd'] = macd['macd']
df['macdsignal'] = macd['macdsignal']
df['macdhist'] = macd['macdhist']
# inverse Fisher rsi/ RSI
df['rsi'] = ta.RSI(df)
rsi = 0.1 - (df['rsi'] - 50)
df['i_rsi'] = (np.exp(2 * rsi) - 1) / (np.exp(2 * rsi) + 1)
# Stoch fast
stoch_fast = ta.STOCHF(df)
df['fastd'] = stoch_fast['fastd']
df['fastk'] = stoch_fast['fastk']
# Stock slow
stoch_slow = ta.STOCH(df)
df['slowd'] = stoch_slow['slowd']
df['slowk'] = stoch_slow['slowk']
# Bollinger bands
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(df),
window=20,
stds=2)
df['bb_lowerband'] = bollinger['lower']
df['bb_middleband'] = bollinger['mid']
df['bb_upperband'] = bollinger['upper']
# ROC
df['roc'] = ta.ROC(df, 10)
# CCI
df['cci'] = ta.CCI(df, 14)
# on balance volume
df['obv'] = ta.OBV(df)
# Average True Range
df['atr'] = ta.ATR(df, 14)
df = ichimoku(df)
return df
def populate_indicators(self, dataframe: DataFrame,
metadata: dict) -> DataFrame:
macd = ta.MACD(dataframe)
dataframe['macd'] = macd['macd']
dataframe['macdsignal'] = macd['macdsignal']
dataframe['macdhist'] = macd['macdhist']
dataframe['cci'] = ta.CCI(dataframe)
return dataframe
def technical_index(self):
df = self.max_min_price()
df2 = self.institutional_investors()
df['RSI'] = abstract.RSI(df) / 100
df['CMO'] =(abstract.CMO(df)+100) / (2 *100)
df['MACD'] =(abstract.MACD(df)['macd']+abstract.MACD(df)['macd'].max()) / (2 *abstract.MACD(df)['macd'].max())
df['WILLR'] =(abstract.WILLR(df)+100) / (2 *100)
df['WMA'] =abstract.WMA(df) / abstract.WMA(df).max()
df['PPO'] =(abstract.PPO(df)+abstract.PPO(df).max()) / (2 *abstract.PPO(df).max())
df['EMA'] =abstract.EMA(df) / abstract.EMA(df).max()
df['ROC'] =(abstract.ROC(df)+abstract.ROC(df).max()) / (2 *abstract.ROC(df).max())
df['SMA'] =abstract.SMA(df) / abstract.SMA(df).max()
df['TEMA'] =abstract.TEMA(df) / abstract.TEMA(df).max()
df['CCI'] =(abstract.CCI(df)+abstract.CCI(df).max()) / (2 *abstract.CCI(df).max())
df['investment_trust'] = (df2['investment_trust'] + df2['investment_trust'].max()) / (2*df2['investment_trust'].max())
df['foreign_investor'] = (df2['foreign_investor'] + df2['foreign_investor'].max()) / (2*df2['foreign_investor'].max())
df = df.drop(columns=['volume', 'open', 'high', 'low', 'close', 'close_max', 'close_min'])
df = df.dropna()
return df
def populate_indicators(dataframe: DataFrame, metadata: dict) -> DataFrame:
macd = ta.MACD(dataframe)
dataframe["macd"] = macd["macd"]
dataframe["macdsignal"] = macd["macdsignal"]
for cciTime in cciTimeRange:
cciName = "cci-" + str(cciTime)
dataframe[cciName] = ta.CCI(dataframe, timeperiod=cciTime)
return dataframe
def CCI(self):
CCI = tb.CCI(self.dataframe, timeperiod=20)
value = CCI[len(CCI) - 1]
preValue = CCI[len(CCI) - 2]
if (value > cciUpperLimit):
return "sell"
elif (value < cciLowerLimit):
return "buy"
else:
return "neutral"
def populate_indicators(self, dataframe: DataFrame) -> DataFrame:
macd = ta.MACD(dataframe)
dataframe = CCIStrategy.resample(dataframe, self.ticker_interval, 5)
dataframe['cci_one'] = ta.CCI(dataframe, timeperiod=170)
dataframe['cci_two'] = ta.CCI(dataframe, timeperiod=34)
dataframe['rsi'] = ta.RSI(dataframe)
dataframe['mfi'] = ta.MFI(dataframe)
dataframe['cmf'] = self.chaikin_mf(dataframe)
# required for graphing
bollinger = qtpylib.bollinger_bands(dataframe['close'],
window=20,
stds=2)
dataframe['bb_lowerband'] = bollinger['lower']
dataframe['bb_upperband'] = bollinger['upper']
dataframe['bb_middleband'] = bollinger['mid']
return dataframe
def populate_indicators(self, dataframe: DataFrame,
metadata: dict) -> DataFrame:
dataframe['hma'] = hull_moving_average(dataframe, 14, 'close')
dataframe['cci'] = ta.CCI(dataframe, timeperiod=14)
dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
rsi = 0.1 * (dataframe['rsi'] - 50)
dataframe['fisher_rsi'] = (np.exp(2 * rsi) - 1) / (np.exp(2 * rsi) + 1)
return dataframe
def populate_indicators(self, dataframe: DataFrame,
metadata: dict) -> DataFrame:
dataframe = self.resample(dataframe, self.timeframe, 5)
dataframe["cci_one"] = ta.CCI(dataframe, timeperiod=170)
dataframe["cci_two"] = ta.CCI(dataframe, timeperiod=34)
dataframe["rsi"] = ta.RSI(dataframe)
dataframe["mfi"] = ta.MFI(dataframe)
dataframe["cmf"] = self.chaikin_mf(dataframe)
# required for graphing
bollinger = qtpylib.bollinger_bands(dataframe["close"],
window=20,
stds=2)
dataframe["bb_lowerband"] = bollinger["lower"]
dataframe["bb_upperband"] = bollinger["upper"]
dataframe["bb_middleband"] = bollinger["mid"]
return dataframe
def populate_indicators(self, dataframe: DataFrame,
metadata: dict) -> DataFrame:
bollinger = qtpylib.bollinger_bands(dataframe['close'],
window=20,
stds=2)
dataframe['bb_lowerband'] = bollinger['lower']
dataframe['bb_middleband'] = bollinger['mid']
dataframe['bb_upperband'] = bollinger['upper']
dataframe['bb_width'] = (
(dataframe['bb_upperband'] - dataframe['bb_lowerband']) /
dataframe['bb_middleband'])
dataframe['bb_bottom_cross'] = qtpylib.crossed_below(
dataframe['close'], dataframe['bb_lowerband']).astype('int')
dataframe['rsi'] = ta.RSI(dataframe, timeperiod=10)
dataframe['plus_di'] = ta.PLUS_DI(dataframe)
dataframe['minus_di'] = ta.MINUS_DI(dataframe)
dataframe['cci'] = ta.CCI(dataframe, 30)
dataframe['mfi'] = ta.MFI(dataframe, timeperiod=14)
dataframe['cmf'] = chaikin_mf(dataframe)
dataframe['rmi'] = RMI(dataframe, length=8, mom=4)
stoch = ta.STOCHRSI(dataframe, 15, 20, 2, 2)
dataframe['srsi_fk'] = stoch['fastk']
dataframe['srsi_fd'] = stoch['fastd']
dataframe['fastEMA'] = ta.EMA(dataframe['volume'], timeperiod=12)
dataframe['slowEMA'] = ta.EMA(dataframe['volume'], timeperiod=26)
dataframe['pvo'] = ((dataframe['fastEMA'] - dataframe['slowEMA']) /
dataframe['slowEMA']) * 100
dataframe['is_dip'] = ((dataframe['rmi'] < 20)
& (dataframe['cci'] <= -150)
& (dataframe['srsi_fk'] < 20)
# Maybe comment mfi and cmf to make more trades
& (dataframe['mfi'] < 25)
& (dataframe['cmf'] <= -0.1)).astype('int')
dataframe['is_break'] = (
(dataframe['bb_width'] > 0.025)
& (dataframe['bb_bottom_cross'].rolling(10).sum() > 1)
& (dataframe['close'] < 0.99 * dataframe['bb_lowerband'])
).astype('int')
dataframe['buy_signal'] = ((dataframe['is_dip'] > 0)
& (dataframe['is_break'] > 0)).astype('int')
return dataframe
def populate_indicators(dataframe: DataFrame, metadata: dict) -> DataFrame:
macd = ta.MACD(dataframe, fastperiod=12, slowperiod=26, signalperiod=9)
dataframe['macdhist'] = macd['macdhist']
dataframe['macd'] = macd['macd']
dataframe['macdsignal'] = macd['macdsignal']
for cciTime in cciTimeRange:
cciName = "cci-" + str(cciTime)
dataframe[cciName] = ta.CCI(dataframe, timeperiod=cciTime)
return dataframe
def populate_indicators(dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Dynamic TA indicators
Used so hyperopt can optimized around the period of various indicators
"""
for kshort in range(kshortStart, (kshortEnd + 1)):
dataframe[f'kama-short({kshort})'] = ta.KAMA(dataframe,
timeperiod=kshort)
for klong in range(klongStart, (klongEnd + 1)):
dataframe[f'kama-long({klong})'] = ta.KAMA(dataframe,
timeperiod=klong)
for klong in range(klongStart, (klongEnd + 1)):
dataframe[f'kama-long-slope({klong})'] = (
dataframe[f'kama-long({klong})'] /
dataframe[f'kama-long({klong})'].shift())
for ccip in range(cciStart, (cciEnd + 1)):
dataframe[f'cci({ccip})'] = ta.CCI(dataframe, timeperiod=ccip)
for rsip in range(rsiStart, (rsiEnd + 1)):
dataframe[f'rsi({rsip})'] = ta.RSI(dataframe, timeperiod=rsip)
"""
Static TA indicators.
Only used when --spaces does not include buy or sell
"""
dataframe['cci'] = ta.CCI(dataframe, timeperiod=cciStatic)
# RSI
dataframe['rsi'] = ta.RSI(dataframe, timeperiod=rsiStatic)
# KAMA - Kaufman Adaptive Moving Average
dataframe['kama-short'] = ta.KAMA(dataframe,
timeperiod=kamaShortStatic)
dataframe['kama-long'] = ta.KAMA(dataframe, timeperiod=kamaLongStatic)
dataframe['kama-long-slope'] = (dataframe['kama-long'] /
dataframe['kama-long'].shift())
return dataframe
def populate_indicators(self, dataframe: DataFrame,
metadata: dict) -> DataFrame:
"""
Dynamic TA indicators
Used so hyperopt can optimized around the period of various indicators
"""
dataframe['kama-short'] = ta.KAMA(dataframe, timeperiod=5)
dataframe['kama-long'] = ta.KAMA(dataframe, timeperiod=20)
dataframe['cci'] = ta.CCI(dataframe, timeperiod=21)
dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
return dataframe
def populate_indicators(dataframe: DataFrame, metadata: dict) -> DataFrame:
for cciTime in cciTimeRange:
cciName = "cci-" + str(cciTime)
dataframe[cciName] = ta.CCI(dataframe, timeperiod=cciTime)
for rsiTime in rsiTimeRange:
rsiName = "rsi-" + str(rsiTime)
dataframe[rsiName] = ta.RSI(dataframe, timeperiod=rsiTime)
return dataframe
def populate_indicators(dataframe: DataFrame) -> DataFrame:
"""
Adds several different TA indicators to the given DataFrame
"""
dataframe['sar'] = ta.SAR(dataframe)
dataframe['adx'] = ta.ADX(dataframe)
stoch = ta.STOCHF(dataframe)
dataframe['fastd'] = stoch['fastd']
dataframe['fastk'] = stoch['fastk']
dataframe['blower'] = ta.BBANDS(dataframe, nbdevup=2, nbdevdn=2)['lowerband']
dataframe['sma'] = ta.SMA(dataframe, timeperiod=40)
dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9)
dataframe['mfi'] = ta.MFI(dataframe)
dataframe['cci'] = ta.CCI(dataframe)
return dataframe
def populate_indicators(self, dataframe: DataFrame,
metadata: dict) -> DataFrame:
dataframe = self.resample(dataframe, self.ticker_interval,
self.resample_factor)
##################################################################################
# buy and sell indicators
dataframe['ema_{}'.format(self.EMA_SHORT_TERM)] = ta.EMA(
dataframe, timeperiod=self.EMA_SHORT_TERM)
dataframe['ema_{}'.format(self.EMA_MEDIUM_TERM)] = ta.EMA(
dataframe, timeperiod=self.EMA_MEDIUM_TERM)
dataframe['ema_{}'.format(self.EMA_LONG_TERM)] = ta.EMA(
dataframe, timeperiod=self.EMA_LONG_TERM)
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe),
window=20,
stds=2)
dataframe['bb_lowerband'] = bollinger['lower']
dataframe['bb_middleband'] = bollinger['mid']
dataframe['bb_upperband'] = bollinger['upper']
dataframe['min'] = ta.MIN(dataframe, timeperiod=self.EMA_MEDIUM_TERM)
dataframe['max'] = ta.MAX(dataframe, timeperiod=self.EMA_MEDIUM_TERM)
dataframe['cci'] = ta.CCI(dataframe)
dataframe['mfi'] = ta.MFI(dataframe)
dataframe['rsi'] = ta.RSI(dataframe, timeperiod=7)
dataframe['average'] = (dataframe['close'] + dataframe['open'] +
dataframe['high'] + dataframe['low']) / 4
##################################################################################
# required for graphing
bollinger = qtpylib.bollinger_bands(dataframe['close'],
window=20,
stds=2)
dataframe['bb_lowerband'] = bollinger['lower']
dataframe['bb_upperband'] = bollinger['upper']
dataframe['bb_middleband'] = bollinger['mid']
macd = ta.MACD(dataframe)
dataframe['macd'] = macd['macd']
dataframe['macdsignal'] = macd['macdsignal']
dataframe['macdhist'] = macd['macdhist']
return dataframe
def populate_indicators(self, dataframe: DataFrame) -> DataFrame:
"""
Adds several different TA indicators to the given DataFrame
Performance Note: For the best performance be frugal on the number of indicators
you are using. Let uncomment only the indicator you are using in your strategies
or your hyperopt configuration, otherwise you will waste your memory and CPU usage.
"""
# Commodity Channel Index: values Oversold:<-100, Overbought:>100
dataframe['cci'] = ta.CCI(dataframe)
# MFI
dataframe['mfi'] = ta.MFI(dataframe)
# CMO
dataframe['cmo'] = ta.CMO(dataframe)
return dataframe
def TA_processing(dataframe):
bias(dataframe, days=[3, 6, 10, 25])
moving_average(dataframe, days=[5, 10, 20])
dataframe['ROC'] = abstract.ROC(dataframe, timeperiod=10)
dataframe['MACD'] = abstract.MACD(dataframe, fastperiod=12, slowperiod=26, signalperiod=9)['macd']
dataframe['MACD_signal'] = abstract.MACD(dataframe, fastperiod=12, slowperiod=26, signalperiod=9)['macdsignal']
dataframe['UBBANDS'] = abstract.BBANDS(dataframe, timeperiod=20, nbdevup=2, nbdevdn=2, matype=0)['upperband']
dataframe['MBBANDS'] = abstract.BBANDS(dataframe, timeperiod=20, nbdevup=2, nbdevdn=2, matype=0)['middleband']
dataframe['LBBANDS'] = abstract.BBANDS(dataframe, timeperiod=20, nbdevup=2, nbdevdn=2, matype=0)['lowerband']
dataframe['%K'] = abstract.STOCH(dataframe, fastk_period=9)['slowk']/100
dataframe['%D'] = abstract.STOCH(dataframe, fastk_period=9)['slowd']/100
dataframe['W%R'] = abstract.WILLR(dataframe, timeperiod=14)/100
dataframe['RSI9'] = abstract.RSI(dataframe, timeperiod = 9)/100
dataframe['RSI14'] = abstract.RSI(dataframe, timeperiod = 14)/100
dataframe['CCI'] = abstract.CCI(dataframe, timeperiod=14)/100
counter_daily_potential(dataframe)
dataframe['MOM'] = abstract.MOM(dataframe, timeperiod=10)
dataframe['DX'] = abstract.DX(dataframe, timeperiod=14)/100
psy_line(dataframe)
volumn_ratio(dataframe, d=26)
on_balance_volume(dataframe)
def populate_indicators(self, dataframe: DataFrame) -> DataFrame:
dataframe = self.resample(dataframe, self.ticker_interval, self.resample_factor)
dataframe['ema_high'] = ta.EMA(dataframe, timeperiod=5, price='high')
dataframe['ema_close'] = ta.EMA(dataframe, timeperiod=5, price='close')
dataframe['ema_low'] = ta.EMA(dataframe, timeperiod=5, price='low')
stoch_fast = ta.STOCHF(dataframe, 5.0, 3.0, 0.0, 3.0, 0.0)
dataframe['fastd'] = stoch_fast['fastd']
dataframe['fastk'] = stoch_fast['fastk']
dataframe['adx'] = ta.ADX(dataframe)
dataframe['cci'] = ta.CCI(dataframe, timeperiod=20)
dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
dataframe['mfi'] = ta.MFI(dataframe)
# required for graphing
bollinger = qtpylib.bollinger_bands(dataframe['close'], window=20, stds=2)
dataframe['bb_lowerband'] = bollinger['lower']
dataframe['bb_upperband'] = bollinger['upper']
dataframe['bb_middleband'] = bollinger['mid']
return dataframe
def populate_indicators(self, dataframe: DataFrame,
metadata: dict) -> DataFrame:
"""
Adds several different TA indicators to the given DataFrame
Performance Note: For the best performance be frugal on the number of indicators
you are using. Let uncomment only the indicator you are using in your strategies
or your hyperopt configuration, otherwise you will waste your memory and CPU usage.
"""
# ADX
dataframe['adx'] = ta.ADX(dataframe)
dataframe['slowadx'] = ta.ADX(dataframe, 35)
# Commodity Channel Index: values Oversold:<-100, Overbought:>100
dataframe['cci'] = ta.CCI(dataframe)
# Stoch
stoch = ta.STOCHF(dataframe, 5)
dataframe['fastd'] = stoch['fastd']
dataframe['fastk'] = stoch['fastk']
dataframe['fastk-previous'] = dataframe.fastk.shift(1)
dataframe['fastd-previous'] = dataframe.fastd.shift(1)
# Slow Stoch
slowstoch = ta.STOCHF(dataframe, 50)
dataframe['slowfastd'] = slowstoch['fastd']
dataframe['slowfastk'] = slowstoch['fastk']
dataframe['slowfastk-previous'] = dataframe.slowfastk.shift(1)
dataframe['slowfastd-previous'] = dataframe.slowfastd.shift(1)
# EMA - Exponential Moving Average
dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5)
dataframe['mean-volume'] = dataframe['volume'].mean()
return dataframe
def TKE(dataframe, *, length=14, emaperiod=5):
"""
Source: https://www.tradingview.com/script/Pcbvo0zG/
Author: Dr Yasar ERDINC
The calculation is simple:
TKE=(RSI+STOCHASTIC+ULTIMATE OSCILLATOR+MFI+WIILIAMS %R+MOMENTUM+CCI)/7
Buy signal: when TKE crosses above 20 value
Oversold region: under 20 value
Overbought region: over 80 value
Another usage of TKE is with its EMA ,
the default value is defined as 5 bars of EMA of the TKE line,
Go long: when TKE crosses above EMALine
Go short: when TKE crosses below EMALine
Usage:
`dataframe['TKE'], dataframe['TKEema'] = TKE1(dataframe)`
"""
import talib.abstract as ta
df = dataframe.copy()
# TKE=(RSI+STOCHASTIC+ULTIMATE OSCILLATOR+MFI+WIILIAMS %R+MOMENTUM+CCI)/7
df["rsi"] = ta.RSI(df, timeperiod=length)
df['stoch'] = (100 *
(df['close'] - df['low'].rolling(window=length).min()) /
(df['high'].rolling(window=length).max() -
df['low'].rolling(window=length).min()))
df["ultosc"] = ta.ULTOSC(df, timeperiod1=7, timeperiod2=14, timeperiod3=28)
df["mfi"] = ta.MFI(df, timeperiod=length)
df["willr"] = ta.WILLR(df, timeperiod=length)
df["mom"] = ta.ROCR100(df, timeperiod=length)
df["cci"] = ta.CCI(df, timeperiod=length)
df['TKE'] = df[['rsi', 'stoch', 'ultosc', 'mfi', 'willr', 'mom',
'cci']].mean(axis='columns')
df["TKEema"] = ta.EMA(df["TKE"], timeperiod=emaperiod)
return df["TKE"], df["TKEema"]
def populate_indicators(self, dataframe: DataFrame,
metadata: dict) -> DataFrame:
# Momentum Indicators
# ------------------------------------
# ADX
dataframe['adx'] = ta.ADX(dataframe)
# Plus Directional Indicator / Movement
dataframe['plus_dm'] = ta.PLUS_DM(dataframe)
dataframe['plus_di'] = ta.PLUS_DI(dataframe)
# # Minus Directional Indicator / Movement
dataframe['minus_dm'] = ta.MINUS_DM(dataframe)
dataframe['minus_di'] = ta.MINUS_DI(dataframe)
# Aroon, Aroon Oscillator
aroon = ta.AROON(dataframe)
dataframe['aroonup'] = aroon['aroonup']
dataframe['aroondown'] = aroon['aroondown']
dataframe['aroonosc'] = ta.AROONOSC(dataframe)
# Awesome Oscillator
dataframe['ao'] = qtpylib.awesome_oscillator(dataframe)
# # Keltner Channel
# keltner = qtpylib.keltner_channel(dataframe)
# dataframe["kc_upperband"] = keltner["upper"]
# dataframe["kc_lowerband"] = keltner["lower"]
# dataframe["kc_middleband"] = keltner["mid"]
# dataframe["kc_percent"] = (
# (dataframe["close"] - dataframe["kc_lowerband"]) /
# (dataframe["kc_upperband"] - dataframe["kc_lowerband"])
# )
# dataframe["kc_width"] = (
# (dataframe["kc_upperband"] - dataframe["kc_lowerband"]) / dataframe["kc_middleband"]
# )
# Ultimate Oscillator
dataframe['uo'] = ta.ULTOSC(dataframe)
# Commodity Channel Index: values [Oversold:-100, Overbought:100]
dataframe['cci'] = ta.CCI(dataframe)
# RSI
dataframe['rsi'] = ta.RSI(dataframe)
# Inverse Fisher transform on RSI: values [-1.0, 1.0] (https://goo.gl/2JGGoy)
rsi = 0.1 * (dataframe['rsi'] - 50)
dataframe['fisher_rsi'] = (np.exp(2 * rsi) - 1) / (np.exp(2 * rsi) + 1)
# Inverse Fisher transform on RSI normalized: values [0.0, 100.0] (https://goo.gl/2JGGoy)
dataframe['fisher_rsi_norma'] = 50 * (dataframe['fisher_rsi'] + 1)
# Stochastic Slow
stoch = ta.STOCH(dataframe)
dataframe['slowd'] = stoch['slowd']
dataframe['slowk'] = stoch['slowk']
# Stochastic Fast
stoch_fast = ta.STOCHF(dataframe)
dataframe['fastd'] = stoch_fast['fastd']
dataframe['fastk'] = stoch_fast['fastk']
# Stochastic RSI
stoch_rsi = ta.STOCHRSI(dataframe)
dataframe['fastd_rsi'] = stoch_rsi['fastd']
dataframe['fastk_rsi'] = stoch_rsi['fastk']
# MACD
macd = ta.MACD(dataframe)
dataframe['macd'] = macd['macd']
dataframe['macdsignal'] = macd['macdsignal']
dataframe['macdhist'] = macd['macdhist']
# MFI
dataframe['mfi'] = ta.MFI(dataframe)
# # ROC
dataframe['roc'] = ta.ROC(dataframe)
# Overlap Studies
# ------------------------------------
# # Bollinger Bands
# bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
# dataframe['bb_lowerband'] = bollinger['lower']
# dataframe['bb_middleband'] = bollinger['mid']
# dataframe['bb_upperband'] = bollinger['upper']
# dataframe["bb_percent"] = (
# (dataframe["close"] - dataframe["bb_lowerband"]) /
# (dataframe["bb_upperband"] - dataframe["bb_lowerband"])
# )
# dataframe["bb_width"] = (
# (dataframe["bb_upperband"] - dataframe["bb_lowerband"]) / dataframe["bb_middleband"]
# )
# # Bollinger Bands - Weighted (EMA based instead of SMA)
# weighted_bollinger = qtpylib.weighted_bollinger_bands(
# qtpylib.typical_price(dataframe), window=20, stds=2
# )
# dataframe["wbb_upperband"] = weighted_bollinger["upper"]
# dataframe["wbb_lowerband"] = weighted_bollinger["lower"]
# dataframe["wbb_middleband"] = weighted_bollinger["mid"]
# dataframe["wbb_percent"] = (
# (dataframe["close"] - dataframe["wbb_lowerband"]) /
# (dataframe["wbb_upperband"] - dataframe["wbb_lowerband"])
# )
# dataframe["wbb_width"] = (
# (dataframe["wbb_upperband"] - dataframe["wbb_lowerband"]) /
# dataframe["wbb_middleband"]
# )
# # EMA - Exponential Moving Average
# dataframe['ema3'] = ta.EMA(dataframe, timeperiod=3)
# dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5)
# dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10)
# dataframe['ema21'] = ta.EMA(dataframe, timeperiod=21)
# dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50)
# dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100)
# # SMA - Simple Moving Average
# dataframe['sma3'] = ta.SMA(dataframe, timeperiod=3)
# dataframe['sma5'] = ta.SMA(dataframe, timeperiod=5)
# dataframe['sma10'] = ta.SMA(dataframe, timeperiod=10)
# dataframe['sma21'] = ta.SMA(dataframe, timeperiod=21)
# dataframe['sma50'] = ta.SMA(dataframe, timeperiod=50)
# dataframe['sma100'] = ta.SMA(dataframe, timeperiod=100)
# Parabolic SAR
# dataframe['sar'] = ta.SAR(dataframe)
# TEMA - Triple Exponential Moving Average
# dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9)
# # Cycle Indicator
# # ------------------------------------
# # Hilbert Transform Indicator - SineWave
# hilbert = ta.HT_SINE(dataframe)
# dataframe['htsine'] = hilbert['sine']
# dataframe['htleadsine'] = hilbert['leadsine']
# # Pattern Recognition - Bullish candlestick patterns
# # ------------------------------------
# # Hammer: values [0, 100]
# dataframe['CDLHAMMER'] = ta.CDLHAMMER(dataframe)
# # Inverted Hammer: values [0, 100]
# dataframe['CDLINVERTEDHAMMER'] = ta.CDLINVERTEDHAMMER(dataframe)
# # Dragonfly Doji: values [0, 100]
# dataframe['CDLDRAGONFLYDOJI'] = ta.CDLDRAGONFLYDOJI(dataframe)
# # Piercing Line: values [0, 100]
# dataframe['CDLPIERCING'] = ta.CDLPIERCING(dataframe) # values [0, 100]
# # Morningstar: values [0, 100]
# dataframe['CDLMORNINGSTAR'] = ta.CDLMORNINGSTAR(dataframe) # values [0, 100]
# # Three White Soldiers: values [0, 100]
# dataframe['CDL3WHITESOLDIERS'] = ta.CDL3WHITESOLDIERS(dataframe) # values [0, 100]
# # Pattern Recognition - Bearish candlestick patterns
# # ------------------------------------
# # Hanging Man: values [0, 100]
# dataframe['CDLHANGINGMAN'] = ta.CDLHANGINGMAN(dataframe)
# # Shooting Star: values [0, 100]
# dataframe['CDLSHOOTINGSTAR'] = ta.CDLSHOOTINGSTAR(dataframe)
# # Gravestone Doji: values [0, 100]
# dataframe['CDLGRAVESTONEDOJI'] = ta.CDLGRAVESTONEDOJI(dataframe)
# # Dark Cloud Cover: values [0, 100]
# dataframe['CDLDARKCLOUDCOVER'] = ta.CDLDARKCLOUDCOVER(dataframe)
# # Evening Doji Star: values [0, 100]
# dataframe['CDLEVENINGDOJISTAR'] = ta.CDLEVENINGDOJISTAR(dataframe)
# # Evening Star: values [0, 100]
# dataframe['CDLEVENINGSTAR'] = ta.CDLEVENINGSTAR(dataframe)
# # Pattern Recognition - Bullish/Bearish candlestick patterns
# # ------------------------------------
# # Three Line Strike: values [0, -100, 100]
# dataframe['CDL3LINESTRIKE'] = ta.CDL3LINESTRIKE(dataframe)
# # Spinning Top: values [0, -100, 100]
# dataframe['CDLSPINNINGTOP'] = ta.CDLSPINNINGTOP(dataframe) # values [0, -100, 100]
# # Engulfing: values [0, -100, 100]
# dataframe['CDLENGULFING'] = ta.CDLENGULFING(dataframe) # values [0, -100, 100]
# # Harami: values [0, -100, 100]
# dataframe['CDLHARAMI'] = ta.CDLHARAMI(dataframe) # values [0, -100, 100]
# # Three Outside Up/Down: values [0, -100, 100]
# dataframe['CDL3OUTSIDE'] = ta.CDL3OUTSIDE(dataframe) # values [0, -100, 100]
# # Three Inside Up/Down: values [0, -100, 100]
# dataframe['CDL3INSIDE'] = ta.CDL3INSIDE(dataframe) # values [0, -100, 100]
# # Chart type
# # ------------------------------------
# # Heikin Ashi Strategy
# heikinashi = qtpylib.heikinashi(dataframe)
# dataframe['ha_open'] = heikinashi['open']
# dataframe['ha_close'] = heikinashi['close']
# dataframe['ha_high'] = heikinashi['high']
# dataframe['ha_low'] = heikinashi['low']
# Retrieve best bid and best ask from the orderbook
# ------------------------------------
"""
# first check if dataprovider is available
if self.dp:
if self.dp.runmode in ('live', 'dry_run'):
ob = self.dp.orderbook(metadata['pair'], 1)
dataframe['best_bid'] = ob['bids'][0][0]
dataframe['best_ask'] = ob['asks'][0][0]
"""
return dataframe
def populate_indicators(dataframe: DataFrame) -> DataFrame:
"""
Adds several different TA indicators to the given DataFrame
"""
dataframe['sar'] = ta.SAR(dataframe)
dataframe['adx'] = ta.ADX(dataframe)
stoch = ta.STOCHF(dataframe)
dataframe['fastd'] = stoch['fastd']
dataframe['fastk'] = stoch['fastk']
dataframe['blower'] = ta.BBANDS(dataframe, nbdevup=2,
nbdevdn=2)['lowerband']
dataframe['sma'] = ta.SMA(dataframe, timeperiod=40)
dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9)
dataframe['mfi'] = ta.MFI(dataframe)
dataframe['cci'] = ta.CCI(dataframe)
dataframe['rsi'] = ta.RSI(dataframe)
dataframe['mom'] = ta.MOM(dataframe)
dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5)
dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10)
dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50)
dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100)
dataframe['ao'] = awesome_oscillator(dataframe)
macd = ta.MACD(dataframe)
dataframe['macd'] = macd['macd']
dataframe['macdsignal'] = macd['macdsignal']
dataframe['macdhist'] = macd['macdhist']
# add volatility indicators
dataframe['natr'] = ta.NATR(dataframe)
# add volume indicators
dataframe['obv'] = ta.OBV(dataframe)
# add more momentum indicators
dataframe['rocp'] = ta.ROCP(dataframe)
# add some pattern recognition
dataframe['CDL2CROWS'] = ta.CDL2CROWS(dataframe)
dataframe['CDL3BLACKCROWS'] = ta.CDL3BLACKCROWS(dataframe)
dataframe['CDL3INSIDE'] = ta.CDL3INSIDE(dataframe)
dataframe['CDL3LINESTRIKE'] = ta.CDL3LINESTRIKE(dataframe)
dataframe['CDL3OUTSIDE'] = ta.CDL3OUTSIDE(dataframe)
dataframe['CDL3STARSINSOUTH'] = ta.CDL3STARSINSOUTH(dataframe)
dataframe['CDL3WHITESOLDIERS'] = ta.CDL3WHITESOLDIERS(dataframe)
dataframe['CDLADVANCEBLOCK'] = ta.CDLADVANCEBLOCK(dataframe)
dataframe['CDLBELTHOLD'] = ta.CDLBELTHOLD(dataframe)
dataframe['CDLBREAKAWAY'] = ta.CDLBREAKAWAY(dataframe)
dataframe['CDLDOJI'] = ta.CDLDOJI(dataframe)
dataframe['CDLDOJISTAR'] = ta.CDLDOJISTAR(dataframe)
dataframe['CDLDRAGONFLYDOJI'] = ta.CDLDRAGONFLYDOJI(dataframe)
dataframe['CDLENGULFING'] = ta.CDLENGULFING(dataframe)
dataframe['CDLHAMMER'] = ta.CDLHAMMER(dataframe)
dataframe['CDLBREAKAWAY'] = ta.CDLBREAKAWAY(dataframe)
dataframe['CDLBREAKAWAY'] = ta.CDLBREAKAWAY(dataframe)
# enter categorical time
hour = datetime.strptime(str(dataframe['date'][len(dataframe) - 1]),
"%Y-%m-%d %H:%M:%S").hour
for h in range(24):
dataframe['hour_{0:02}'.format(h)] = int(h == hour)
return dataframe
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Adds several different TA indicators to the given DataFrame
Performance Note: For the best performance be frugal on the number of indicators
you are using. Let uncomment only the indicator you are using in your strategies
or your hyperopt configuration, otherwise you will waste your memory and CPU usage.
:param dataframe: Dataframe with data from the exchange
:param metadata: Additional information, like the currently traded pair
:return: a Dataframe with all mandatory indicators for the strategies
"""
#divergences
# - - - -
# -
# 4 3 2 1 0
#src[4] > src[2] and src[3] > src[2] and src[2] < src[1] and src[2] < src[0]
dataframe['bullish_div'] = (
( dataframe['close'].shift(4) > dataframe['close'].shift(2) ) &
( dataframe['close'].shift(3) > dataframe['close'].shift(2) ) &
( dataframe['close'].shift(2) < dataframe['close'].shift(1) ) &
( dataframe['close'].shift(2) < dataframe['close'] )
)
#queremos el volumen medio de las ultimas 24 velas, si es mayor queremos comprar, si es que no es volumen a la baja, esto habria que compararlo tomando el precio unas horas antes
dataframe['mean24volume'] = dataframe.volume.rolling(24).mean()
dataframe['mean68close'] = dataframe.close.rolling(68).mean()
# -
# - - - -
# 4 3 2 1 0
#src[4] < src[2] and src[3] < src[2] and src[2] > src[1] and src[2] > src[0]
dataframe['bearish_div'] = (
( dataframe['close'].shift(4) < dataframe['close'].shift(2) ) &
( dataframe['close'].shift(3) < dataframe['close'].shift(2) ) &
( dataframe['close'].shift(2) > dataframe['close'].shift(1) ) &
( dataframe['close'].shift(2) > dataframe['close'] )
)
dataframe['cci_one'] = ta.CCI(dataframe, timeperiod=170)
dataframe['cci_two'] = ta.CCI(dataframe, timeperiod=34)
# Momentum Indicators
# ------------------------------------
# ADX
dataframe['adx'] = ta.ADX(dataframe)
# # Plus Directional Indicator / Movement
# dataframe['plus_dm'] = ta.PLUS_DM(dataframe)
# dataframe['plus_di'] = ta.PLUS_DI(dataframe)
# # Minus Directional Indicator / Movement
# dataframe['minus_dm'] = ta.MINUS_DM(dataframe)
# dataframe['minus_di'] = ta.MINUS_DI(dataframe)
# # Aroon, Aroon Oscillator
# aroon = ta.AROON(dataframe)
# dataframe['aroonup'] = aroon['aroonup']
# dataframe['aroondown'] = aroon['aroondown']
# dataframe['aroonosc'] = ta.AROONOSC(dataframe)
# # Awesome Oscillator
# dataframe['ao'] = qtpylib.awesome_oscillator(dataframe)
# # Keltner Channel
# keltner = qtpylib.keltner_channel(dataframe)
# dataframe["kc_upperband"] = keltner["upper"]
# dataframe["kc_lowerband"] = keltner["lower"]
# dataframe["kc_middleband"] = keltner["mid"]
# dataframe["kc_percent"] = (
# (dataframe["close"] - dataframe["kc_lowerband"]) /
# (dataframe["kc_upperband"] - dataframe["kc_lowerband"])
# )
# dataframe["kc_width"] = (
# (dataframe["kc_upperband"] - dataframe["kc_lowerband"]) / dataframe["kc_middleband"]
# )
# # Ultimate Oscillator
# dataframe['uo'] = ta.ULTOSC(dataframe)
# # Commodity Channel Index: values [Oversold:-100, Overbought:100]
dataframe['cci'] = ta.CCI(dataframe)
# RSI
dataframe['rsi'] = ta.RSI(dataframe)
# # Inverse Fisher transform on RSI: values [-1.0, 1.0] (https://goo.gl/2JGGoy)
# rsi = 0.1 * (dataframe['rsi'] - 50)
# dataframe['fisher_rsi'] = (np.exp(2 * rsi) - 1) / (np.exp(2 * rsi) + 1)
# # Inverse Fisher transform on RSI normalized: values [0.0, 100.0] (https://goo.gl/2JGGoy)
# dataframe['fisher_rsi_norma'] = 50 * (dataframe['fisher_rsi'] + 1)
# # Stochastic Slow
# stoch = ta.STOCH(dataframe)
# dataframe['slowd'] = stoch['slowd']
# dataframe['slowk'] = stoch['slowk']
# Stochastic Fast
stoch_fast = ta.STOCHF(dataframe)
dataframe['fastd'] = stoch_fast['fastd']
dataframe['fastk'] = stoch_fast['fastk']
# # Stochastic RSI
# Please read https://github.com/freqtrade/freqtrade/issues/2961 before using this.
# STOCHRSI is NOT aligned with tradingview, which may result in non-expected results.
# stoch_rsi = ta.STOCHRSI(dataframe)
# dataframe['fastd_rsi'] = stoch_rsi['fastd']
# dataframe['fastk_rsi'] = stoch_rsi['fastk']
# MACD
macd = ta.MACD(dataframe)
dataframe['macd'] = macd['macd']
dataframe['macdsignal'] = macd['macdsignal']
dataframe['macdhist'] = macd['macdhist']
# MFI
dataframe['mfi'] = ta.MFI(dataframe)
# # ROC
# dataframe['roc'] = ta.ROC(dataframe)
# Overlap Studies
# ------------------------------------
# Bollinger Bands
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
dataframe['bb_lowerband'] = bollinger['lower']
dataframe['bb_middleband'] = bollinger['mid']
dataframe['bb_upperband'] = bollinger['upper']
dataframe["bb_percent"] = (
(dataframe["close"] - dataframe["bb_lowerband"]) /
(dataframe["bb_upperband"] - dataframe["bb_lowerband"])
)
dataframe["bb_width"] = (
(dataframe["bb_upperband"] - dataframe["bb_lowerband"]) / dataframe["bb_middleband"]
)
# Bollinger Bands - Weighted (EMA based instead of SMA)
# weighted_bollinger = qtpylib.weighted_bollinger_bands(
# qtpylib.typical_price(dataframe), window=20, stds=2
# )
# dataframe["wbb_upperband"] = weighted_bollinger["upper"]
# dataframe["wbb_lowerband"] = weighted_bollinger["lower"]
# dataframe["wbb_middleband"] = weighted_bollinger["mid"]
# dataframe["wbb_percent"] = (
# (dataframe["close"] - dataframe["wbb_lowerband"]) /
# (dataframe["wbb_upperband"] - dataframe["wbb_lowerband"])
# )
# dataframe["wbb_width"] = (
# (dataframe["wbb_upperband"] - dataframe["wbb_lowerband"]) /
# dataframe["wbb_middleband"]
# )
# # EMA - Exponential Moving Average
dataframe['ema3'] = ta.EMA(dataframe, timeperiod=3)
dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5)
dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10)
dataframe['ema21'] = ta.EMA(dataframe, timeperiod=21)
dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50)
dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100)
dataframe['ema200'] = ta.EMA(dataframe, timeperiod=200)
# # SMA - Simple Moving Average
# dataframe['sma3'] = ta.SMA(dataframe, timeperiod=3)
# dataframe['sma5'] = ta.SMA(dataframe, timeperiod=5)
# dataframe['sma10'] = ta.SMA(dataframe, timeperiod=10)
# dataframe['sma21'] = ta.SMA(dataframe, timeperiod=21)
# dataframe['sma50'] = ta.SMA(dataframe, timeperiod=50)
# dataframe['sma100'] = ta.SMA(dataframe, timeperiod=100)
# Parabolic SAR
dataframe['sar'] = ta.SAR(dataframe)
# TEMA - Triple Exponential Moving Average
dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9)
# Cycle Indicator
# ------------------------------------
# Hilbert Transform Indicator - SineWave
hilbert = ta.HT_SINE(dataframe)
dataframe['htsine'] = hilbert['sine']
dataframe['htleadsine'] = hilbert['leadsine']
# Pattern Recognition - Bullish candlestick patterns
# ------------------------------------
# # Hammer: values [0, 100]
# dataframe['CDLHAMMER'] = ta.CDLHAMMER(dataframe)
# # Inverted Hammer: values [0, 100]
# dataframe['CDLINVERTEDHAMMER'] = ta.CDLINVERTEDHAMMER(dataframe)
# # Dragonfly Doji: values [0, 100]
# dataframe['CDLDRAGONFLYDOJI'] = ta.CDLDRAGONFLYDOJI(dataframe)
# # Piercing Line: values [0, 100]
# dataframe['CDLPIERCING'] = ta.CDLPIERCING(dataframe) # values [0, 100]
# # Morningstar: values [0, 100]
# dataframe['CDLMORNINGSTAR'] = ta.CDLMORNINGSTAR(dataframe) # values [0, 100]
# # Three White Soldiers: values [0, 100]
# dataframe['CDL3WHITESOLDIERS'] = ta.CDL3WHITESOLDIERS(dataframe) # values [0, 100]
# Pattern Recognition - Bearish candlestick patterns
# ------------------------------------
# # Hanging Man: values [0, 100]
# dataframe['CDLHANGINGMAN'] = ta.CDLHANGINGMAN(dataframe)
# # Shooting Star: values [0, 100]
# dataframe['CDLSHOOTINGSTAR'] = ta.CDLSHOOTINGSTAR(dataframe)
# # Gravestone Doji: values [0, 100]
# dataframe['CDLGRAVESTONEDOJI'] = ta.CDLGRAVESTONEDOJI(dataframe)
# # Dark Cloud Cover: values [0, 100]
# dataframe['CDLDARKCLOUDCOVER'] = ta.CDLDARKCLOUDCOVER(dataframe)
# # Evening Doji Star: values [0, 100]
# dataframe['CDLEVENINGDOJISTAR'] = ta.CDLEVENINGDOJISTAR(dataframe)
# # Evening Star: values [0, 100]
# dataframe['CDLEVENINGSTAR'] = ta.CDLEVENINGSTAR(dataframe)
# Pattern Recognition - Bullish/Bearish candlestick patterns
# ------------------------------------
# # Three Line Strike: values [0, -100, 100]
# dataframe['CDL3LINESTRIKE'] = ta.CDL3LINESTRIKE(dataframe)
# # Spinning Top: values [0, -100, 100]
# dataframe['CDLSPINNINGTOP'] = ta.CDLSPINNINGTOP(dataframe) # values [0, -100, 100]
# # Engulfing: values [0, -100, 100]
# dataframe['CDLENGULFING'] = ta.CDLENGULFING(dataframe) # values [0, -100, 100]
# # Harami: values [0, -100, 100]
# dataframe['CDLHARAMI'] = ta.CDLHARAMI(dataframe) # values [0, -100, 100]
# # Three Outside Up/Down: values [0, -100, 100]
# dataframe['CDL3OUTSIDE'] = ta.CDL3OUTSIDE(dataframe) # values [0, -100, 100]
# # Three Inside Up/Down: values [0, -100, 100]
# dataframe['CDL3INSIDE'] = ta.CDL3INSIDE(dataframe) # values [0, -100, 100]
# # Chart type
# # ------------------------------------
# Heikin Ashi Strategy
heikinashi = qtpylib.heikinashi(dataframe)
dataframe['ha_open'] = heikinashi['open']
dataframe['close'] = heikinashi['close']
dataframe['ha_high'] = heikinashi['high']
dataframe['ha_low'] = heikinashi['low']
dataframe['haclosestrat'] = (dataframe['ha_open'] + dataframe['ha_high'] + dataframe['ha_low'] + dataframe['close']) / 4
dataframe['haopenstrat'] = (dataframe['ha_open'] + dataframe['close']) / 2
dataframe['highstrat'] = max(dataframe['ha_high'] , max(dataframe['ha_open'], dataframe['close'] ))
dataframe['lowstrat'] = min(dataframe['haLow'] , min(dataframe['ha_open'], dataframe['close'] ))
# Retrieve best bid and best ask from the orderbook
# ------------------------------------
return dataframe
while True:
cci_total_today_buy = []
cci_total_yesterday_buy = []
df_sell = []
print("START")
try:
for i in range(len(coin_buy)):
df_buy = pyupbit.get_ohlcv(coin_buy[i])
#print(df_buy)
high_buy = df_buy['high']
#print(high_buy)
low_buy = df_buy['low']
close_buy = df_buy['close']
cci_buy = ta.CCI(high_buy, low_buy, close_buy, timeperiod=20)
print(coin_buy[i], cci_buy[-1])
cci_total_today_buy.append(int(cci_buy[-1]))
cci_total_yesterday_buy.append(int(cci_buy[-2]))
time.sleep(0.1)
if cci_total_yesterday_buy[i] <= 50 < cci_total_today_buy[i]:
print("매수시작")
my_coin_buy = []
coin_list = []
for j in range(len(upbit.get_balances()[0])):
coin_list.append(upbit.get_balances()[0][j])
my_coin_buy.append("KRW-" + coin_list[j].get('currency'))
if my_coin_buy != "KRW-BTC":
if my_coin_buy.count(coin_buy[i]) == 0:
upbit.buy_market_order(coin_buy[i], 5000)
def populate_indicators(self, dataframe: DataFrame) -> DataFrame:
# resampled dataframe to establish if we are in an uptrend, downtrend or sideways trend
dataframe = StrategyHelper.resample(dataframe, self.ticker_interval, self.resample_factor)
##################################################################################
# required for entry and exit
# CCI
dataframe['cci'] = ta.CCI(dataframe, timeperiod=20)
dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
dataframe['adx'] = ta.ADX(dataframe)
dataframe['mfi'] = ta.MFI(dataframe)
dataframe['mfi_smooth'] = ta.EMA(dataframe, timeperiod=11, price='mfi')
dataframe['cci_smooth'] = ta.EMA(dataframe, timeperiod=11, price='cci')
dataframe['rsi_smooth'] = ta.EMA(dataframe, timeperiod=11, price='rsi')
##################################################################################
# required for graphing
bollinger = qtpylib.bollinger_bands(dataframe['close'], window=20, stds=2)
dataframe['bb_lowerband'] = bollinger['lower']
dataframe['bb_upperband'] = bollinger['upper']
dataframe['bb_middleband'] = bollinger['mid']
# MACD
macd = ta.MACD(dataframe)
dataframe['macd'] = macd['macd']
dataframe['macdsignal'] = macd['macdsignal']
dataframe['macdhist'] = macd['macdhist']
##################################################################################
# required for entry
bollinger = qtpylib.bollinger_bands(dataframe['close'], window=20, stds=1.6)
dataframe['entry_bb_lowerband'] = bollinger['lower']
dataframe['entry_bb_upperband'] = bollinger['upper']
dataframe['entry_bb_middleband'] = bollinger['mid']
dataframe['bpercent'] = (dataframe['close'] - dataframe['bb_lowerband']) / (
dataframe['bb_upperband'] - dataframe['bb_lowerband']) * 100
dataframe['bsharp'] = (dataframe['bb_upperband'] - dataframe['bb_lowerband']) / (
dataframe['bb_middleband'])
# these seem to be kind useful to measure when bands widen
# but than they are directly based on the moving average
dataframe['bsharp_slow'] = ta.SMA(dataframe, price='bsharp', timeperiod=11)
dataframe['bsharp_medium'] = ta.SMA(dataframe, price='bsharp', timeperiod=8)
dataframe['bsharp_fast'] = ta.SMA(dataframe, price='bsharp', timeperiod=5)
##################################################################################
# rsi and mfi are slightly weighted
dataframe['mfi_rsi_cci_smooth'] = (dataframe['rsi_smooth'] * 1.125 + dataframe['mfi_smooth'] * 1.125 +
dataframe[
'cci_smooth']) / 3
dataframe['mfi_rsi_cci_smooth'] = ta.TEMA(dataframe, timeperiod=21, price='mfi_rsi_cci_smooth')
# playgound
dataframe['candle_size'] = (dataframe['close'] - dataframe['open']) * (
dataframe['close'] - dataframe['open']) / 2
# helps with pattern recognition
dataframe['average'] = (dataframe['close'] + dataframe['open'] + dataframe['high'] + dataframe['low']) / 4
dataframe['sma_slow'] = ta.SMA(dataframe, timeperiod=200, price='close')
dataframe['sma_medium'] = ta.SMA(dataframe, timeperiod=100, price='close')
dataframe['sma_fast'] = ta.SMA(dataframe, timeperiod=50, price='close')
return dataframe
def populate_indicators(dataframe: DataFrame) -> DataFrame:
"""
Adds several different TA indicators to the given DataFrame
"""
dataframe['adx'] = ta.ADX(dataframe)
dataframe['ao'] = qtpylib.awesome_oscillator(dataframe)
dataframe['cci'] = ta.CCI(dataframe)
macd = ta.MACD(dataframe)
dataframe['macd'] = macd['macd']
dataframe['macdsignal'] = macd['macdsignal']
dataframe['macdhist'] = macd['macdhist']
dataframe['mfi'] = ta.MFI(dataframe)
dataframe['minus_dm'] = ta.MINUS_DM(dataframe)
dataframe['minus_di'] = ta.MINUS_DI(dataframe)
dataframe['plus_dm'] = ta.PLUS_DM(dataframe)
dataframe['plus_di'] = ta.PLUS_DI(dataframe)
dataframe['roc'] = ta.ROC(dataframe)
dataframe['rsi'] = ta.RSI(dataframe)
# Inverse Fisher transform on RSI, values [-1.0, 1.0] (https://goo.gl/2JGGoy)
rsi = 0.1 * (dataframe['rsi'] - 50)
dataframe['fisher_rsi'] = (numpy.exp(2 * rsi) - 1) / (numpy.exp(2 * rsi) + 1)
# Inverse Fisher transform on RSI normalized, value [0.0, 100.0] (https://goo.gl/2JGGoy)
dataframe['fisher_rsi_norma'] = 50 * (dataframe['fisher_rsi'] + 1)
# Stoch
stoch = ta.STOCH(dataframe)
dataframe['slowd'] = stoch['slowd']
dataframe['slowk'] = stoch['slowk']
# Stoch fast
stoch_fast = ta.STOCHF(dataframe)
dataframe['fastd'] = stoch_fast['fastd']
dataframe['fastk'] = stoch_fast['fastk']
# Stoch RSI
stoch_rsi = ta.STOCHRSI(dataframe)
dataframe['fastd_rsi'] = stoch_rsi['fastd']
dataframe['fastk_rsi'] = stoch_rsi['fastk']
# Bollinger bands
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
dataframe['bb_lowerband'] = bollinger['lower']
dataframe['bb_middleband'] = bollinger['mid']
dataframe['bb_upperband'] = bollinger['upper']
# EMA - Exponential Moving Average
dataframe['ema3'] = ta.EMA(dataframe, timeperiod=3)
dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5)
dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10)
dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50)
dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100)
# SAR Parabolic
dataframe['sar'] = ta.SAR(dataframe)
# SMA - Simple Moving Average
dataframe['sma'] = ta.SMA(dataframe, timeperiod=40)
# TEMA - Triple Exponential Moving Average
dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9)
# Hilbert Transform Indicator - SineWave
hilbert = ta.HT_SINE(dataframe)
dataframe['htsine'] = hilbert['sine']
dataframe['htleadsine'] = hilbert['leadsine']
# Pattern Recognition - Bullish candlestick patterns
# ------------------------------------
"""
# Hammer: values [0, 100]
dataframe['CDLHAMMER'] = ta.CDLHAMMER(dataframe)
# Inverted Hammer: values [0, 100]
dataframe['CDLINVERTEDHAMMER'] = ta.CDLINVERTEDHAMMER(dataframe)
# Dragonfly Doji: values [0, 100]
dataframe['CDLDRAGONFLYDOJI'] = ta.CDLDRAGONFLYDOJI(dataframe)
# Piercing Line: values [0, 100]
dataframe['CDLPIERCING'] = ta.CDLPIERCING(dataframe) # values [0, 100]
# Morningstar: values [0, 100]
dataframe['CDLMORNINGSTAR'] = ta.CDLMORNINGSTAR(dataframe) # values [0, 100]
# Three White Soldiers: values [0, 100]
dataframe['CDL3WHITESOLDIERS'] = ta.CDL3WHITESOLDIERS(dataframe) # values [0, 100]
"""
# Pattern Recognition - Bearish candlestick patterns
# ------------------------------------
"""
# Hanging Man: values [0, 100]
dataframe['CDLHANGINGMAN'] = ta.CDLHANGINGMAN(dataframe)
# Shooting Star: values [0, 100]
dataframe['CDLSHOOTINGSTAR'] = ta.CDLSHOOTINGSTAR(dataframe)
# Gravestone Doji: values [0, 100]
dataframe['CDLGRAVESTONEDOJI'] = ta.CDLGRAVESTONEDOJI(dataframe)
# Dark Cloud Cover: values [0, 100]
dataframe['CDLDARKCLOUDCOVER'] = ta.CDLDARKCLOUDCOVER(dataframe)
# Evening Doji Star: values [0, 100]
dataframe['CDLEVENINGDOJISTAR'] = ta.CDLEVENINGDOJISTAR(dataframe)
# Evening Star: values [0, 100]
dataframe['CDLEVENINGSTAR'] = ta.CDLEVENINGSTAR(dataframe)
"""
# Pattern Recognition - Bullish/Bearish candlestick patterns
# ------------------------------------
"""
# Three Line Strike: values [0, -100, 100]
dataframe['CDL3LINESTRIKE'] = ta.CDL3LINESTRIKE(dataframe)
# Spinning Top: values [0, -100, 100]
dataframe['CDLSPINNINGTOP'] = ta.CDLSPINNINGTOP(dataframe) # values [0, -100, 100]
# Engulfing: values [0, -100, 100]
dataframe['CDLENGULFING'] = ta.CDLENGULFING(dataframe) # values [0, -100, 100]
# Harami: values [0, -100, 100]
dataframe['CDLHARAMI'] = ta.CDLHARAMI(dataframe) # values [0, -100, 100]
# Three Outside Up/Down: values [0, -100, 100]
dataframe['CDL3OUTSIDE'] = ta.CDL3OUTSIDE(dataframe) # values [0, -100, 100]
# Three Inside Up/Down: values [0, -100, 100]
dataframe['CDL3INSIDE'] = ta.CDL3INSIDE(dataframe) # values [0, -100, 100]
"""
# Chart type
# ------------------------------------
# Heikinashi stategy
heikinashi = qtpylib.heikinashi(dataframe)
dataframe['ha_open'] = heikinashi['open']
dataframe['ha_close'] = heikinashi['close']
dataframe['ha_high'] = heikinashi['high']
dataframe['ha_low'] = heikinashi['low']
return dataframe
def cal_cci(self, period=14):
self.analysis_data['cci'] = ta.CCI(self.high,
self.low,
self.close,
timeperiod=period)
def populate_indicators(self, dataframe: DataFrame,
metadata: dict) -> DataFrame:
"""
Adds several different TA indicators to the given DataFrame
Performance Note: For the best performance be frugal on the number of indicators
you are using. Let uncomment only the indicator you are using in your strategies
or your hyperopt configuration, otherwise you will waste your memory and CPU usage.
"""
dataframe['ema20'] = ta.EMA(dataframe, timeperiod=20)
dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50)
dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100)
heikinashi = qtpylib.heikinashi(dataframe)
dataframe['ha_open'] = heikinashi['open']
dataframe['ha_close'] = heikinashi['close']
dataframe['adx'] = ta.ADX(dataframe)
dataframe['rsi'] = ta.RSI(dataframe)
macd = ta.MACD(dataframe)
dataframe['macd'] = macd['macd']
dataframe['macdsignal'] = macd['macdsignal']
dataframe['macdhist'] = macd['macdhist']
bollinger = ta.BBANDS(dataframe,
timeperiod=20,
nbdevup=2.0,
nbdevdn=2.0)
dataframe['bb_lowerband'] = bollinger['lowerband']
dataframe['bb_middleband'] = bollinger['middleband']
dataframe['bb_upperband'] = bollinger['upperband']
# Stoch
stoch = ta.STOCH(dataframe)
dataframe['slowk'] = stoch['slowk']
# Commodity Channel Index: values Oversold:<-100, Overbought:>100
dataframe['cci'] = ta.CCI(dataframe)
# Stoch
stoch = ta.STOCHF(dataframe, 5)
dataframe['fastd'] = stoch['fastd']
dataframe['fastk'] = stoch['fastk']
dataframe['fastk-previous'] = dataframe.fastk.shift(1)
dataframe['fastd-previous'] = dataframe.fastd.shift(1)
# Slow Stoch
slowstoch = ta.STOCHF(dataframe, 50)
dataframe['slowfastd'] = slowstoch['fastd']
dataframe['slowfastk'] = slowstoch['fastk']
dataframe['slowfastk-previous'] = dataframe.slowfastk.shift(1)
dataframe['slowfastd-previous'] = dataframe.slowfastd.shift(1)
# RSI
dataframe['rsi'] = ta.RSI(dataframe)
# Inverse Fisher transform on RSI, values [-1.0, 1.0] (https://goo.gl/2JGGoy)
rsi = 0.1 * (dataframe['rsi'] - 50)
dataframe['fisher_rsi'] = (numpy.exp(2 * rsi) -
1) / (numpy.exp(2 * rsi) + 1)
# Bollinger bands
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe),
window=20,
stds=2)
dataframe['bb_lowerband'] = bollinger['lower']
# SAR Parabol
dataframe['sar'] = ta.SAR(dataframe)
# Hammer: values [0, 100]
dataframe['CDLHAMMER'] = ta.CDLHAMMER(dataframe)
# SMA - Simple Moving Average
dataframe['sma'] = ta.SMA(dataframe, timeperiod=40)
return dataframe
def CCI(self): #14
cci = abstract.CCI(self.company_stock, timeperiod=14)
self.company_stock['CCI'] = cci
def __countCCI(self): self.cci = ta.CCI(self.stock.inputs)
