def populate_indicators(self, dataframe: DataFrame) -> DataFrame:
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']
macd = ta.MACD(dataframe)
dataframe['macd'] = macd['macd']
dataframe['macdsignal'] = macd['macdsignal']
dataframe['macdhist'] = macd['macdhist']
dataframe['cci'] = ta.CCI(dataframe)
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.
"""
dataframe['gap'] = dataframe['close'].shift(1) - (
(dataframe['high'].shift(1) - dataframe['low'].shift(1)) * 0.1)
dataframe['adx'] = ta.ADX(dataframe)
# MFI
dataframe['mfi'] = ta.MFI(dataframe)
# RSI
dataframe['rsi'] = ta.RSI(dataframe)
# Stochastic Fast
stoch_fast = ta.STOCH(dataframe)
dataframe['slowd'] = stoch_fast['slowd']
# Bollinger bands
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe),
window=20,
stds=2)
dataframe['bb_lowerband'] = bollinger['lower']
dataframe['bb_upperband'] = bollinger['upper']
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']
return dataframe
def populate_indicators(dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Add several indicators needed for buy and sell strategies defined below.
"""
# ADX
dataframe['adx'] = ta.ADX(dataframe)
# MACD
macd = ta.MACD(dataframe)
dataframe['macd'] = macd['macd']
dataframe['macdsignal'] = macd['macdsignal']
# MFI
dataframe['mfi'] = ta.MFI(dataframe)
# RSI
dataframe['rsi'] = ta.RSI(dataframe)
# Stochastic Fast
stoch_fast = ta.STOCHF(dataframe)
dataframe['fastd'] = stoch_fast['fastd']
# Minus-DI
dataframe['minus_di'] = ta.MINUS_DI(dataframe)
# Bollinger bands
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
dataframe['bb_lowerband'] = bollinger['lower']
dataframe['bb_upperband'] = bollinger['upper']
# SAR
dataframe['sar'] = ta.SAR(dataframe)
return dataframe
def populate_indicators(self, dataframe: DataFrame,
metadata: dict) -> DataFrame:
# RSI
dataframe['rsi'] = ta.RSI(dataframe)
dataframe['mfi'] = ta.MFI(dataframe)
# Bollinger Bands 1,2,3 and 4
bollinger1 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe),
window=20,
stds=1)
dataframe['bb_lowerband1'] = bollinger1['lower']
dataframe['bb_middleband1'] = bollinger1['mid']
dataframe['bb_upperband1'] = bollinger1['upper']
bollinger2 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe),
window=20,
stds=2)
dataframe['bb_lowerband2'] = bollinger2['lower']
dataframe['bb_middleband2'] = bollinger2['mid']
dataframe['bb_upperband2'] = bollinger2['upper']
bollinger3 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe),
window=20,
stds=3)
dataframe['bb_lowerband3'] = bollinger3['lower']
dataframe['bb_middleband3'] = bollinger3['mid']
dataframe['bb_upperband3'] = bollinger3['upper']
bollinger4 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe),
window=20,
stds=4)
dataframe['bb_lowerband4'] = bollinger4['lower']
dataframe['bb_middleband4'] = bollinger4['mid']
dataframe['bb_upperband4'] = bollinger4['upper']
return dataframe
def do_populate_indicators(self, dataframe: DataFrame,
metadata: dict) -> DataFrame:
"""
Adds multiple TA indicators to MoniGoMani's DataFrame per pair.
Should be called with 'informative_pair' (1h candles) during backtesting/hyperopting with TimeFrame-Zoom!
Performance Note: For the best performance be frugal on the number of indicators you are using.
Only add in indicators that you are using in your weighted signal configuration for MoniGoMani,
otherwise you will waste your memory and CPU usage.
:param dataframe: (DataFrame) DataFrame with data from the exchange
:param metadata: (dict) Additional information, like the currently traded pair
:return DataFrame: DataFrame for MoniGoMani with all mandatory indicator data populated
"""
# Momentum Indicators (timeperiod is expressed in candles)
# -------------------
# Parabolic SAR
dataframe['sar'] = ta.SAR(dataframe)
# Stochastic Slow
stoch = ta.STOCH(dataframe)
dataframe['slowk'] = stoch['slowk']
# MACD - Moving Average Convergence Divergence
macd = ta.MACD(dataframe)
dataframe['macd'] = macd[
'macd'] # MACD - Blue TradingView Line (Bullish if on top)
dataframe['macdsignal'] = macd[
'macdsignal'] # Signal - Orange TradingView Line (Bearish if on top)
# MFI - Money Flow Index (Under bought / Over sold & Over bought / Under sold / volume Indicator)
dataframe['mfi'] = ta.MFI(dataframe)
# Overlap Studies
# ---------------
# Bollinger Bands
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe),
window=20,
stds=2)
dataframe['bb_middleband'] = bollinger['mid']
# SMA's & EMA's are trend following tools (Should not be used when line goes sideways)
# SMA - Simple Moving Average (Moves slower compared to EMA, price trend over X periods)
dataframe['sma9'] = ta.SMA(dataframe, timeperiod=9)
dataframe['sma50'] = ta.SMA(dataframe, timeperiod=50)
dataframe['sma200'] = ta.SMA(dataframe, timeperiod=200)
# TEMA - Triple Exponential Moving Average
dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9)
# Volume Indicators
# -----------------
# Rolling VWAP - Volume Weighted Average Price
dataframe['rolling_vwap'] = qtpylib.rolling_vwap(dataframe)
return dataframe
def analyze(self,
historical_data,
period_count=14,
signal=['mfi'],
hot_thresh=None,
cold_thresh=None):
"""Performs MFI analysis on the historical data
Args:
historical_data (list): A matrix of historical OHCLV data.
period_count (int, optional): Defaults to 14. The number of data points to consider for
our MFI.
signal (list, optional): Defaults to mfi. The indicator line to check hot/cold
against.
hot_thresh (float, optional): Defaults to None. The threshold at which this might be
good to purchase.
cold_thresh (float, optional): Defaults to None. The threshold at which this might be
good to sell.
Returns:
pandas.DataFrame: A dataframe containing the indicators and hot/cold values.
"""
dataframe = self.convert_to_dataframe(historical_data)
mfi_values = abstract.MFI(dataframe, period_count).to_frame()
mfi_values.dropna(how='all', inplace=True)
mfi_values.rename(columns={0: 'mfi'}, inplace=True)
if mfi_values[signal[0]].shape[0]:
mfi_values['is_hot'] = mfi_values[signal[0]] > hot_thresh
mfi_values['is_cold'] = mfi_values[signal[0]] < cold_thresh
return mfi_values
def populate_indicators(self, dataframe: DataFrame,
metadata: dict) -> DataFrame:
tf_res = timeframe_to_minutes(self.timeframe) * 5
df_res = resample_to_interval(dataframe, tf_res)
df_res['sma'] = ta.SMA(df_res, 50, price='close')
dataframe = resampled_merge(dataframe, df_res, fill_na=True)
dataframe['resample_sma'] = dataframe[f'resample_{tf_res}_sma']
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, 3, 0, 3, 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:
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 add_indicators(self, df: pd.DataFrame = None) -> pd.DataFrame:
"""Add indicators."""
cols = ['high', 'low', 'open', 'close', 'volume']
HLOCV = {key: df[key].values for key in df if key in cols}
try:
df['volume'] = df['volumeto']
except:
pass
# Moving Averages
df['sma'] = abstract_ta.SMA(df, timeperiod=25)
df['ema20'] = abstract_ta.EMA(df, timeperiod=20)
df['ema50'] = abstract_ta.EMA(df, timeperiod=50)
df['ema100'] = abstract_ta.EMA(df, timeperiod=100)
df['ema200'] = abstract_ta.EMA(df, timeperiod=200)
df['ema300'] = abstract_ta.EMA(df, timeperiod=300)
# Bollinger Bands
u, m, l = abstract_ta.BBANDS(HLOCV,
timeperiod=24,
nbdevup=2.5,
nbdevdn=2.5,
matype=MA_Type.T3)
df['upper'] = u
df['middle'] = m
df['lower'] = l
# Stochastic
# uses high, low, close (default)
slowk, slowd = abstract_ta.STOCH(HLOCV, 5, 3, 0, 3,
0) # uses high, low, close by default
df['slowk'] = slowk
df['slowd'] = slowd
df['slow_stoch'] = (slowk + slowd) / 2
df['slow_stoch_sma14'] = df.slow_stoch.rolling(window=14).mean()
df['slow_stoch_sma26'] = df.slow_stoch.rolling(window=26).mean()
# Relative Strength Index
rsi = abstract_ta.RSI(df, timeperiod=14)
df['rsi'] = rsi
# Money Flow Index
mfi = abstract_ta.MFI(df, timeperiod=14)
df['mfi'] = mfi
# Medivh Relative Flow Index
mrfi_df = MRFI(df)
df['mrfi'] = mrfi_df['mrfi'].astype(float)
df['smrfi'] = mrfi_df['smrfi'].astype(float)
df['mrfi_basis'] = mrfi_df['mrfi_basis'].astype(float)
df['mrfi_inverse'] = mrfi_df['mrfi_inverse'].astype(float)
return df
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 analyze(self,
historical_data,
signal=['bbp'],
hot_thresh=0,
cold_thresh=0.8,
period_count=20,
std_dev=2):
"""Check when close price cross the Upper/Lower bands.
Args:
historical_data (list): A matrix of historical OHCLV data.
period_count (int, optional): Defaults to 20. The number of data points to consider for the BB bands indicator.
signal (list, optional): Defaults bbp value.
hot_thresh (float, optional): Defaults to 0. The threshold at which this might be
good to purchase.
cold_thresh (float, optional): Defaults to 0.8. The threshold at which this might be
good to sell.
std_dev (int, optional): number of std dev to use. Common values are 2 or 1
Returns:
pandas.DataFrame: A dataframe containing the indicator and hot/cold values.
"""
dataframe = self.convert_to_dataframe(historical_data)
mfi = abstract.MFI(dataframe, period_count=14)
# Required to avoid getting same values for low, middle, up
dataframe['close_10k'] = dataframe['close'] * 10000
up_band, mid_band, low_band = BBANDS(dataframe['close_10k'],
timeperiod=period_count,
nbdevup=std_dev,
nbdevdn=std_dev,
matype=0)
bbp = (dataframe['close_10k'] - low_band) / (up_band - low_band)
bollinger = pandas.concat([dataframe, bbp, mfi], axis=1)
bollinger.rename(columns={0: 'bbp', 1: 'mfi'}, inplace=True)
bollinger['is_hot'] = False
bollinger['is_cold'] = False
bollinger['is_hot'].iloc[
-1] = bollinger['bbp'].iloc[-2] <= hot_thresh and bollinger[
'bbp'].iloc[-2] < bollinger['bbp'].iloc[-1]
bollinger['is_cold'].iloc[
-1] = bollinger['bbp'].iloc[-1] >= cold_thresh
return bollinger
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(dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe['adx'] = ta.ADX(dataframe)
macd = ta.MACD(dataframe)
dataframe['macd'] = macd['macd']
dataframe['macdsignal'] = macd['macdsignal']
dataframe['mfi'] = ta.MFI(dataframe)
dataframe['rsi'] = ta.RSI(dataframe)
stoch_fast = ta.STOCHF(dataframe)
dataframe['fastd'] = stoch_fast['fastd']
dataframe['minus_di'] = ta.MINUS_DI(dataframe)
# Bollinger bands
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
dataframe['bb_lowerband'] = bollinger['lower']
dataframe['bb_upperband'] = bollinger['upper']
dataframe['sar'] = ta.SAR(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,
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
"""
# Momentum Indicators
# ------------------------------------
dataframe['adx'] = ta.ADX(dataframe)
dataframe['sar'] = ta.SAR(dataframe)
# RSI
dataframe['rsi'] = ta.RSI(dataframe)
dataframe['mfi'] = ta.MFI(dataframe)
stoch_fast = ta.STOCHF(dataframe)
dataframe['fastd'] = stoch_fast['fastd']
# Bollinger Bands 1 STD
bollinger1 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe),
window=20,
stds=1)
dataframe['bb_lowerband1'] = bollinger1['lower']
# dataframe['bb_middleband1'] = bollinger1['mid']
# dataframe['bb_upperband1'] = bollinger1['upper']
# bollinger2 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
# dataframe['bb_lowerband2'] = bollinger2['lower']
# dataframe['bb_middleband2'] = bollinger2['mid']
# dataframe['bb_upperband2'] = bollinger2['upper']
# bollinger3 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=3)
# dataframe['bb_lowerband3'] = bollinger3['lower']
# dataframe['bb_middleband3'] = bollinger3['mid']
# dataframe['bb_upperband3'] = bollinger3['upper']
# Bollinger Bands 4 STD
bollinger4 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe),
window=20,
stds=4)
dataframe['bb_lowerband4'] = bollinger4['lower']
# dataframe['bb_middleband4'] = bollinger4['mid']
# dataframe['bb_upperband4'] = bollinger4['upper']
macd = ta.MACD(dataframe)
dataframe['macd'] = macd['macd']
dataframe['macdsignal'] = macd['macdsignal']
return dataframe
def populate_indicators(self, dataframe: DataFrame) -> DataFrame:
dataframe = self.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:
"""
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.
"""
# MFI
dataframe['mfi'] = ta.MFI(dataframe)
# Stoch fast
stoch_fast = ta.STOCHF(dataframe)
dataframe['fastd'] = stoch_fast['fastd']
dataframe['fastk'] = stoch_fast['fastk']
# 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']
# EMA - Exponential Moving Average
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 Parabol
dataframe['sar'] = ta.SAR(dataframe)
# SMA - Simple Moving Average
dataframe['sma'] = ta.SMA(dataframe, timeperiod=40)
return dataframe
def populate_indicators(dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
This method can also be loaded from the strategy, if it doesn't exist in the hyperopt class.
"""
dataframe['adx'] = ta.ADX(dataframe)
macd = ta.MACD(dataframe)
dataframe['macd'] = macd['macd']
dataframe['macdsignal'] = macd['macdsignal']
dataframe['mfi'] = ta.MFI(dataframe)
dataframe['rsi'] = ta.RSI(dataframe)
stoch_fast = ta.STOCHF(dataframe)
dataframe['fastd'] = stoch_fast['fastd']
dataframe['minus_di'] = ta.MINUS_DI(dataframe)
# Bollinger bands
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
dataframe['bb_lowerband'] = bollinger['lower']
dataframe['bb_upperband'] = bollinger['upper']
dataframe['sar'] = ta.SAR(dataframe)
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 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 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 analyze(self,
historical_data,
period_count=14,
hot_thresh=None,
cold_thresh=None,
all_data=False):
"""Performs MFI analysis on the historical data
Args:
historical_data (list): A matrix of historical OHCLV data.
period_count (int, optional): Defaults to 14. The number of data points to consider for
our simple moving average.
hot_thresh (float, optional): Defaults to None. The threshold at which this might be
good to purchase.
cold_thresh (float, optional): Defaults to None. The threshold at which this might be
good to sell.
all_data (bool, optional): Defaults to False. If True, we return the momentum
associated with each data point in our historical dataset. Otherwise just return
the last one.
Returns:
dict: A dictionary containing a tuple of indicator values and booleans for buy / sell
indication.
"""
dataframe = self.convert_to_dataframe(historical_data)
mom_values = abstract.MFI(dataframe, period_count)
analyzed_data = [(value, ) for value in mom_values]
return self.analyze_results(analyzed_data,
is_hot=lambda v: v > hot_thresh
if hot_thresh else False,
is_cold=lambda v: v < cold_thresh
if cold_thresh else False,
all_data=all_data)
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
"""
# Momentum Indicators
# ------------------------------------
# momentum
high = dataframe['high']
low = dataframe['low']
close = dataframe['close']
volume = dataframe['volume']
dataframe['mfi'] = ta.MFI(high, low, close, volume, 14)
dataframe['signal'] = 0
dataframe.loc[((dataframe['mfi'] <= 20)), 'signal'] = 1
dataframe.loc[((dataframe['mfi'] >= 80)), 'signal'] = -1
dataframe['signal'] = dataframe['signal'].diff()
dataframe.loc[((dataframe['mfi'] > 20) & (dataframe['mfi'] < 80)),
'signal'] = 0
#relative volume
dataframe['rv'] = (volume /
dataframe['volume'].rolling(14).max()) * 100
# 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['sma34'] = ta.SMA(dataframe, timeperiod=34)
# 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(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
"""
# Momentum Indicators
# ------------------------------------
# ADX
dataframe['adx'] = ta.ADX(dataframe)
# RSI
dataframe['rsi'] = ta.RSI(dataframe)
# Stochastic Fast
stoch_fast = ta.STOCHF(dataframe)
dataframe['fastd'] = stoch_fast['fastd']
dataframe['fastk'] = stoch_fast['fastk']
# MACD
macd = ta.MACD(dataframe)
dataframe['macd'] = macd['macd']
dataframe['macdsignal'] = macd['macdsignal']
dataframe['macdhist'] = macd['macdhist']
# MFI
dataframe['mfi'] = ta.MFI(dataframe)
# 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"])
# 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']
"""
# 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(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(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: Raw data from the exchange and parsed by parse_ticker_dataframe()
:param metadata: Additional information, like the currently traded pair
:return: a Dataframe with all mandatory indicators for the strategies
"""
# Momentum Indicators
# ------------------------------------
# RSI
dataframe['rsi'] = ta.RSI(dataframe)
# ADX
dataframe['adx'] = ta.ADX(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)
# # Commodity Channel Index: values Oversold:<-100, Overbought:>100
# dataframe['cci'] = ta.CCI(dataframe)
# MACD
macd = ta.MACD(dataframe)
dataframe['macd'] = macd['macd']
dataframe['macdsignal'] = macd['macdsignal']
dataframe['macdhist'] = macd['macdhist']
# MFI
dataframe['mfi'] = ta.MFI(dataframe)
# # Minus Directional Indicator / Movement
# dataframe['minus_dm'] = ta.MINUS_DM(dataframe)
# dataframe['minus_di'] = ta.MINUS_DI(dataframe)
# # Plus Directional Indicator / Movement
# dataframe['plus_dm'] = ta.PLUS_DM(dataframe)
# dataframe['plus_di'] = ta.PLUS_DI(dataframe)
# dataframe['minus_di'] = ta.MINUS_DI(dataframe)
# # ROC
# dataframe['roc'] = ta.ROC(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, 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']
# 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']
# # 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)
# # SMA - Simple Moving Average
# dataframe['sma'] = ta.SMA(dataframe, timeperiod=40)
# SAR Parabol
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
# # ------------------------------------
# # 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']
# 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(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 _build_indicators(self, df):
if not self.realtime:
inputs = df.to_dict(orient="list")
for col in inputs:
inputs[col] = np.array(inputs[col])
c = df["close"]
for n in range(2, 40):
inputs["bband_u_" +
str(n)], inputs["bband_m_" +
str(n)], inputs["bband_l_" +
str(n)] = ta.BBANDS(
inputs, n)
inputs["sma_" + str(n)] = ta.SMA(inputs, timeperiod=n)
inputs["adx_" + str(n)] = ta.ADX(inputs, timeperiod=n)
# fast_ema = c.ewm(span = n, adjust = False).mean()
# slow_ema = c.ewm(span = n*2, adjust = False).mean()
# macd1 = fast_ema - slow_ema
# macd2 = macd1.ewm(span = int(n*2/3), adjust = False).mean()
# macd3 = macd1 - macd2
# inputs["macd_"+str(n)] = macd1.values
# inputs["macdsignal_"+str(n)] = macd2.values
# inputs["macdhist_"+str(n)] = macd3.values
if n != 2:
inputs["macd_" +
str(n)], inputs["macdsignal_" +
str(n)], inputs["macdhist_" +
str(n)] = ta.MACD(
inputs, n,
n * 2,
int(n * 2 / 3))
else:
inputs["macd_" +
str(n)], inputs["macdsignal_" +
str(n)], inputs["macdhist_" +
str(n)] = ta.MACD(
inputs, n,
n * 2, 1)
# macd = [macd1.values, macd2.values, macd3.values]
# for idx, i in enumerate(["macd_"+str(n), "macdsignal_"+str(n), "macdhist_"+str(n)]):
# for day in zip(inputs[i], macd[idx]):
# print("Type: %s N: %d PD: %.3f TA: %.3f, " % (i, n, day[1], day[0]))
inputs["mfi_" + str(n)] = ta.MFI(inputs, n)
inputs["ult_" + str(n)] = ta.ULTOSC(inputs, n, n * 2, n * 4)
inputs["willr_" + str(n)] = ta.WILLR(inputs, n)
inputs["slowk"], inputs["slowd"] = ta.STOCH(inputs)
inputs["mom_" + str(n)] = ta.MOM(inputs, n)
inputs["volume"] = list(map(lambda x: x / 10000, inputs["volume"]))
df = pd.DataFrame().from_dict(inputs)
# df = df.ix[100:]
# print(df.tail(5)["macd_3"], df.tail(5)["macdsignal_3"], df.tail(5)["macdhist_3"])
return df
else:
# Build data one-by-one, as if it's coming in one at a time
output = pd.DataFrame()
sliding_window = pd.DataFrame()
for idx, day in df.iterrows():
print("\rNow building day", str(idx), end="", flush=True)
day = copy.deepcopy(day) # Avoid reference vs copy bullshit
sliding_window = sliding_window.append(day, ignore_index=True)
# print(day, type(day))
day_out = {}
# print(sliding_window)
o = sliding_window["open"].values
h = sliding_window["high"].values
l = sliding_window["low"].values
c_series = sliding_window["close"]
c = sliding_window["close"].values
# print("----")
# print(c)
v = sliding_window["volume"].values
for t in ["open", "high", "low", "close"]:
day_out[t] = sliding_window[t].values[-1]
for n in range(2, 40):
# time.sleep(0.1)
day_out["bband_u_" +
str(n)], day_out["bband_m_" + str(n)], day_out[
"bband_l_" + str(n)] = stream.BBANDS(c, n)
day_out["sma_" + str(n)] = stream.SMA(c, timeperiod=n)
day_out["adx_" + str(n)] = stream.ADX(h,
l,
c,
timeperiod=n)
fast_ema = c_series.ewm(span=n, adjust=False).mean()
slow_ema = c_series.ewm(span=n * 2, adjust=False).mean()
macd1 = fast_ema - slow_ema
macd2 = macd1.ewm(span=int(n * 2 / 3), adjust=False).mean()
macd3 = macd1 - macd2
day_out["macd_" + str(n)] = macd1.values[-1]
day_out["macdsignal_" + str(n)] = macd2.values[-1]
day_out["macdhist_" + str(n)] = macd3.values[-1]
# if n != 2:
# day_out["macd_"+str(n)], day_out["macdsignal_"+str(n)], day_out["macdhist_"+str(n)] = stream.MACD(c, n, n*2, int(n*2/3))
# elif idx > 100:
# macd = ta.MACD({"close":c}, n, n*2, 1)
# day_out["macd_2"], day_out["macdsignal_2"], day_out["macdhist_2"] = (x[-1] for x in macd)
# else:
# day_out["macd_2"], day_out["macdsignal_2"], day_out["macdhist_2"] = None, None, None
# macd = [macd1.values, macd2.values, macd3.values]
# for idx, i in enumerate(["macd_"+str(n), "macdsignal_"+str(n), "macdhist_"+str(n)]):
# for day in zip(inputs[i], macd[idx]):
# print("Type: %s N: %d PD: %.3f TA: %.3f, " % (i, n, day[1], day[0]))
day_out["mfi_" + str(n)] = stream.MFI(h, l, c, v, n)
day_out["ult_" + str(n)] = stream.ULTOSC(
h, l, c, n, n * 2, n * 4)
day_out["willr_" + str(n)] = stream.WILLR(h, l, c, n)
day_out["slowk"], day_out["slowd"] = stream.STOCH(h, l, c)
day_out["mom_" + str(n)] = stream.MOM(c, n)
day_out["volume"] = v[-1] / 10000
# print(day_out["macd_2"], day_out["macdsignal_2"], day_out["macdhist_2"])
output = output.append(day_out, ignore_index=True)
# print(output.tail(5)["macd_3"], output.tail(5)["macdsignal_3"], output.tail(5)["macdhist_3"])
return output
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
