def __init__(self):
"""
Class Constructor.
"""
tableName = "Indicator_ADI"
self._insertQuery = "insert or replace into %s (Date, ADI, ADI_SUM, ADI_ROC, ADI_EMA) values (?,?,?,?,?)" % (
tableName)
self._selectQuery = "SELECT Date, SUM(CASE WHEN Close > Open THEN 1 ELSE 0 END) - SUM(CASE WHEN Close < Open THEN 1 ELSE 0 END) as ADI FROM Equities group by Date"
connection = sqlite3.connect(pyswing.database.pySwingDatabase)
self._indicatorDataFrame = read_sql_query(self._selectQuery,
connection, 'Date')
self._indicatorDataFrame['ADI'] = self._indicatorDataFrame[
'ADI'].astype(float)
self._indicatorDataFrame['ADI_SUM'] = self._indicatorDataFrame[
'ADI'].cumsum()
self._indicatorDataFrame['ADI_ROC'] = abstract.ROC(
self._indicatorDataFrame, timeperiod=5, price='ADI_SUM')
self._indicatorDataFrame['ADI_EMA'] = abstract.EMA(
self._indicatorDataFrame, timeperiod=5, price='ADI')
self._tableName = "Indicator_ADI"
def __init__(self, equityDataFrame, tickerCode):
"""
Create the Average Directional-specific logic. Everything else lives in the Indicator Base Class.
:param equityDataFrame: A Pandas DataFrame from the Equity Class.
:param tickerCode: Ticker Code (String).
"""
tableName = "Indicator_ADX"
tickerCode = tickerCode
insertQuery = "insert or replace into %s (Date, Code, ADX, ADX_ROC) values (?,?,?,?)" % (
tableName)
# Create a new DataFrame with just Date and Code
indicatorDataFrame = equityDataFrame.ix[:, 'Code':]
# Stick the Indicator Values into the new DataFrame
indicatorDataFrame['ADX'] = abstract.ADX(
equityDataFrame, timeperiod=14, prices=['High', 'Low', 'Close'])
indicatorDataFrame['ADX_ROC'] = abstract.ROC(indicatorDataFrame,
timeperiod=5,
price='ADX')
Indicator.__init__(self, tableName, tickerCode, insertQuery,
equityDataFrame, indicatorDataFrame)
def populate_indicators(self, dataframe: DataFrame,
metadata: dict) -> DataFrame:
self.custom_trade_info[metadata['pair']] = self.populate_trades(
metadata['pair'])
dataframe['rmi-slow'] = RMI(dataframe, length=21, mom=5)
dataframe['rmi-fast'] = RMI(dataframe, length=8, mom=4)
dataframe['roc'] = ta.ROC(dataframe, timeperiod=6)
dataframe['mp'] = ta.RSI(dataframe['roc'], timeperiod=6)
dataframe['rmi-up'] = np.where(
dataframe['rmi-slow'] >= dataframe['rmi-slow'].shift(), 1, 0)
dataframe['rmi-dn'] = np.where(
dataframe['rmi-slow'] <= dataframe['rmi-slow'].shift(), 1, 0)
dataframe['rmi-up-trend'] = np.where(
dataframe['rmi-up'].rolling(3, min_periods=1).sum() >= 2, 1, 0)
dataframe['rmi-dn-trend'] = np.where(
dataframe['rmi-dn'].rolling(3, min_periods=1).sum() >= 2, 1, 0)
informative = self.dp.get_pair_dataframe(pair=metadata['pair'],
timeframe=self.inf_timeframe)
informative['rsi'] = ta.RSI(informative, timeperiod=14)
informative['1d_high'] = informative['close'].rolling(24).max()
informative['3d_low'] = informative['close'].rolling(72).min()
informative['adr'] = informative['1d_high'] - informative['3d_low']
dataframe = merge_informative_pair(dataframe,
informative,
self.timeframe,
self.inf_timeframe,
ffill=True)
return dataframe
def populate_indicators(self, dataframe: DataFrame,
metadata: dict) -> DataFrame:
# Base Thiccness
dataframe['color'] = np.where(dataframe['close'] > dataframe['open'],
'green', 'red')
dataframe['how-thicc'] = (dataframe['close'] - dataframe['open']).abs()
dataframe['avg-thicc'] = dataframe['how-thicc'].abs().rolling(
36).mean()
dataframe['not-thicc'] = dataframe['how-thicc'] < (
dataframe['avg-thicc'])
dataframe['rly-thicc'] = dataframe['how-thicc'] > (
dataframe['avg-thicc'])
dataframe['xtra-thicc'] = np.where(
dataframe['rly-thicc'].rolling(8).sum() >= 5, 1, 0)
dataframe['roc'] = ta.ROC(dataframe, timeperiod=6)
informative = self.dp.get_pair_dataframe(pair=metadata['pair'],
timeframe=self.inf_timeframe)
informative['3d-low'] = informative['close'].rolling(72).min()
informative['3d-high'] = informative['close'].rolling(72).max()
dataframe = merge_informative_pair(dataframe,
informative,
self.timeframe,
self.inf_timeframe,
ffill=True)
return dataframe
def populate_indicators(self, dataframe: DataFrame,
metadata: dict) -> DataFrame:
self.custom_trade_info[metadata['pair']] = self.populate_trades(
metadata['pair'])
# Relative Momentum Index
dataframe['rmi-slow'] = RMI(dataframe, length=21, mom=5)
dataframe['rmi-fast'] = RMI(dataframe, length=8, mom=4)
# Momentum Pinball
dataframe['roc'] = ta.ROC(dataframe, timeperiod=6)
dataframe['mp'] = ta.RSI(dataframe['roc'], timeperiod=6)
# Trend Calculations
dataframe['rmi-up'] = np.where(
dataframe['rmi-slow'] >= dataframe['rmi-slow'].shift(), 1, 0)
dataframe['rmi-dn'] = np.where(
dataframe['rmi-slow'] <= dataframe['rmi-slow'].shift(), 1, 0)
dataframe['rmi-up-trend'] = np.where(
dataframe['rmi-up'].rolling(3, min_periods=1).sum() >= 2, 1, 0)
dataframe['rmi-dn-trend'] = np.where(
dataframe['rmi-dn'].rolling(3, min_periods=1).sum() >= 2, 1, 0)
# Informative for STAKE/FIAT and COIN/FIAT on default timeframe, only relevant if stake currency is BTC or ETH
if self.config['stake_currency'] in ('BTC', 'ETH'):
coin, stake = metadata['pair'].split('/')
fiat = self.custom_fiat
coin_fiat = f"{coin}/{fiat}"
stake_fiat = f"{stake}/{fiat}"
# COIN/FIAT (e.g. XLM/USD) - timeframe
coin_fiat_tf = self.dp.get_pair_dataframe(pair=coin_fiat,
timeframe=self.timeframe)
dataframe[f"{fiat}_rsi"] = ta.RSI(coin_fiat_tf, timeperiod=14)
# STAKE/FIAT (e.g. BTC/USD) - inf_timeframe
stake_fiat_tf = self.dp.get_pair_dataframe(
pair=stake_fiat, timeframe=self.timeframe)
stake_fiat_inf_tf = self.dp.get_pair_dataframe(
pair=stake_fiat, timeframe=self.inf_timeframe)
dataframe[f"{stake}_rsi"] = ta.RSI(stake_fiat_tf, timeperiod=14)
dataframe[f"{stake}_rmi_{self.inf_timeframe}"] = RMI(
stake_fiat_inf_tf, length=21, mom=5)
# Informative indicators for current pair on inf_timeframe
informative = self.dp.get_pair_dataframe(pair=metadata['pair'],
timeframe=self.inf_timeframe)
informative['rsi'] = ta.RSI(informative, timeperiod=14)
dataframe = merge_informative_pair(dataframe,
informative,
self.timeframe,
self.inf_timeframe,
ffill=True)
return dataframe
def populate_indicators(self, dataframe: DataFrame,
metadata: dict) -> DataFrame:
# RSI
dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
dataframe['rsi_84'] = ta.RSI(dataframe, timeperiod=84)
dataframe['rsi_112'] = ta.RSI(dataframe, timeperiod=112)
# Bollinger bands
bollinger1 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe),
window=17,
stds=1)
dataframe['bb_lowerband'] = bollinger1['lower']
dataframe['bb_middleband'] = bollinger1['mid']
dataframe['bb_upperband'] = bollinger1['upper']
# Close delta
dataframe['closedelta'] = (dataframe['close'] -
dataframe['close'].shift()).abs()
# Dip Protection
dataframe['tpct_change_0'] = top_percent_change(dataframe, 0)
dataframe['tpct_change_1'] = top_percent_change(dataframe, 1)
dataframe['tpct_change_2'] = top_percent_change(dataframe, 2)
dataframe['tpct_change_4'] = top_percent_change(dataframe, 4)
dataframe['tpct_change_5'] = top_percent_change(dataframe, 5)
dataframe['tpct_change_9'] = top_percent_change(dataframe, 9)
# SMA
dataframe['sma_50'] = ta.SMA(dataframe['close'], timeperiod=50)
dataframe['sma_200'] = ta.SMA(dataframe['close'], timeperiod=200)
# CTI
dataframe['cti'] = pta.cti(dataframe["close"], length=20)
# ADX
dataframe['adx'] = ta.ADX(dataframe)
# %R
dataframe['r_14'] = williams_r(dataframe, period=14)
dataframe['r_96'] = williams_r(dataframe, period=96)
# MAMA / FAMA
dataframe['hl2'] = (dataframe['high'] + dataframe['low']) / 2
dataframe['mama'], dataframe['fama'] = ta.MAMA(dataframe['hl2'], 0.5,
0.05)
dataframe['mama_diff'] = ((dataframe['mama'] - dataframe['fama']) /
dataframe['hl2'])
# CRSI (3, 2, 100)
crsi_closechange = dataframe['close'] / dataframe['close'].shift(1)
crsi_updown = np.where(crsi_closechange.gt(1), 1.0,
np.where(crsi_closechange.lt(1), -1.0, 0.0))
dataframe['crsi'] = (ta.RSI(dataframe['close'], timeperiod=3) + ta.RSI(
crsi_updown, timeperiod=2) + ta.ROC(dataframe['close'], 100)) / 3
return dataframe
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 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(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
self.custom_trade_info[metadata['pair']] = self.populate_trades(metadata['pair'])
# Base timeframe indicators
dataframe['rmi-slow'] = RMI(dataframe, length=21, mom=5)
dataframe['rmi-fast'] = RMI(dataframe, length=8, mom=4)
# Momentum Pinball:
dataframe['roc'] = ta.ROC(dataframe, timeperiod=6)
dataframe['mp'] = ta.RSI(dataframe['roc'], timeperiod=6)
# RMI Trends and Peaks
dataframe['rmi-up'] = np.where(dataframe['rmi-slow'] >= dataframe['rmi-slow'].shift(),1,0)
dataframe['rmi-dn'] = np.where(dataframe['rmi-slow'] <= dataframe['rmi-slow'].shift(),1,0)
dataframe['rmi-up-trend'] = np.where(dataframe['rmi-up'].rolling(3, min_periods=1).sum() >= 2,1,0)
dataframe['rmi-dn-trend'] = np.where(dataframe['rmi-dn'].rolling(3, min_periods=1).sum() >= 2,1,0)
dataframe['rmi-max'] = dataframe['rmi-slow'].rolling(10, min_periods=1).max()
# See: https://github.com/freqtrade/technical/blob/master/technical/indicators/indicators.py#L1059
# matype = vidya, src = hl2
dataframe = PMAX(dataframe, period=10, multiplier=3, length=10, MAtype=5, src=2)
# Other stake specific informative indicators
# TODO: Re-evaluate the value of these informative indicators
if self.config['stake_currency'] in ('BTC', 'ETH'):
coin, stake = metadata['pair'].split('/')
fiat = self.custom_fiat
coin_fiat = f"{coin}/{fiat}"
stake_fiat = f"{stake}/{fiat}"
coin_fiat_tf = self.dp.get_pair_dataframe(pair=coin_fiat, timeframe=self.timeframe)
dataframe[f"{fiat}_rsi"] = ta.RSI(coin_fiat_tf, timeperiod=14)
stake_fiat_tf = self.dp.get_pair_dataframe(pair=stake_fiat, timeframe=self.timeframe)
stake_fiat_inf_tf = self.dp.get_pair_dataframe(pair=stake_fiat, timeframe=self.inf_timeframe)
dataframe[f"{stake}_rsi"] = ta.RSI(stake_fiat_tf, timeperiod=14)
dataframe[f"{stake}_rmi_{self.inf_timeframe}"] = RMI(stake_fiat_inf_tf, length=21, mom=5)
# Base pair informative timeframe indicators
informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=self.inf_timeframe)
informative['rsi'] = ta.RSI(informative, timeperiod=14)
# Get the "average day range" between the 1d high and 3d low to set up guards
informative['1d_high'] = informative['close'].rolling(24).max()
informative['3d_low'] = informative['close'].rolling(72).min()
informative['adr'] = informative['1d_high'] - informative['3d_low']
dataframe = merge_informative_pair(dataframe, informative, self.timeframe, self.inf_timeframe, ffill=True)
dataframe.to_csv('user_data/foo.csv')
return dataframe
def populate_indicators(self, dataframe: DataFrame,
metadata: dict) -> DataFrame:
# Populate/update the trade data if there is any, set trades to false if not live/dry
self.custom_trade_info[metadata['pair']] = self.populate_trades(
metadata['pair'])
# Set up primary indicators
dataframe['rmi-slow'] = ti.RMI(dataframe, length=21, mom=5)
dataframe['rmi-fast'] = ti.RMI(dataframe, length=8, mom=4)
# MA Streak
dataframe['mac'] = self.mac(dataframe, 20, 50)
dataframe['streak'] = self.ma_streak(dataframe, period=4)
streak = abs(int(dataframe['streak'].iloc[-1]))
streak_back_close = dataframe['close'].shift(streak + 1)
dataframe['streak-roc'] = 100 * (dataframe['close'] -
streak_back_close) / streak_back_close
# Percent Change Channel
pcc = self.pcc(dataframe, period=20, mult=2)
dataframe['pcc-lowerband'] = pcc.lowerband
# Momentum Pinball
dataframe['roc'] = ta.ROC(dataframe, timeperiod=1)
dataframe['mp'] = ta.RSI(dataframe['roc'], timeperiod=3)
# Trend Calculations
dataframe['rmi-up'] = np.where(
dataframe['rmi-slow'] >= dataframe['rmi-slow'].shift(), 1, 0)
dataframe['rmi-dn'] = np.where(
dataframe['rmi-slow'] <= dataframe['rmi-slow'].shift(), 1, 0)
dataframe['rmi-up-trend'] = np.where(
dataframe['rmi-up'].rolling(3, min_periods=1).sum() >= 2, 1, 0)
dataframe['rmi-dn-trend'] = np.where(
dataframe['rmi-dn'].rolling(3, min_periods=1).sum() >= 2, 1, 0)
# Informative indicators for current pair on inf_timeframe
informative = self.dp.get_pair_dataframe(pair=metadata['pair'],
timeframe=self.inf_timeframe)
informative['1d_high'] = informative['close'].rolling(24).max()
informative['3d_low'] = informative['close'].rolling(72).min()
informative['adr'] = informative['1d_high'] - informative['3d_low']
dataframe = merge_informative_pair(dataframe,
informative,
self.timeframe,
self.inf_timeframe,
ffill=True)
return dataframe
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe['color'] = dataframe['close'] > dataframe['open']
macd = ta.MACD(dataframe)
dataframe['macd'] = macd['macd']
dataframe['macdsignal'] = macd['macdsignal']
dataframe['macdhist'] = macd['macdhist']
dataframe['rsi'] = ta.RSI(dataframe, timeperiod=7)
dataframe['roc'] = ta.ROC(dataframe, timeperiod=6)
dataframe['primed'] = np.where(dataframe['color'].rolling(3).sum() == 3,1,0)
dataframe['in-the-mood'] = dataframe['rsi'] > dataframe['rsi'].rolling(12).mean()
dataframe['moist'] = qtpylib.crossed_above(dataframe['macd'], dataframe['macdsignal'])
dataframe['throbbing'] = dataframe['roc'] > dataframe['roc'].rolling(12).mean()
dataframe['ready-to-go'] = np.where(dataframe['close'] > dataframe['open'].rolling(12).mean(), 1,0)
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: 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
"""
length = 20
dataframe['roc'] = ta.ROC(dataframe, timeperiod=length)
dataframe['max'] = ta.MAX(dataframe, timeperiod=length)
dataframe['min'] = ta.MIN(dataframe, timeperiod=length)
return dataframe
def populate_indicators(self, dataframe: DataFrame,
metadata: dict) -> DataFrame:
# Populate/update the trade data if there is any, set trades to false if not live/dry
self.custom_trade_info[metadata['pair']] = self.populate_trades(
metadata['pair'])
# Momentum Pinball
dataframe['roc'] = ta.ROC(dataframe, timeperiod=1)
dataframe['mp'] = ta.RSI(dataframe['roc'], timeperiod=3)
# MA Streak
dataframe['mac'] = cta.macross(dataframe, 21, 55)
dataframe['streak'] = cta.mastreak(dataframe, period=4)
lookup_idxs = dataframe.index.values - (
abs(dataframe['streak'].values) + 1)
valid_lookups = lookup_idxs >= 0
dataframe['sbc'] = np.nan
dataframe.loc[valid_lookups, 'sbc'] = dataframe['close'].to_numpy()[
lookup_idxs[valid_lookups].astype(int)]
dataframe['streak-roc'] = 100 * (dataframe['close'] -
dataframe['sbc']) / dataframe['sbc']
# Percent Change Channel
upper, mid, lower = cta.pcc(dataframe, period=20, mult=2)
dataframe['pcc-lowerband'] = lower
dataframe['pcc-upperband'] = upper
# RMI Trend Strength
dataframe['rmi'] = RMI(dataframe, length=21, mom=5)
# RMI Trend Calculations
dataframe['rmi-up'] = np.where(
dataframe['rmi'] >= dataframe['rmi'].shift(), 1, 0)
dataframe['rmi-dn'] = np.where(
dataframe['rmi'] <= dataframe['rmi'].shift(), 1, 0)
dataframe['rmi-up-trend'] = np.where(
dataframe['rmi-up'].rolling(3, min_periods=1).sum() >= 2, 1, 0)
dataframe['rmi-dn-trend'] = np.where(
dataframe['rmi-dn'].rolling(3, min_periods=1).sum() >= 2, 1, 0)
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, metadata: dict) -> DataFrame:
buy_params = self.get_pair_params(metadata['pair'], 'buy')
sell_params = self.get_pair_params(metadata['pair'], 'sell')
self.custom_trade_info[metadata['pair']] = self.populate_trades(metadata['pair'])
# Base timeframe indicators
dataframe['rmi-slow'] = cta.RMI(dataframe, length=21, mom=5)
dataframe['rmi-fast'] = cta.RMI(dataframe, length=8, mom=4)
# Momentum Pinball: https://www.tradingview.com/script/fBpVB1ez-Momentum-Pinball-Indicator/
dataframe['roc'] = ta.ROC(dataframe, timeperiod=6)
dataframe['mp'] = ta.RSI(dataframe['roc'], timeperiod=6)
# RMI Trends and Peaks
dataframe['rmi-up'] = np.where(dataframe['rmi-slow'] >= dataframe['rmi-slow'].shift(),1,0)
dataframe['rmi-dn'] = np.where(dataframe['rmi-slow'] <= dataframe['rmi-slow'].shift(),1,0)
dataframe['rmi-up-trend'] = np.where(dataframe['rmi-up'].rolling(3, min_periods=1).sum() >= 2,1,0)
dataframe['rmi-dn-trend'] = np.where(dataframe['rmi-dn'].rolling(3, min_periods=1).sum() >= 2,1,0)
dataframe['rmi-max'] = dataframe['rmi-slow'].rolling(10, min_periods=1).max()
# Fibonacci Retracements
dataframe['fib-ret'] = cta.fib_ret(dataframe)
# PMAX
# matype = ema, src = hl2
# pmax is really slow, only enable it if its actually being used.
if buy_params['base-pmax-enable'] == True or sell_params['sell-pmax-enable'] == True:
atr, ma, pmax, pm_loc = cta.PMAX(dataframe, period=10, multiplier=3, length=12, MAtype=1, src=2)
dataframe['pmax-trend'] = pm_loc
else:
dataframe['pmax-trend'] = False
# Other stake specific informative indicators
# e.g if stake is BTC and current coin is XLM (pair: XLM/BTC)
# TODO: Re-evaluate the value of these informative indicators
if self.config['stake_currency'] in ('BTC', 'ETH'):
coin, stake = metadata['pair'].split('/')
fiat = self.custom_fiat
coin_fiat = f"{coin}/{fiat}"
stake_fiat = f"{stake}/{fiat}"
# Informative COIN/FIAT e.g. XLM/USD
coin_fiat_tf = self.dp.get_pair_dataframe(pair=coin_fiat, timeframe=self.timeframe)
dataframe[f"{fiat}_rsi"] = ta.RSI(coin_fiat_tf, timeperiod=14)
# Informative STAKE/FIAT e.g. BTC/USD
stake_fiat_tf = self.dp.get_pair_dataframe(pair=stake_fiat, timeframe=self.timeframe)
stake_fiat_inf_tf = self.dp.get_pair_dataframe(pair=stake_fiat, timeframe=self.inf_timeframe)
dataframe[f"{stake}_rsi"] = ta.RSI(stake_fiat_tf, timeperiod=14)
dataframe[f"{stake}_rmi_{self.inf_timeframe}"] = cta.RMI(stake_fiat_inf_tf, length=21, mom=5)
# Base pair informative timeframe indicators
informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=self.inf_timeframe)
informative['rsi'] = ta.RSI(informative, timeperiod=14)
# Get the "average day range" between the 1d high and 3d low to set up guards
informative['1d_high'] = informative['close'].rolling(24).max()
informative['3d_low'] = informative['close'].rolling(72).min()
informative['adr'] = informative['1d_high'] - informative['3d_low']
informative['fib-ret'] = cta.fib_ret(informative)
dataframe = merge_informative_pair(dataframe, informative, self.timeframe, self.inf_timeframe, ffill=True)
return dataframe
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# Populate/update the trade data if there is any, set trades to false if not live/dry
self.custom_trade_info[metadata['pair']] = self.populate_trades(metadata['pair'])
# Indicators for ROI and Custom Stoploss
dataframe['atr'] = ta.ATR(dataframe, timeperiod=24)
dataframe['roc'] = ta.ROC(dataframe, timeperiod=9)
dataframe['rmi-slow'] = cta.RMI(dataframe, length=21, mom=5)
# Trends, Peaks and Crosses
dataframe['rmi-up'] = np.where(dataframe['rmi-slow'] >= dataframe['rmi-slow'].shift(),1,0)
dataframe['rmi-up-trend'] = np.where(dataframe['rmi-up'].rolling(3, min_periods=1).sum() >= 2,1,0)
# strategy BinHV45
mid, lower = cta.bollinger_bands(dataframe['close'], window_size=40, num_of_std=2)
dataframe['lower'] = lower
dataframe['bbdelta'] = (mid - dataframe['lower']).abs()
dataframe['closedelta'] = (dataframe['close'] - dataframe['close'].shift()).abs()
dataframe['tail'] = (dataframe['close'] - dataframe['low']).abs()
# strategy ClucMay72018
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
dataframe['bb_lowerband'] = bollinger['lower']
dataframe['bb_middleband'] = bollinger['mid']
dataframe['ema_slow'] = ta.EMA(dataframe, timeperiod=50)
dataframe['volume_mean_slow'] = dataframe['volume'].rolling(window=30).mean()
# Base pair informative timeframe indicators
informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=self.inf_timeframe)
# Get the "average day range" between the 1d high and 3d low to set up guards
informative['1d_high'] = informative['close'].rolling(24).max()
informative['3d_low'] = informative['close'].rolling(72).min()
informative['adr'] = informative['1d_high'] - informative['3d_low']
dataframe = merge_informative_pair(dataframe, informative, self.timeframe, self.inf_timeframe, ffill=True)
# Other stake specific informative indicators
# e.g if stake is BTC and current coin is XLM (pair: XLM/BTC)
if self.config['stake_currency'] in ('BTC', 'ETH'):
coin, stake = metadata['pair'].split('/')
fiat = self.custom_fiat
coin_fiat = f"{coin}/{fiat}"
stake_fiat = f"{stake}/{fiat}"
# Informative COIN/FIAT e.g. XLM/USD - Base Timeframe
coin_fiat_tf = self.dp.get_pair_dataframe(pair=coin_fiat, timeframe=self.timeframe)
dataframe[f"{fiat}_rmi"] = cta.RMI(coin_fiat_tf, length=21, mom=5)
# Informative STAKE/FIAT e.g. BTC/USD - Base Timeframe
stake_fiat_tf = self.dp.get_pair_dataframe(pair=stake_fiat, timeframe=self.timeframe)
dataframe[f"{stake}_rmi"] = cta.RMI(stake_fiat_tf, length=21, mom=5)
# Informative STAKE/FIAT e.g. BTC/USD - Informative Timeframe
stake_fiat_inf_tf = self.dp.get_pair_dataframe(pair=stake_fiat, timeframe=self.inf_timeframe)
stake_fiat_inf_tf[f"{stake}_rmi"] = cta.RMI(stake_fiat_inf_tf, length=48, mom=5)
dataframe = merge_informative_pair(dataframe, stake_fiat_inf_tf, self.timeframe, self.inf_timeframe, ffill=True)
# Informatives for BTC/STAKE if not in whitelist
else:
pairs = self.dp.current_whitelist()
btc_stake = f"BTC/{self.config['stake_currency']}"
if not btc_stake in pairs:
# BTC/STAKE - Base Timeframe
btc_stake_tf = self.dp.get_pair_dataframe(pair=btc_stake, timeframe=self.timeframe)
dataframe['BTC_rmi'] = cta.RMI(btc_stake_tf, length=14, mom=3)
# BTC/STAKE - Informative Timeframe
btc_stake_inf_tf = self.dp.get_pair_dataframe(pair=btc_stake, timeframe=self.inf_timeframe)
btc_stake_inf_tf['BTC_rmi'] = cta.RMI(btc_stake_inf_tf, length=48, mom=5)
dataframe = merge_informative_pair(dataframe, btc_stake_inf_tf, self.timeframe, self.inf_timeframe, ffill=True)
# Slam some indicators into the trade_info dict so we can dynamic roi and custom stoploss in backtest
if self.dp.runmode.value in ('backtest', 'hyperopt'):
self.custom_trade_info[metadata['pair']]['roc'] = dataframe[['date', 'roc']].copy().set_index('date')
self.custom_trade_info[metadata['pair']]['atr'] = dataframe[['date', 'atr']].copy().set_index('date')
self.custom_trade_info[metadata['pair']]['rmi-slow'] = dataframe[['date', 'rmi-slow']].copy().set_index('date')
self.custom_trade_info[metadata['pair']]['rmi-up-trend'] = dataframe[['date', 'rmi-up-trend']].copy().set_index('date')
return dataframe
def ROC(self): #12
roc = abstract.ROC(self.company_stock, timeperiod=12)
self.company_stock['ROC'] = roc
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 do_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# Stoch fast - mainly due to 5m timeframes
stoch_fast = ta.STOCHF(dataframe)
dataframe['fastd'] = stoch_fast['fastd']
dataframe['fastk'] = stoch_fast['fastk']
#StochRSI for double checking things
period = 14
smoothD = 3
SmoothK = 3
dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
stochrsi = (dataframe['rsi'] - dataframe['rsi'].rolling(period).min()
) / (dataframe['rsi'].rolling(period).max() -
dataframe['rsi'].rolling(period).min())
dataframe['srsi_k'] = stochrsi.rolling(SmoothK).mean() * 100
dataframe['srsi_d'] = dataframe['srsi_k'].rolling(smoothD).mean()
# Bollinger Bands because obviously
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe),
window=20,
stds=1)
dataframe['bb_lowerband'] = bollinger['lower']
dataframe['bb_middleband'] = bollinger['mid']
dataframe['bb_upperband'] = bollinger['upper']
# SAR Parabol - probably don't need this
dataframe['sar'] = ta.SAR(dataframe)
## confirm wideboi variance signal with bbw expansion
dataframe["bb_width"] = (
(dataframe["bb_upperband"] - dataframe["bb_lowerband"]) /
dataframe["bb_middleband"])
dataframe['bbw_expansion'] = dataframe['bb_width'].rolling(
window=4).apply(self.bbw_expansion)
# confirm entry and exit on smoothed HA
dataframe = self.HA(dataframe, 4)
# thanks to Hansen_Khornelius for this idea that I apply to the 1hr informative
# https://github.com/hansen1015/freqtrade_strategy
hansencalc = self.hansen_HA(dataframe, 6)
dataframe['emac'] = hansencalc['emac']
dataframe['emao'] = hansencalc['emao']
# money flow index (MFI) for in/outflow of money, like RSI adjusted for vol
dataframe['mfi'] = fta.MFI(dataframe)
## sqzmi to detect quiet periods
dataframe['sqzmi'] = fta.SQZMI(dataframe) #, MA=hansencalc['emac'])
# Volume Flow Indicator (MFI) for volume based on the direction of price movement
dataframe['vfi'] = fta.VFI(dataframe, period=14)
dmi = fta.DMI(dataframe, period=14)
dataframe['dmi_plus'] = dmi['DI+']
dataframe['dmi_minus'] = dmi['DI-']
dataframe['adx'] = fta.ADX(dataframe, period=14)
## for stoploss - all from Solipsis4
## simple ATR and ROC for stoploss
dataframe['atr'] = ta.ATR(dataframe, timeperiod=14)
dataframe['roc'] = ta.ROC(dataframe, timeperiod=9)
dataframe['rmi'] = RMI(dataframe, length=24, mom=5)
ssldown, sslup = SSLChannels_ATR(dataframe, length=21)
dataframe['sroc'] = SROC(dataframe, roclen=21, emalen=13, smooth=21)
dataframe['ssl-dir'] = np.where(sslup > ssldown, 'up', 'down')
dataframe['rmi-up'] = np.where(
dataframe['rmi'] >= dataframe['rmi'].shift(), 1, 0)
dataframe['rmi-up-trend'] = np.where(
dataframe['rmi-up'].rolling(5).sum() >= 3, 1, 0)
dataframe['candle-up'] = np.where(
dataframe['close'] >= dataframe['close'].shift(), 1, 0)
dataframe['candle-up-trend'] = np.where(
dataframe['candle-up'].rolling(5).sum() >= 3, 1, 0)
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 populate_indicators(self, dataframe: DataFrame,
metadata: dict) -> DataFrame:
# Populate/update the trade data if there is any, set trades to false if not live/dry
self.custom_trade_info[metadata['pair']] = self.populate_trades(
metadata['pair'])
# Momentum Pinball
dataframe['roc'] = ta.ROC(dataframe, timeperiod=1)
dataframe['mp'] = ta.RSI(dataframe['roc'], timeperiod=3)
# MA Streak
dataframe['mac'] = ci_mac(dataframe, 20, 50)
dataframe['streak'] = ci_mastreak(dataframe, period=4)
streak = abs(int(dataframe['streak'].iloc[-1]))
streak_back_close = dataframe['close'].shift(streak + 1)
dataframe['streak-roc'] = 100 * (dataframe['close'] -
streak_back_close) / streak_back_close
# Percent Change Channel
pcc = ci_pcc(dataframe, period=20, mult=2)
dataframe['pcc-lowerband'] = pcc.lowerband
dataframe['pcc-upperband'] = pcc.upperband
# RMI Trend Strength
dataframe['rmi'] = RMI(dataframe, length=21, mom=5)
# RMI Trend Calculations
dataframe['rmi-up'] = np.where(
dataframe['rmi'] >= dataframe['rmi'].shift(), 1, 0)
dataframe['rmi-dn'] = np.where(
dataframe['rmi'] <= dataframe['rmi'].shift(), 1, 0)
dataframe['rmi-up-trend'] = np.where(
dataframe['rmi-up'].rolling(3, min_periods=1).sum() >= 2, 1, 0)
dataframe['rmi-dn-trend'] = np.where(
dataframe['rmi-dn'].rolling(3, min_periods=1).sum() >= 2, 1, 0)
# Informative for STAKE/FIAT and COIN/FIAT on default timeframe, only relevant if stake currency is BTC or ETH
if self.config['stake_currency'] in ('BTC', 'ETH'):
coin, stake = metadata['pair'].split('/')
fiat = self.custom_fiat
coin_fiat = f"{coin}/{fiat}"
stake_fiat = f"{stake}/{fiat}"
# COIN/FIAT (e.g. XLM/USD) - timeframe
coin_fiat_tf = self.dp.get_pair_dataframe(pair=coin_fiat,
timeframe=self.timeframe)
dataframe[f"{fiat}_rsi"] = ta.RSI(coin_fiat_tf, timeperiod=14)
# STAKE/FIAT (e.g. BTC/USD) - inf_timeframe
stake_fiat_tf = self.dp.get_pair_dataframe(
pair=stake_fiat, timeframe=self.timeframe)
stake_fiat_inf_tf = self.dp.get_pair_dataframe(
pair=stake_fiat, timeframe=self.inf_timeframe)
dataframe[f"{stake}_rsi"] = ta.RSI(stake_fiat_tf, timeperiod=14)
dataframe[f"{stake}_rmi_{self.inf_timeframe}"] = RMI(
stake_fiat_inf_tf, length=21, mom=5)
# Informative indicators for current pair on inf_timeframe
informative = self.dp.get_pair_dataframe(pair=metadata['pair'],
timeframe=self.inf_timeframe)
informative['rsi'] = ta.RSI(informative, timeperiod=14)
dataframe = merge_informative_pair(dataframe,
informative,
self.timeframe,
self.inf_timeframe,
ffill=True)
return dataframe
def populate_indicators(self, dataframe: DataFrame,
metadata: dict) -> DataFrame:
if not metadata['pair'] in self.custom_trade_info:
self.custom_trade_info[metadata['pair']] = {}
c = Consensus(dataframe)
# Overlap / MA's
c.evaluate_tema(period=12)
c.evaluate_ema(period=6)
c.evaluate_ema(period=12)
c.evaluate_sma(period=12)
c.evaluate_ichimoku() # slightly slow
#c.evaluate_hull() # very slow
c.evaluate_vwma(period=20)
# Oscillators
c.evaluate_rsi(period=14)
c.evaluate_stoch()
#c.evaluate_cci() # slightly slow
#c.evaluate_adx()
c.evaluate_macd()
c.evaluate_momentum()
c.evaluate_williams()
# c.evaluate_ultimate_oscilator() # extremely slow
# missing: awesome osc
# missing: bull bear
# missing: stoch rsi
#c.evaluate_macd_cross_over()
c.evaluate_osc()
c.evaluate_cmf()
c.evaluate_cmo() # slightly slow
#c.evaluate_laguerre() # slow
dataframe['consensus-buy'] = c.score()['buy']
dataframe['consensus-sell'] = c.score()['sell']
# Indicators for ROI and Custom Stoploss
dataframe['atr'] = ta.ATR(dataframe, timeperiod=24)
dataframe['roc'] = ta.ROC(dataframe, timeperiod=9)
# Trend Indicators for ROI
dataframe['rmi-slow'] = cta.RMI(dataframe, length=21, mom=5)
dataframe['rmi-up'] = np.where(
dataframe['rmi-slow'] >= dataframe['rmi-slow'].shift(), 1, 0)
dataframe['rmi-up-trend'] = np.where(
dataframe['rmi-up'].rolling(3, min_periods=1).sum() >= 2, 1, 0)
# Base pair informative timeframe indicators
informative = self.dp.get_pair_dataframe(pair=metadata['pair'],
timeframe=self.inf_timeframe)
# Get the "average day range" between the 1d high and 3d low to set up guards
informative['1d_high'] = informative['close'].rolling(24).max()
informative['3d_low'] = informative['close'].rolling(72).min()
informative['adr'] = informative['1d_high'] - informative['3d_low']
dataframe = merge_informative_pair(dataframe,
informative,
self.timeframe,
self.inf_timeframe,
ffill=True)
# Other stake specific informative indicators
# e.g if stake is BTC and current coin is XLM (pair: XLM/BTC)
if self.config['stake_currency'] in ('BTC', 'ETH'):
coin, stake = metadata['pair'].split('/')
fiat = self.custom_fiat
coin_fiat = f"{coin}/{fiat}"
stake_fiat = f"{stake}/{fiat}"
# Informative COIN/FIAT e.g. XLM/USD - Base Timeframe
coin_fiat_tf = self.dp.get_pair_dataframe(pair=coin_fiat,
timeframe=self.timeframe)
dataframe[f"{fiat}_rmi"] = cta.RMI(coin_fiat_tf, length=21, mom=5)
# Informative STAKE/FIAT e.g. BTC/USD - Base Timeframe
stake_fiat_tf = self.dp.get_pair_dataframe(
pair=stake_fiat, timeframe=self.timeframe)
dataframe[f"{stake}_rmi"] = cta.RMI(stake_fiat_tf,
length=21,
mom=5)
# Informative STAKE/FIAT e.g. BTC/USD - Informative Timeframe
stake_fiat_inf_tf = self.dp.get_pair_dataframe(
pair=stake_fiat, timeframe=self.inf_timeframe)
stake_fiat_inf_tf[f"{stake}_rmi"] = cta.RMI(stake_fiat_inf_tf,
length=48,
mom=5)
dataframe = merge_informative_pair(dataframe,
stake_fiat_inf_tf,
self.timeframe,
self.inf_timeframe,
ffill=True)
# Informatives for BTC/STAKE if not in whitelist
else:
pairs = self.dp.current_whitelist()
btc_stake = f"BTC/{self.config['stake_currency']}"
if not btc_stake in pairs:
# BTC/STAKE - Base Timeframe
btc_stake_tf = self.dp.get_pair_dataframe(
pair=btc_stake, timeframe=self.timeframe)
cbtc = Consensus(btc_stake_tf)
cbtc.evaluate_tema(period=12)
cbtc.evaluate_ema(period=12)
cbtc.evaluate_ema(period=24)
cbtc.evaluate_sma(period=12)
cbtc.evaluate_ichimoku()
cbtc.evaluate_vwma(period=20)
cbtc.evaluate_rsi(period=14)
cbtc.evaluate_stoch()
cbtc.evaluate_macd()
cbtc.evaluate_momentum()
cbtc.evaluate_williams()
cbtc.evaluate_osc()
cbtc.evaluate_cmf()
cbtc.evaluate_cmo()
cbtc.evaluate_laguerre()
dataframe['BTC_consensus-buy'] = cbtc.score()['buy']
# BTC/STAKE - Informative Timeframe
# btc_stake_inf_tf = self.dp.get_pair_dataframe(pair=btc_stake, timeframe=self.inf_timeframe)
# dataframe = merge_informative_pair(dataframe, btc_stake_inf_tf, self.timeframe, self.inf_timeframe, ffill=True)
# Slam some indicators into the trade_info dict so we can dynamic roi and custom stoploss in backtest
if self.dp.runmode.value in ('backtest', 'hyperopt'):
# Attempting to use a temporary holding place for dynamic roi backtest return value...
if not 'backtest' in self.custom_trade_info:
self.custom_trade_info['backtest'] = {}
self.custom_trade_info[metadata['pair']]['roc'] = dataframe[[
'date', 'roc'
]].copy().set_index('date')
self.custom_trade_info[metadata['pair']]['atr'] = dataframe[[
'date', 'atr'
]].copy().set_index('date')
self.custom_trade_info[metadata['pair']]['rmi-slow'] = dataframe[[
'date', 'rmi-slow'
]].copy().set_index('date')
self.custom_trade_info[
metadata['pair']]['rmi-up-trend'] = dataframe[[
'date', 'rmi-up-trend'
]].copy().set_index('date')
self.custom_trade_info[
metadata['pair']]['consensus-buy'] = dataframe[[
'date', 'consensus-buy'
]].copy().set_index('date')
self.custom_trade_info[
metadata['pair']]['consensus-sell'] = dataframe[[
'date', 'consensus-sell'
]].copy().set_index('date')
return dataframe
def populate_indicators(self, dataframe: DataFrame,
metadata: dict) -> DataFrame:
# Populate/update the trade data if there is any, set trades to false if not live/dry
self.custom_trade_info[metadata['pair']] = self.populate_trades(
metadata['pair'])
# Base timeframe indicators
dataframe['rmi-slow'] = cta.RMI(dataframe, length=21, mom=5)
dataframe['rmi-fast'] = cta.RMI(dataframe, length=8, mom=4)
# Indicators for ROI and Custom Stoploss
dataframe['atr'] = ta.ATR(dataframe, timeperiod=24)
dataframe['roc'] = ta.ROC(dataframe, timeperiod=9)
# Momentum Pinball: https://www.tradingview.com/script/fBpVB1ez-Momentum-Pinball-Indicator/
dataframe['roc-mp'] = ta.ROC(dataframe, timeperiod=6)
dataframe['mp'] = ta.RSI(dataframe['roc-mp'], timeperiod=6)
# Trends, Peaks and Crosses
dataframe['rmi-up'] = np.where(
dataframe['rmi-slow'] >= dataframe['rmi-slow'].shift(), 1, 0)
dataframe['rmi-dn'] = np.where(
dataframe['rmi-slow'] <= dataframe['rmi-slow'].shift(), 1, 0)
dataframe['rmi-up-trend'] = np.where(
dataframe['rmi-up'].rolling(3, min_periods=1).sum() >= 2, 1, 0)
dataframe['rmi-dn-trend'] = np.where(
dataframe['rmi-dn'].rolling(3, min_periods=1).sum() >= 2, 1, 0)
# Base pair informative timeframe indicators
informative = self.dp.get_pair_dataframe(pair=metadata['pair'],
timeframe=self.inf_timeframe)
# Get the "average day range" between the 1d high and 3d low to set up guards
informative['1d_high'] = informative['close'].rolling(24).max()
informative['3d_low'] = informative['close'].rolling(72).min()
informative['adr'] = informative['1d_high'] - informative['3d_low']
dataframe = merge_informative_pair(dataframe,
informative,
self.timeframe,
self.inf_timeframe,
ffill=True)
# Other stake specific informative indicators
# e.g if stake is BTC and current coin is XLM (pair: XLM/BTC)
if self.config['stake_currency'] in ('BTC', 'ETH'):
coin, stake = metadata['pair'].split('/')
fiat = self.custom_fiat
coin_fiat = f"{coin}/{fiat}"
stake_fiat = f"{stake}/{fiat}"
# Informative COIN/FIAT e.g. XLM/USD - Base Timeframe
coin_fiat_tf = self.dp.get_pair_dataframe(pair=coin_fiat,
timeframe=self.timeframe)
dataframe[f"{fiat}_rmi"] = cta.RMI(coin_fiat_tf, length=21, mom=5)
# Informative STAKE/FIAT e.g. BTC/USD - Base Timeframe
stake_fiat_tf = self.dp.get_pair_dataframe(
pair=stake_fiat, timeframe=self.timeframe)
dataframe[f"{stake}_rmi"] = cta.RMI(stake_fiat_tf,
length=21,
mom=5)
# Informative STAKE/FIAT e.g. BTC/USD - Informative Timeframe
stake_fiat_inf_tf = self.dp.get_pair_dataframe(
pair=stake_fiat, timeframe=self.inf_timeframe)
stake_fiat_inf_tf[f"{stake}_rmi"] = cta.RMI(stake_fiat_inf_tf,
length=48,
mom=5)
dataframe = merge_informative_pair(dataframe,
stake_fiat_inf_tf,
self.timeframe,
self.inf_timeframe,
ffill=True)
# Informatives for BTC/STAKE if not in whitelist
else:
pairs = self.dp.current_whitelist()
btc_stake = f"BTC/{self.config['stake_currency']}"
if not btc_stake in pairs:
# BTC/STAKE - Base Timeframe
btc_stake_tf = self.dp.get_pair_dataframe(
pair=btc_stake, timeframe=self.timeframe)
dataframe['BTC_rmi'] = cta.RMI(btc_stake_tf, length=14, mom=3)
# BTC/STAKE - Informative Timeframe
btc_stake_inf_tf = self.dp.get_pair_dataframe(
pair=btc_stake, timeframe=self.inf_timeframe)
btc_stake_inf_tf['BTC_rmi'] = cta.RMI(btc_stake_inf_tf,
length=48,
mom=5)
dataframe = merge_informative_pair(dataframe,
btc_stake_inf_tf,
self.timeframe,
self.inf_timeframe,
ffill=True)
# Slam some indicators into the trade_info dict so we can dynamic roi and custom stoploss in backtest
if self.dp.runmode.value in ('backtest', 'hyperopt'):
self.custom_trade_info[metadata['pair']]['roc'] = dataframe[[
'date', 'roc'
]].copy().set_index('date')
self.custom_trade_info[metadata['pair']]['atr'] = dataframe[[
'date', 'atr'
]].copy().set_index('date')
self.custom_trade_info[metadata['pair']]['rmi-slow'] = dataframe[[
'date', 'rmi-slow'
]].copy().set_index('date')
self.custom_trade_info[
metadata['pair']]['rmi-up-trend'] = dataframe[[
'date', 'rmi-up-trend'
]].copy().set_index('date')
return dataframe
def populate_indicators(self, dataframe: DataFrame,
metadata: dict) -> DataFrame:
if not metadata['pair'] in self.custom_trade_info:
self.custom_trade_info[metadata['pair']] = {}
## Base Timeframe / Pair
dataframe['kama'] = ta.KAMA(dataframe, length=233)
# RMI: https://www.tradingview.com/script/kwIt9OgQ-Relative-Momentum-Index/
dataframe['rmi'] = cta.RMI(dataframe, length=24, mom=5)
# Momentum Pinball: https://www.tradingview.com/script/fBpVB1ez-Momentum-Pinball-Indicator/
dataframe['roc-mp'] = ta.ROC(dataframe, timeperiod=1)
dataframe['mp'] = ta.RSI(dataframe['roc-mp'], timeperiod=3)
# MA Streak: https://www.tradingview.com/script/Yq1z7cIv-MA-Streak-Can-Show-When-a-Run-Is-Getting-Long-in-the-Tooth/
dataframe['mastreak'] = cta.mastreak(dataframe, period=4)
# Percent Change Channel: https://www.tradingview.com/script/6wwAWXA1-MA-Streak-Change-Channel/
upper, mid, lower = cta.pcc(dataframe, period=40, mult=3)
dataframe['pcc-lowerband'] = lower
dataframe['pcc-upperband'] = upper
lookup_idxs = dataframe.index.values - (
abs(dataframe['mastreak'].values) + 1)
valid_lookups = lookup_idxs >= 0
dataframe['sbc'] = np.nan
dataframe.loc[valid_lookups, 'sbc'] = dataframe['close'].to_numpy()[
lookup_idxs[valid_lookups].astype(int)]
dataframe['streak-roc'] = 100 * (dataframe['close'] -
dataframe['sbc']) / dataframe['sbc']
# Trends, Peaks and Crosses
dataframe['candle-up'] = np.where(
dataframe['close'] >= dataframe['close'].shift(), 1, 0)
dataframe['candle-up-trend'] = np.where(
dataframe['candle-up'].rolling(5).sum() >= 3, 1, 0)
dataframe['rmi-up'] = np.where(
dataframe['rmi'] >= dataframe['rmi'].shift(), 1, 0)
dataframe['rmi-up-trend'] = np.where(
dataframe['rmi-up'].rolling(5).sum() >= 3, 1, 0)
dataframe['rmi-dn'] = np.where(
dataframe['rmi'] <= dataframe['rmi'].shift(), 1, 0)
dataframe['rmi-dn-count'] = dataframe['rmi-dn'].rolling(8).sum()
dataframe['streak-bo'] = np.where(
dataframe['streak-roc'] < dataframe['pcc-lowerband'], 1, 0)
dataframe['streak-bo-count'] = dataframe['streak-bo'].rolling(8).sum()
# Indicators used only for ROI and Custom Stoploss
ssldown, sslup = cta.SSLChannels_ATR(dataframe, length=21)
dataframe['sroc'] = cta.SROC(dataframe,
roclen=21,
emalen=13,
smooth=21)
dataframe['ssl-dir'] = np.where(sslup > ssldown, 'up', 'down')
# Base pair informative timeframe indicators
informative = self.dp.get_pair_dataframe(pair=metadata['pair'],
timeframe=self.inf_timeframe)
# Get the "average day range" between the 1d high and 1d low to set up guards
informative['1d-high'] = informative['close'].rolling(24).max()
informative['1d-low'] = informative['close'].rolling(24).min()
informative['adr'] = informative['1d-high'] - informative['1d-low']
dataframe = merge_informative_pair(dataframe,
informative,
self.timeframe,
self.inf_timeframe,
ffill=True)
# Other stake specific informative indicators
# e.g if stake is BTC and current coin is XLM (pair: XLM/BTC)
if self.config['stake_currency'] in ('BTC', 'ETH'):
coin, stake = metadata['pair'].split('/')
fiat = self.custom_fiat
coin_fiat = f"{coin}/{fiat}"
stake_fiat = f"{stake}/{fiat}"
# Informative COIN/FIAT e.g. XLM/USD - Base Timeframe
coin_fiat_tf = self.dp.get_pair_dataframe(pair=coin_fiat,
timeframe=self.timeframe)
dataframe[f"{fiat}_rmi"] = cta.RMI(coin_fiat_tf, length=55, mom=5)
# Informative STAKE/FIAT e.g. BTC/USD - Base Timeframe
stake_fiat_tf = self.dp.get_pair_dataframe(
pair=stake_fiat, timeframe=self.timeframe)
dataframe[f"{stake}_rmi"] = cta.RMI(stake_fiat_tf,
length=55,
mom=5)
# Informatives for BTC/STAKE if not in whitelist
else:
pairs = self.dp.current_whitelist()
btc_stake = f"BTC/{self.config['stake_currency']}"
if not btc_stake in pairs:
self.custom_btc_inf = True
# BTC/STAKE - Base Timeframe
btc_stake_tf = self.dp.get_pair_dataframe(
pair=btc_stake, timeframe=self.timeframe)
dataframe['BTC_rmi'] = cta.RMI(btc_stake_tf, length=55, mom=5)
dataframe['BTC_close'] = btc_stake_tf['close']
dataframe['BTC_kama'] = ta.KAMA(btc_stake_tf, length=144)
# Slam some indicators into the trade_info dict so we can dynamic roi and custom stoploss in backtest
if self.dp.runmode.value in ('backtest', 'hyperopt'):
self.custom_trade_info[metadata['pair']]['sroc'] = dataframe[[
'date', 'sroc'
]].copy().set_index('date')
self.custom_trade_info[metadata['pair']]['ssl-dir'] = dataframe[[
'date', 'ssl-dir'
]].copy().set_index('date')
self.custom_trade_info[
metadata['pair']]['rmi-up-trend'] = dataframe[[
'date', 'rmi-up-trend'
]].copy().set_index('date')
self.custom_trade_info[
metadata['pair']]['candle-up-trend'] = dataframe[[
'date', 'candle-up-trend'
]].copy().set_index('date')
return dataframe
