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:
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:
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
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:
self.custom_trade_info[metadata['pair']] = self.populate_trades(
metadata['pair'])
c = Consensus(dataframe)
# Overlap / MA's
c.evaluate_tema(period=12)
c.evaluate_ema(period=8)
c.evaluate_ema(period=13)
c.evaluate_ema(period=21)
c.evaluate_ema(period=34)
c.evaluate_ema(period=55)
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']
# 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=8)
cbtc.evaluate_ema(period=13)
cbtc.evaluate_ema(period=21)
cbtc.evaluate_ema(period=34)
cbtc.evaluate_ema(period=55)
cbtc.evaluate_ichimoku()
cbtc.evaluate_vwma(period=20)
cbtc.evaluate_rsi(period=14)
cbtc.evaluate_stoch()
cbtc.evaluate_cci()
cbtc.evaluate_adx()
cbtc.evaluate_macd()
cbtc.evaluate_momentum()
cbtc.evaluate_williams()
cbtc.evaluate_macd_cross_over()
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)
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
