def pcc(self):
return cta.pcc(self.candles, period=20, mult=2, sequential=False)
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