def _run(self):
# Bollinger Bands
bb_indicator = BollingerBands(close=self._close,
n=self._chan_period,
ndev=self._bol_band_std_dev,
fillna=self._fillna)
self._bb_hband = bb_indicator.bollinger_hband()
self._bb_lband = bb_indicator.bollinger_lband()
# Keltner Channel
kb_indicator = KeltnerChannel(high=self._high,
low=self._low,
close=self._close,
n=self._chan_period,
n_atr=self._kelt_std_dev,
fillna=self._fillna,
ov=False)
self._kb_hband = kb_indicator.keltner_channel_hband()
self._kb_lband = kb_indicator.keltner_channel_lband()
# Momentum Oscillator
smo_indicator = SMOIndicator(high=self._high,
low=self._low,
close=self._close,
n=self._mom_period,
fillna=self._fillna)
self._oscillator = smo_indicator.smo()
# Bar and Signal Colors
self._squeeze = bb_indicator.bollinger_wband(
) - kb_indicator.keltner_channel_wband()
self._squeeze = self._squeeze.ge(0).astype(int)
def add_volatility_ta(df: pd.DataFrame, high: str, low: str, close: str,
fillna: bool = False, colprefix: str = "") -> pd.DataFrame:
"""Add volatility technical analysis features to dataframe.
Args:
df (pandas.core.frame.DataFrame): Dataframe base.
high (str): Name of 'high' column.
low (str): Name of 'low' column.
close (str): Name of 'close' column.
fillna(bool): if True, fill nan values.
colprefix(str): Prefix column names inserted
Returns:
pandas.core.frame.DataFrame: Dataframe with new features.
"""
# Average True Range
df[f'{colprefix}volatility_atr'] = AverageTrueRange(
close=df[close], high=df[high], low=df[low], n=10, fillna=fillna).average_true_range()
# Bollinger Bands
indicator_bb = BollingerBands(close=df[close], n=20, ndev=2, fillna=fillna)
df[f'{colprefix}volatility_bbm'] = indicator_bb.bollinger_mavg()
df[f'{colprefix}volatility_bbh'] = indicator_bb.bollinger_hband()
df[f'{colprefix}volatility_bbl'] = indicator_bb.bollinger_lband()
df[f'{colprefix}volatility_bbw'] = indicator_bb.bollinger_wband()
df[f'{colprefix}volatility_bbp'] = indicator_bb.bollinger_pband()
df[f'{colprefix}volatility_bbhi'] = indicator_bb.bollinger_hband_indicator()
df[f'{colprefix}volatility_bbli'] = indicator_bb.bollinger_lband_indicator()
# Keltner Channel
indicator_kc = KeltnerChannel(close=df[close], high=df[high], low=df[low], n=10, fillna=fillna)
df[f'{colprefix}volatility_kcc'] = indicator_kc.keltner_channel_mband()
df[f'{colprefix}volatility_kch'] = indicator_kc.keltner_channel_hband()
df[f'{colprefix}volatility_kcl'] = indicator_kc.keltner_channel_lband()
df[f'{colprefix}volatility_kcw'] = indicator_kc.keltner_channel_wband()
df[f'{colprefix}volatility_kcp'] = indicator_kc.keltner_channel_pband()
df[f'{colprefix}volatility_kchi'] = indicator_kc.keltner_channel_hband_indicator()
df[f'{colprefix}volatility_kcli'] = indicator_kc.keltner_channel_lband_indicator()
# Donchian Channel
indicator_dc = DonchianChannel(high=df[high], low=df[low], close=df[close], n=20, offset=0, fillna=fillna)
df[f'{colprefix}volatility_dcl'] = indicator_dc.donchian_channel_lband()
df[f'{colprefix}volatility_dch'] = indicator_dc.donchian_channel_hband()
df[f'{colprefix}volatility_dcm'] = indicator_dc.donchian_channel_mband()
df[f'{colprefix}volatility_dcw'] = indicator_dc.donchian_channel_wband()
df[f'{colprefix}volatility_dcp'] = indicator_dc.donchian_channel_pband()
# Ulcer Index
df[f'{colprefix}volatility_ui'] = UlcerIndex(close=df[close], n=14, fillna=fillna).ulcer_index()
return df