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_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_central()
df[f'{colprefix}volatility_kch'] = indicator_kc.keltner_channel_hband()
df[f'{colprefix}volatility_kcl'] = indicator_kc.keltner_channel_lband()
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(close=df[close], n=20, 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_dchi'] = indicator_dc.donchian_channel_hband_indicator(
)
df[f'{colprefix}volatility_dcli'] = indicator_dc.donchian_channel_lband_indicator(
)
return df
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 = "",
vectorized: bool = False,
) -> 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
vectorized(bool): if True, use only vectorized functions indicators
Returns:
pandas.core.frame.DataFrame: Dataframe with new features.
"""
# Bollinger Bands
indicator_bb = BollingerBands(close=df[close],
window=20,
window_dev=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],
window=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],
window=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()
if not vectorized:
# Average True Range
df[f"{colprefix}volatility_atr"] = AverageTrueRange(
close=df[close],
high=df[high],
low=df[low],
window=10,
fillna=fillna).average_true_range()
# Ulcer Index
df[f"{colprefix}volatility_ui"] = UlcerIndex(
close=df[close], window=14, fillna=fillna).ulcer_index()
return df
def applyIndicator(self, full_company_price):
self.data = full_company_price
high = self.data['high']
low = self.data['low']
close = self.data['close']
volume = self.data['volume']
EMA12 = EMAIndicator(close, 12, fillna=False)
EMA30 = EMAIndicator(close, 20, fillna=False)
EMA60 = EMAIndicator(close, 60, fillna=False)
MACD1226 = MACD(close, 26, 12, 9, fillna=False)
MACD2452 = MACD(close, 52, 24, 18, fillna=False)
ROC12 = ROCIndicator(close, 12, fillna=False)
ROC30 = ROCIndicator(close, 30, fillna=False)
ROC60 = ROCIndicator(close, 60, fillna=False)
RSI14 = RSIIndicator(close, 14, fillna=False)
RSI28 = RSIIndicator(close, 28, fillna=False)
RSI60 = RSIIndicator(close, 60, fillna=False)
AROON25 = AroonIndicator(close, 25, fillna=False)
AROON50 = AroonIndicator(close, 50, fillna=False)
AROON80 = AroonIndicator(close, 80, fillna=False)
MFI14 = MFIIndicator(high, low, close, volume, 14, fillna=False)
MFI28 = MFIIndicator(high, low, close, volume, 28, fillna=False)
MFI80 = MFIIndicator(high, low, close, volume, 80, fillna=False)
CCI20 = CCIIndicator(high, low, close, 20, 0.015, fillna=False)
CCI40 = CCIIndicator(high, low, close, 40, 0.015, fillna=False)
CCI100 = CCIIndicator(high, low, close, 100, 0.015, fillna=False)
WILLR14 = WilliamsRIndicator(high, low, close, 14, fillna=False)
WILLR28 = WilliamsRIndicator(high, low, close, 28, fillna=False)
WILLR60 = WilliamsRIndicator(high, low, close, 60, fillna=False)
BBANDS20 = BollingerBands(close, 20, 2, fillna=False)
KC20 = KeltnerChannel(high, low, close, 20, 10, fillna=False)
STOCH14 = StochasticOscillator(high, low, close, 14, 3, fillna=False)
STOCH28 = StochasticOscillator(high, low, close, 28, 6, fillna=False)
STOCH60 = StochasticOscillator(high, low, close, 60, 12, fillna=False)
CMI20 = ChaikinMoneyFlowIndicator(high,
low,
close,
volume,
20,
fillna=False)
CMI40 = ChaikinMoneyFlowIndicator(high,
low,
close,
volume,
40,
fillna=False)
CMI100 = ChaikinMoneyFlowIndicator(high,
low,
close,
volume,
100,
fillna=False)
self.data['ema12'] = (close - EMA12.ema_indicator()) / close
self.data['ema30'] = (close - EMA30.ema_indicator()) / close
self.data['ema60'] = (close - EMA60.ema_indicator()) / close
self.data['macd1226'] = MACD1226.macd() - MACD1226.macd_signal()
self.data['macd2452'] = MACD2452.macd() - MACD2452.macd_signal()
self.data['roc12'] = ROC12.roc()
self.data['roc30'] = ROC30.roc()
self.data['roc60'] = ROC60.roc()
self.data['rsi14'] = RSI14.rsi()
self.data['rsi28'] = RSI28.rsi()
self.data['rsi60'] = RSI60.rsi()
self.data['aroon25'] = AROON25.aroon_indicator()
self.data['aroon50'] = AROON50.aroon_indicator()
self.data['aroon80'] = AROON80.aroon_indicator()
self.data['mfi14'] = MFI14.money_flow_index()
self.data['mfi28'] = MFI28.money_flow_index()
self.data['mfi80'] = MFI80.money_flow_index()
self.data['cci20'] = CCI20.cci()
self.data['cci40'] = CCI40.cci()
self.data['cci100'] = CCI100.cci()
self.data['willr14'] = WILLR14.wr()
self.data['willr28'] = WILLR28.wr()
self.data['willr60'] = WILLR60.wr()
self.data['bband20up'] = (BBANDS20.bollinger_hband() - close) / close
self.data['bband20down'] = (close - BBANDS20.bollinger_lband()) / close
self.data['stoch14'] = STOCH14.stoch()
self.data['stoch28'] = STOCH28.stoch()
self.data['stoch60'] = STOCH60.stoch()
self.data['cmi20'] = CMI20.chaikin_money_flow()
self.data['cmi40'] = CMI40.chaikin_money_flow()
self.data['cmi100'] = CMI100.chaikin_money_flow()
self.data['kc20up'] = (KC20.keltner_channel_hband() - close) / close
self.data['kc20down'] = (close - KC20.keltner_channel_lband()) / close
return self.data