def setUpClass(cls):
cls._df = pd.read_csv(cls._filename, sep=',')
cls._params = dict(high=cls._df['high'],
low=cls._df['low'],
close=cls._df['close'],
n=20,
offset=1,
fillna=False)
cls._indicator = DonchianChannel(**cls._params)
def donchian(df):
indicator_don = DonchianChannel(high=df["High"],
low=df["Low"],
close=df["Close"])
df['don_h'] = indicator_don.donchian_channel_hband()
df['don_l'] = indicator_don.donchian_channel_lband()
df['don_m'] = indicator_don.donchian_channel_mband()
df['don_p'] = indicator_don.donchian_channel_pband()
df['don_w'] = indicator_don.donchian_channel_wband()
def setUpClass(cls):
cls._df = pd.read_csv(cls._filename, sep=",")
cls._params = dict(
high=cls._df["high"],
low=cls._df["low"],
close=cls._df["close"],
window=20,
offset=1,
fillna=False,
)
cls._indicator = DonchianChannel(**cls._params)
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 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
