Revision f3ec5bbfc93ac610e967a87d128bbfcdf7c43082 authored by Darío López Padial on 29 March 2020, 17:17:14 UTC, committed by GitHub on 29 March 2020, 17:17:14 UTC
1 parent 2bc3254
volatility.py
"""
.. module:: volatility
:synopsis: Volatility Indicators.
.. moduleauthor:: Dario Lopez Padial (Bukosabino)
"""
import numpy as np
import pandas as pd
from ta.utils import IndicatorMixin, ema
class AverageTrueRange(IndicatorMixin):
"""Average True Range (ATR)
The indicator provide an indication of the degree of price volatility.
Strong moves, in either direction, are often accompanied by large ranges,
or large True Ranges.
http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:average_true_range_atr
Args:
high(pandas.Series): dataset 'High' column.
low(pandas.Series): dataset 'Low' column.
close(pandas.Series): dataset 'Close' column.
n(int): n period.
fillna(bool): if True, fill nan values.
"""
def __init__(self, high: pd.Series, low: pd.Series, close: pd.Series, n: int = 14, fillna: bool = False):
self._high = high
self._low = low
self._close = close
self._n = n
self._fillna = fillna
self._run()
def _run(self):
cs = self._close.shift(1)
tr = self._high.combine(cs, max) - self._low.combine(cs, min)
atr = np.zeros(len(self._close))
atr[self._n-1] = tr[0:self._n].mean()
for i in range(self._n, len(atr)):
atr[i] = (atr[i-1] * (self._n-1) + tr.iloc[i]) / float(self._n)
self._atr = pd.Series(data=atr, index=tr.index)
def average_true_range(self) -> pd.Series:
"""Average True Range (ATR)
Returns:
pandas.Series: New feature generated.
"""
atr = self._check_fillna(self._atr, value=0)
return pd.Series(atr, name='atr')
class BollingerBands(IndicatorMixin):
"""Bollinger Bands
https://school.stockcharts.com/doku.php?id=technical_indicators:bollinger_bands
Args:
close(pandas.Series): dataset 'Close' column.
n(int): n period.
ndev(int): n factor standard deviation
fillna(bool): if True, fill nan values.
"""
def __init__(self, close: pd.Series, n: int = 20, ndev: int = 2, fillna: bool = False):
self._close = close
self._n = n
self._ndev = ndev
self._fillna = fillna
self._run()
def _run(self):
self._mavg = self._close.rolling(self._n, min_periods=0).mean()
self._mstd = self._close.rolling(self._n, min_periods=0).std(ddof=0)
self._hband = self._mavg + self._ndev * self._mstd
self._lband = self._mavg - self._ndev * self._mstd
def bollinger_mavg(self) -> pd.Series:
"""Bollinger Channel Middle Band
Returns:
pandas.Series: New feature generated.
"""
mavg = self._check_fillna(self._mavg, value=-1)
return pd.Series(mavg, name='mavg')
def bollinger_hband(self) -> pd.Series:
"""Bollinger Channel High Band
Returns:
pandas.Series: New feature generated.
"""
hband = self._check_fillna(self._hband, value=-1)
return pd.Series(hband, name='hband')
def bollinger_lband(self) -> pd.Series:
"""Bollinger Channel Low Band
Returns:
pandas.Series: New feature generated.
"""
lband = self._check_fillna(self._lband, value=-1)
return pd.Series(lband, name='lband')
def bollinger_wband(self) -> pd.Series:
"""Bollinger Channel Band Width
From: https://school.stockcharts.com/doku.php?id=technical_indicators:bollinger_band_width
Returns:
pandas.Series: New feature generated.
"""
wband = ((self._hband - self._lband) / self._mavg) * 100
wband = self._check_fillna(wband, value=0)
return pd.Series(wband, name='bbiwband')
def bollinger_pband(self) -> pd.Series:
"""Bollinger Channel Percentage Band
From: https://school.stockcharts.com/doku.php?id=technical_indicators:bollinger_band_perce
Returns:
pandas.Series: New feature generated.
"""
pband = (self._close - self._lband) / (self._hband - self._lband)
pband = self._check_fillna(pband, value=0)
return pd.Series(pband, name='bbipband')
def bollinger_hband_indicator(self) -> pd.Series:
"""Bollinger Channel Indicator Crossing High Band (binary).
It returns 1, if close is higher than bollinger_hband. Else, it returns 0.
Returns:
pandas.Series: New feature generated.
"""
hband = pd.Series(np.where(self._close > self._hband, 1.0, 0.0), index=self._close.index)
hband = self._check_fillna(hband, value=0)
return pd.Series(hband, index=self._close.index, name='bbihband')
def bollinger_lband_indicator(self) -> pd.Series:
"""Bollinger Channel Indicator Crossing Low Band (binary).
It returns 1, if close is lower than bollinger_lband. Else, it returns 0.
Returns:
pandas.Series: New feature generated.
"""
lband = pd.Series(np.where(self._close < self._lband, 1.0, 0.0), index=self._close.index)
lband = self._check_fillna(lband, value=0)
return pd.Series(lband, name='bbilband')
class KeltnerChannel(IndicatorMixin):
"""KeltnerChannel
Keltner Channels are a trend following indicator used to identify reversals with channel breakouts and
channel direction. Channels can also be used to identify overbought and oversold levels when the trend
is flat.
https://school.stockcharts.com/doku.php?id=technical_indicators:keltner_channels
Args:
high(pandas.Series): dataset 'High' column.
low(pandas.Series): dataset 'Low' column.
close(pandas.Series): dataset 'Close' column.
n(int): n period.
fillna(bool): if True, fill nan values.
ov(bool): if True, use original version as the centerline (SMA of typical price)
if False, use EMA of close as the centerline. More info:
https://school.stockcharts.com/doku.php?id=technical_indicators:keltner_channels
"""
def __init__(
self, high: pd.Series, low: pd.Series, close: pd.Series, n: int = 14, fillna: bool = False,
ov: bool = True):
self._high = high
self._low = low
self._close = close
self._n = n
self._fillna = fillna
self._ov = ov
self._run()
def _run(self):
if self._ov:
self._tp = ((self._high + self._low + self._close) / 3.0).rolling(self._n, min_periods=0).mean()
self._tp_high = (((4 * self._high) - (2 * self._low) + self._close) / 3.0).rolling(
self._n, min_periods=0).mean()
self._tp_low = (((-2 * self._high) + (4 * self._low) + self._close) / 3.0).rolling(
self._n, min_periods=0).mean()
else:
self._tp = self._close.ewm(span=self._n, min_periods=0, adjust=False).mean()
atr = AverageTrueRange(
close=self._close, high=self._high, low=self._high, n=10, fillna=self._fillna
).average_true_range()
self._tp_high = self._tp + (2*atr)
self._tp_low = self._tp - (2*atr)
def keltner_channel_mband(self) -> pd.Series:
"""Keltner Channel Middle Band
Returns:
pandas.Series: New feature generated.
"""
tp = self._check_fillna(self._tp, value=-1)
return pd.Series(tp, name='mavg')
def keltner_channel_hband(self) -> pd.Series:
"""Keltner Channel High Band
Returns:
pandas.Series: New feature generated.
"""
tp = self._check_fillna(self._tp_high, value=-1)
return pd.Series(tp, name='kc_hband')
def keltner_channel_lband(self) -> pd.Series:
"""Keltner Channel Low Band
Returns:
pandas.Series: New feature generated.
"""
tp_low = self._check_fillna(self._tp_low, value=-1)
return pd.Series(tp_low, name='kc_lband')
def keltner_channel_wband(self) -> pd.Series:
"""Keltner Channel Band Width
Returns:
pandas.Series: New feature generated.
"""
wband = ((self._tp_high - self._tp_low) / self._tp) * 100
wband = self._check_fillna(wband, value=0)
return pd.Series(wband, name='bbiwband')
def keltner_channel_pband(self) -> pd.Series:
"""Keltner Channel Percentage Band
Returns:
pandas.Series: New feature generated.
"""
pband = (self._close - self._tp_low) / (self._tp_high - self._tp_low)
pband = self._check_fillna(pband, value=0)
return pd.Series(pband, name='bbipband')
def keltner_channel_hband_indicator(self) -> pd.Series:
"""Keltner Channel Indicator Crossing High Band (binary)
It returns 1, if close is higher than keltner_channel_hband. Else, it returns 0.
Returns:
pandas.Series: New feature generated.
"""
hband = pd.Series(np.where(self._close > self._tp_high, 1.0, 0.0), index=self._close.index)
hband = self._check_fillna(hband, value=0)
return pd.Series(hband, name='dcihband')
def keltner_channel_lband_indicator(self) -> pd.Series:
"""Keltner Channel Indicator Crossing Low Band (binary)
It returns 1, if close is lower than keltner_channel_lband. Else, it returns 0.
Returns:
pandas.Series: New feature generated.
"""
lband = pd.Series(np.where(self._close < self._tp_low, 1.0, 0.0), index=self._close.index)
lband = self._check_fillna(lband, value=0)
return pd.Series(lband, name='dcilband')
class DonchianChannel(IndicatorMixin):
"""Donchian Channel
https://www.investopedia.com/terms/d/donchianchannels.asp
Args:
close(pandas.Series): dataset 'Close' column.
n(int): n period.
ndev(int): n factor standard deviation
fillna(bool): if True, fill nan values.
"""
def __init__(self, close: pd.Series, n: int = 20, fillna: bool = False):
self._close = close
self._n = n
self._fillna = fillna
self._run()
def _run(self):
self._hband = self._close.rolling(self._n, min_periods=0).max()
self._lband = self._close.rolling(self._n, min_periods=0).min()
def donchian_channel_hband(self) -> pd.Series:
"""Donchian Channel High Band
Returns:
pandas.Series: New feature generated.
"""
hband = self._check_fillna(self._hband, value=-1)
return pd.Series(hband, name='dchband')
def donchian_channel_lband(self) -> pd.Series:
"""Donchian Channel Low Band
Returns:
pandas.Series: New feature generated.
"""
lband = self._check_fillna(self._lband, value=-1)
return pd.Series(lband, name='dclband')
def donchian_channel_hband_indicator(self) -> pd.Series:
"""Donchian Channel Indicator Crossing High Band (binary)
It returns 1, if close is higher than donchian_channel_hband. Else, it returns 0.
Returns:
pandas.Series: New feature generated.
"""
hband = pd.Series(np.where(self._close >= self._hband, 1.0, 0.0), index=self._close.index)
hband = self._check_fillna(hband, value=0)
return pd.Series(hband, name='dcihband')
def donchian_channel_lband_indicator(self) -> pd.Series:
"""Donchian Channel Indicator Crossing Low Band (binary)
It returns 1, if close is lower than donchian_channel_lband. Else, it returns 0.
Returns:
pandas.Series: New feature generated.
"""
lband = pd.Series(np.where(self._close <= self._lband, 1.0, 0.0), index=self._close.index)
lband = self._check_fillna(lband, value=0)
return pd.Series(lband, name='dcilband')
def average_true_range(high, low, close, n=14, fillna=False):
"""Average True Range (ATR)
The indicator provide an indication of the degree of price volatility.
Strong moves, in either direction, are often accompanied by large ranges,
or large True Ranges.
http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:average_true_range_atr
Args:
high(pandas.Series): dataset 'High' column.
low(pandas.Series): dataset 'Low' column.
close(pandas.Series): dataset 'Close' column.
n(int): n period.
fillna(bool): if True, fill nan values.
Returns:
pandas.Series: New feature generated.
"""
indicator = AverageTrueRange(high=high, low=low, close=close, n=n, fillna=fillna)
return indicator.average_true_range()
def bollinger_mavg(close, n=20, fillna=False):
"""Bollinger Bands (BB)
N-period simple moving average (MA).
https://en.wikipedia.org/wiki/Bollinger_Bands
Args:
close(pandas.Series): dataset 'Close' column.
n(int): n period.
fillna(bool): if True, fill nan values.
Returns:
pandas.Series: New feature generated.
"""
indicator = BollingerBands(close=close, n=n, fillna=fillna)
return indicator.bollinger_mavg()
def bollinger_hband(close, n=20, ndev=2, fillna=False):
"""Bollinger Bands (BB)
Upper band at K times an N-period standard deviation above the moving
average (MA + Kdeviation).
https://en.wikipedia.org/wiki/Bollinger_Bands
Args:
close(pandas.Series): dataset 'Close' column.
n(int): n period.
ndev(int): n factor standard deviation
fillna(bool): if True, fill nan values.
Returns:
pandas.Series: New feature generated.
"""
indicator = BollingerBands(close=close, n=n, ndev=ndev, fillna=fillna)
return indicator.bollinger_hband()
def bollinger_lband(close, n=20, ndev=2, fillna=False):
"""Bollinger Bands (BB)
Lower band at K times an N-period standard deviation below the moving
average (MA − Kdeviation).
https://en.wikipedia.org/wiki/Bollinger_Bands
Args:
close(pandas.Series): dataset 'Close' column.
n(int): n period.
ndev(int): n factor standard deviation
fillna(bool): if True, fill nan values.
Returns:
pandas.Series: New feature generated.
"""
indicator = BollingerBands(close=close, n=n, ndev=ndev, fillna=fillna)
return indicator.bollinger_lband()
def bollinger_hband_indicator(close, n=20, ndev=2, fillna=False):
"""Bollinger High Band Indicator
Returns 1, if close is higher than bollinger high band. Else, return 0.
https://en.wikipedia.org/wiki/Bollinger_Bands
Args:
close(pandas.Series): dataset 'Close' column.
n(int): n period.
ndev(int): n factor standard deviation
fillna(bool): if True, fill nan values.
Returns:
pandas.Series: New feature generated.
"""
indicator = BollingerBands(close=close, n=n, ndev=ndev, fillna=fillna)
return indicator.bollinger_hband_indicator()
def bollinger_lband_indicator(close, n=20, ndev=2, fillna=False):
"""Bollinger Low Band Indicator
Returns 1, if close is lower than bollinger low band. Else, return 0.
https://en.wikipedia.org/wiki/Bollinger_Bands
Args:
close(pandas.Series): dataset 'Close' column.
n(int): n period.
ndev(int): n factor standard deviation
fillna(bool): if True, fill nan values.
Returns:
pandas.Series: New feature generated.
"""
indicator = BollingerBands(close=close, n=n, ndev=ndev, fillna=fillna)
return indicator.bollinger_lband_indicator()
def keltner_channel_mband(high, low, close, n=10, fillna=False):
"""Keltner channel (KC)
Showing a simple moving average line (central) of typical price.
https://en.wikipedia.org/wiki/Keltner_channel
Args:
high(pandas.Series): dataset 'High' column.
low(pandas.Series): dataset 'Low' column.
close(pandas.Series): dataset 'Close' column.
n(int): n period.
fillna(bool): if True, fill nan values.
Returns:
pandas.Series: New feature generated.
"""
indicator = KeltnerChannel(high=high, low=low, close=close, n=n, fillna=False)
return indicator.keltner_channel_mband()
def keltner_channel_hband(high, low, close, n=10, fillna=False):
"""Keltner channel (KC)
Showing a simple moving average line (high) of typical price.
https://en.wikipedia.org/wiki/Keltner_channel
Args:
high(pandas.Series): dataset 'High' column.
low(pandas.Series): dataset 'Low' column.
close(pandas.Series): dataset 'Close' column.
n(int): n period.
fillna(bool): if True, fill nan values.
Returns:
pandas.Series: New feature generated.
"""
indicator = KeltnerChannel(high=high, low=low, close=close, n=n, fillna=False)
return indicator.keltner_channel_hband()
def keltner_channel_lband(high, low, close, n=10, fillna=False):
"""Keltner channel (KC)
Showing a simple moving average line (low) of typical price.
https://en.wikipedia.org/wiki/Keltner_channel
Args:
high(pandas.Series): dataset 'High' column.
low(pandas.Series): dataset 'Low' column.
close(pandas.Series): dataset 'Close' column.
n(int): n period.
fillna(bool): if True, fill nan values.
Returns:
pandas.Series: New feature generated.
"""
indicator = KeltnerChannel(high=high, low=low, close=close, n=n, fillna=False)
return indicator.keltner_channel_lband()
def keltner_channel_hband_indicator(high, low, close, n=10, fillna=False):
"""Keltner Channel High Band Indicator (KC)
Returns 1, if close is higher than keltner high band channel. Else,
return 0.
https://en.wikipedia.org/wiki/Keltner_channel
Args:
high(pandas.Series): dataset 'High' column.
low(pandas.Series): dataset 'Low' column.
close(pandas.Series): dataset 'Close' column.
n(int): n period.
fillna(bool): if True, fill nan values.
Returns:
pandas.Series: New feature generated.
"""
indicator = KeltnerChannel(high=high, low=low, close=close, n=n, fillna=False)
return indicator.keltner_channel_hband_indicator()
def keltner_channel_lband_indicator(high, low, close, n=10, fillna=False):
"""Keltner Channel Low Band Indicator (KC)
Returns 1, if close is lower than keltner low band channel. Else, return 0.
https://en.wikipedia.org/wiki/Keltner_channel
Args:
high(pandas.Series): dataset 'High' column.
low(pandas.Series): dataset 'Low' column.
close(pandas.Series): dataset 'Close' column.
n(int): n period.
fillna(bool): if True, fill nan values.
Returns:
pandas.Series: New feature generated.
"""
indicator = KeltnerChannel(high=high, low=low, close=close, n=n, fillna=False)
return indicator.keltner_channel_lband_indicator()
def donchian_channel_hband(close, n=20, fillna=False):
"""Donchian channel (DC)
The upper band marks the highest price of an issue for n periods.
https://www.investopedia.com/terms/d/donchianchannels.asp
Args:
close(pandas.Series): dataset 'Close' column.
n(int): n period.
fillna(bool): if True, fill nan values.
Returns:
pandas.Series: New feature generated.
"""
indicator = DonchianChannel(close=close, n=n, fillna=fillna)
return indicator.donchian_channel_hband()
def donchian_channel_lband(close, n=20, fillna=False):
"""Donchian channel (DC)
The lower band marks the lowest price for n periods.
https://www.investopedia.com/terms/d/donchianchannels.asp
Args:
close(pandas.Series): dataset 'Close' column.
n(int): n period.
fillna(bool): if True, fill nan values.
Returns:
pandas.Series: New feature generated.
"""
indicator = DonchianChannel(close=close, n=n, fillna=fillna)
return indicator.donchian_channel_lband()
def donchian_channel_hband_indicator(close, n=20, fillna=False):
"""Donchian High Band Indicator
Returns 1, if close is higher than donchian high band channel. Else,
return 0.
https://www.investopedia.com/terms/d/donchianchannels.asp
Args:
close(pandas.Series): dataset 'Close' column.
n(int): n period.
fillna(bool): if True, fill nan values.
Returns:
pandas.Series: New feature generated.
"""
indicator = DonchianChannel(close=close, n=n, fillna=fillna)
return indicator.donchian_channel_hband_indicator()
def donchian_channel_lband_indicator(close, n=20, fillna=False):
"""Donchian Low Band Indicator
Returns 1, if close is lower than donchian low band channel. Else,
return 0.
https://www.investopedia.com/terms/d/donchianchannels.asp
Args:
close(pandas.Series): dataset 'Close' column.
n(int): n period.
fillna(bool): if True, fill nan values.
Returns:
pandas.Series: New feature generated.
"""
indicator = DonchianChannel(close=close, n=n, fillna=fillna)
return indicator.donchian_channel_lband_indicator()
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