Indicadores de volatilidad: ATR, Bollinger, Keltner y Donchian
Código Technical Analysis
Resumen
Este módulo de código calcula varios indicadores basados en precios que describen la volatilidad, la posición dentro de un canal o posibles rupturas. El rango verdadero promedio usa los máximos, mínimos y el cierre anterior para calcular rangos verdaderos y luego los suaviza durante una ventana elegida. Las bandas de Bollinger sitúan bandas de desviación estándar alrededor de una media móvil y ofrecen la anchura de las bandas, la posición relativa del cierre y señales binarias para los cierres fuera de ellas.
Los canales de Keltner ofrecen bandas alternativas, con una media móvil del precio típico o una media exponencial del cierre y una anchura basada en ATR. El fragmento también incluye envoltorios para las bandas del canal de Donchian y el índice Ulcer, aunque no muestra todos sus cálculos. Son definiciones de funciones, no un sistema de trading probado: el documento no aporta ejemplos de mercado, pruebas de rendimiento ni orientación para elegir parámetros. La configuración de la ventana y los valores ausentes afecta a las series resultantes, y las señales de los indicadores por sí solas no demuestran rentabilidad.
Ideas clave
- ATR suaviza rangos verdaderos que incorporan las brechas respecto al cierre anterior.
- Las bandas de Bollinger ajustan su distancia respecto a una media móvil según la dispersión de los precios.
- Los resultados de Bollinger incluyen la posición normalizada dentro de las bandas, su anchura y los indicadores de cruce de bandas.
- Las bandas del canal de Keltner pueden usar medias del precio típico o un centro de EMA con anchura de ATR.
- El fragmento menciona los canales de Donchian y el índice Ulcer, pero no muestra sus cálculos completos.
Etiquetas
Texto completo
# volatility.py
```py
"""
.. module:: volatility
:synopsis: Volatility Indicators.
.. moduleauthor:: Dario Lopez Padial (Bukosabino)
"""
import numpy as np
import pandas as pd
from ta.utils import IndicatorMixin
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.
window(int): n period.
fillna(bool): if True, fill nan values.
"""
def __init__(
self,
high: pd.Series,
low: pd.Series,
close: pd.Series,
window: int = 14,
fillna: bool = False,
):
self._high = high
self._low = low
self._close = close
self._window = window
self._fillna = fillna
self._run()
def _run(self):
close_shift = self._close.shift(1)
true_range = self._true_range(self._high, self._low, close_shift)
atr = np.zeros(len(self._close))
atr[self._window - 1] = true_range[0 : self._window].mean()
for i in range(self._window, len(atr)):
atr[i] = (atr[i - 1] * (self._window - 1) + true_range.iloc[i]) / float(
self._window
)
self._atr = pd.Series(data=atr, index=true_range.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.
window(int): n period.
window_dev(int): n factor standard deviation
fillna(bool): if True, fill nan values.
"""
def __init__(
self,
close: pd.Series,
window: int = 20,
window_dev: int = 2,
fillna: bool = False,
):
self._close = close
self._window = window
self._window_dev = window_dev
self._fillna = fillna
self._run()
def _run(self):
min_periods = 0 if self._fillna else self._window
self._mavg = self._close.rolling(self._window, min_periods=min_periods).mean()
self._mstd = self._close.rolling(self._window, min_periods=min_periods).std(
ddof=0
)
self._hband = self._mavg + self._window_dev * self._mstd
self._lband = self._mavg - self._window_dev * 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).where(
self._hband != self._lband, np.nan
)
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.
window(int): n period.
window_atr(int): n atr period. Only valid if original_version param is False.
fillna(bool): if True, fill nan values.
original_version(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
multiplier(int): The multiplier has the most effect on the channel width. default is 2
"""
def __init__(
self,
high: pd.Series,
low: pd.Series,
close: pd.Series,
window: int = 20,
window_atr: int = 10,
fillna: bool = False,
original_version: bool = True,
multiplier: int = 2,
):
self._high = high
self._low = low
self._close = close
self._window = window
self._window_atr = window_atr
self._fillna = fillna
self._original_version = original_version
self._multiplier = multiplier
self._run()
def _run(self):
min_periods = 1 if self._fillna else self._window
if self._original_version:
self._tp = (
((self._high + self._low + self._close) / 3.0)
.rolling(self._window, min_periods=min_periods)
.mean()
)
self._tp_high = (
(((4 * self._high) - (2 * self._low) + self._close) / 3.0)
.rolling(self._window, min_periods=0)
.mean()
)
self._tp_low = (
(((-2 * self._high) + (4 * self._low) + self._close) / 3.0)
.rolling(self._window, min_periods=0)
.mean()
)
else:
self._tp = self._close.ewm(
span=self._window, min_periods=min_periods, adjust=False
).mean()
atr = AverageTrueRange(
close=self._close,
high=self._high,
low=self._low,
window=self._window_atr,
fillna=self._fillna,
).average_true_range()
self._tp_high = self._tp + (self._multiplier * atr)
self._tp_low = self._tp - (self._multiplier * atr)
def keltner_channel_mband(self) -> pd.Series:
"""Keltner Channel Middle Band
Returns:
pandas.Series: New feature generated.
"""
tp_middle = self._check_fillna(self._tp, value=-1)
return pd.Series(tp_middle, name="mavg")
def keltner_channel_hband(self) -> pd.Series:
"""Keltner Channel High Band
Returns:
pandas.Series: New feature generated.
"""
tp_high = self._check_fillna(self._tp_high, value=-1)
return pd.Series(tp_high, 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:
high(pandas.Series): dataset 'High' column.
low(pandas.Series): dataset 'Low' column.
close(pandas.Series): dataset 'Close' column.
window(int): n period.
fillna(bool): if True, fill nan values.
"""
def __init__(
self,
high: pd.Series,
low: pd.Series,
close: pd.Series,
window: int = 20,
offset: int = 0,
fillna: bool = False,
):
self._offset = offset
self._close = close
self._high = high
self._low = low
self._window = window
self._fillna = fillna
self._run()
def _run(self):
self._min_periods = 1 if self._fillna else self._window
self._hband = self._high.rolling(
self._window, min_periods=self._min_periods
).max()
self._lband = self._low.rolling(
self._window, min_periods=self._min_periods
).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)
if self._offset != 0:
hband = hband.shift(self._offset)
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)
if self._offset != 0:
lband = lband.shift(self._offset)
return pd.Series(lband, name="dclband")
def donchian_channel_mband(self) -> pd.Series:
"""Donchian Channel Middle Band
Returns:
pandas.Series: New feature generated.
"""
mband = ((self._hband - self._lband) / 2.0) + self._lband
mband = self._check_fillna(mband, value=-1)
if self._offset != 0:
mband = mband.shift(self._offset)
return pd.Series(mband, name="dcmband")
def donchian_channel_wband(self) -> pd.Series:
"""Donchian Channel Band Width
Returns:
pandas.Series: New feature generated.
"""
mavg = self._close.rolling(self._window, min_periods=self._min_periods).mean()
wband = ((self._hband - self._lband) / mavg) * 100
wband = self._check_fillna(wband, value=0)
if self._offset != 0:
wband = wband.shift(self._offset)
return pd.Series(wband, name="dcwband")
def donchian_channel_pband(self) -> pd.Series:
"""Donchian Channel Percentage Band
Returns:
pandas.Series: New feature generated.
"""
pband = (self._close - self._lband) / (self._hband - self._lband)
pband = self._check_fillna(pband, value=0)
if self._offset != 0:
pband = pband.shift(self._offset)
return pd.Series(pband, name="dcpband")
class UlcerIndex(IndicatorMixin):
"""Ulcer Index
https://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:ulcer_index
Args:
close(pandas.Series): dataset 'Close' column.
window(int): n period.
fillna(bool): if True, fill nan values.
"""
def __init__(self, close: pd.Series, window: int = 14, fillna: bool = False):
self._close = close
self._window = window
self._fillna = fillna
self._run()
def _run(self):
_ui_max = self._close.rolling(self._window, min_periods=1).max()
_r_i = 100 * (self._close - _ui_max) / _ui_max
def ui_function():
def _ui_function(x):
return np.sqrt((x**2 / self._window).sum())
return _ui_function
self._ulcer_idx = _r_i.rolling(self._window).apply(ui_function(), raw=True)
def ulcer_index(self) -> pd.Series:
"""Ulcer Index (UI)
Returns:
pandas.Series: New feature generated.
"""
ulcer_idx = self._check_fillna(self._ulcer_idx)
return pd.Series(ulcer_idx, name="ui")
def average_true_range(high, low, close, window=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.
window(int): n period.
fillna(bool): if True, fill nan values.
Returns:
pandas.Series: New feature generated.
"""
indicator = AverageTrueRange(
high=high, low=low, close=close, window=window, fillna=fillna
)
return indicator.average_true_range()
def bollinger_mavg(close, window=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.
window(int): n period.
fillna(bool): if True, fill nan values.
Returns:
pandas.Series: New feature generated.
"""
indicator = BollingerBands(close=close, window=window, fillna=fillna)
return indicator.bollinger_mavg()
def bollinger_hband(close, window=20, window_dev=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.
window(int): n period.
window_dev(int): n factor standard deviation
fillna(bool): if True, fill nan values.
Returns:
pandas.Series: New feature generated.
"""
indicator = BollingerBands(
close=close, window=window, window_dev=window_dev, fillna=fillna
)
return indicator.bollinger_hband()
def bollinger_lband(close, window=20, window_dev=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.
window(int): n period.
window_dev(int): n factor standard deviation
fillna(bool): if True, fill nan values.
Returns:
pandas.Series: New feature generated.
"""
indicator = BollingerBands(
close=close, window=window, window_dev=window_dev, fillna=fillna
)
return indicator.bollinger_lband()
def bollinger_wband(close, window=20, window_dev=2, fillna=False):
"""Bollinger Channel Band Width
From: https://school.stockcharts.com/doku.php?id=technical_indicators:bollinger_band_width
Args:
close(pandas.Series): dataset 'Close' column.
window(int): n period.
window_dev(int): n factor standard deviation
fillna(bool): if True, fill nan values.
Returns:
pandas.Series: New feature generated.
"""
indicator = BollingerBands(
close=close, window=window, window_dev=window_dev, fillna=fillna
)
return indicator.bollinger_wband()
def bollinger_pband(close, window=20, window_dev=2, fillna=False):
"""Bollinger Channel Percentage Band
From: https://school.stockcharts.com/doku.php?id=technical_indicators:bollinger_band_perce
Args:
close(pandas.Series): dataset 'Close' column.
window(int): n period.
window_dev(int): n factor standard deviation
fillna(bool): if True, fill nan values.
Returns:
pandas.Series: New feature generated.
"""
indicator = BollingerBands(
close=close, window=window, window_dev=window_dev, fillna=fillna
)
return indicator.bollinger_pband()
def bollinger_hband_indicator(close, window=20, window_dev=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.
window(int): n period.
window_dev(int): n factor standard deviation
fillna(bool): if True, fill nan values.
Returns:
pandas.Series: New feature generated.
"""
indicator = BollingerBands(
close=close, window=window, window_dev=window_dev, fillna=fillna
)
return indicator.bollinger_hband_indicator()
def bollinger_lband_indicator(close, window=20, window_dev=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.
window(int): n period.
window_dev(int): n factor standard deviation
fillna(bool): if True, fill nan values.
Returns:
pandas.Series: New feature generated.
"""
indicator = BollingerBands(
close=close, window=window, window_dev=window_dev, fillna=fillna
)
return indicator.bollinger_lband_indicator()
def keltner_channel_mband(
high, low, close, window=20, window_atr=10, fillna=False, original_version=True
):
"""Keltner channel (KC)
Showing a simple moving average line (central) of typical price.
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.
window(int): n period.
window_atr(int): n atr period. Only valid if original_version param is False.
fillna(bool): if True, fill nan values.
original_version(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
Returns:
pandas.Series: New feature generated.
"""
indicator = KeltnerChannel(
high=high,
low=low,
close=close,
window=window,
window_atr=window_atr,
fillna=fillna,
original_version=original_version,
)
return indicator.keltner_channel_mband()
def keltner_channel_hband(
high, low, close, window=20, window_atr=10, fillna=False, original_version=True
):
"""Keltner channel (KC)
Showing a simple moving average line (high) of typical price.
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.
window(int): n period.
window_atr(int): n atr period. Only valid if original_version param is False.
fillna(bool): if True, fill nan values.
original_version(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
Returns:
pandas.Series: New feature generated.
"""
indicator = KeltnerChannel(
high=high,
low=low,
close=close,
window=window,
window_atr=window_atr,
fillna=fillna,
original_version=original_version,
)
return indicator.keltner_channel_hband()
def keltner_channel_lband(
high, low, close, window=20, window_atr=10, fillna=False, original_version=True
):
"""Keltner channel (KC)
Showing a simple moving average line (low) of typical price.
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.
window(int): n period.
window_atr(int): n atr period. Only valid if original_version param is False.
fillna(bool): if True, fill nan values.
original_version(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
Returns:
pandas.Series: New feature generated.
"""
indicator = KeltnerChannel(
high=high,
low=low,
close=close,
window=window,
window_atr=window_atr,
fillna=fillna,
original_version=original_version,
)
return indicator.keltner_channel_lband()
def keltner_channel_wband(
high, low, close, window=20, window_atr=10, fillna=False, original_version=True
):
"""Keltner Channel Band Width
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.
window(int): n period.
window_atr(int): n atr period. Only valid if original_version param is False.
fillna(bool): if True, fill nan values.
original_version(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
Returns:
pandas.Series: New feature generated.
"""
indicator = KeltnerChannel(
high=high,
low=low,
close=close,
window=window,
window_atr=window_atr,
fillna=fillna,
original_version=original_version,
)
return indicator.keltner_channel_wband()
def keltner_channel_pband(
high, low, close, window=20, window_atr=10, fillna=False, original_version=True
):
"""Keltner Channel Percentage Band
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.
window(int): n period.
window_atr(int): n atr period. Only valid if original_version param is False.
fillna(bool): if True, fill nan values.
original_version(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
Returns:
pandas.Series: New feature generated.
"""
indicator = KeltnerChannel(
high=high,
low=low,
close=close,
window=window,
window_atr=window_atr,
fillna=fillna,
original_version=original_version,
)
return indicator.keltner_channel_pband()
def keltner_channel_hband_indicator(
high, low, close, window=20, window_atr=10, fillna=False, original_version=True
):
"""Keltner Channel High Band Indicator (KC)
Returns 1, if close is higher than keltner high band channel. Else,
return 0.
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.
window(int): n period.
window_atr(int): n atr period. Only valid if original_version param is False.
fillna(bool): if True, fill nan values.
original_version(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
Returns:
pandas.Series: New feature generated.
"""
indicator = KeltnerChannel(
high=high,
low=low,
close=close,
window=window,
window_atr=window_atr,
fillna=fillna,
original_version=original_version,
)
return indicator.keltner_channel_hband_indicator()
def keltner_channel_lband_indicator(
high, low, close, window=20, window_atr=10, fillna=False, original_version=True
):
"""Keltner Channel Low Band Indicator (KC)
Returns 1, if close is lower than keltner low band channel. Else, return 0.
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.
window(int): n period.
window_atr(int): n atr period. Only valid if original_version param is False.
fillna(bool): if True, fill nan values.
original_version(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
Returns:
pandas.Series: New feature generated.
"""
indicator = KeltnerChannel(
high=high,
low=low,
close=close,
window=window,
window_atr=window_atr,
fillna=fillna,
original_version=original_version,
)
return indicator.keltner_channel_lband_indicator()
def donchian_channel_hband(high, low, close, window=20, offset=0, fillna=False):
"""Donchian Channel High Band (DC)
The upper band marks the highest price of an issue for n periods.
https://www.investopedia.com/terms/d/donchianchannels.asp
Args:
high(pandas.Series): dataset 'High' column.
low(pandas.Series): dataset 'Low' column.
close(pandas.Series): dataset 'Close' column.
window(int): n period.
fillna(bool): if True, fill nan values.
Returns:
pandas.Series: New feature generated.
"""
indicator = DonchianChannel(
high=high, low=low, close=close, window=window, offset=offset, fillna=fillna
)
return indicator.donchian_channel_hband()
def donchian_channel_lband(high, low, close, window=20, offset=0, fillna=False):
"""Donchian Channel Low Band (DC)
The lower band marks the lowest price for n periods.
https://www.investopedia.com/terms/d/donchianchannels.asp
Args:
high(pandas.Series): dataset 'High' column.
low(pandas.Series): dataset 'Low' column.
close(pandas.Series): dataset 'Close' column.
window(int): n period.
fillna(bool): if True, fill nan values.
Returns:
pandas.Series: New feature generated.
"""
indicator = DonchianChannel(
high=high, low=low, close=close, window=window, offset=offset, fillna=fillna
)
return indicator.donchian_channel_lband()
def donchian_channel_mband(high, low, close, window=10, offset=0, fillna=False):
"""Donchian Channel Middle Band (DC)
https://www.investopedia.com/terms/d/donchianchannels.asp
Args:
high(pandas.Series): dataset 'High' column.
low(pandas.Series): dataset 'Low' column.
close(pandas.Series): dataset 'Close' column.
window(int): n period.
fillna(bool): if True, fill nan values.
Returns:
pandas.Series: New feature generated.
"""
indicator = DonchianChannel(
high=high, low=low, close=close, window=window, offset=offset, fillna=fillna
)
return indicator.donchian_channel_mband()
def donchian_channel_wband(high, low, close, window=10, offset=0, fillna=False):
"""Donchian Channel Band Width (DC)
https://www.investopedia.com/terms/d/donchianchannels.asp
Args:
high(pandas.Series): dataset 'High' column.
low(pandas.Series): dataset 'Low' column.
close(pandas.Series): dataset 'Close' column.
window(int): n period.
fillna(bool): if True, fill nan values.
Returns:
pandas.Series: New feature generated.
"""
indicator = DonchianChannel(
high=high, low=low, close=close, window=window, offset=offset, fillna=fillna
)
return indicator.donchian_channel_wband()
def donchian_channel_pband(high, low, close, window=10, offset=0, fillna=False):
"""Donchian Channel Percentage Band (DC)
https://www.investopedia.com/terms/d/donchianchannels.asp
Args:
high(pandas.Series): dataset 'High' column.
low(pandas.Series): dataset 'Low' column.
close(pandas.Series): dataset 'Close' column.
window(int): n period.
fillna(bool): if True, fill nan values.
Returns:
pandas.Series: New feature generated.
"""
indicator = DonchianChannel(
high=high, low=low, close=close, window=window, offset=offset, fillna=fillna
)
return indicator.donchian_channel_pband()
def ulcer_index(close, window=14, fillna=False):
"""Ulcer Index
https://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:ulcer_index
Args:
close(pandas.Series): dataset 'Close' column.
window(int): n period.
fillna(bool): if True, fill nan values.
Returns:
pandas.Series: New feature generated.
"""
indicator = UlcerIndex(close=close, window=window, fillna=fillna)
return indicator.ulcer_index()
```Se muestra íntegramente con atribución según la licencia de la fuente. Licencia: MIT
Este resumen lo redactó el agente de investigación de Stratmill a partir del original; no es una copia de la fuente.