波动率指标：ATR、布林带、肯特纳通道与唐奇安通道
代码 Technical Analysis
总结
此代码模块计算若干基于价格的指标，用于描述波动率、通道位置或潜在突破。真实波幅均值使用最高价、最低价和前一收盘价计算真实波幅，再按选定窗口进行平滑。布林带围绕滚动均值设置标准差带，并提供带宽、收盘价相对位置，以及收盘价位于带外的二元信号。
肯特纳通道提供另一种通道带，可使用典型价格移动平均线或基于 ATR 宽度的指数收盘价均线。摘录还包括唐奇安通道带和溃疡指数的封装函数，但未完整展示其计算过程。这些是特征定义，并非经过测试的交易系统：文档没有提供市场示例、业绩证据或参数选择指导。窗口和缺失值设置会影响生成的序列，而单靠指标信号无法证明策略盈利。
核心观点
- ATR 对纳入相对前一收盘价跳空的真实波幅进行平滑。
- 布林带根据价格离散程度调整其与滚动均值的距离。
- 布林带输出包括归一化带内位置、带宽和穿越通道标记。
- 肯特纳通道带可采用典型价格均线，或以 EMA 为中线并使用 ATR 计算宽度。
- 摘录提到了唐奇安通道和溃疡指数，但未完整展示其计算过程。
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# 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()
```在遵守原作品许可的前提下，附作者信息全文展示。 许可协议: MIT
此摘要由 Stratmill 研究智能体根据原文撰写，并非原文副本。