ボラティリティ指標：ATR、ボリンジャー、ケルトナー、ドンチャンチャネル
コード Technical Analysis
サマリー
このコードモジュールは、ボラティリティ、チャネル内の位置、ブレイクアウトの可能性を示す、価格ベースの複数の指標を計算します。Average True Rangeは高値、安値、前回終値から真の値幅を求め、指定した期間で平滑化します。ボリンジャーバンドは移動平均の周囲に標準偏差のバンドを設定し、バンド幅、終値の相対位置、終値がバンドの外にあることを示す二値シグナルを出力します。
ケルトナーチャネルは、典型価格の移動平均または終値の指数移動平均を使い、ATRに基づく幅で別のバンドを設定します。抜粋にはドンチャンチャネルのバンドとUlcer Indexのラッパーも含まれますが、計算の全容は示されていません。これらは機能の定義であり、検証済みのトレードシステムではありません。この文書には市場での使用例、運用成績の証拠、パラメータ選択の指針がありません。期間や欠損値の設定によって結果の時系列は変わり、指標のシグナルだけでは収益性は証明されません。
主なアイデア
- ATRは、前回終値とのギャップを含む真の値幅を平滑化します。
- ボリンジャーバンドは、価格のばらつきに応じて移動平均からの距離を調整します。
- ボリンジャーの出力には、正規化したバンド内位置、幅、バンド越えのフラグが含まれます。
- ケルトナーチャネルのバンドには、典型価格の平均、またはEMAを中心線としATRを幅に使う方法があります。
- 抜粋ではドンチャンチャネルとUlcer Indexに触れていますが、計算の全容は示されていません。
タグ
全文
# 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()
```出典を明記したうえで、ライセンスに従って全文を掲載しています。 ライセンス: MIT
この要約は原文をもとにStratmillのリサーチエージェントが作成したもので、出典の複製ではありません。