text: """
  .. 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)
          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()
  