code: """
  .. module:: volume
     :synopsis: Volume Indicators.
  
  .. moduleauthor:: Dario Lopez Padial (Bukosabino)
  
  """
  
  import numpy as np
  import pandas as pd
  
  from ta.utils import IndicatorMixin, ema
  
  
  class AccDistIndexIndicator(IndicatorMixin):
      """Accumulation/Distribution Index (ADI)
  
      Acting as leading indicator of price movements.
  
      https://school.stockcharts.com/doku.php?id=technical_indicators:accumulation_distribution_line
  
      Args:
          high(pandas.Series): dataset 'High' column.
          low(pandas.Series): dataset 'Low' column.
          close(pandas.Series): dataset 'Close' column.
          volume(pandas.Series): dataset 'Volume' column.
          fillna(bool): if True, fill nan values.
      """
  
      def __init__(self, high: pd.Series, low: pd.Series, close: pd.Series, volume: pd.Series, fillna: bool = False):
          self._high = high
          self._low = low
          self._close = close
          self._volume = volume
          self._fillna = fillna
          self._run()
  
      def _run(self):
          clv = ((self._close - self._low) - (self._high - self._close)) / (self._high - self._low)
          clv = clv.fillna(0.0)  # float division by zero
          ad = clv * self._volume
          self._ad = ad.cumsum()
  
      def acc_dist_index(self) -> pd.Series:
          """Accumulation/Distribution Index (ADI)
  
          Returns:
              pandas.Series: New feature generated.
          """
          ad = self._check_fillna(self._ad, value=0)
          return pd.Series(ad, name='adi')
  
  
  class OnBalanceVolumeIndicator(IndicatorMixin):
      """On-balance volume (OBV)
  
      It relates price and volume in the stock market. OBV is based on a
      cumulative total volume.
  
      https://en.wikipedia.org/wiki/On-balance_volume
  
      Args:
          close(pandas.Series): dataset 'Close' column.
          volume(pandas.Series): dataset 'Volume' column.
          fillna(bool): if True, fill nan values.
      """
  
      def __init__(self, close: pd.Series, volume: pd.Series, fillna: bool = False):
          self._close = close
          self._volume = volume
          self._fillna = fillna
          self._run()
  
      def _run(self):
          obv = np.where(self._close < self._close.shift(1), -self._volume, self._volume)
          self._obv = pd.Series(obv, index=self._close.index).cumsum()
  
      def on_balance_volume(self) -> pd.Series:
          """On-balance volume (OBV)
  
          Returns:
              pandas.Series: New feature generated.
          """
          obv = self._check_fillna(self._obv, value=0)
          return pd.Series(obv, name='obv')
  
  
  class ChaikinMoneyFlowIndicator(IndicatorMixin):
      """Chaikin Money Flow (CMF)
  
      It measures the amount of Money Flow Volume over a specific period.
  
      http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:chaikin_money_flow_cmf
  
      Args:
          high(pandas.Series): dataset 'High' column.
          low(pandas.Series): dataset 'Low' column.
          close(pandas.Series): dataset 'Close' column.
          volume(pandas.Series): dataset 'Volume' column.
          n(int): n period.
          fillna(bool): if True, fill nan values.
      """
  
      def __init__(self, high: pd.Series, low: pd.Series, close: pd.Series,
                   volume: pd.Series, n: int = 20, fillna: bool = False):
          self._high = high
          self._low = low
          self._close = close
          self._volume = volume
          self._n = n
          self._fillna = fillna
          self._run()
  
      def _run(self):
          mfv = ((self._close - self._low) - (self._high - self._close)) / (self._high - self._low)
          mfv = mfv.fillna(0.0)  # float division by zero
          mfv *= self._volume
          self._cmf = mfv.rolling(self._n, min_periods=0).sum() / self._volume.rolling(self._n, min_periods=0).sum()
  
      def chaikin_money_flow(self) -> pd.Series:
          """Chaikin Money Flow (CMF)
  
          Returns:
              pandas.Series: New feature generated.
          """
          cmf = self._check_fillna(self._cmf, value=0)
          return pd.Series(cmf, name='cmf')
  
  
  class ForceIndexIndicator(IndicatorMixin):
      """Force Index (FI)
  
      It illustrates how strong the actual buying or selling pressure is. High
      positive values mean there is a strong rising trend, and low values signify
      a strong downward trend.
  
      http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:force_index
  
      Args:
          high(pandas.Series): dataset 'High' column.
          low(pandas.Series): dataset 'Low' column.
          close(pandas.Series): dataset 'Close' column.
          volume(pandas.Series): dataset 'Volume' column.
          n(int): n period.
          fillna(bool): if True, fill nan values.
      """
  
      def __init__(self, close: pd.Series, volume: pd.Series, n: int = 13, fillna: bool = False):
          self._close = close
          self._volume = volume
          self._n = n
          self._fillna = fillna
          self._run()
  
      def _run(self):
          fi = (self._close - self._close.shift(1)) * self._volume
          self._fi = ema(fi, self._n, fillna=self._fillna)
  
      def force_index(self) -> pd.Series:
          """Force Index (FI)
  
          Returns:
              pandas.Series: New feature generated.
          """
          fi = self._check_fillna(self._fi, value=0)
          return pd.Series(fi, name=f'fi_{self._n}')
  
  
  class EaseOfMovementIndicator(IndicatorMixin):
      """Ease of movement (EoM, EMV)
  
      It relate an asset's price change to its volume and is particularly useful
      for assessing the strength of a trend.
  
      https://en.wikipedia.org/wiki/Ease_of_movement
  
      Args:
          high(pandas.Series): dataset 'High' column.
          low(pandas.Series): dataset 'Low' column.
          volume(pandas.Series): dataset 'Volume' column.
          n(int): n period.
          fillna(bool): if True, fill nan values.
      """
  
      def __init__(self, high: pd.Series, low: pd.Series, volume: pd.Series, n: int = 14, fillna: bool = False):
          self._high = high
          self._low = low
          self._volume = volume
          self._n = n
          self._fillna = fillna
          self._run()
  
      def _run(self):
          self._emv = (self._high.diff(1) + self._low.diff(1)) * (self._high - self._low) / (2 * self._volume)
          self._emv *= 100000000
  
      def ease_of_movement(self) -> pd.Series:
          """Ease of movement (EoM, EMV)
  
          Returns:
              pandas.Series: New feature generated.
          """
          emv = self._check_fillna(self._emv, value=0)
          return pd.Series(emv, name=f'eom_{self._n}')
  
      def sma_ease_of_movement(self) -> pd.Series:
          """Signal Ease of movement (EoM, EMV)
  
          Returns:
              pandas.Series: New feature generated.
          """
          emv = self._emv.rolling(self._n, min_periods=0).mean()
          emv = self._check_fillna(emv, value=0)
          return pd.Series(emv, name=f'sma_eom_{self._n}')
  
  
  class VolumePriceTrendIndicator(IndicatorMixin):
      """Volume-price trend (VPT)
  
      Is based on a running cumulative volume that adds or substracts a multiple
      of the percentage change in share price trend and current volume, depending
      upon the investment's upward or downward movements.
  
      https://en.wikipedia.org/wiki/Volume%E2%80%93price_trend
  
      Args:
          close(pandas.Series): dataset 'Close' column.
          volume(pandas.Series): dataset 'Volume' column.
          fillna(bool): if True, fill nan values.
      """
  
      def __init__(self, close: pd.Series, volume: pd.Series, fillna: bool = False):
          self._close = close
          self._volume = volume
          self._fillna = fillna
          self._run()
  
      def _run(self):
          vpt = (self._volume * ((self._close - self._close.shift(1, fill_value=self._close.mean()))
                                 / self._close.shift(1, fill_value=self._close.mean())))
          self._vpt = vpt.shift(1, fill_value=vpt.mean()) + vpt
  
      def volume_price_trend(self) -> pd.Series:
          """Volume-price trend (VPT)
  
          Returns:
              pandas.Series: New feature generated.
          """
          vpt = self._check_fillna(self._vpt, value=0)
          return pd.Series(vpt, name='vpt')
  
  
  class NegativeVolumeIndexIndicator(IndicatorMixin):
      """Negative Volume Index (NVI)
  
      http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:negative_volume_inde
  
      Args:
          close(pandas.Series): dataset 'Close' column.
          volume(pandas.Series): dataset 'Volume' column.
          fillna(bool): if True, fill nan values with 1000.
      """
  
      def __init__(self, close: pd.Series, volume: pd.Series, fillna: bool = False):
          self._close = close
          self._volume = volume
          self._fillna = fillna
          self._run()
  
      def _run(self):
          price_change = self._close.pct_change()
          vol_decrease = (self._volume.shift(1) > self._volume)
          self._nvi = pd.Series(data=np.nan, index=self._close.index, dtype='float64', name='nvi')
          self._nvi.iloc[0] = 1000
          for i in range(1, len(self._nvi)):
              if vol_decrease.iloc[i]:
                  self._nvi.iloc[i] = self._nvi.iloc[i - 1] * (1.0 + price_change.iloc[i])
              else:
                  self._nvi.iloc[i] = self._nvi.iloc[i - 1]
  
      def negative_volume_index(self) -> pd.Series:
          """Negative Volume Index (NVI)
  
          Returns:
              pandas.Series: New feature generated.
          """
          # IDEA: There shouldn't be any na; might be better to throw exception
          nvi = self._check_fillna(self._nvi, value=1000)
          return pd.Series(nvi, name='nvi')
  
  
  def acc_dist_index(high, low, close, volume, fillna=False):
      """Accumulation/Distribution Index (ADI)
  
      Acting as leading indicator of price movements.
  
      https://en.wikipedia.org/wiki/Accumulation/distribution_index
  
      Args:
          high(pandas.Series): dataset 'High' column.
          low(pandas.Series): dataset 'Low' column.
          close(pandas.Series): dataset 'Close' column.
          volume(pandas.Series): dataset 'Volume' column.
          fillna(bool): if True, fill nan values.
  
      Returns:
          pandas.Series: New feature generated.
      """
      return AccDistIndexIndicator(high=high, low=low, close=close, volume=volume, fillna=fillna).acc_dist_index()
  
  
  def on_balance_volume(close, volume, fillna=False):
      """On-balance volume (OBV)
  
      It relates price and volume in the stock market. OBV is based on a
      cumulative total volume.
  
      https://en.wikipedia.org/wiki/On-balance_volume
  
      Args:
          close(pandas.Series): dataset 'Close' column.
          volume(pandas.Series): dataset 'Volume' column.
          fillna(bool): if True, fill nan values.
  
      Returns:
          pandas.Series: New feature generated.
      """
      return OnBalanceVolumeIndicator(close=close, volume=volume, fillna=fillna).on_balance_volume()
  
  
  def chaikin_money_flow(high, low, close, volume, n=20, fillna=False):
      """Chaikin Money Flow (CMF)
  
      It measures the amount of Money Flow Volume over a specific period.
  
      http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:chaikin_money_flow_cmf
  
      Args:
          high(pandas.Series): dataset 'High' column.
          low(pandas.Series): dataset 'Low' column.
          close(pandas.Series): dataset 'Close' column.
          volume(pandas.Series): dataset 'Volume' column.
          n(int): n period.
          fillna(bool): if True, fill nan values.
  
      Returns:
          pandas.Series: New feature generated.
      """
      return ChaikinMoneyFlowIndicator(
          high=high, low=low, close=close, volume=volume, n=n, fillna=fillna).chaikin_money_flow()
  
  
  def force_index(close, volume, n=13, fillna=False):
      """Force Index (FI)
  
      It illustrates how strong the actual buying or selling pressure is. High
      positive values mean there is a strong rising trend, and low values signify
      a strong downward trend.
  
      http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:force_index
  
      Args:
          close(pandas.Series): dataset 'Close' column.
          volume(pandas.Series): dataset 'Volume' column.
          n(int): n period.
          fillna(bool): if True, fill nan values.
  
      Returns:
          pandas.Series: New feature generated.
      """
      return ForceIndexIndicator(close=close, volume=volume, n=n, fillna=fillna).force_index()
  
  
  def ease_of_movement(high, low, volume, n=14, fillna=False):
      """Ease of movement (EoM, EMV)
  
      It relate an asset's price change to its volume and is particularly useful
      for assessing the strength of a trend.
  
      https://en.wikipedia.org/wiki/Ease_of_movement
  
      Args:
          high(pandas.Series): dataset 'High' column.
          low(pandas.Series): dataset 'Low' column.
          volume(pandas.Series): dataset 'Volume' column.
          n(int): n period.
          fillna(bool): if True, fill nan values.
  
      Returns:
          pandas.Series: New feature generated.
      """
      return EaseOfMovementIndicator(
          high=high, low=low, volume=volume, n=n, fillna=fillna).ease_of_movement()
  
  
  def sma_ease_of_movement(high, low, volume, n=14, fillna=False):
      """Ease of movement (EoM, EMV)
  
      It relate an asset's price change to its volume and is particularly useful
      for assessing the strength of a trend.
  
      https://en.wikipedia.org/wiki/Ease_of_movement
  
      Args:
          high(pandas.Series): dataset 'High' column.
          low(pandas.Series): dataset 'Low' column.
          volume(pandas.Series): dataset 'Volume' column.
          n(int): n period.
          fillna(bool): if True, fill nan values.
  
      Returns:
          pandas.Series: New feature generated.
      """
      return EaseOfMovementIndicator(
          high=high, low=low, volume=volume, n=n, fillna=fillna).sma_ease_of_movement()
  
  
  def volume_price_trend(close, volume, fillna=False):
      """Volume-price trend (VPT)
  
      Is based on a running cumulative volume that adds or substracts a multiple
      of the percentage change in share price trend and current volume, depending
      upon the investment's upward or downward movements.
  
      https://en.wikipedia.org/wiki/Volume%E2%80%93price_trend
  
      Args:
          close(pandas.Series): dataset 'Close' column.
          volume(pandas.Series): dataset 'Volume' column.
          fillna(bool): if True, fill nan values.
  
      Returns:
          pandas.Series: New feature generated.
      """
      return VolumePriceTrendIndicator(close=close, volume=volume, fillna=fillna).volume_price_trend()
  
  
  def negative_volume_index(close, volume, fillna=False):
      """Negative Volume Index (NVI)
  
      http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:negative_volume_inde
  
      The Negative Volume Index (NVI) is a cumulative indicator that uses the
      change in volume to decide when the smart money is active. Paul Dysart
      first developed this indicator in the 1930s. [...] Dysart's Negative Volume
      Index works under the assumption that the smart money is active on days
      when volume decreases and the not-so-smart money is active on days when
      volume increases.
  
      The cumulative NVI line was unchanged when volume increased from one
      period to the other. In other words, nothing was done. Norman Fosback, of
      Stock Market Logic, adjusted the indicator by substituting the percentage
      price change for Net Advances.
  
      This implementation is the Fosback version.
  
      If today's volume is less than yesterday's volume then:
          nvi(t) = nvi(t-1) * ( 1 + (close(t) - close(t-1)) / close(t-1) )
      Else
          nvi(t) = nvi(t-1)
  
      Please note: the "stockcharts.com" example calculation just adds the
      percentange change of price to previous NVI when volumes decline; other
      sources indicate that the same percentage of the previous NVI value should
      be added, which is what is implemented here.
  
      Args:
          close(pandas.Series): dataset 'Close' column.
          volume(pandas.Series): dataset 'Volume' column.
          fillna(bool): if True, fill nan values with 1000.
  
      Returns:
          pandas.Series: New feature generated.
  
      See also:
          https://en.wikipedia.org/wiki/Negative_volume_index
      """
      return NegativeVolumeIndexIndicator(close=close, volume=volume, fillna=fillna).negative_volume_index()
  
  
  # TODO
  def put_call_ratio():
      """Put/Call ratio (PCR)
      https://en.wikipedia.org/wiki/Put/call_ratio
      """
      # TODO
      pass
  
apis: pandas.Series