code: # -*- coding: utf-8 -*-
  from numpy import log as nplog
  from pandas_ta.utils import get_offset, verify_series
  
  
  def log_return(close, length=None, cumulative=False, offset=None, **kwargs):
      """Indicator: Log Return"""
      # Validate Arguments
      close = verify_series(close)
      length = int(length) if length and length > 0 else 1
      offset = get_offset(offset)
  
      # Calculate Result
      log_return = nplog(close).diff(periods=length)
  
      if cumulative:
          log_return = log_return.cumsum()
  
      # Offset
      if offset != 0:
          log_return = log_return.shift(offset)
  
      # Handle fills
      if "fillna" in kwargs:
          log_return.fillna(kwargs["fillna"], inplace=True)
      if "fill_method" in kwargs:
          log_return.fillna(method=kwargs["fill_method"], inplace=True)
  
      # Name & Category
      log_return.name = f"{'CUM' if cumulative else ''}LOGRET_{length}"
      log_return.category = "performance"
  
      return log_return
  
  
  log_return.__doc__ = \
  """Log Return
  
  Calculates the logarithmic return of a Series.
  See also: help(df.ta.log_return) for additional **kwargs a valid 'df'.
  
  Sources:
      https://stackoverflow.com/questions/31287552/logarithmic-returns-in-pandas-dataframe
  
  Calculation:
      Default Inputs:
          length=1, cumulative=False
      LOGRET = log( close.diff(periods=length) )
      CUMLOGRET = LOGRET.cumsum() if cumulative
  
  Args:
      close (pd.Series): Series of 'close's
      length (int): It's period.  Default: 20
      cumulative (bool): If True, returns the cumulative returns.  Default: False
      offset (int): How many periods to offset the result.  Default: 0
  
  Kwargs:
      fillna (value, optional): pd.DataFrame.fillna(value)
      fill_method (value, optional): Type of fill method
  
  Returns:
      pd.Series: New feature generated.
  """
  
apis: numpy.log