Source code for pandas_ta.performance.log_return

# -*- coding: utf-8 -*-
from numpy import log as nplog
from pandas_ta.utils import get_offset, verify_series

[docs]def log_return(close, length=None, cumulative=None, offset=None, **kwargs): """Indicator: Log Return""" # Validate Arguments length = int(length) if length and length > 0 else 1 cumulative = bool(cumulative) if cumulative is not None and cumulative else False close = verify_series(close, length) offset = get_offset(offset) if close is None: return # Calculate Result if cumulative: # log_return = nplog(close).diff(length).cumsum() log_return = nplog(close / close.iloc[0]) else: log_return = nplog(close / close.shift(length)) # nplog(close).diff(length) # 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 = 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: 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. """