# 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
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.

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.
"""
```