Source code for pandas_ta.statistics.variance
# -*- coding: utf-8 -*-
from pandas_ta import Imports
from pandas_ta.utils import get_offset, verify_series
[docs]def variance(close, length=None, ddof=None, talib=None, offset=None, **kwargs):
"""Indicator: Variance"""
# Validate Arguments
length = int(length) if length and length > 1 else 30
ddof = int(ddof) if isinstance(ddof, int) and ddof >= 0 and ddof < length else 1
min_periods = int(kwargs["min_periods"]) if "min_periods" in kwargs and kwargs["min_periods"] is not None else length
close = verify_series(close, max(length, min_periods))
offset = get_offset(offset)
mode_tal = bool(talib) if isinstance(talib, bool) else True
if close is None: return
# Calculate Result
if Imports["talib"] and mode_tal:
from talib import VAR
variance = VAR(close, length)
else:
variance = close.rolling(length, min_periods=min_periods).var(ddof)
# Offset
if offset != 0:
variance = variance.shift(offset)
# Handle fills
if "fillna" in kwargs:
variance.fillna(kwargs["fillna"], inplace=True)
if "fill_method" in kwargs:
variance.fillna(method=kwargs["fill_method"], inplace=True)
# Name & Category
variance.name = f"VAR_{length}"
variance.category = "statistics"
return variance
variance.__doc__ = \
"""Rolling Variance
Sources:
Calculation:
Default Inputs:
length=30
VARIANCE = close.rolling(length).var()
Args:
close (pd.Series): Series of 'close's
length (int): It's period. Default: 30
ddof (int): Delta Degrees of Freedom.
The divisor used in calculations is N - ddof,
where N represents the number of elements. Default: 0
talib (bool): If TA Lib is installed and talib is True, Returns the TA Lib
version. Default: True
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.
"""