Source code for pandas_ta.statistics.stdev
# -*- coding: utf-8 -*-
from numpy import sqrt as npsqrt
from .variance import variance
from pandas_ta import Imports
from pandas_ta.utils import get_offset, verify_series
[docs]def stdev(close, length=None, ddof=None, talib=None, offset=None, **kwargs):
"""Indicator: Standard Deviation"""
# Validate Arguments
length = int(length) if length and length > 0 else 30
ddof = int(ddof) if isinstance(ddof, int) and ddof >= 0 and ddof < length else 1
close = verify_series(close, length)
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 STDDEV
stdev = STDDEV(close, length)
else:
stdev = variance(close=close, length=length, ddof=ddof).apply(npsqrt)
# Offset
if offset != 0:
stdev = stdev.shift(offset)
# Handle fills
if "fillna" in kwargs:
stdev.fillna(kwargs["fillna"], inplace=True)
if "fill_method" in kwargs:
stdev.fillna(method=kwargs["fill_method"], inplace=True)
# Name & Category
stdev.name = f"STDEV_{length}"
stdev.category = "statistics"
return stdev
stdev.__doc__ = \
"""Rolling Standard Deviation
Sources:
Calculation:
Default Inputs:
length=30
VAR = Variance
STDEV = variance(close, length).apply(np.sqrt)
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: 1
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.
"""