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Python

# -*- coding: utf-8 -*-
from numpy import sqrt
from pandas import Series
from pandas_ta._typing import DictLike, Int
from pandas_ta.maps import Imports
from pandas_ta.utils import v_offset, v_pos_default, v_series, v_talib
from .variance import variance
def stdev(
close: Series, length: Int = None,
ddof: Int = None, talib: bool = None,
offset: Int = None, **kwargs: DictLike
) -> Series:
"""Rolling Standard Deviation
Calculates the Standard Deviation over a rolling period.
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. The 'talib'
argument must be false for 'ddof' to work. Default: 1
talib (bool): If TA Lib is installed and talib is True, Returns
the TA Lib version. TA Lib does not have a 'ddof' argument.
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.
"""
# Validate
length = v_pos_default(length, 30)
close = v_series(close, length)
if close is None:
return
ddof = int(ddof) if isinstance(ddof, int) and 0 <= ddof < length else 1
mode_tal = v_talib(talib)
offset = v_offset(offset)
# Calculate
if Imports["talib"] and mode_tal:
from talib import STDDEV
stdev = STDDEV(close, length)
else:
stdev = variance(
close=close, length=length, ddof=ddof, talib=mode_tal
).apply(sqrt)
# Offset
if offset != 0:
stdev = stdev.shift(offset)
# Fill
if "fillna" in kwargs:
stdev.fillna(kwargs["fillna"], inplace=True)
if "fill_method" in kwargs:
stdev.fillna(method=kwargs["fill_method"], inplace=True)
# Name and Category
stdev.name = f"STDEV_{length}"
stdev.category = "statistics"
return stdev