# -*- 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