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 = 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. """