mirror of
https://github.com/wassname/pandas-ta.git
synced 2026-07-30 12:20:22 +08:00
YuvalWeinandGitHub
4cf1d86996
Same as variance explanation, since std = sqrt(variance): When calculation variance we can divide the calculation by N, or by N-1. When calculating sample variance we divide by N-1 (like in the current code).(https://towardsdatascience.com/why-sample-variance-is-divided-by-n-1-89821b83ef6d) Sometimes We want to divide by N like the basis formula, to calculate population variance. (https://en.wikipedia.org/wiki/Variance#Discrete_random_variable) For me its useful to add another parameter that will make it possible to calculate both of then using one function. Is it useful for you?
55 lines
1.4 KiB
Python
55 lines
1.4 KiB
Python
# -*- coding: utf-8 -*-
|
|
from numpy import sqrt as npsqrt
|
|
from .variance import variance
|
|
from ..utils import get_offset, verify_series
|
|
|
|
def stdev(close, length=None, ddof=1, offset=None, **kwargs):
|
|
"""Indicator: Standard Deviation"""
|
|
# Validate Arguments
|
|
close = verify_series(close)
|
|
length = int(length) if length and length > 0 else 30
|
|
ddof = int(ddof) if ddof >= 0 and ddof < length else 1
|
|
offset = get_offset(offset)
|
|
|
|
# Calculate Result
|
|
stdev = variance(close=close, length=length, ddof=ddof).apply(npsqrt)
|
|
|
|
# Offset
|
|
if offset != 0:
|
|
stdev = stdev.shift(offset)
|
|
|
|
# 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
|
|
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.
|
|
"""
|