mirror of
https://github.com/wassname/pandas-ta.git
synced 2026-06-27 16:10:07 +08:00
James Sawyer
54 lines
1.4 KiB
Python
54 lines
1.4 KiB
Python
# -*- coding: utf-8 -*-
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from numpy import sqrt as npsqrt
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from .variance import variance
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from ..utils import get_offset, verify_series
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def stdev(close, length=None, ddof=1, offset=None, **kwargs):
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"""Indicator: Standard Deviation"""
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# Validate Arguments
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close = verify_series(close)
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length = int(length) if length and length > 0 else 30
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ddof = int(ddof) if ddof >= 0 and ddof < length else 1
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offset = get_offset(offset)
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# Calculate Result
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stdev = variance(close=close, length=length, ddof=ddof).apply(npsqrt)
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# Offset
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if offset != 0:
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stdev = stdev.shift(offset)
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# Name & Category
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stdev.name = f"STDEV_{length}"
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stdev.category = "statistics"
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return stdev
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stdev.__doc__ = """Rolling Standard Deviation
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Sources:
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Calculation:
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Default Inputs:
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length=30
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VAR = Variance
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STDEV = variance(close, length).apply(np.sqrt)
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Args:
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close (pd.Series): Series of 'close's
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length (int): It's period. Default: 30
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ddof (int): Delta Degrees of Freedom.
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The divisor used in calculations is N - ddof,
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where N represents the number of elements. Default: 1
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offset (int): How many periods to offset the result. Default: 0
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Kwargs:
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fillna (value, optional): pd.DataFrame.fillna(value)
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fill_method (value, optional): Type of fill method
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Returns:
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pd.Series: New feature generated.
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"""
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