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54 lines
1.4 KiB
Python
54 lines
1.4 KiB
Python
# -*- coding: utf-8 -*-
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from pandas_ta.utils import get_offset, verify_series
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def variance(close, length=None, ddof=None, offset=None, **kwargs):
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"""Indicator: Variance"""
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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 > 1 else 30
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ddof = int(ddof) if ddof and ddof >= 0 and ddof < length else 0
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min_periods = int(kwargs["min_periods"]) if "min_periods" in kwargs and kwargs["min_periods"] is not None else length
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offset = get_offset(offset)
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# Calculate Result
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variance = close.rolling(length, min_periods=min_periods).var(ddof)
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# Offset
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if offset != 0:
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variance = variance.shift(offset)
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# Name & Category
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variance.name = f"VAR_{length}"
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variance.category = "statistics"
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return variance
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variance.__doc__ = \
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"""Rolling Variance
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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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VARIANCE = close.rolling(length).var()
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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: 0
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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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