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74 lines
2.2 KiB
Python
74 lines
2.2 KiB
Python
# -*- coding: utf-8 -*-
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from pandas import Series
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from pandas_ta._typing import DictLike, Int
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from pandas_ta.maps import Imports
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from pandas_ta.utils import v_lowerbound, v_offset, v_series, v_talib
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def variance(
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close: Series, length: Int = None,
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ddof: Int = None, talib: bool = None,
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offset: Int = None, **kwargs: DictLike
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) -> Series:
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"""Rolling Variance
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Calculates the Variance over a rolling period.
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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.
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The 'talib' argument must be false for 'ddof' to work.
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Default: 1
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talib (bool): If TA Lib is installed and talib is True, Returns
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the TA Lib version. Note: TA Lib does not have a 'ddof' argument.
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Default: True
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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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# Validate
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length = v_lowerbound(length, 1, 30)
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if "min_periods" in kwargs and kwargs["min_periods"] is not None:
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min_periods = int(kwargs["min_periods"])
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else:
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min_periods = length
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close = v_series(close, max(length, min_periods))
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if close is None:
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return
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ddof = int(ddof) if isinstance(ddof, int) and 0 <= ddof < length else 1
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mode_tal = v_talib(talib)
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offset = v_offset(offset)
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# Calculate
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if Imports["talib"] and mode_tal:
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from talib import VAR
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variance = VAR(close, length)
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else:
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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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# Fill
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if "fillna" in kwargs:
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variance.fillna(kwargs["fillna"], inplace=True)
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if "fill_method" in kwargs:
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variance.fillna(method=kwargs["fill_method"], inplace=True)
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# Name and 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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