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76 lines
2.1 KiB
Python
76 lines
2.1 KiB
Python
# -*- coding: utf-8 -*-
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from numpy import arange as nparange
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from pandas import Series
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from ..utils import get_offset, verify_series
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def wma(close, length=None, asc=None, offset=None, **kwargs):
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"""Indicator: Weighted Moving Average (WMA)"""
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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 10
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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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asc = asc if asc else True
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offset = get_offset(offset)
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# Calculate Result
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total_weight = 0.5 * length * (length + 1)
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weights_ = Series(nparange(1, length + 1))
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weights = weights_ if asc else weights_[::-1]
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def linear(w):
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def _compute(x):
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return (w * x).sum() / total_weight
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return _compute
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close_ = close.rolling(length, min_periods=length)
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wma = close_.apply(linear(weights), raw=True)
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# Offset
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if offset != 0:
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wma = wma.shift(offset)
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# Name & Category
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wma.name = f"WMA_{length}"
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wma.category = 'overlap'
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return wma
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wma.__doc__ = \
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"""Weighted Moving Average (WMA)
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The Weighted Moving Average where the weights are linearly increasing and
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the most recent data has the heaviest weight.
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Sources:
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https://en.wikipedia.org/wiki/Moving_average#Weighted_moving_average
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Calculation:
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Default Inputs:
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length=10, asc=True
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total_weight = 0.5 * length * (length + 1)
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weights_ = [1, 2, ..., length + 1] # Ascending
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weights = weights if asc else weights[::-1]
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def linear_weights(w):
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def _compute(x):
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return (w * x).sum() / total_weight
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return _compute
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WMA = close.rolling(length)_.apply(linear_weights(weights), raw=True)
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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: 10
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asc (bool): Recent values weigh more. 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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