Source code for pandas_ta.overlap.wma

# -*- coding: utf-8 -*-
from pandas import Series
from pandas_ta import Imports
from pandas_ta.utils import get_offset, verify_series

[docs]def wma(close, length=None, asc=None, talib=None, offset=None, **kwargs): """Indicator: Weighted Moving Average (WMA)""" # Validate Arguments length = int(length) if length and length > 0 else 10 asc = asc if asc else True close = verify_series(close, length) offset = get_offset(offset) mode_tal = bool(talib) if isinstance(talib, bool) else True if close is None: return # Calculate Result if Imports["talib"] and mode_tal: from talib import WMA wma = WMA(close, length) else: from numpy import arange as npArange from numpy import dot as npDot total_weight = 0.5 * length * (length + 1) weights_ = Series(npArange(1, length + 1)) weights = weights_ if asc else weights_[::-1] def linear(w): def _compute(x): return npDot(x, w) / total_weight return _compute close_ = close.rolling(length, min_periods=length) wma = close_.apply(linear(weights), raw=True) # Offset if offset != 0: wma = wma.shift(offset) # Handle fills if "fillna" in kwargs: wma.fillna(kwargs["fillna"], inplace=True) if "fill_method" in kwargs: wma.fillna(method=kwargs["fill_method"], inplace=True) # Name & Category = f"WMA_{length}" wma.category = "overlap" return wma
wma.__doc__ = \ """Weighted Moving Average (WMA) The Weighted Moving Average where the weights are linearly increasing and the most recent data has the heaviest weight. Sources: Calculation: Default Inputs: length=10, asc=True total_weight = 0.5 * length * (length + 1) weights_ = [1, 2, ..., length + 1] # Ascending weights = weights if asc else weights[::-1] def linear_weights(w): def _compute(x): return (w * x).sum() / total_weight return _compute WMA = close.rolling(length)_.apply(linear_weights(weights), raw=True) Args: close (pd.Series): Series of 'close's length (int): It's period. Default: 10 asc (bool): Recent values weigh more. Default: True talib (bool): If TA Lib is installed and talib is True, Returns the TA Lib version. Default: True 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. """