Source code for pandas_ta.overlap.pwma

# -*- coding: utf-8 -*-
from pandas_ta.utils import get_offset, pascals_triangle, verify_series, weights

[docs]def pwma(close, length=None, asc=None, offset=None, **kwargs): """Indicator: Pascals Weighted Moving Average (PWMA)""" # 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) if close is None: return # Calculate Result triangle = pascals_triangle(n=length - 1, weighted=True) pwma = close.rolling(length, min_periods=length).apply(weights(triangle), raw=True) # Offset if offset != 0: pwma = pwma.shift(offset) # Handle fills if "fillna" in kwargs: pwma.fillna(kwargs["fillna"], inplace=True) if "fill_method" in kwargs: pwma.fillna(method=kwargs["fill_method"], inplace=True) # Name & Category = f"PWMA_{length}" pwma.category = "overlap" return pwma
pwma.__doc__ = \ """Pascal's Weighted Moving Average (PWMA) Pascal's Weighted Moving Average is similar to a symmetric triangular window except PWMA's weights are based on Pascal's Triangle. Source: Kevin Johnson Calculation: Default Inputs: length=10 def weights(w): def _compute(x): return * x) return _compute triangle = utils.pascals_triangle(length + 1) PWMA = close.rolling(length)_.apply(weights(triangle), 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 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. """