Source code for pandas_ta.overlap.sinwma

# -*- coding: utf-8 -*-
from numpy import pi as npPi
from numpy import sin as npSin
from pandas import Series
from pandas_ta.utils import get_offset, verify_series, weights

[docs]def sinwma(close, length=None, offset=None, **kwargs): """Indicator: Sine Weighted Moving Average (SINWMA) by Everget of TradingView""" # Validate Arguments length = int(length) if length and length > 0 else 14 close = verify_series(close, length) offset = get_offset(offset) if close is None: return # Calculate Result sines = Series([npSin((i + 1) * npPi / (length + 1)) for i in range(0, length)]) w = sines / sines.sum() sinwma = close.rolling(length, min_periods=length).apply(weights(w), raw=True) # Offset if offset != 0: sinwma = sinwma.shift(offset) # Handle fills if "fillna" in kwargs: sinwma.fillna(kwargs["fillna"], inplace=True) if "fill_method" in kwargs: sinwma.fillna(method=kwargs["fill_method"], inplace=True) # Name & Category = f"SINWMA_{length}" sinwma.category = "overlap" return sinwma
sinwma.__doc__ = \ """Sine Weighted Moving Average (SWMA) A weighted average using sine cycles. The middle term(s) of the average have the highest weight(s). Source: Author: Everget ( Calculation: Default Inputs: length=10 def weights(w): def _compute(x): return * x) return _compute sines = Series([sin((i + 1) * pi / (length + 1)) for i in range(0, length)]) w = sines / sines.sum() SINWMA = close.rolling(length, min_periods=length).apply(weights(w), raw=True) Args: close (pd.Series): Series of 'close's length (int): It's period. Default: 10 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. """