Source code for pandas_ta.overlap.hma

# -*- coding: utf-8 -*-
from numpy import sqrt as npSqrt
from .wma import wma
from pandas_ta.utils import get_offset, verify_series


[docs]def hma(close, length=None, offset=None, **kwargs): """Indicator: Hull Moving Average (HMA)""" # Validate Arguments length = int(length) if length and length > 0 else 10 close = verify_series(close, length) offset = get_offset(offset) if close is None: return # Calculate Result half_length = int(length / 2) sqrt_length = int(npSqrt(length)) wmaf = wma(close=close, length=half_length) wmas = wma(close=close, length=length) hma = wma(close=2 * wmaf - wmas, length=sqrt_length) # Offset if offset != 0: hma = hma.shift(offset) # Handle fills if "fillna" in kwargs: hma.fillna(kwargs["fillna"], inplace=True) if "fill_method" in kwargs: hma.fillna(method=kwargs["fill_method"], inplace=True) # Name & Category hma.name = f"HMA_{length}" hma.category = "overlap" return hma
hma.__doc__ = \ """Hull Moving Average (HMA) The Hull Exponential Moving Average attempts to reduce or remove lag in moving averages. Sources: https://alanhull.com/hull-moving-average Calculation: Default Inputs: length=10 WMA = Weighted Moving Average half_length = int(0.5 * length) sqrt_length = int(sqrt(length)) wmaf = WMA(close, half_length) wmas = WMA(close, length) HMA = WMA(2 * wmaf - wmas, sqrt_length) 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. """