alma

API documentation for pandas_ta.overlap.alma Python function.

alma(close, length=None, sigma=None, distribution_offset=None, offset=None, **kwargs)

Arnaud Legoux Moving Average (ALMA)

The ALMA moving average uses the curve of the Normal (Gauss) distribution, which can be shifted from 0 to 1. This allows regulating the smoothness and high sensitivity of the indicator. Sigma is another parameter that is responsible for the shape of the curve coefficients. This moving average reduces lag of the data in conjunction with smoothing to reduce noise.

Implemented for Pandas TA by rengel8 based on the source provided below.

Sources:

https://www.prorealcode.com/prorealtime-indicators/alma-arnaud-legoux-moving-average/

Calculation:

refer to provided source

Args:

close (pd.Series): Series of ‘close’s length (int): It’s period, window size. Default: 10 sigma (float): Smoothing value. Default 6.0 distribution_offset (float): Value to offset the distribution min 0

(smoother), max 1 (more responsive). Default 0.85

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.