roll_mad

Defined in fynance.features.metrics

roll_mad(X, w=None, axis=0, dtype=None)[source]

Compute rolling Mean Absolut Deviation for each X’ series.

Compute the moving average of the absolute value of the distance to the moving average [6].

Parameters:
Xnp.ndarray[dtype, ndim=1 or 2]

Time series (price, performance or index).

wint, optional

Size of the lagged window of the rolling function, must be positive. If w is None or w=0, then w=X.shape[axis]. Default is None.

axis{0, 1}, optional

Axis along wich the computation is done. Default is 0.

dtypenp.dtype, optional

The type of the output array. If dtype is not given, infer the data type from X input.

Returns:
np.ndarray[dtype, ndim=1 or 2]

Series of mean absolute deviation.

See also

mad

References

Examples

>>> X = np.array([70, 100, 90, 110, 150, 80])
>>> roll_mad(X, dtype=np.float64)
array([ 0.        , 15.        , 11.11111111, 12.5       , 20.8       ,
       20.        ])
>>> X = np.array([60, 100, 80, 120, 160, 80]).astype(np.float64)
>>> roll_mad(X, w=3, dtype=np.float64)
array([ 0.        , 20.        , 13.33333333, 13.33333333, 26.66666667,
       26.66666667])