fynance.features.momentums.wmstd

fynance.features.momentums.wmstd(X, w=None, axis=0, dtype=None)

Compute weighted moving standard(s) deviation for each X’ series’.

\[\begin{split}wma^w_t(X) = \frac{2}{w (w-1)} \sum^{w-1}_{i=0} (w-i) \times X_{t-i} \\ wmstd^w_t(X) = \sqrt{\frac{2}{w(w-1)} \sum^{w-1}_{i=0} (w-i) \times (X_{t-i} - wma^w_t(X))^2}\end{split}\]

Parameters:

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

Elements to compute the moving standard deviation.

w : int, optional

Size of the lagged window of the moving average, 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.

dtype : np.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]

Weighted moving standard deviation of each series.

See also

wma, smstd, emstd

Examples

>>> X = np.array([60, 100, 80, 120, 160, 80])
>>> wmstd(X, w=3, dtype=np.float64)
array([ 0.        , 18.85618083, 13.74368542, 17.95054936, 29.8142397 ,
       35.90109871])
>>> wmstd(X.reshape([6, 1]), w=3, dtype=np.float64).flatten()
array([ 0.        , 18.85618083, 13.74368542, 17.95054936, 29.8142397 ,
       35.90109871])