fynance.features.momentums.wma

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

Compute weighted moving average(s) of size w for each X’ series.

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

Parameters:

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

Elements to compute the moving average.

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 average of each series.

See also

sma, ema, wmstd

Examples

>>> X = np.array([60, 100, 80, 120, 160, 80])
>>> wma(X, w=3, dtype=np.float64)
array([ 60.        ,  86.66666667,  83.33333333, 103.33333333,
       133.33333333, 113.33333333])
>>> X = X.reshape([6, 1])
>>> wma(X, w=3, dtype=np.float64).flatten()
array([ 60.        ,  86.66666667,  83.33333333, 103.33333333,
       133.33333333, 113.33333333])