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# 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. np.ndarray[dtype, ndim=1 or 2] Weighted moving average of each series.

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])