fynance.features.scale.roll_normalize¶

fynance.features.scale.roll_normalize(X, w=None, a=0, b=1, axis=0)¶

Scale the data between a and b.

Substitutes the rolling minimum and divid by the difference between the rolling maximum and the minimum. Then multiply by b minus a and add a.

\[RollNormalize(X)^w_t = (b - a) \times \frac{X_t - RollMin(X)^w_t} {RollMax(X)^w_t - RollMin(X)^w_t} + a\]

Parameters:	X : np.ndarray[dtype, ndim=1 or 2] Data to scale. w : int, optional Size of the lagged window of the rolling minimum/maximum, must be positive. If `w is None` or `w=0`, then `w=X.shape[axis]`. Default is None. a, b : float or array_like, optional Respectively the lower and upper bound of the transformation. axis : int, optional Axis along which to scale the data.
Returns:	np.ndarray[dtype, ndim=1 or 2] The scaled data.