roll_normalize¶

Defined in fynance.features.scale

roll_normalize(X, w=None, a=0, b=1, axis=0)[source]

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:

Xnp.ndarray[dtype, ndim=1 or 2]: Data to scale.
wint, 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, bfloat or array_like, optional: Respectively the lower and upper bound of the transformation.
axisint, optional: Axis along which to scale the data.

Returns: