Rolling window REVISITED - Adding window rolling quantity as a parameter- Walk Forward Analysis

So, giving my two cents (with all the help of @Ben.T), here goes the code to create a Walk Forward Analysis basic tool to get a view on how will your model/models perform in a more generalized manner.

Non-anchored WFA

def walkForwardAnal(myArr, windowSize, rollQty):

    from numpy.lib.stride_tricks import as_strided

    ArrRows, ArrCols = myArr.shape

    ArrItems = myArr.itemsize

    sliceQtyAndShape = (int((ArrRows - windowSize) / rollQty + 1), windowSize, ArrCols)
    print('The final view shape is {}'.format(sliceQtyAndShape))

    ArrStrides = (rollQty * ArrCols * ArrItems, ArrCols * ArrItems, ArrItems)
    print('The final strides are {}'.format(ArrStrides))

    sliceList = list(as_strided(myArr, shape=sliceQtyAndShape, strides=ArrStrides, writeable=False))

    return sliceList

wSizeTr = 400
wSizeTe = 100
wSizeTot = wSizeTr + wSizeTe
rQty = 200

sliceListX = wf.walkForwardAnal(X, wSizeTot, rQty)
sliceListY = wf.walkForwardAnal(y, wSizeTot, rQty)

for sliceArrX, sliceArrY in zip(sliceListX, sliceListY):

    ## Consider having to make a .copy() of each array, so that we don't modify the original one. 

    # XArr = sliceArrX.copy() and hence, changing Xtrain, Xtest = XArr[...]
    # YArr = sliceArrY.copy() and hence, changing Ytrain, Ytest = XArr[...]

    Xtrain = sliceArrX[:-wSizeTe,:]
    Xtest = sliceArrX[-wSizeTe:,:]

    Ytrain = sliceArrY[:-wSizeTe,:]
    Ytest = sliceArrY[-wSizeTe:,:]

Anchored WFA

timeSeriesCrossVal = TimeSeriesSplit(n_splits=5)

    for trainIndex, testIndex in timeSeriesCrossVal.split(X):
        ## Check if the training and testing quantities make sense. If not, increase or decrease the n_splits parameter. 

        Xtrain = X[trainIndex]
        Xtest = X[testIndex]

        Ytrain = y[trainIndex]
        Ytest = y[testIndex]

Then, you could just create the following (in any of the two approaches) and keep modelling:

        # Fit on training set only - The targets (y) are already encoded in dummy variables, so no need to standarize them.
    scaler = StandardScaler()
    scaler.fit(Xtrain)

    # Apply transform to both the training set and the test set.
    trainX = scaler.transform(Xtrain)
    testX = scaler.transform(Xtest)

    ## PCA - Principal Component Analysis #### APPLY PCA TO THE STANDARIZED TRAINING SET! :::: Fit on training set only.
    pca = PCA(.95)
    pca.fit(trainX)

    # Apply transform to both the training set and the test set.
    trainX = pca.transform(trainX)
    testX = pca.transform(testX)

    ## Predict and append predictions...

The one liner for a non-anchored case with generalized window rolling quantity:

sliceListX = [arr[i: i + wSizeTot] for i in range(0, arr.shape[0] - wSizeTot+1, rQty)]

IIUC what you want, you can use np.lib.stride_tricks.as_strided to create the view of the windows size and the rolling quantity such as:

#redefine arr to see better what is happening than with random numbers
arr = np.arange(30).reshape((10,3))
#get arr properties
arr_0, arr_1 = arr.shape
arr_is = arr.itemsize #the size of element in arr
#parameter window and rolling
win_size = 5
roll_qty = 2
# use as_stribed by defining the right parameters:
from numpy.lib.stride_tricks import as_strided
print (as_strided( arr, 
                   shape=(int((arr_0 - win_size)/roll_qty+1), win_size,arr_1),
                   strides=(roll_qty*arr_1*arr_is, arr_1*arr_is, arr_is)))

array([[[ 0,  1,  2],
        [ 3,  4,  5],
        [ 6,  7,  8],
        [ 9, 10, 11],
        [12, 13, 14]],

       [[ 6,  7,  8],
        [ 9, 10, 11],
        [12, 13, 14],
        [15, 16, 17],
        [18, 19, 20]],

       [[12, 13, 14],
        [15, 16, 17],
        [18, 19, 20],
        [21, 22, 23],
        [24, 25, 26]]])

and for another window size and rolling quantity:

win_size = 4
roll_qty = 3
print( as_strided( arr, 
                   shape=(int((arr_0 - win_size)/roll_qty+1), win_size,arr_1),
                   strides=(roll_qty*arr_1*arr_is, arr_1*arr_is, arr_is)))

array([[[ 0,  1,  2],
        [ 3,  4,  5],
        [ 6,  7,  8],
        [ 9, 10, 11]],

       [[ 9, 10, 11],
        [12, 13, 14],
        [15, 16, 17],
        [18, 19, 20]],

       [[18, 19, 20],
        [21, 22, 23],
        [24, 25, 26],
        [27, 28, 29]]])