Find the maximum price change over any 1 to 5 day rolling window
A first pass will see (as Vishesh suggests) collapsing one dimension, and properly unpacking the index tuple:
import numpy as np

# Window size
W = 5

prices = np.arange(1, 10)  # npr.random([1000,1])*1000

max_array = np.zeros(prices.size-W)
for (index,), elem in np.ndenumerate(prices[:-W]):
    local_max = 0
    for i in range(1, W+1):
        price_return = prices[index + i] / elem
        local_max = max(local_max, price_return)
    max_array[index] = local_max
global_max = np.amax(max_array)

I've also replaced your random array with a simple one so that this is repeatable and verifiable. You should make your window size parametric or at least configurable via constant.
Unfortunately, none of the native Numpy options are great. Your best bet is to use Pandas:
import numpy as np
import pandas as pd

# Window size
W = 5

rolled = (
    pd.Series(prices)
    .rolling(window=W)
    .min()
    .to_numpy()
)
max_array = prices[1-W:] / rolled[W-1:-1]

I have tested this to be equivalent. Note that you need to start with a rolling minimum in the denominator to get a maximum numerator.