I am trying to run Monte Carlo simulation for 520 weeks and 10000 trials. I am using the below code for the purpose.

my code:

import numpy as np
import pandas as pd
from pandas_datareader import data as wb
import matplotlib.pyplot as plt
import seaborn as sns
from scipy.stats import norm


u=log_return.mean()
var=log_return.var()
drift=u-(0.5*var)

stdev=log_return.std()
weeks=520
trials=10000
z=norm.ppf(np.random.rand(weeks,trials))
weekly_returns=np.exp(drift+stdev * z)

price_paths=np.zeros_like(weekly_returns)
price_paths[0]=data2.iloc[-1]
for t in range(1,weeks):
    price_paths[t]=(price_paths[t-1]*weekly_returns[t])

What I want to do is to set upper cut (bound) and lower cut (bound) so that the simulated values remain within these range.

for example: if a series is to have lower bound as 10 and upper as 100, it means, the entire series values will lie within 10 and 100 (both inclusive).

Can it be done?