I am currently using random_sample to generate weightage allocation for 3 stocks where each row values add up to 1.

for portfolio in range (10):
    weights = np.random.random_sample(3)
    weights  = weights/ np.sum(weights)
    print (weights)

[0.39055438 0.44055996 0.16888567]
[0.22401792 0.26961926 0.50636282]
[0.67856154 0.21523207 0.10620639]
[0.33449127 0.36491387 0.30059486]
[0.55274192 0.23291811 0.21433997]
[0.20980909 0.38639029 0.40380063]
[0.24600751 0.199761   0.5542315 ]
[0.50743661 0.26633377 0.22622962]
[0.1154567  0.36803903 0.51650427]
[0.29092731 0.34675988 0.36231281]

I am able to do it but is there any way to ensure that the minimum weightage allocation is greater than 0.05? Meaning that the minimum weight allocation could only be something like [0.05 0.9 0.05]

CodePudding user response：

You can ignore them:

n = 0
while n < 10:
    weights = np.random.random_sample(3)
    weights  = weights/ np.sum(weights)
    if any(i < 0.05 for i in weights):
        continue
    n  = 1
    print (weights)

CodePudding user response：

Have a look at the docs

Results are from the “continuous uniform” distribution over the stated interval. To sample Unif(a,b), b>a multiply the output of random_sample by (b-a) and add a.

In this case, 0.95 * weight 0.05