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Cedric
Cedric

Reputation: 79

python random_sample minimum value

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]

Upvotes: 1

Views: 239

Answers (2)

Nin17
Nin17

Reputation: 3492

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)

Upvotes: 3

gimix
gimix

Reputation: 3833

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

Upvotes: 0

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