Yep. You'll end up with a lopsided allocation, with one or two allocations holding a very large percentage of the total allocation.
Distributing the allocation weights could help, e.g. with ensembling, regularization or some other kind of penalty.
When i compared them a year or so ago, my conclusion that OR-Tools had more focus on engineering, making the whole thing easy to integrate etc, whilst COIN-OR had more sophisticated solvers, and gave you lots and lots of knobs to tweak.
Convex optimization is also useful if you care about optimizing convex functions. Which I bet you do.
This is a basic LP problem that you'll come across in a textbook.
By contrast, you effectively said, "Heh, I've seen fancier models. BTW, you can just plug your stuff into Google."
Well, yeah, for something so rudimentary. Use a library. Nothing to see here.
I also doubt that it's optimal. An implementation using Hierarchical Risk Parity for instance would be more interesting. I assume you can model risk/uncertainty here.
By the way, Google has an OR tools framework that implements these kind of solvers for you:
https://developers.google.com/optimization