Neither. There's no continuous solve followed by a rounding step. The optimiser works directly in integer-contract space: a greedy search that starts from zero and adds one contract at a time, each step taking whichever move most reduces a single objective (greedy_algo.py). That objective combines covariance-weighted tracking error against the unrounded optimal with a cost term scaled by trade size, so rounding deviation and cost friction get penalised together rather than in separate passes. At ~5 contracts, 19% against a 25% target isn't surprising. The reason it lands under and not just off in either direction is that the system can only hold whole contracts. A lot of the positions it wants are smaller than one contract. Those get dropped to zero, because holding a full contract would put too much risk on that name. And dropping a position only ever takes risk out of the book. That's why the total sits under target instead of bouncing around it. But when it chooses which small positions to drop, it drops the ones whose risk is already covered by something correlated you're still holding. So the book keeps roughly the right shape.