13 NaN Features. Same Code. Different Data.
When V5 went live, every single inference run logged the same warning: WARNING: 13 features contain NaN values. Filling with 0. I saw it on day one. I told myself it was a data warmup issue — the live feed just needed more bars to stabilize. That was wrong. Same Code, Different Data V5 solved the training/live code divergence problem from V4. One shared feature_core.py. Every pipeline — training, backtest, all five live scripts — imports from the same file. ...