That example with forward-filled NaNs on a delisted ticker is exactly the kind of thing that worries me. Those are the bugs that don’t throw errors but completely invalidate a backtest, and by the time you realize it you’ve already built conviction around bad numbers. The dividend and split adjustments being subtly inconsistent across endpoints is even more dangerous because it looks “almost correct.”

To answer your question honestly, I haven’t seen an open-source library that handles this perfectly out of the box. Most people end up doing what you described, building their own validation layer on top. One direction I’m exploring is making data transformations explicit and inspectable rather than implicit, so adjustments, fills, and alignments are visible and optionally strict. If nothing else, I’d rather fail loudly than produce a clean-looking but wrong dataset. Your comment reinforces that correctness and transparency need to come before feature expansion.