Survivorship bias and silent restatements quietly inflate backtest returns. Here is how point-in-time data keeps research honest.
Most backtests are optimistic — not because the strategy is good, but because the data has been cleaned with the benefit of hindsight. Instruments that were delisted vanish from the universe. Prices that were later corrected are shown as if they were always right. The result is a model that looks brilliant on paper and disappoints in production.
Point-in-time data solves this by recording not just what a value is, but what it was known to be at each moment. When you query as of a historical timestamp, you see exactly the world your strategy would have seen — no lookahead, no restatements, no ghosts of assets that no longer exist.
At QXX, every dataset is versioned this way. History is immutable and additive: corrections are recorded as new observations rather than overwriting the past. That makes a backtest reproducible, and a discrepancy explainable.
The practical upshot is simple. When live performance matches the backtest, you can trust the edge. When it doesn't, the data isn't the reason — and that alone saves research teams weeks of second-guessing.