Dashboards Researchers Actually Check

It's tempting to imagine each strategy needs its own custom monitoring setup, but in practice most quant desks converge on the same handful of dashboards checked daily regardless of which strategy is running. A data-health dashboard flags missing, delayed, or anomalous input data before it can silently corrupt a model's signals. A live-versus-backtest dashboard overlays realized P&L against what the backtest predicted for the same period, since a growing gap between the two is usually the earliest sign something has broken. And a drift dashboard tracks whether the statistical properties a model was trained on — feature distributions, hit rates, turnover — are still holding up in current data, since models trained on stale conditions degrade quietly rather than failing loudly.

The common thread is that these dashboards catch the same few failure modes over and over — bad data, model decay, and unexpected implementation slippage — which is why researchers gravitate toward a small reusable set rather than reinventing monitoring for every new strategy.

New strategies typically get bolted onto these existing dashboards as another row or panel rather than a wholly separate report, since the point of the exercise is comparability: a researcher scanning the data-health dashboard across ten strategies at once notices a shared upstream data problem far faster than if each strategy's health were checked in isolation.