Finance learned an uncomfortable lesson long before today’s AI boom: a model can look excellent in simulation and still fail when it meets reality. Backtests can contain look-ahead bias, data can be incomplete, costs can be underestimated and live execution can diverge from the model.
QuantConnect explicitly separates research, backtesting, live trading and reconciliation. Its documentation explains why backtests and live deployments can diverge and gives users tools to compare live performance with an out-of-sample backtest over the same period.
Scientific and industrial AI need the same discipline
A CFD surrogate is not a CFD solver. A CFD solver is not a wind tunnel. A predicted binding affinity is not an assay. An assay is not a clinical outcome. A digital twin is not the physical plant.
LargeQuant keeps those evidence classes separate. This matters because autonomy amplifies whatever distinctions the system fails to make.
The future of quantitative AI will depend not only on faster models, but on better infrastructure for reconciling predicted, simulated, measured and realised outcomes.