Turn price drivers into
a defensible forecast.

The difference is not the prediction model, but the translation of real-world events into causally calibrated, position-specific price signals. Every forecast comes with a verifiable track record.

A five-model baseline ensemble reads the price history, a causal event layer turns news into weighted signals, and integration fuses both onto the specific grade and position. A rolling forecast history keeps every projection on record against the realized price.

Your context, built in. Event signals are combined with baseline forecasts and your specific business assumptions — so projections reflect both statistical patterns and the developments that actually matter to your positions.

Classic models rely on pure time series or a handful of manual variables, and data terminals deliver prices and news without causal models or confidence-banded forecasts. DSA closes the gap: each news item becomes a modelable, evidence-backed signal tied to real price movements.