SILO ALG

Picks which proprietary forecasting pipeline MAG runs. This is a MAG-wide setting because the choice is ingrained into every forecast AND every configurator walk — the configurator must measure accuracy under the same regime the forecaster will run, otherwise the saved accuracy does not represent what is actually produced. Changes take effect on the next forecast or configurate kickoff; in-flight jobs continue under the regime they started with.

SILO ALG 1 is the two-point compose method. The model is trained to predict the raw closing price two trading days ahead. At inference the runner makes two point-predictions — one for T+1, one for T+2 — and the reported magnitude is the percent difference between them. This is the preserved proprietary baseline. Susceptible to compounding error because the reported magnitude is a difference of two model outputs.

SILO ALG 2 is the direct-magnitude method. Each row of the history is massaged so its training target IS the percent change from that row's T+1 close to its T+2 close, and the model emits a single point-prediction at inference that IS the reported magnitude. No difference of two predictions, no compounding error. Higher accuracy in practice.

Training History

Maximum number of recent rows (days) to train on from TDM's derivative data for a symbol when training for its magnitude forecast.

Minimum SIG Confidence

Minimum SIG confidence a symbol must clear in SIG's latest report to enter MAG's bulk forecast. 0 disables the gate; 100 admits only perfect-confidence rows.

Minimum SIG Probability

Minimum SIG classifier probability a symbol must clear in SIG's latest report to enter MAG's bulk forecast. Applied alongside the minimum confidence above; both thresholds must be cleared. 0 disables the gate; 100 admits only perfect-probability rows. The bulk configurator does not apply this filter; it walks the same inventory SIG's bulk configurator walks (TDM Derivative Inventory) so MAG and SIG stay configured against the same actionable symbol set.