Port the paper's ARIMA(p,0,q)-GARCH(1,1) daily-return forecasting rule to SPY.USEQ 1-DAY. Preserve daily parameter estimation and quarterly order selection. Baselines are training_bars=756, max_order=5 and order_criterion=AIC. Fit Gaussian joint ARMA-GARCH conditional likelihood on log returns with an intercept. Enumerate p and q from zero through max_order, including (0,0). Initialize residual history at zero and conditional variance at training sample variance; require positive omega, nonnegative alpha and beta, alpha+beta<1 and stationary/invertible ARMA coefficients. At the first eligible session and first session of January, April, July and October select orders by the lowest information criterion; ties prefer lower p+q, then lower p. Daily re-estimate the chosen order on the trailing training window. On estimation failure reuse the last valid model and advance its state with observed returns; remain cash if none exists. Implement genuine joint likelihood estimation with sandbox-allowed NumPy; statsmodels and arch imports are unavailable, and an AR-only substitute is not faithful. Interpret source DFL=1 as exposure capped at 100% account NAV, whole shares, no leverage. Risk route is signal exit plus capped sizing, with no added stop. Fills occur at the next session open rather than the paper's decision-price accounting. Daily model refitting is part of the fixed algorithm; optimizer-selected meta-parameters stay frozen.