Algorithm 2 Entropy-regulated quantitative trading.

1:Input: Trained C-T-GPR model, rolling window length T, entropy risk aversion $\kappa$, CVaR level $\alpha$, CVaR bound $\overline{c}$, initial capital $W_0$.   2:for each trading day t do   3:    Construct features $(X_t,Z_t)$ from rolling window $[t-T,t-1]$.   4:    Obtain $({\widehat{\mu }}_t^{*},{\widehat{v}}_t^{*},{\mathcal{H}}_t^{*})$ from Algorithm 1.   5:    Compute ${\mathrm{EUI}}_t$ via Equation (34).   6:    Compute entropy-penalised Kelly fraction $w_t^{*}=max(0,{\widehat{\mu }}_t^{*}/({\widehat{v}}_t^{*}·exp(\kappa ·{\mathrm{EUI}}_t)))$.   7:    If multiple assets: solve CVaR-constrained Equation (37) to obtain ${\mathit{w}}_t^{*}$.   8:     Trade execution:   If ${\widehat{\mu }}_t^{*}\le 0$, set $w_t^{*}=0$ (close any long position; short selling is not allowed).   Set target holdings ${\mathit{w}}_t^{*}$ from the CVaR step (multi-asset) or $(w_t^{*},1-w_t^{*})$ for single asset and cash.   Submit market orders to rebalance from ${\mathit{w}}_{t-1}$ to ${\mathit{w}}_t^{*}$.   Update portfolio value: $W_t=W_{t-1}\left(1+{\mathit{w}}_t^{*\top }{\mathbf{Y}}_t-c{\parallel {\mathit{w}}_t^{*}-{\mathit{w}}_{t-1}\parallel }_1\right)$, where ${\mathbf{Y}}_t$ is the realised return vector and c is the proportional transaction cost.   9:    Record ${\mathcal{H}}_t^{*}$, ${\mathrm{EUI}}_t$, $I_t$ for monitoring. 10:end for 11:Output: Portfolio value path, performance metrics, entropy time series.