How should an event-driven Kelly allocator price capital reserved for stochastic future signals?

I am building an event-driven capital allocator for three trading strategies. This is a portfolio-optimization and validation question, not a request for investment advice. Signals arrive asynchronously. Several signals can be available at the same time, positions lock capital until they close, and the arrival times, holding periods, and returns of future signals are uncertain. For each current signal $i$ , I have: a posterior predictive return distribution $R_i$ ; a stop-loss risk estimate; an estimated holding period; model-level and portfolio-level risk limits. The allocator must make three related decisions: How much free capital should be invested now? How much capacity should be reserved for signals that may arrive later? How should current capital be divided among simultaneous signals, while generally favoring signals with higher posterior expected value? My intended objective is long-run geometric growth, subject to explicit drawdown, gross-exposure, per-trade, and per-model constraints. A natural formulation appears to be $V_t(s_t)=\max_{x_t \in X(s_t)}\inf_{P \in \mathcal{U}_t}\mathbb{E}_P\left[\log\left(\frac{W_{t+1}}{W_t}\right)+V_{t+1}(s_{t+1})\right].$ Here, the state includes free capital, open positions, time, and remaining holding periods. The marginal value of one unit of free capacity would then be a shadow price such as $\lambda_t(c)=\frac{\partial V_t(s_t)}{\partial c_{\mathrm{free}}}.$ The current implementation approximates the continuation-value curve by block-bootstrapping historical sequences of future signal arrivals and applying a greedy recourse allocator to each simulated sequence. Current signals are then allocated with a joint expected-log optimizer. However, this approach appears to overvalue future capacity. I have only a few hundred event decisions. In a chronological development test using identical signals and initial capital: the trained policy finished with only about 40% of the final wealth of an equal-stop-risk baseline; its maximum drawdown was worse, 78.7% versus 73.1%; it accepted 176 signals versus 128 for the baseline; in a five-seed ablation at the same current-exposure level, setting continuation value to zero outperformed the full continuation model in every seed. The policy search evaluated 3,000 calibration configurations, but the continuation-value weight itself had been structurally fixed at one. Therefore, the search could not select the empirically superior zero-continuation case. My main question is: What statistically defensible, low-sample method would you use to estimate and cross-validate the marginal continuation value of free capital, so that the allocator does not systematically over-reserve for hypothetical future signals while still accounting for their stochastic arrival? I am particularly interested in whether this should be treated as: a dynamic stochastic-knapsack or bid-price problem; a receding-horizon stochastic program with an approximate terminal value function; or a simpler current-only robust Kelly allocator until continuation value demonstrates out-of-sample value. A useful answer would ideally describe the state variables, the continuation-value estimator, and an appropriate nested chronological validation procedure. I am not looking for a package recommendation or a neural-network solution.