AlphaRouter: An Expert Routing Approach for Adaptive Portfolio Selection

Portfolio selection requires adapting asset allocations to changing market conditions while controlling risk and satisfying investment constraints. Existing learning-based methods often predict asset weights directly from market data, making portfolio decisions sensitive to noisy and nonstationary observations. We propose AlphaRouter, an adaptive portfolio selection framework that separates portfolio construction from market-dependent selection. AlphaRouter maintains a set of complementary experts, each capturing distinct historical market patterns to produce a complete portfolio under shared investment constraints. To overcome the high optimization variance of single neural networks in noisy markets, an ensemble of independently trained Transformer routers evaluates expert suitability from recent states, aggregating their calibrated decisions through majority voting to form a robust consensus. A historically best-performing expert serves as a stable reference, and routing adjustments are applied only when router consensus is sufficiently confident, mitigating unstable switching caused by short-term noise. By reducing the learned decision from high-dimensional weight prediction to expert-level selection, AlphaRouter guarantees constraint satisfaction by construction and improves robustness to noisy, nonstationary markets. Experiments on DJIA, HSI, CSI, and CRYPTO show that AlphaRouter achieves the best performance among the compared methods in cumulative wealth, annualized percentage yield, annualized Sharpe ratio, and Calmar ratio.