R2 PT: Relation-Probed Dynamic Graphs with Regime Adaptive Evolutionary Factor Pruning for Quantitative Trading

Recent large‑model‑driven factor mining combined with static graph neural networks for quantitative trading suffers from two critical drawbacks: static asset relation graphs ignore dynamic causal spillover among stocks, and one‑shot global factor selection overlooks factor validity shifting under different market regimes. To address these limitations, we present Relation‑Probed Trading (R²PT), a practical end‑to‑end trading framework. First, we design a relation probe module to build time‑varying directed asset graphs by fusing news text signals, lead‑lag statistical correlation and industry prior knowledge. Second, we introduce a regime‑aware evolutionary pruning mechanism, which adaptively screens effective factors conditioned on current market status. Moreover, we adopt overlapping stratified backtesting to mitigate phase artifacts caused by fixed rebalancing offsets. Extensive out‑of‑sample rolling experiments and ablation studies are conducted on both S&P 500 and CSI 300 constituents. Empirical results demonstrate consistent performance gain and verify the independent contribution of each core component. We further discuss survivorship bias and hyper‑parameter sensitivity as key limitations for real‑world deployment.