ParaFusionBench: A Unified Benchmark for Cross-Paradigm Fusion in Factor Mining and Trading Decision-Making

Quantitative investment research has evolved from conventional models to large language models (LLMs) and multi-agent collaboration, forming two primary research trajectories: factor mining and trading decision-making. Their intersection yields four paradigms: Conventional Factor Mining (CFM), LLM-driven Factor Mining (LFM), Conventional Trading Decision (CTD), and LLM-driven Trading Decision (LTD). Existing works mostly develop individual paradigms in isolation, lacking unified formal characterization and cross-paradigm fusion evaluation. Current benchmarks similarly tend to focus on a single paradigm or task, limiting themselves to model ranking while offering little insight into paradigm fusion. To address this gap, we propose ParaFusionBench, a cross-paradigm fusion benchmark, and construct a quantitative paradigm fusion framework, including formal fusion definitions, a seven-dimensional feasibility criterion, fusion injection methods, candidate fusion screening methods, and fusion effect evaluation metrics. It designs the EDD three-layer evaluation system to guide multi-dimensional evaluation of different paradigm instances. Experimental validation shows that the complementary strengths and weaknesses of individual paradigms provide feasible conditions for paradigm fusion. 83% of fusion combinations help reduce drawdown, and 66.7% and 58.3% of combinations achieve Sharpe and ARR gains, respectively. ParaFusionBench also identifies effective combinations such as AlphaGen + TradingAgents and AlphaProbe + FinMem, demonstrating its ability to uncover beneficial cross-paradigm compositions. These results further provide practical insights into when and how heterogeneous quantitative paradigms can be effectively integrated. Our code is available at https://anonymous.4open.science/r/ParaFusionBench-301F.