QuantaAlpha: 一种基于大语言模型的alpha挖掘进化框架
QuantaAlpha: An Evolutionary Framework for LLM-Driven Alpha Mining
AI总结：
本文提出QuantaAlpha框架，通过进化算法改进alpha挖掘过程，通过轨迹级突变和交叉实现多轮搜索和经验重用，实验表明其在多个市场指数上均表现出稳健的性能。
摘要：
金融市场噪声和非平稳性使得alpha挖掘对回测噪声和制度转换高度敏感。尽管近期代理框架提高了自动化水平，但通常缺乏可控的多轮搜索和可靠的经验重用。为了解决这些挑战，我们提出了QuantaAlpha，一种进化alpha挖掘框架，将每个端到端挖掘运行视为轨迹，并通过轨迹级突变和交叉改进因素。QuantaAlpha定位次优步骤以进行针对性修订，并重新组合互补的高收益段以重用有效模式，从而在迭代中实现结构化探索和细化。在因子生成过程中，它强制假设、因子表达和可执行代码之间的语义一致性，并约束生成因子的复杂性和冗余性以缓解拥挤。在CSI 300上的大量实验表明，QuantaAlpha在强基线和先前代理系统上均表现出一致的优势。使用GPT-5.2，QuantaAlpha实现了IC为0.0472，ARR为4.68%，MDD为11.8%。此外，基于CSI 300挖掘的因子有效转移到CSI 500和S&P 500，分别在四年内分别产生约40.28%和19.1%的累计超额收益，这表明其在市场分布转换下的稳健性。
英文摘要：
Financial markets are noisy and non-stationary, making alpha mining highly sensitive to backtest noise and regime shifts. While recent agentic frameworks improve automation, they often lack controllable multi-round search and reliable reuse of validated experience. To address these challenges, we propose QuantaAlpha, an evolutionary alpha mining framework that treats each end-to-end mining run as a trajectory and improves factors via trajectory-level mutation and crossover. QuantaAlpha localizes suboptimal steps for targeted revision and recombines complementary high-reward segments to reuse effective patterns, enabling structured exploration and refinement across iterations. During factor generation, it enforces semantic consistency across hypothesis, factor expression, and executable code, and constrains the complexity and redundancy of the generated factor to mitigate crowding. Extensive experiments on CSI 300 show consistent gains over strong baselines and prior agentic systems. Using GPT-5.2, QuantaAlpha achieves an IC of 0.0472 with ARR of 4.68% and MDD of 11.8%. Moreover, factors mined on CSI 300 transfer effectively to CSI 500 and the S&P 500, delivering about 40.28% and 19.1% cumulative excess return over four years, respectively, which indicates strong robustness under market distribution shifts.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
