AlphaEval: A Comprehensive and Efficient Evaluation Framework for Formula Alpha Mining
Hongjun Ding, Binqi Chen, Jinsheng Huang, Taian Guo, Zhengyang Mao, Guoyi Shao, Lutong Zou, Luchen Liu, Ming Zhang

TL;DR
AlphaEval is a new evaluation framework for formula alpha mining that is comprehensive, efficient, and open-source, assessing multiple quality dimensions without relying on computationally intensive backtesting.
Contribution
It introduces a unified, parallelizable, backtest-free evaluation method that assesses alpha quality across five key dimensions, improving over existing metrics and enabling reproducibility.
Findings
AlphaEval achieves evaluation consistency comparable to backtesting.
It provides more comprehensive insights into alpha quality.
It effectively identifies superior alphas compared to traditional methods.
Abstract
Formula alpha mining, which generates predictive signals from financial data, is critical for quantitative investment. Although various algorithmic approaches-such as genetic programming, reinforcement learning, and large language models-have significantly expanded the capacity for alpha discovery, systematic evaluation remains a key challenge. Existing evaluation metrics predominantly include backtesting and correlation-based measures. Backtesting is computationally intensive, inherently sequential, and sensitive to specific strategy parameters. Correlation-based metrics, though efficient, assess only predictive ability and overlook other crucial properties such as temporal stability, robustness, diversity, and interpretability. Additionally, the closed-source nature of most existing alpha mining models hinders reproducibility and slows progress in this field. To address these issues,…
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