面向自主公式化阿尔法发现：进化计算视角
Towards Autonomous Formulaic Alpha Discovery: An Evolutionary Computation Perspective
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中文总结 AI 辅助
本文从进化计算视角，提出自动化公式化阿尔法发现的统一分析与评估框架，为开发可靠的自主阿尔法发现系统奠定基础。
中文摘要 AI 辅助
自动化公式化阿尔法发现旨在从庞大的符号因子空间中生成具有预测性和可解释性的交易信号，但其有效性受限于有噪的适应度估计、市场非平稳性、回测成本高、语义冗余以及相互冲突的实际目标。现有研究采用了多种技术，包括遗传编程（GP）、进化算法（EAs）、强化学习（RL）、生成流网络（GFlowNets）、蒙特卡洛树搜索（MCTS）、大语言模型（LLMs）和智能体工作流，但通常将这些技术作为独立的算法家族进行研究。本文首次提出了自动化公式化阿尔法发现的统一进化计算（EC）视角，将其表述为一个有噪、动态且多目标的符号进化优化问题。研究开发了一个包含六个组件的分析框架，通过表示、变异、适应度评估、选择、记忆和适应来刻画现有方法；还提出了一个八维的、面向自主的评估框架，涵盖搜索效率、适应度可靠性、残余阿尔法质量、经济多样性、可交易性、进化自主性、对非平稳性的鲁棒性以及可复现性。这些框架共同为统一异构方法、诊断组件级限制以及指导可靠、自适应、可解释且可复现的自主阿尔法发现系统的开发提供了系统基础。
英文摘要
Automated formulaic alpha discovery aims to generate predictive and interpretable trading signals from large symbolic factor spaces. Its effectiveness is constrained by noisy fitness estimates, market nonstationarity, costly backtesting, semantic redundancy, and conflicting practical objectives. Existing studies employ diverse techniques, including genetic programming (GP), evolutionary algorithms (EAs), reinforcement learning (RL), generative flow networks (GFlowNets), Monte Carlo tree search (MCTS), large language models (LLMs), and agentic workflows, but generally examine them as separate algorithmic families. This article introduces, for the first time, a unified evolutionary computation (EC) perspective on automated formulaic alpha discovery, formulating it as a noisy, dynamic, and multiobjective symbolic evolutionary optimization problem. A six-component analytical framework is developed to characterize existing methods through representation, variation, fitness evaluation, selection, memory, and adaptation. Furthermore, an eight-dimensional, autonomy-oriented evaluation framework is proposed, covering search efficiency, fitness reliability, residual alpha quality, economic diversity, tradability, evolutionary autonomy, robustness to nonstationarity, and reproducibility. Together, these frameworks provide a systematic foundation for unifying heterogeneous approaches, diagnosing component-level limitations, and guiding the development of reliable, adaptive, interpretable, and reproducible autonomous alpha discovery systems.
