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AutoAlpha: an Efficient Hierarchical Evolutionary Algorithm for Mining Alpha Factors in Quantitative Investment

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arxiv 2002.08245 v2 pith:COPGZ3ZI submitted 2020-02-09 q-fin.CP

Tianping Zhang , Yuanqi Li , Yifei Jin , Jian Li This is my paper
classification q-fin.CP
keywords modelalphasformulaicautoalphaproposequantitativesearchalgorithm
verification ladder T0 review T1 audit T2 compute T3 formal
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The multi-factor model is a widely used model in quantitative investment. The success of a multi-factor model is largely determined by the effectiveness of the alpha factors used in the model. This paper proposes a new evolutionary algorithm called AutoAlpha to automatically generate effective formulaic alphas from massive stock datasets. Specifically, first we discover an inherent pattern of the formulaic alphas and propose a hierarchical structure to quickly locate the promising part of space for search. Then we propose a new Quality Diversity search based on the Principal Component Analysis (PCA-QD) to guide the search away from the well-explored space for more desirable results. Next, we utilize the warm start method and the replacement method to prevent the premature convergence problem. Based on the formulaic alphas we discover, we propose an ensemble learning-to-rank model for generating the portfolio. The backtests in the Chinese stock market and the comparisons with several baselines further demonstrate the effectiveness of AutoAlpha in mining formulaic alphas for quantitative trading.

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Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Cognitive Alpha Mining via LLM-Driven Code-Based Evolution

    cs.CL 2025-11 unverdicted novelty 7.0 of 10

    CogAlpha combines LLM reasoning with code-level evolutionary search to discover financial alphas that show higher predictive accuracy and generalization than prior methods on five stock datasets.

  2. AlphaSchema: Exploring the Space of Trading Semantics for LLM-Based Alpha Mining

    cs.AI 2026-07 conditional novelty 6.0 of 10

    Searching over explicit trading-semantics plans (event/context/qualities/direction/output), instead of factor code, yields competitive alpha pools and makes the search space controllable and learnable.

  3. AlphaEval: A Comprehensive and Efficient Evaluation Framework for Formula Alpha Mining

    cs.AI 2025-08 conditional novelty 6.0 of 10

    AlphaEval scores alpha mining models on prediction, stability, robustness, logic, and diversity, replacing backtests with fast parallel metrics that the paper claims align with backtest outcomes.

  4. Learning from Expert Factors: Trajectory-level Reward Shaping for Formulaic Alpha Mining

    cs.LG 2025-07 conditional novelty 6.0 of 10

    A trajectory-level reward shaping method for RL-based formulaic alpha mining uses exact subsequence matching against expert formulas and reward centering to accelerate training and slightly improve mined factors.

  5. Towards Autonomous Formulaic Alpha Discovery: An Evolutionary Computation Perspective

    cs.NE 2026-08 conditional novelty 4.0 of 10

    A review that reframes automated trading-signal (alpha) discovery as noisy, dynamic, multiobjective evolutionary optimization, and proposes six-component and eight-dimension frameworks for comparing and evaluating methods.

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