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

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  • Tianping Zhang
  • Yuanqi Li
  • Yifei Jin
  • Jian Li

Abstract

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.

Suggested Citation

  • Tianping Zhang & Yuanqi Li & Yifei Jin & Jian Li, 2020. "AutoAlpha: an Efficient Hierarchical Evolutionary Algorithm for Mining Alpha Factors in Quantitative Investment," Papers 2002.08245, arXiv.org, revised Apr 2020.
    • Handle: RePEc:arx:papers:2002.08245
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    References listed on IDEAS

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    1. Stephen A. Ross, 2013. "The Arbitrage Theory of Capital Asset Pricing," World Scientific Book Chapters, in: Leonard C MacLean & William T Ziemba (ed.), HANDBOOK OF THE FUNDAMENTALS OF FINANCIAL DECISION MAKING Part I, chapter 1, pages 11-30, World Scientific Publishing Co. Pte. Ltd..
    2. Fama, Eugene F. & French, Kenneth R., 1993. "Common risk factors in the returns on stocks and bonds," Journal of Financial Economics, Elsevier, vol. 33(1), pages 3-56, February.
    3. William F. Sharpe, 1964. "Capital Asset Prices: A Theory Of Market Equilibrium Under Conditions Of Risk," Journal of Finance, American Finance Association, vol. 19(3), pages 425-442, September.
    4. Jegadeesh, Narasimhan & Titman, Sheridan, 1993. "Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency," Journal of Finance, American Finance Association, vol. 48(1), pages 65-91, March.
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    Cited by:

    1. Hang Yuan & Saizhuo Wang & Jian Guo, 2024. "Alpha-GPT 2.0: Human-in-the-Loop AI for Quantitative Investment," Papers 2402.09746, arXiv.org.
    2. Saizhuo Wang & Hang Yuan & Leon Zhou & Lionel M. Ni & Heung-Yeung Shum & Jian Guo, 2023. "Alpha-GPT: Human-AI Interactive Alpha Mining for Quantitative Investment," Papers 2308.00016, arXiv.org.
    3. Bruno Gav{s}perov & Marko {DJ}urasevi'c & Domagoj Jakobovic, 2024. "Finding Near-Optimal Portfolios With Quality-Diversity," Papers 2402.16118, arXiv.org.
    4. Shuo Sun & Rundong Wang & Bo An, 2021. "Reinforcement Learning for Quantitative Trading," Papers 2109.13851, arXiv.org.
    5. Tao Ren & Ruihan Zhou & Jinyang Jiang & Jiafeng Liang & Qinghao Wang & Yijie Peng, 2024. "RiskMiner: Discovering Formulaic Alphas via Risk Seeking Monte Carlo Tree Search," Papers 2402.07080, arXiv.org, revised Feb 2024.
    6. Chuheng Zhang & Yuanqi Li & Xi Chen & Yifei Jin & Pingzhong Tang & Jian Li, 2020. "DoubleEnsemble: A New Ensemble Method Based on Sample Reweighting and Feature Selection for Financial Data Analysis," Papers 2010.01265, arXiv.org, revised Jan 2021.

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