• AlphaPROBE: Alpha Mining Via Principled Retrieval and On-graph Biased Evolution

  Taian Guo^1,2, Haiyang Shen^*1, Junyu Luo¹, Binqi Chen^{1, 2}, Hongjun Ding³, Jinsheng Huang^{1, 2}, Luchen Liu², Yun Ma^1†, Ming Zhang^1†,

  ¹Peking University, ²Zhengren Quant, ³Baruch College

  ^* Project Leader. ⁺ Corresponding author.

  

  This repository contains the official implementation of AlphaPROBE (Alpha Mining Via Principled Retrieval and On-graph Biased Evolution), a framework that reframes alpha mining as the strategic navigation of a DAG.

  🎯 Overview

  

  AlphaPROBE is a closed-loop alpha mining pipeline consisting of the Bayesian Factor Retriever and the DAG-aware Factor Generator. The Bayesian Factor Retriever retrieves the most promising factors for evolution, and the DAG-aware Factor Generator is a a structural multi-agent pipeline that generate high-quality and novel offspring from the selected factors.

  👉 Quick Start of AlphaPROBE

  Dependency Installation

  conda create --name your_env  python==3.11.0
  conda activate your_env
  pip install pdm
  pdm install

  Dataset Retrieval

  Run the following command to retrive data from microsoft Qlib

  wget https://github.com/chenditc/investment_data/releases/latest/download/qlib_bin.tar.gz
  mkdir -p ~/.qlib/qlib_data/cn_data
  tar -zxvf qlib_bin.tar.gz -C ~/.qlib/qlib_data/cn_data --strip-components=1
  rm -f qlib_bin.tar.gz

  RUN AlphaPROBE

  Factor Generation

  1. modify dataset name (default csi 300.)
  2. modfiy your model url, model name and model key.
  3. run AlphaPROBE

  python train_new_work.py
  

  Factor combination

  python run_adaptive_combination.py --expression_file your_expression_file
  

☎️ Contact

Please feel free to contact the authors below if you have more questions:

- Taian Guo, taianguo@stu.pku.edu.cn

- Haiyang Shen, hyshen@stu.pku.edu.cn

🌟 Citation

If you find our work useful, please kindly consider citing our work as follows:

@misc{guo2026alphaprobealphaminingprincipled,
      title={AlphaPROBE: Alpha Mining via Principled Retrieval and On-graph biased evolution}, 
      author={Taian Guo and Haiyang Shen and Junyu Luo and Binqi Chen and Hongjun Ding and Jinsheng Huang and Luchen Liu and Yun Ma and Ming Zhang},
      year={2026},
      eprint={2602.11917},
      archivePrefix={arXiv},
      primaryClass={cs.AI},
      url={https://arxiv.org/abs/2602.11917}, 
}