--- language: - en - zh license: apache-2.0 task_categories: - time-series-forecasting pretty_name: QuantaAlpha Qlib CSI300 Dataset tags: - finance - quantitative - qlib - factor - time-series arxiv: 2602.07085 --- # QuantaAlpha Qlib CSI300 Dataset [**Project Page**](https://quantaalpha.github.io/) | [**Paper**](https://huggingface.co/papers/2602.07085) | [**GitHub**](https://github.com/QuantaAlpha/QuantaAlpha) Qlib market data and pre-computed HDF5 files for QuantaAlpha factor mining (A-share, CSI 300). ## Dataset description | Filename | Description | | -------------- | ------------------------------------------------- | | daily_pv.h5 | Adjusted daily price and volume data. | | daily_pv_debug.h5 | Debug subset (smaller) of price-volume data. | ## How to load from Hugging Face ```python from huggingface_hub import hf_hub_download import pandas as pd # Download a file from this dataset path = hf_hub_download( repo_id="QuantaAlpha/qlib_csi300", filename="daily_pv.h5", repo_type="dataset" ) df = pd.read_hdf(path, key="data") ``` **Note:** The key is always `"data"` for all HDF5 files in this dataset. ## How to read the files locally If you have already downloaded the files: ```python import pandas as pd df = pd.read_hdf("daily_pv.h5", key="data") ``` ## Field description (daily price and volume) | Field | Description | | -------- | ------------------------------------ | | open | Open price of the stock on that day | | close | Close price of the stock on that day | | high | High price of the stock on that day | | low | Low price of the stock on that day | | volume | Trading volume on that day | | factor | Adjusted factor value | ## Citation If you find QuantaAlpha useful in your research, please cite the following work: ```bibtex @misc{han2026quantaalphaevolutionaryframeworkllmdriven, title={QuantaAlpha: An Evolutionary Framework for LLM-Driven Alpha Mining}, author={Jun Han and Shuo Zhang and Wei Li and Zhi Yang and Yifan Dong and Tu Hu and Jialuo Yuan and Xiaomin Yu and Yumo Zhu and Fangqi Lou and Xin Guo and Zhaowei Liu and Tianyi Jiang and Ruichuan An and Jingping Liu and Biao Wu and Rongze Chen and Kunyi Wang and Yifan Wang and Sen Hu and Xinbing Kong and Liwen Zhang and Ronghao Chen and Huacan Wang}, year={2026}, eprint={2602.07085}, archivePrefix={arXiv}, primaryClass={q-fin.ST}, url={https://arxiv.org/abs/2602.07085}, } ```