Stock Selection via Spatiotemporal Hypergraph Attention Network: A Learning to Rank Approach

This codebase contains the python scripts for STHAN-SR, the model for the AAAI 2021 paper link.

Environment & Installation Steps

Python 3.6, Pytorch, Pytorch-Geometric and networkx.

pip install -r requirements.txt

Dataset and Preprocessing

Download the dataset and follow preprocessing steps from here.

Run

Execute the following python command to train STHAN-SR:

python train_nyse.py -m NYSE -l 16 -u 64 -a 1 -e NYSE_rank_lstm_seq-8_unit-32_0.csv.npy 
python train_tse.py
python train_nasdaq.py -l 16 -u 64 -a 0.1

Cite

Consider citing our work if you use our codebase

@inproceedings{sawhney2021stock,
  title={Stock Selection via Spatiotemporal Hypergraph Attention Network: A Learning to Rank Approach},
  author={Sawhney, Ramit and Agarwal, Shivam and Wadhwa, Arnav and Derr, Tyler and Shah, Rajiv Ratn},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={35},
  number={1},
  pages={497--504},
  year={2021}
}