https://github.com/midas-research/sthan-sr-aaai

https://github.com/midas-research/sthan-sr-aaai

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• Host: GitHub
• URL: https://github.com/midas-research/sthan-sr-aaai
• Owner: midas-research
• Created: 2021-01-17T12:47:27.000Z (almost 4 years ago)
• Default Branch: main
• Last Pushed: 2021-06-08T13:05:38.000Z (over 3 years ago)
• Last Synced: 2024-09-29T14:31:38.862Z (4 months ago)
• Language: Python
• Size: 23.4 KB
• Stars: 98
• Watchers: 9
• Forks: 32
• Open Issues: 11
• Metadata Files:
  • Readme: README.md

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README

        
# 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](https://ojs.aaai.org/index.php/AAAI/article/view/16127).

## Environment & Installation Steps

Python 3.6, Pytorch, Pytorch-Geometric and networkx.

```python

pip install -r requirements.txt

```

## Dataset and Preprocessing 

Download the dataset and follow preprocessing steps from [here](https://github.com/fulifeng/Temporal_Relational_Stock_Ranking). 

## Run

Execute the following python command to train STHAN-SR: 

```python

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

```c

@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}

}

```