# HIST

**Repository Path**: spring-water-driver/HIST

## Basic Information

- **Project Name**: HIST
- **Description**: 基于图的可通过挖掘概念共享信息进行股票趋势预测，由中山大学和微软亚洲研究院合作在 2022 年 1 月发布于 arXiv。文章中提出了一种新的股票趋势预测框架，能够充分挖掘股票预定义概念和隐藏概念中的动态共 享信息。作者在沪深 300 和中证 100 的数据上验证了该模型具有较好的预测效果。
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2025-11-15
- **Last Updated**: 2025-11-15

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# The HIST framework for stock trend forecasting
The implementation of the paper "[HIST: A Graph-based Framework for Stock Trend Forecasting via Mining Concept-Oriented Shared Information](https://arxiv.org/abs/2110.13716)".
![image](https://user-images.githubusercontent.com/25242325/139006788-b51b18c2-1bcf-44b8-921a-b10d4b197e91.png)

## Environment
1. Install python3.7, 3.8 or 3.9. 
2. Install the requirements in [requirements.txt](https://github.com/Wentao-Xu/HIST/blob/main/requirements.txt).
3. Install the quantitative investment platform [Qlib](https://github.com/microsoft/qlib) and download the data from Qlib:
	```
	# install Qlib from source
	pip install --upgrade  cython
	git clone https://github.com/microsoft/qlib.git && cd qlib
	python setup.py install

	# Download the stock features of Alpha360 from Qlib
	python scripts/get_data.py qlib_data --target_dir ~/.qlib/qlib_data/cn_data --region cn --version v2
	```
## Reproduce the stock trend forecasting results
![image](https://user-images.githubusercontent.com/25242325/139006416-c0847b7e-6090-4bd9-9990-567d74198ad0.png)
```
git clone https://github.com/Wentao-Xu/HIST.git
cd HIST
mkdir output
```
### Reproduce our HIST framework
```
# CSI 100
python learn.py --model_name HIST --data_set csi100 --hidden_size 128 --num_layers 2 --outdir ./output/csi100_HIST

# CSI 300
python learn.py --model_name HIST --data_set csi300 --hidden_size 128 --num_layers 2 --outdir ./output/csi300_HIST
```
### Reproduce the baselines
* MLP 
```
# MLP on CSI 100
python learn.py --model_name MLP --data_set csi100 --hidden_size 512 --num_layers 3 --outdir ./output/csi100_MLP

# MLP on CSI 300
python learn.py --model_name MLP --data_set csi300 --hidden_size 512 --num_layers 3 --outdir ./output/csi300_MLP
```

* LSTM
```
# LSTM on CSI 100
python learn.py --model_name LSTM --data_set csi100 --hidden_size 128 --num_layers 2 --outdir ./output/csi100_LSTM

# LSTM on CSI 300
python learn.py --model_name LSTM --data_set csi300 --hidden_size 128 --num_layers 2 --outdir ./output/csi300_LSTM
```

* GRU
```
# GRU on CSI 100
python learn.py --model_name GRU --data_set csi100 --hidden_size 128 --num_layers 2 --outdir ./output/csi100_GRU

# GRU on CSI 300
python learn.py --model_name GRU --data_set csi300 --hidden_size 64 --num_layers 2 --outdir ./output/csi300_GRU
```

* SFM
```
# SFM on CSI 100
python learn.py --model_name SFM --data_set csi100 --hidden_size 64 --num_layers 2 --outdir ./output/csi100_SFM

# SFM on CSI 300
python learn.py --model_name SFM --data_set csi300 --hidden_size 128 --num_layers 2 --outdir ./output/csi300_SFM
```

* GATs
```
# GATs on CSI 100
python learn.py --model_name GATs --data_set csi100 --hidden_size 128 --num_layers 2 --outdir ./output/csi100_GATs

# GATs on CSI 300
python learn.py --model_name GATs --data_set csi300 --hidden_size 64 --num_layers 2 --outdir ./output/csi300_GATs
```

* ALSTM
```
# ALSTM on CSI 100
python learn.py --model_name ALSTM --data_set csi100 --hidden_size 64 --num_layers 2 --outdir ./output/csi100_ALSTM

# ALSTM on CSI 300
python learn.py --model_name ALSTM --data_set csi300 --hidden_size 128 --num_layers 2 --outdir ./output/csi300_ALSTM
```

* Transformer
```
# Transformer on CSI 100
python learn.py --model_name Transformer --data_set csi100 --hidden_size 32 --num_layers 3 --outdir ./output/csi100_Transformer

# Transformer on CSI 300
python learn.py --model_name Transformer --data_set csi300 --hidden_size 32 --num_layers 3 --outdir ./output/csi300_Transformer
```

* ALSTM+TRA 

	We reproduce the ALSTM+TRA with its [source code](https://github.com/microsoft/qlib/tree/main/examples/benchmarks/TRA).

## Citation
Please cite the following paper if you use this code in your work.
```
@article{xu2021hist,
  title={HIST: A Graph-based Framework for Stock Trend Forecasting via Mining Concept-Oriented Shared Information},
  author={Xu, Wentao and Liu, Weiqing and Wang, Lewen and Xia, Yingce and Bian, Jiang and Yin, Jian and Liu, Tie-Yan},
  journal={arXiv preprint arXiv:2110.13716},
  year={2021}
}
```