MAGNN

Financial time series forecasting with multi-modality graph neural network

Usage

1. Prepare your data

   • We have retained the data used as a sample in the event, news, and price subfolders in the data folder and stored it in the form of .pkl. These files can be opened using the np.load() function to view their specific styles.
   • At the same time, the sample relationship diagram between companies is placed in the example_company_relation.pkl under the dataset folder.

2. Setup your env

   • We recommend you to create a new python environment for MAGNN. You might run this command in your anaconda prompt in order to create a new environment:

     conda create -n magnn python==3.6.13
     

     We recommend to use Python 3.6 in our model.

   • After enter the magnn folder, you might install required packages using command below:

     pip install -r requirements.txt
     

3. Run MAGNN

• All functions are integrated in magnn.py, you only need to execute python magnn.py in your virtual environment to run. The results of the operation will be placed in magnn_result.csv. You might use pandas to reveal it.
• If you want to load your own data to MAGNN, do not forget to change train/test period tuples which are defined in ./dataset/constant.py

Project Description

• dataset
  This module contains helper functions to initiate train/valid/test datasets including price data, stock event and stock news data for our models.

• model
  This module contains all our models(magnn, price-lstm, event-embedding, news-embedding).

• tools This module contains some simple implementation versions of some non-open source functions. Readers can modify the functions as needed.

Citing

• If you find MAGNN is useful for your research, please consider citing the following papers:

  @article{cheng2022financial,
    title={Financial time series forecasting with multi-modality graph neural network},
    author={Cheng, Dawei and Yang, Fangzhou and Xiang, Sheng and Liu, Jin},
    journal={Pattern Recognition},
    volume={121},
    pages={108218},
    year={2022},
    publisher={Elsevier}
  }