This is our implementation for the paper: FinGAT: A Financial Graph Attention Networkto Recommend Top-K Profitable Stocks

• pytorch==1.0.0
• numpy==1.16.4
• pandas==0.25.3

1. Run clean_data.py This script would run the preprocessing for raw data and dump a preprocessed file.
2. Run train.py you can tune the hyper parameters by adding args after train.py e.g. python3 train.py --epoch 10 --l2 1e-6 etc.

--epoch: number of epochs
--l2: l2 regularization
--dim: dimension for hidden layer
--alpha: The adaptive weight on MAE loss
--beta: The adaptive weight on classification loss
--gamma: The adaptive weight on ranking loss
--lr: learning rate
--device: The device name for training, if train with cpu please use:"cpu"