FinGAT: A Financial Graph Attention Networkto Recommend Top-K Profitable Stocks

Last update: Dec 13, 2022

Related tags

Deep Learning Financial-GraphAttention

Overview

FinGAT: A Financial Graph Attention Networkto Recommend Top-K Profitable Stocks

This is our implementation for the paper:

FinGAT: A Financial Graph Attention Networkto Recommend Top-K Profitable Stocks

Under review of TKDE

Requirements

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

Model architecture

How to train the model

Run clean_data.py This script would run the preprocessing for raw data and dump a preprocessed file.
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"

Reslut

Owner

Yu-Che Tsai

Major in Computer Science, Statistics and interested in machine learning, deep learning with it's application in rec sys, network analysis.

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