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

Related tags

Deep LearningFinancial-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

  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"

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.
Yu-Che Tsai
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