Table of Contents

• Introduction
  • Differences from the article version
• Dependencies
• Dataset
• Build
  • Running
  • Configurations
• Logging
• Model
• Acknowledgement
• Citation
• References

Introduction

This repo is the Pytorch code for our paper “Relation-Aware Transformer for Portfolio Policy Learning”^[1] in IJCAI 2020.

Differences from the article version

Note that this library is a part of our main project, and it is several versions ahead of the article.

• In decision-making layer, we construct the leverage operation through two softmax heads, and it's performance is similar to the result of using three softmax heads.

Dependencies

python 3.7.3 (Anaconda)

pytorch 0.4.1.post2

cudnn 7.4.1

Dataset

The raw data of Crypto-A is accessed with Poloniex^[2]. We provide the Crypto-A dataset in link^[3], where data selection is based on the method in ^[4]. Please download ./database to the same directory as the main.py. The statistics of Crypto-A are summarized as below.

Dataset	Assets	Training	Test
Crypto-A	12	2016.01-2017.11	2017.11-2018.01

Build

File main.py mainly contains the construction of RAT network, data preprocessing, the fitting model process and testing process. File run_mian.sh mainly contains the parameter configurations of training RAT.

Running

cd ${RAT_ROOT}/RAT-master

./run_main.sh

Configurations

The figure shows the entire structure of RAT, and we detail some related parameter configurations in run_main.sh as below.

--x_window_size

The length of the price series.

--local_context_length

The length of local price context.

--daily_interest_rate

The interest rate of the loan for one day.

--log_dir

The directory to save the log.

--model_dir

The directory to save the model.

--model_index

Set a unique ID for the model.

Logging

After training process, the model is saved in ${SAVE_MODEL_DIR}/${MODEL_INDEX}.pkl. After testing process, the backtest results are saved in ${LOG_DIR}/train_summary.csv. It contains metrics such as fAPV, SR, CR and basktest_history.

Model

We provide a model with 495.28 fAPV in https://drive.google.com/drive/folders/11MK2QSj30G9pYE8qx_-80zgCDmJOHS9U?usp=sharing. You can download the model to ${SAVE_MODEL_DIR}/ and test it. When testing the given model, please comment out line 1443 in main.py to avoid the training process.

Acknowledgement

This project is constructed based on the open source project:

• [PGPortfolio toolbox(https://github.com/ZhengyaoJiang/PGPortfolio)]

• [Zhang Y, Zhao P, Li B, et al. Cost-sensitive portfolio selection via deep reinforcement learning[J]. IEEE Transactions on Knowledge and Data Engineering, 2020(https://github.com/Vanint/PPN)]

Citation

If you use this code for your research, please consider citing:

@inproceedings{xu-relation,
  title = {Relation-Aware Transformer for Portfolio Policy Learning},
  author = {Xu, Ke and Zhang, Yifan and Ye, Deheng and Zhao, Peilin and Tan, Mingkui},
  booktitle = {Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence}, 
  pages = {4647--4653},
  year = {2020},
  month = {7},
  note = {Special Track on AI in FinTech}
}

References

• [1] Xu Ke, Zhang Yifan, Ye Deheng, Zhao Peilin and Tan Mingkui. Relation-Aware Transformer for Portfolio Policy Learning. IJCAI, 2020.

• [2] Poloniex’s official API.

• [3] Crypto-A dataset.

• [4] Zhengyao Jiang, Dixing Xu, and Jinjun Liang. A deep reinforcement learning framework for the financial port-folio management problem. arXiv, 2017.