Table of Contents
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}
}