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TRA

README.md

Learning Multiple Stock Trading Patterns with Temporal Routing Adaptor and Optimal Transport

Temporal Routing Adaptor (TRA) is designed to capture multiple trading patterns in the stock market data. Please refer to our paper for more details.

If you find our work useful in your research, please cite:

@inproceedings{HengxuKDD2021,
 author = {Hengxu Lin and Dong Zhou and Weiqing Liu and Jiang Bian},
 title = {Learning Multiple Stock Trading Patterns with Temporal Routing Adaptor and Optimal Transport},
 booktitle = {Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery \& Data Mining},
 series = {KDD '21},
 year = {2021},
 publisher = {ACM},
}

@article{yang2020qlib,
  title={Qlib: An AI-oriented Quantitative Investment Platform},
  author={Yang, Xiao and Liu, Weiqing and Zhou, Dong and Bian, Jiang and Liu, Tie-Yan},
  journal={arXiv preprint arXiv:2009.11189},
  year={2020}
}

Usage (Recommended)

Update: TRA has been moved to qlib.contrib.model.pytorch_tra to support other Qlib components like qlib.workflow and Alpha158/Alpha360 dataset.

Please follow the official doc to use TRA with workflow. Here we also provide several example config files:

  • workflow_config_tra_Alpha360.yaml: running TRA with Alpha360 dataset
  • workflow_config_tra_Alpha158.yaml: running TRA with Alpha158 dataset (with feature subsampling)
  • workflow_config_tra_Alpha158_full.yaml: running TRA with Alpha158 dataset (without feature subsampling)

The performances of TRA are reported in Benchmarks.

Usage (Not Maintained)

This section is used to reproduce the results in the paper.

Running

We attach our running scripts for the paper in run.sh.

And here are two ways to run the model:

  • Running from scripts with default parameters

    You can directly run from Qlib command qrun:

    qrun configs/config_alstm.yaml

  • Running from code with self-defined parameters

    Setting different parameters is also allowed. See codes in example.py:

    python example.py --config_file configs/config_alstm.yaml

Here we trained TRA on a pretrained backbone model. Therefore we run *_init.yaml before TRA's scripts.

Results

After running the scripts, you can find result files in path ./output:

  • info.json - config settings and result metrics.
  • log.csv - running logs.
  • model.bin - the model parameter dictionary.
  • pred.pkl - the prediction scores and output for inference.

Evaluation metrics reported in the paper: This result is generated by qlib==0.7.1.

Methods MSE MAE IC ICIR AR AV SR MDD
Linear 0.163 0.327 0.020 0.132 -3.2% 16.8% -0.191 32.1%
LightGBM 0.160(0.000) 0.323(0.000) 0.041 0.292 7.8% 15.5% 0.503 25.7%
MLP 0.160(0.002) 0.323(0.003) 0.037 0.273 3.7% 15.3% 0.264 26.2%
SFM 0.159(0.001) 0.321(0.001) 0.047 0.381 7.1% 14.3% 0.497 22.9%
ALSTM 0.158(0.001) 0.320(0.001) 0.053 0.419 12.3% 13.7% 0.897 20.2%
Trans. 0.158(0.001) 0.322(0.001) 0.051 0.400 14.5% 14.2% 1.028 22.5%
ALSTM+TS 0.160(0.002) 0.321(0.002) 0.039 0.291 6.7% 14.6% 0.480 22.3%
Trans.+TS 0.160(0.004) 0.324(0.005) 0.037 0.278 10.4% 14.7% 0.722 23.7%
ALSTM+TRA(Ours) 0.157(0.000) 0.318(0.000) 0.059 0.460 12.4% 14.0% 0.885 20.4%
Trans.+TRA(Ours) 0.157(0.000) 0.320(0.000) 0.056 0.442 16.1% 14.2% 1.133 23.1%

A more detailed demo for our experiment results in the paper can be found in Report.ipynb.

Common Issues

For help or issues using TRA, please submit a GitHub issue.

Sometimes we might encounter situation where the loss is NaN, please check the epsilon parameter in the sinkhorn algorithm, adjusting the epsilon according to input's scale is important.