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

This code provides a PyTorch implementation for TRA (Temporal Routing Adaptor), as described in the paper Learning Multiple Stock Trading Patterns with Temporal Routing Adaptor and Optimal Transport.

TRA (Temporal Routing Adaptor) is a lightweight module that consists of a set of independent predictors for learning multiple patterns as well as a router to dispatch samples to different predictors.
We also design a learning algorithm based on Optimal Transport (OT) to obtain the optimal sample to predictor assignment and effectively optimize the router with such assignment through an auxiliary loss term.

Running TRA

Requirements

Install Qlib main branch

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 scipts.

Results

Outputs

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.

Our Results

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.

Citation

If you find this repository 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},
}

3.4 KiB Raw Blame History