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[DDG-DA] Update crowd-sourced data results (#1405)

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* [DDG-DA] Update crowd-sourced data experiments

* Remove internal data version

* Modify README
This commit is contained in:
Wendi Li
2023-01-02 10:15:50 -06:00
committed by GitHub
parent 4fe3ffccfd
commit ebb8ec34f3
2 changed files with 12 additions and 6 deletions
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2
examples/benchmarks_dynamic/DDG-DA/workflow.py
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@@ -170,7 +170,7 @@ class DDGDA:
# 3) train and logging meta model # 3) train and logging meta model
with R.start(experiment_name=self.meta_exp_name): with R.start(experiment_name=self.meta_exp_name):
R.log_params(**kwargs) R.log_params(**kwargs)
mm = MetaModelDS(step=self.step, hist_step_n=kwargs["hist_step_n"], lr=0.001, max_epoch=200, seed=43) mm = MetaModelDS(step=self.step, hist_step_n=kwargs["hist_step_n"], lr=0.001, max_epoch=100, seed=43)
mm.fit(md) mm.fit(md)
R.save_objects(model=mm) R.save_objects(model=mm)

16
examples/benchmarks_dynamic/README.md
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@@ -4,15 +4,21 @@ So adapting the forecasting models/strategies to market dynamics is very importa
The table below shows the performances of different solutions on different forecasting models. The table below shows the performances of different solutions on different forecasting models.
## Alpha158 dataset ## Alpha158 Dataset
Here is the [crowd sourced version of qlib data](data_collector/crowd_source/README.md): https://github.com/chenditc/investment_data/releases
```bash
wget https://github.com/chenditc/investment_data/releases/download/20220720/qlib_bin.tar.gz
tar -zxvf qlib_bin.tar.gz -C ~/.qlib/qlib_data/cn_data --strip-components=2
```
| Model Name | Dataset | IC | ICIR | Rank IC | Rank ICIR | Annualized Return | Information Ratio | Max Drawdown | | Model Name | Dataset | IC | ICIR | Rank IC | Rank ICIR | Annualized Return | Information Ratio | Max Drawdown |
|------------------|---------|----|------|---------|-----------|-------------------|-------------------|--------------| |------------------|---------|----|------|---------|-----------|-------------------|-------------------|--------------|
| RR[Linear] |Alpha158 |0.088|0.570|0.102 |0.622 |0.077 |1.175 |-0.086 | | RR[Linear] |Alpha158 |0.089|0.577|0.102 |0.627 |0.093 |1.458 |-0.073 |
| DDG-DA[Linear] |Alpha158 |0.093|0.622|0.106 |0.670 |0.085 |1.213 |-0.093 | | DDG-DA[Linear] |Alpha158 |0.096|0.636|0.107 |0.677 |0.067 |0.996 |-0.091 |
| RR[LightGBM] |Alpha158 |0.079|0.566|0.088 |0.592 |0.075 |1.226 |-0.096 | | RR[LightGBM] |Alpha158 |0.082|0.589|0.091 |0.626 |0.077 |1.320 |-0.091 |
| DDG-DA[LightGBM] |Alpha158 |0.084|0.639|0.093 |0.664 |0.099 |1.442 |-0.071 | | DDG-DA[LightGBM] |Alpha158 |0.085|0.658|0.094 |0.686 |0.115 |1.792 |-0.068 |
- The label horizon of the `Alpha158` dataset is set to 20. - The label horizon of the `Alpha158` dataset is set to 20.
- The rolling time intervals are set to 20 trading days. - The rolling time intervals are set to 20 trading days.
- The test rolling periods are from January 2017 to August 2020. - The test rolling periods are from January 2017 to August 2020.
- The results are based on the crowd-sourced version. The Yahoo version of qlib data does not contain `VWAP`, so all related factors are missing and filled with 0, which leads to a rank-deficient matrix (a matrix does not have full rank) and makes lower-level optimization of DDG-DA can not be solved.