Here are the results of each benchmark model running on Qlib's Alpha360 and Alpha158 dataset with China's A shared-stock & CSI300 data respectively. The values of each metric are the mean and std calculated based on 20 runs.
The numbers shown below demonstrate the performance of the entire workflow of each model. We will update the workflow as well as models in the near future for better results.
If you need to reproduce the results below, please use the v1 dataset:
python scripts/get_data.py qlib_data --target_dir ~/.qlib/qlib_data/qlib_cn_1d --region cn --version v1In the new version of qlib, the default dataset is v2. Since the data is collected from the YahooFinance API (which is not very stable), the results of v2 and v1 may differ
| Model Name | Dataset | IC | ICIR | Rank IC | Rank ICIR | Annualized Return | Information Ratio | Max Drawdown |
|---|---|---|---|---|---|---|---|---|
| Linear | Alpha360 | 0.0150±0.00 | 0.1049±0.00 | 0.0284±0.00 | 0.1970±0.00 | -0.0659±0.00 | -0.7072±0.00 | -0.2955±0.00 |
| CatBoost (Liudmila Prokhorenkova, et al.) | Alpha360 | 0.0397±0.00 | 0.2878±0.00 | 0.0470±0.00 | 0.3703±0.00 | 0.0342±0.00 | 0.4092±0.00 | -0.1057±0.00 |
| XGBoost (Tianqi Chen, et al.) | Alpha360 | 0.0400±0.00 | 0.3031±0.00 | 0.0461±0.00 | 0.3862±0.00 | 0.0528±0.00 | 0.6307±0.00 | -0.1113±0.00 |
| LightGBM (Guolin Ke, et al.) | Alpha360 | 0.0399±0.00 | 0.3075±0.00 | 0.0492±0.00 | 0.4019±0.00 | 0.0323±0.00 | 0.4370±0.00 | -0.0917±0.00 |
| MLP | Alpha360 | 0.0285±0.00 | 0.1981±0.02 | 0.0402±0.00 | 0.2993±0.02 | 0.0073±0.02 | 0.0880±0.22 | -0.1446±0.03 |
| GRU (Kyunghyun Cho, et al.) | Alpha360 | 0.0490±0.01 | 0.3787±0.05 | 0.0581±0.00 | 0.4664±0.04 | 0.0726±0.02 | 0.9817±0.34 | -0.0902±0.03 |
| LSTM (Sepp Hochreiter, et al.) | Alpha360 | 0.0443±0.01 | 0.3401±0.05 | 0.0536±0.01 | 0.4248±0.05 | 0.0627±0.03 | 0.8441±0.48 | -0.0882±0.03 |
| ALSTM (Yao Qin, et al.) | Alpha360 | 0.0493±0.01 | 0.3778±0.06 | 0.0585±0.00 | 0.4606±0.04 | 0.0513±0.03 | 0.6727±0.38 | -0.1085±0.02 |
| GATs (Petar Velickovic, et al.) | Alpha360 | 0.0475±0.00 | 0.3515±0.02 | 0.0592±0.00 | 0.4585±0.01 | 0.0876±0.02 | 1.1513±0.27 | -0.0795±0.02 |
| DoubleEnsemble (Chuheng Zhang, et al.) | Alpha360 | 0.0407±0.00 | 0.3053±0.00 | 0.0490±0.00 | 0.3840±0.00 | 0.0380±0.02 | 0.5000±0.21 | -0.0984±0.02 |
| TabNet (Sercan O. Arik, et al.) | Alpha360 | 0.0192±0.00 | 0.1401±0.00 | 0.0291±0.00 | 0.2163±0.00 | -0.0258±0.00 | -0.2961±0.00 | -0.1429±0.00 |
| Model Name | Dataset | IC | ICIR | Rank IC | Rank ICIR | Annualized Return | Information Ratio | Max Drawdown |
|---|---|---|---|---|---|---|---|---|
| Linear | Alpha158 | 0.0393±0.00 | 0.2980±0.00 | 0.0475±0.00 | 0.3546±0.00 | 0.0795±0.00 | 1.0712±0.00 | -0.1449±0.00 |
| CatBoost (Liudmila Prokhorenkova, et al.) | Alpha158 | 0.0503±0.00 | 0.3586±0.00 | 0.0483±0.00 | 0.3667±0.00 | 0.1080±0.00 | 1.1561±0.00 | -0.0787±0.00 |
| XGBoost (Tianqi Chen, et al.) | Alpha158 | 0.0481±0.00 | 0.3659±0.00 | 0.0495±0.00 | 0.4033±0.00 | 0.1111±0.00 | 1.2915±0.00 | -0.0893±0.00 |
| LightGBM (Guolin Ke, et al.) | Alpha158 | 0.0475±0.00 | 0.3979±0.00 | 0.0485±0.00 | 0.4123±0.00 | 0.1143±0.00 | 1.2744±0.00 | -0.0800±0.00 |
| MLP | Alpha158 | 0.0358±0.00 | 0.2738±0.03 | 0.0425±0.00 | 0.3221±0.01 | 0.0836±0.02 | 1.0323±0.25 | -0.1127±0.02 |
| TFT (Bryan Lim, et al.) | Alpha158 (with selected 20 features) | 0.0343±0.00 | 0.2071±0.02 | 0.0107±0.00 | 0.0660±0.02 | 0.0623±0.02 | 0.5818±0.20 | -0.1762±0.01 |
| GRU (Kyunghyun Cho, et al.) | Alpha158 (with selected 20 features) | 0.0311±0.00 | 0.2418±0.04 | 0.0425±0.00 | 0.3434±0.02 | 0.0330±0.02 | 0.4805±0.30 | -0.1021±0.02 |
| LSTM (Sepp Hochreiter, et al.) | Alpha158 (with selected 20 features) | 0.0312±0.00 | 0.2394±0.04 | 0.0418±0.00 | 0.3324±0.03 | 0.0298±0.02 | 0.4198±0.33 | -0.1348±0.03 |
| ALSTM (Yao Qin, et al.) | Alpha158 (with selected 20 features) | 0.0385±0.01 | 0.3022±0.06 | 0.0478±0.00 | 0.3874±0.04 | 0.0486±0.03 | 0.7141±0.45 | -0.1088±0.03 |
| GATs (Petar Velickovic, et al.) | Alpha158 (with selected 20 features) | 0.0349±0.00 | 0.2511±0.01 | 0.0457±0.00 | 0.3537±0.01 | 0.0578±0.02 | 0.8221±0.25 | -0.0824±0.02 |
| DoubleEnsemble (Chuheng Zhang, et al.) | Alpha158 | 0.0544±0.00 | 0.4338±0.01 | 0.0523±0.00 | 0.4257±0.01 | 0.1253±0.01 | 1.4105±0.14 | -0.0902±0.01 |
| TabNet (Sercan O. Arik, et al.) | Alpha158 | 0.0383±0.00 | 0.3414±0.00 | 0.0388±0.00 | 0.3460±0.00 | 0.0226±0.00 | 0.2652±0.00 | -0.1072±0.00 |
- The selected 20 features are based on the feature importance of a lightgbm-based model.
- The base model of DoubleEnsemble is LGBM.