This page lists a batch of methods designed for alpha seeking. Each method tries to give scores/predictions for all stocks each day(e.g. forecasting the future excess return of stocks). The scores/predictions of the models will be used as the mined alpha. Investing in stocks with higher scores is expected to yield more profit.
The alpha is evaluated in two ways.
- The correlation between the alpha and future return.
- Constructing portfolio based on the alpha and evaluating the final total return.
- The explanation of metrics can be found here
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 with different random seeds.
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.
NOTE: The backtest start from 0.8.0 is quite different from previous version. Please check out the changelog for the difference.
NOTE: We have very limited resources to implement and finetune the models. We tried our best effort to fairly compare these models. But some models may have greater potential than what it looks like in the table below. Your contribution is highly welcomed to explore their potential.
| Model Name | Dataset | IC | ICIR | Rank IC | Rank ICIR | Annualized Return | Information Ratio | Max Drawdown |
|---|---|---|---|---|---|---|---|---|
| TCN(Shaojie Bai, et al.) | Alpha158 | 0.0275±0.00 | 0.2157±0.01 | 0.0411±0.00 | 0.3379±0.01 | 0.0190±0.02 | 0.2887±0.27 | -0.1202±0.03 |
| TabNet(Sercan O. Arik, et al.) | Alpha158 | 0.0204±0.01 | 0.1554±0.07 | 0.0333±0.00 | 0.2552±0.05 | 0.0227±0.04 | 0.3676±0.54 | -0.1089±0.08 |
| Transformer(Ashish Vaswani, et al.) | Alpha158 | 0.0264±0.00 | 0.2053±0.02 | 0.0407±0.00 | 0.3273±0.02 | 0.0273±0.02 | 0.3970±0.26 | -0.1101±0.02 |
| GRU(Kyunghyun Cho, et al.) | Alpha158(with selected 20 features) | 0.0315±0.00 | 0.2450±0.04 | 0.0428±0.00 | 0.3440±0.03 | 0.0344±0.02 | 0.5160±0.25 | -0.1017±0.02 |
| LSTM(Sepp Hochreiter, et al.) | Alpha158(with selected 20 features) | 0.0318±0.00 | 0.2367±0.04 | 0.0435±0.00 | 0.3389±0.03 | 0.0381±0.03 | 0.5561±0.46 | -0.1207±0.04 |
| Localformer(Juyong Jiang, et al.) | Alpha158 | 0.0356±0.00 | 0.2756±0.03 | 0.0468±0.00 | 0.3784±0.03 | 0.0438±0.02 | 0.6600±0.33 | -0.0952±0.02 |
| SFM(Liheng Zhang, et al.) | Alpha158 | 0.0379±0.00 | 0.2959±0.04 | 0.0464±0.00 | 0.3825±0.04 | 0.0465±0.02 | 0.5672±0.29 | -0.1282±0.03 |
| ALSTM (Yao Qin, et al.) | Alpha158(with selected 20 features) | 0.0362±0.01 | 0.2789±0.06 | 0.0463±0.01 | 0.3661±0.05 | 0.0470±0.03 | 0.6992±0.47 | -0.1072±0.03 |
| GATs (Petar Velickovic, et al.) | Alpha158(with selected 20 features) | 0.0349±0.00 | 0.2511±0.01 | 0.0462±0.00 | 0.3564±0.01 | 0.0497±0.01 | 0.7338±0.19 | -0.0777±0.02 |
| TRA(Hengxu Lin, et al.) | Alpha158(with selected 20 features) | 0.0404±0.00 | 0.3197±0.05 | 0.0490±0.00 | 0.4047±0.04 | 0.0649±0.02 | 1.0091±0.30 | -0.0860±0.02 |
| Linear | Alpha158 | 0.0397±0.00 | 0.3000±0.00 | 0.0472±0.00 | 0.3531±0.00 | 0.0692±0.00 | 0.9209±0.00 | -0.1509±0.00 |
| TRA(Hengxu Lin, et al.) | Alpha158 | 0.0440±0.00 | 0.3535±0.05 | 0.0540±0.00 | 0.4451±0.03 | 0.0718±0.02 | 1.0835±0.35 | -0.0760±0.02 |
| CatBoost(Liudmila Prokhorenkova, et al.) | Alpha158 | 0.0481±0.00 | 0.3366±0.00 | 0.0454±0.00 | 0.3311±0.00 | 0.0765±0.00 | 0.8032±0.01 | -0.1092±0.00 |
| XGBoost(Tianqi Chen, et al.) | Alpha158 | 0.0498±0.00 | 0.3779±0.00 | 0.0505±0.00 | 0.4131±0.00 | 0.0780±0.00 | 0.9070±0.00 | -0.1168±0.00 |
| TFT (Bryan Lim, et al.) | Alpha158(with selected 20 features) | 0.0358±0.00 | 0.2160±0.03 | 0.0116±0.01 | 0.0720±0.03 | 0.0847±0.02 | 0.8131±0.19 | -0.1824±0.03 |
| MLP | Alpha158 | 0.0376±0.00 | 0.2846±0.02 | 0.0429±0.00 | 0.3220±0.01 | 0.0895±0.02 | 1.1408±0.23 | -0.1103±0.02 |
| LightGBM(Guolin Ke, et al.) | Alpha158 | 0.0448±0.00 | 0.3660±0.00 | 0.0469±0.00 | 0.3877±0.00 | 0.0901±0.00 | 1.0164±0.00 | -0.1038±0.00 |
| DoubleEnsemble(Chuheng Zhang, et al.) | Alpha158 | 0.0544±0.00 | 0.4340±0.00 | 0.0523±0.00 | 0.4284±0.01 | 0.1168±0.01 | 1.3384±0.12 | -0.1036±0.01 |
| Model Name | Dataset | IC | ICIR | Rank IC | Rank ICIR | Annualized Return | Information Ratio | Max Drawdown |
|---|---|---|---|---|---|---|---|---|
| Transformer(Ashish Vaswani, et al.) | Alpha360 | 0.0114±0.00 | 0.0716±0.03 | 0.0327±0.00 | 0.2248±0.02 | -0.0270±0.03 | -0.3378±0.37 | -0.1653±0.05 |
| TabNet(Sercan O. Arik, et al.) | Alpha360 | 0.0099±0.00 | 0.0593±0.00 | 0.0290±0.00 | 0.1887±0.00 | -0.0369±0.00 | -0.3892±0.00 | -0.2145±0.00 |
| MLP | Alpha360 | 0.0273±0.00 | 0.1870±0.02 | 0.0396±0.00 | 0.2910±0.02 | 0.0029±0.02 | 0.0274±0.23 | -0.1385±0.03 |
| Localformer(Juyong Jiang, et al.) | Alpha360 | 0.0404±0.00 | 0.2932±0.04 | 0.0542±0.00 | 0.4110±0.03 | 0.0246±0.02 | 0.3211±0.21 | -0.1095±0.02 |
| CatBoost((Liudmila Prokhorenkova, et al.) | Alpha360 | 0.0378±0.00 | 0.2714±0.00 | 0.0467±0.00 | 0.3659±0.00 | 0.0292±0.00 | 0.3781±0.00 | -0.0862±0.00 |
| XGBoost(Tianqi Chen, et al.) | Alpha360 | 0.0394±0.00 | 0.2909±0.00 | 0.0448±0.00 | 0.3679±0.00 | 0.0344±0.00 | 0.4527±0.02 | -0.1004±0.00 |
| DoubleEnsemble(Chuheng Zhang, et al.) | Alpha360 | 0.0404±0.00 | 0.3023±0.00 | 0.0495±0.00 | 0.3898±0.00 | 0.0468±0.01 | 0.6302±0.20 | -0.0860±0.01 |
| LightGBM(Guolin Ke, et al.) | Alpha360 | 0.0400±0.00 | 0.3037±0.00 | 0.0499±0.00 | 0.4042±0.00 | 0.0558±0.00 | 0.7632±0.00 | -0.0659±0.00 |
| TCN(Shaojie Bai, et al.) | Alpha360 | 0.0441±0.00 | 0.3301±0.02 | 0.0519±0.00 | 0.4130±0.01 | 0.0604±0.02 | 0.8295±0.34 | -0.1018±0.03 |
| ALSTM (Yao Qin, et al.) | Alpha360 | 0.0497±0.00 | 0.3829±0.04 | 0.0599±0.00 | 0.4736±0.03 | 0.0626±0.02 | 0.8651±0.31 | -0.0994±0.03 |
| LSTM(Sepp Hochreiter, et al.) | Alpha360 | 0.0448±0.00 | 0.3474±0.04 | 0.0549±0.00 | 0.4366±0.03 | 0.0647±0.03 | 0.8963±0.39 | -0.0875±0.02 |
| ADD | Alpha360 | 0.0430±0.00 | 0.3188±0.04 | 0.0559±0.00 | 0.4301±0.03 | 0.0667±0.02 | 0.8992±0.34 | -0.0855±0.02 |
| GRU(Kyunghyun Cho, et al.) | Alpha360 | 0.0493±0.00 | 0.3772±0.04 | 0.0584±0.00 | 0.4638±0.03 | 0.0720±0.02 | 0.9730±0.33 | -0.0821±0.02 |
| AdaRNN(Yuntao Du, et al.) | Alpha360 | 0.0464±0.01 | 0.3619±0.08 | 0.0539±0.01 | 0.4287±0.06 | 0.0753±0.03 | 1.0200±0.40 | -0.0936±0.03 |
| GATs (Petar Velickovic, et al.) | Alpha360 | 0.0476±0.00 | 0.3508±0.02 | 0.0598±0.00 | 0.4604±0.01 | 0.0824±0.02 | 1.1079±0.26 | -0.0894±0.03 |
| TCTS(Xueqing Wu, et al.) | Alpha360 | 0.0508±0.00 | 0.3931±0.04 | 0.0599±0.00 | 0.4756±0.03 | 0.0893±0.03 | 1.2256±0.36 | -0.0857±0.02 |
| TRA(Hengxu Lin, et al.) | Alpha360 | 0.0485±0.00 | 0.3787±0.03 | 0.0587±0.00 | 0.4756±0.03 | 0.0920±0.03 | 1.2789±0.42 | -0.0834±0.02 |
- The selected 20 features are based on the feature importance of a lightgbm-based model.
- The base model of DoubleEnsemble is LGBM.
- The base model of TCTS is GRU.
- About the datasets
- Alpha158 is a tabular dataset. There are less spatial relationships between different features. Each feature are carefully desgined by human (a.k.a feature engineering)
- Alpha360 contains raw price and volue data without much feature engineering. There are strong strong spatial relationships between the features in the time dimension.
- The metrics can be categorized into two
- Signal-based evaluation: IC, ICIR, Rank IC, Rank ICIR
- Portfolio-based metrics: Annualized Return, Information Ratio, Max Drawdown