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* MVP for Indian Stocks in qlib using yahooquery * cleaned with black * cleaned with black * add YahooNormalizeIN and YahooNormalizeIN1d * cleaned the code * added 1min for IN and also updated readme * update comments * fix comments * recorder support upload both raw file and directory * fix comments * Update README.md * Fix docs of QlibRecorder * sort index after loader (#538) make sure the fetch method is based on a index-sorted pd.DataFrame * refactor online serving rolling api * refactor TRA * format by black * fix horizon * fix TRA when use single head * clean up * improve pretrain * update README * fix tra when logdir is None * fix tra when logdir is None * Update strategy.py * Update README.md * Update README.md * Conda Suggestion * code standard docs * Update ensemble.py (#560) * Fix CI Bug (#575) Co-authored-by: yuxwang <anduinnn@foxmail.com> * Update gen.py (#576) * Fix multi-process loop calls (#574) * check lexsort in the 'lazy_sort_index' function (#566) * check lexsort * 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images * fix workflow and update framework Co-authored-by: Gaurav <2796gaurav@gmail.com> Co-authored-by: 2796gaurav <17353992+2796gaurav@users.noreply.github.com> Co-authored-by: bxdd <bxd98@126.com> Co-authored-by: Young <afe.young@gmail.com> Co-authored-by: you-n-g <you-n-g@users.noreply.github.com> Co-authored-by: Dong Zhou <Zhou.Dong@microsoft.com> Co-authored-by: ZhangTP1996 <ztp18@mails.tsinghua.edu.cn> Co-authored-by: demon143 <59681577+demon143@users.noreply.github.com> Co-authored-by: Wangwuyi123 <51237097+Wangwuyi123@users.noreply.github.com> Co-authored-by: yuxwang <anduinnn@foxmail.com> Co-authored-by: Pengrong Zhu <zhu.pengrong@foxmail.com> Co-authored-by: Mark Zhao <50850474+markzhao98@users.noreply.github.com> Co-authored-by: cslwqxx <cslwqxx@users.noreply.github.com> Co-authored-by: Dong Zhou <evanzd@users.noreply.github.com> Co-authored-by: SaintMalik <37118134+saintmalik@users.noreply.github.com> Co-authored-by: Christian Clauss <cclauss@me.com> Co-authored-by: Anurag 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Benchmarks Performance
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.
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
Alpha360 dataset
| 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 |
| TCTS (Xueqing Wu, et al.) | Alpha360 | 0.0485±0.00 | 0.3689±0.04 | 0.0586±0.00 | 0.4669±0.02 | 0.0816±0.02 | 1.1572±0.30 | -0.0689±0.02 |
| Transformer (Ashish Vaswani, et al.) | Alpha360 | 0.0141±0.00 | 0.0917±0.02 | 0.0331±0.00 | 0.2357±0.03 | -0.0259±0.03 | -0.3323±0.43 | -0.1763±0.07 |
| Localformer (Juyong Jiang, et al.) | Alpha360 | 0.0408±0.00 | 0.2988±0.03 | 0.0538±0.00 | 0.4105±0.02 | 0.0275±0.03 | 0.3464±0.37 | -0.1182±0.03 |
| TRA (Hengxu Lin, et al.) | Alpha360 | 0.0491±0.01 | 0.3868±0.06 | 0.0589±0.00 | 0.4802±0.04 | 0.0898±0.02 | 1.2490±0.32 | -0.0778±0.02 |
Alpha158 dataset
| 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 |
| Transformer (Ashish Vaswani, et al.) | Alpha158 | 0.0274±0.00 | 0.2166±0.04 | 0.0409±0.00 | 0.3342±0.04 | 0.0204±0.03 | 0.2888±0.40 | -0.1216±0.04 |
| Localformer (Juyong Jiang, et al.) | Alpha158 | 0.0355±0.00 | 0.2747±0.04 | 0.0466±0.00 | 0.3762±0.03 | 0.0506±0.02 | 0.7447±0.34 | -0.0875±0.02 |
| TRA (Hengxu Lin, et al.) | Alpha158 (with selected 20 features) | 0.0409±0.00 | 0.3253±0.04 | 0.0488±0.00 | 0.4045±0.02 | 0.0673±0.02 | 1.0389±0.39 | -0.0830±0.02 |
| TRA (Hengxu Lin, et al.) | Alpha158 | 0.0442±0.00 | 0.3426±0.03 | 0.0555±0.00 | 0.4395±0.03 | 0.0833±0.03 | 1.2064±0.36 | -0.0849±0.02 |
- The selected 20 features are based on the feature importance of a lightgbm-based model.
- The base model of DoubleEnsemble is LGBM.