Merge nested main (#597)

* 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 * check lexsort * lexsort comment * lexsort comment * Delete .DS_Store * Update README.md * bug fix & use oracle transport pretrain * mend * Add `backend_freq_config` parameter, support multi-freq uri * Add sample_config to QlibDataLoader, support multi-freq * add multi-freq example * get_cls_kwargs renamed get_callable_kwargs * support multi-freq uri * Add inst_processors to D.features * Fix typo * Fix the index type of the multi-freq example * Fix duplicate mlflow directories in tests * Add DataPathManager to QlibConfig && modify inst_processors to supports list only * Modify the default value in the multi_freq example * Modify client-server mode and dataset-cache to disable inst_processor * Add wheel package to github CI * fix comment * Update FAQ.rst * Update README.md Fix wrong link * Update the docs of TaskManager (#586) * Update manage.py * update yaml * update run_all_model * Modify the Feature to be case sensitive (#589) * update README * remove verbose * fix spell bug * fix typos (#592) * Update Release Note * fix portfolio bug * Add calendar support for resample * add freq kwargs * test.yml: Remove redundant code (#595) * Supporting shared processor (#596) * Supporting shared processor * fix readonly reverse bug * remove pytests dependency * with fit bug * fix parameter error * fix comments * Fix undefined names in Python code (#599) * Update pytorch_tabnet.py $ `flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics` ``` ./qlib/qlib/contrib/model/pytorch_tabnet.py:567:38: F821 undefined name 'inp' self.independ.append(GLU(inp, out_dim, vbs=vbs)) ^ ./qlib/examples/model_rolling/task_manager_rolling.py:75:18: F821 undefined name 'task_train' run_task(task_train, self.task_pool, experiment_name=self.experiment_name) ^ 2 F821 undefined name 'task_train' 2 ``` * Fix undefined names in Python code * from qlib.model.trainer import task_train * update seed * fix some docstring * add comments * Fix SimpleDatasetCache * Update setup.py updated classifiers * Update setup.py change to matplotlib==3.3 * Update python-publish.yml added python 3.9 * updategrade version number * Update model list * fix the type of filter_pipe * fix comment * fix record_temp * update cvxpy version * Update code_standard.rst (#587) * Update code_standard.rst * Update docs/developer/code_standard.rst Co-authored-by: you-n-g <you-n-g@users.noreply.github.com> Co-authored-by: you-n-g <you-n-g@users.noreply.github.com> * Add file lock for MLflowExpManager (#619) * fix torch version * Share version number (#620) * Update initialization.rst (#622) * Update initialization.rst * Update docs/start/initialization.rst Co-authored-by: you-n-g <you-n-g@users.noreply.github.com> * Update docs/start/initialization.rst Co-authored-by: you-n-g <you-n-g@users.noreply.github.com> Co-authored-by: you-n-g <you-n-g@users.noreply.github.com> * fix bugs for running previous exmaple * fix deal amount bug * update change doc (#623) * Add files via upload * Update README.md * Update README.md * Update README.md * Delete change doc.gif * Add files via upload * Update README.md * Delete change doc.gif * Add files via upload * Delete change doc.gif * Add files via upload * Update README.md Co-authored-by: you-n-g <you-n-g@users.noreply.github.com> Co-authored-by: you-n-g <you-n-g@users.noreply.github.com> * update doc * simplify run all model * fix run all model bug * Fix Models (#483) * fix gat dataset * fix tft model * Update tft.py * Fix tft.py Co-authored-by: Pengrong Zhu <zhu.pengrong@foxmail.com> * type and skip empty exp * fix model yaml config * fix tft import bug * skip empty result * fix model and yaml bug * fix wrong generate parameter * Modify multi-freq example (#626) * modify the example of multi-freq * add Copyright * add a comment to average_ops.py * modify the example of multi-freq * add comment to multi_freq_handler.py * add the Ref expression description to multi_freq_handler.py * add expression description to multi_freq_handler.py * update 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 Kumar <mailanu98@gmail.com> Co-authored-by: demon143 <785696300@qq.com>
2026-07-12 15:26:54 +08:00 · 2021-10-01 02:15:30 +08:00
parent 163e3c6266
commit 3760a18a8d
145 changed files with 3982 additions and 1221 deletions
--- a/examples/benchmarks/TRA/README.md
+++ b/examples/benchmarks/TRA/README.md
@@ -1,53 +1,78 @@
 # 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](http://arxiv.org/abs/2106.12950).
+Temporal Routing Adaptor (TRA) is designed to capture multiple trading patterns in the stock market data. Please refer to [our paper](http://arxiv.org/abs/2106.12950) for more details.

-* 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.
+If you find our work 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},
+}

+@article{yang2020qlib,
+  title={Qlib: An AI-oriented Quantitative Investment Platform},
+  author={Yang, Xiao and Liu, Weiqing and Zhou, Dong and Bian, Jiang and Liu, Tie-Yan},
+  journal={arXiv preprint arXiv:2009.11189},
+  year={2020}
+}
+```

-# Running TRA 
+## Usage (Recommended)

-## Requirements
- Install `Qlib` main branch
+**Update**: `TRA` has been moved to `qlib.contrib.model.pytorch_tra` to support other `Qlib` components like  `qlib.workflow` and `Alpha158/Alpha360` dataset.

-## Running 
+Please follow the official [doc](https://qlib.readthedocs.io/en/latest/component/workflow.html) to use `TRA` with `workflow`. Here we also provide several example config files:
+
+- `workflow_config_tra_Alpha360.yaml`: running `TRA` with `Alpha360` dataset
+- `workflow_config_tra_Alpha158.yaml`: running `TRA` with `Alpha158` dataset (with feature subsampling)
+- `workflow_config_tra_Alpha158_full.yaml`: running `TRA` with `Alpha158` dataset (without feature subsampling)
+
+The performances of `TRA` are reported in [Benchmarks](https://github.com/microsoft/qlib/tree/main/examples/benchmarks).
+
+## Usage (Not Maintained)
+
+This section is used to reproduce the results in the paper.
+
+### 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
-    ```
+
+  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
-    ```
+
+  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
+### Results

 After running the scripts, you can find result files in path `./output`:

-`info.json` - config settings and result metrics.
+* `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.

-`log.csv` - running logs.
+Evaluation metrics reported in the paper:
+This result is generated by qlib==0.7.1.

-`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%|
@@ -61,21 +86,8 @@ After running the scripts, you can find result files in path `./output`:

 A more detailed demo for our experiment results in the paper can be found in `Report.ipynb`.

-# Common Issues
+## 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},
-}
-```
+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.
--- a/examples/benchmarks/TRA/requirements.txt
+++ b/examples/benchmarks/TRA/requirements.txt
@@ -0,0 +1,5 @@
+pandas==1.1.2
+numpy==1.17.4
+scikit_learn==0.23.2
+torch==1.7.0
+seaborn
--- a/examples/benchmarks/TRA/workflow_config_tra_Alpha158.yaml
+++ b/examples/benchmarks/TRA/workflow_config_tra_Alpha158.yaml
@@ -0,0 +1,132 @@
+qlib_init:
+  provider_uri: "~/.qlib/qlib_data/cn_data"
+  region: cn
+
+market: &market csi300
+benchmark: &benchmark SH000300
+
+data_handler_config: &data_handler_config
+  start_time: 2008-01-01
+  end_time: 2020-08-01
+  fit_start_time: 2008-01-01
+  fit_end_time: 2014-12-31
+  instruments: *market
+  infer_processors:
+    - class: FilterCol
+      kwargs:
+        fields_group: feature
+        col_list: ["RESI5", "WVMA5", "RSQR5", "KLEN", "RSQR10", "CORR5", "CORD5", "CORR10",
+                   "ROC60", "RESI10", "VSTD5", "RSQR60", "CORR60", "WVMA60", "STD5",
+                   "RSQR20", "CORD60", "CORD10", "CORR20", "KLOW"]
+    - class: RobustZScoreNorm
+      kwargs:
+        fields_group: feature
+        clip_outlier: true
+    - class: Fillna
+      kwargs:
+        fields_group: feature
+  learn_processors:
+    - class: CSRankNorm
+      kwargs:
+        fields_group: label
+  label: ["Ref($close, -2) / Ref($close, -1) - 1"]
+
+num_states: &num_states 3
+
+memory_mode: &memory_mode sample
+
+tra_config: &tra_config
+  num_states: *num_states
+  rnn_arch: LSTM
+  hidden_size: 32
+  num_layers: 1
+  dropout: 0.0
+  tau: 1.0
+  src_info: LR_TPE
+
+model_config: &model_config
+  input_size: 20
+  hidden_size: 64
+  num_layers: 2
+  rnn_arch: LSTM
+  use_attn: True
+  dropout: 0.0
+
+port_analysis_config: &port_analysis_config
+    strategy:
+        class: TopkDropoutStrategy
+        module_path: qlib.contrib.strategy
+        kwargs:
+            model: <MODEL>
+            dataset: <DATASET>
+            topk: 50
+            n_drop: 5
+    backtest:
+        limit_threshold: 0.095
+        account: 100000000
+        benchmark: *benchmark
+        deal_price: close
+        open_cost: 0.0005
+        close_cost: 0.0015
+        min_cost: 5
+
+task:
+  model:
+    class: TRAModel
+    module_path: qlib.contrib.model.pytorch_tra
+    kwargs:
+      tra_config: *tra_config
+      model_config: *model_config
+      model_type: RNN
+      lr: 1e-3
+      n_epochs: 100
+      max_steps_per_epoch:
+      early_stop: 20
+      logdir: output/Alpha158
+      seed: 0
+      lamb: 1.0
+      rho: 0.99
+      alpha: 0.5
+      transport_method: router
+      memory_mode: *memory_mode
+      eval_train: False
+      eval_test: True
+      pretrain: True
+      init_state:
+      freeze_model: False
+      freeze_predictors: False
+  dataset:
+    class: MTSDatasetH
+    module_path: qlib.contrib.data.dataset
+    kwargs:
+      handler:
+        class: Alpha158
+        module_path: qlib.contrib.data.handler
+        kwargs: *data_handler_config
+      segments:
+        train: [2008-01-01, 2014-12-31]
+        valid: [2015-01-01, 2016-12-31]
+        test: [2017-01-01, 2020-08-01]
+      seq_len: 60
+      horizon: 2
+      input_size:
+      num_states: *num_states
+      batch_size: 1024
+      n_samples:
+      memory_mode: *memory_mode
+      drop_last: True
+  record:
+    - class: SignalRecord
+      module_path: qlib.workflow.record_temp
+      kwargs: 
+        model: <MODEL>
+        dataset: <DATASET>
+    - class: SigAnaRecord
+      module_path: qlib.workflow.record_temp
+      kwargs: 
+        ana_long_short: False
+        ann_scaler: 252
+    - class: PortAnaRecord
+      module_path: qlib.workflow.record_temp
+      kwargs: 
+        config: *port_analysis_config
--- a/examples/benchmarks/TRA/workflow_config_tra_Alpha158_full.yaml
+++ b/examples/benchmarks/TRA/workflow_config_tra_Alpha158_full.yaml
@@ -0,0 +1,126 @@
+qlib_init:
+  provider_uri: "~/.qlib/qlib_data/cn_data"
+  region: cn
+
+market: &market csi300
+benchmark: &benchmark SH000300
+
+data_handler_config: &data_handler_config
+  start_time: 2008-01-01
+  end_time: 2020-08-01
+  fit_start_time: 2008-01-01
+  fit_end_time: 2014-12-31
+  instruments: *market
+  infer_processors:
+    - class: RobustZScoreNorm
+      kwargs:
+        fields_group: feature
+        clip_outlier: true
+    - class: Fillna
+      kwargs:
+        fields_group: feature
+  learn_processors:
+    - class: CSRankNorm
+      kwargs:
+        fields_group: label
+  label: ["Ref($close, -2) / Ref($close, -1) - 1"]
+
+num_states: &num_states 3
+
+memory_mode: &memory_mode sample
+
+tra_config: &tra_config
+  num_states: *num_states
+  rnn_arch: LSTM
+  hidden_size: 32
+  num_layers: 1
+  dropout: 0.0
+  tau: 1.0
+  src_info: LR_TPE
+
+model_config: &model_config
+  input_size: 158
+  hidden_size: 256
+  num_layers: 2
+  rnn_arch: LSTM
+  use_attn: True
+  dropout: 0.2
+
+port_analysis_config: &port_analysis_config
+    strategy:
+        class: TopkDropoutStrategy
+        module_path: qlib.contrib.strategy
+        kwargs:
+            model: <MODEL>
+            dataset: <DATASET>
+            topk: 50
+            n_drop: 5
+    backtest:
+        limit_threshold: 0.095
+        account: 100000000
+        benchmark: *benchmark
+        deal_price: close
+        open_cost: 0.0005
+        close_cost: 0.0015
+        min_cost: 5
+
+task:
+  model:
+    class: TRAModel
+    module_path: qlib.contrib.model.pytorch_tra
+    kwargs:
+      tra_config: *tra_config
+      model_config: *model_config
+      model_type: RNN
+      lr: 1e-3
+      n_epochs: 100
+      max_steps_per_epoch:
+      early_stop: 20
+      logdir: output/Alpha158_full
+      seed: 0
+      lamb: 1.0
+      rho: 0.99
+      alpha: 0.5
+      transport_method: router
+      memory_mode: *memory_mode
+      eval_train: False
+      eval_test: True
+      pretrain: True
+      init_state:
+      freeze_model: False
+      freeze_predictors: False
+  dataset:
+    class: MTSDatasetH
+    module_path: qlib.contrib.data.dataset
+    kwargs:
+      handler:
+        class: Alpha158
+        module_path: qlib.contrib.data.handler
+        kwargs: *data_handler_config
+      segments:
+        train: [2008-01-01, 2014-12-31]
+        valid: [2015-01-01, 2016-12-31]
+        test: [2017-01-01, 2020-08-01]
+      seq_len: 60
+      horizon: 2
+      input_size:
+      num_states: *num_states
+      batch_size: 1024
+      n_samples:
+      memory_mode: *memory_mode
+      drop_last: True
+  record:
+    - class: SignalRecord
+      module_path: qlib.workflow.record_temp
+      kwargs: 
+        model: <MODEL>
+        dataset: <DATASET>
+    - class: SigAnaRecord
+      module_path: qlib.workflow.record_temp
+      kwargs: 
+        ana_long_short: False
+        ann_scaler: 252
+    - class: PortAnaRecord
+      module_path: qlib.workflow.record_temp
+      kwargs: 
+        config: *port_analysis_config
--- a/examples/benchmarks/TRA/workflow_config_tra_Alpha360.yaml
+++ b/examples/benchmarks/TRA/workflow_config_tra_Alpha360.yaml
@@ -0,0 +1,126 @@
+qlib_init:
+  provider_uri: "~/.qlib/qlib_data/cn_data"
+  region: cn
+
+market: &market csi300
+benchmark: &benchmark SH000300
+
+data_handler_config: &data_handler_config
+  start_time: 2008-01-01
+  end_time: 2020-08-01
+  fit_start_time: 2008-01-01
+  fit_end_time: 2014-12-31
+  instruments: *market
+  infer_processors:
+    - class: RobustZScoreNorm
+      kwargs:
+        fields_group: feature
+        clip_outlier: true
+    - class: Fillna
+      kwargs:
+        fields_group: feature
+  learn_processors:
+    - class: CSRankNorm
+      kwargs:
+        fields_group: label
+  label: ["Ref($close, -2) / Ref($close, -1) - 1"]
+
+num_states: &num_states 3
+
+memory_mode: &memory_mode sample
+
+tra_config: &tra_config
+  num_states: *num_states
+  rnn_arch: LSTM
+  hidden_size: 32
+  num_layers: 1
+  dropout: 0.0
+  tau: 1.0
+  src_info: LR_TPE
+
+model_config: &model_config
+  input_size: 6
+  hidden_size: 64
+  num_layers: 2
+  rnn_arch: LSTM
+  use_attn: True
+  dropout: 0.0
+
+port_analysis_config: &port_analysis_config
+    strategy:
+        class: TopkDropoutStrategy
+        module_path: qlib.contrib.strategy
+        kwargs:
+            model: <MODEL>
+            dataset: <DATASET>
+            topk: 50
+            n_drop: 5
+    backtest:
+        limit_threshold: 0.095
+        account: 100000000
+        benchmark: *benchmark
+        deal_price: close
+        open_cost: 0.0005
+        close_cost: 0.0015
+        min_cost: 5
+
+task:
+  model:
+    class: TRAModel
+    module_path: qlib.contrib.model.pytorch_tra
+    kwargs:
+      tra_config: *tra_config
+      model_config: *model_config
+      model_type: RNN
+      lr: 1e-3
+      n_epochs: 100
+      max_steps_per_epoch:
+      early_stop: 20
+      logdir: output/Alpha360
+      seed: 0
+      lamb: 1.0
+      rho: 0.99
+      alpha: 0.5
+      transport_method: router
+      memory_mode: *memory_mode
+      eval_train: False
+      eval_test: True
+      pretrain: True
+      init_state:
+      freeze_model: False
+      freeze_predictors: False
+  dataset:
+    class: MTSDatasetH
+    module_path: qlib.contrib.data.dataset
+    kwargs:
+      handler:
+        class: Alpha360
+        module_path: qlib.contrib.data.handler
+        kwargs: *data_handler_config
+      segments:
+        train: [2008-01-01, 2014-12-31]
+        valid: [2015-01-01, 2016-12-31]
+        test: [2017-01-01, 2020-08-01]
+      seq_len: 60
+      horizon: 2
+      input_size: 6
+      num_states: *num_states
+      batch_size: 1024
+      n_samples:
+      memory_mode: *memory_mode
+      drop_last: True
+  record:
+    - class: SignalRecord
+      module_path: qlib.workflow.record_temp
+      kwargs: 
+        model: <MODEL>
+        dataset: <DATASET>
+    - class: SigAnaRecord
+      module_path: qlib.workflow.record_temp
+      kwargs: 
+        ana_long_short: False
+        ann_scaler: 252
+    - class: PortAnaRecord
+      module_path: qlib.workflow.record_temp
+      kwargs: 
+        config: *port_analysis_config