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wangwenxi-handsome 3760a18a8d Merge nested main (#597) 
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* Add `backend_freq_config` parameter, support multi-freq uri

* Add sample_config to QlibDataLoader, support multi-freq

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* get_cls_kwargs renamed get_callable_kwargs

* support multi-freq uri

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$ `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
```

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* modify the example of multi-freq

* add Copyright

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* modify the example of multi-freq

* add comment to multi_freq_handler.py

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* add expression description to multi_freq_handler.py

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2021-10-01 02:15:30 +08:00
..
highfreq_handler.py
fix df_features.index conține np.nan
2021-07-21 14:28:20 +08:00
highfreq_ops.py
volume limit update
2021-08-01 16:03:08 +00:00
highfreq_processor.py
fix df_features.index conține np.nan
2021-07-21 14:28:20 +08:00
README.md
update readme
2021-04-14 14:40:28 +08:00
workflow_config_High_Freq_Tree_Alpha158.yaml
Merge nested main (#597)
2021-10-01 02:15:30 +08:00
workflow.py
support check_transform_proc module_path
2021-07-02 08:47:52 +08:00

README.md

High-Frequency Dataset

This dataset is an example for RL high frequency trading.

Get High-Frequency Data

Get high-frequency data by running the following command:

    python workflow.py get_data

Dump & Reload & Reinitialize the Dataset

The High-Frequency Dataset is implemented as qlib.data.dataset.DatasetH in the workflow.py. DatatsetH is the subclass of qlib.utils.serial.Serializable, whose state can be dumped in or loaded from disk in pickle format.

About Reinitialization

After reloading Dataset from disk, Qlib also support reinitializing the dataset. It means that users can reset some states of Dataset or DataHandler such as instruments, start_time, end_time and segments, etc., and generate new data according to the states.

The example is given in workflow.py, users can run the code as follows.

Run the Code

Run the example by running the following command:

    python workflow.py dump_and_load_dataset

Benchmarks Performance

Signal Test

Here are the results of signal test for benchmark models. We will keep updating benchmark models in future.

Model Name Dataset IC ICIR Rank IC Rank ICIR Long precision Short Precision Long-Short Average Return Long-Short Average Sharpe
LightGBM Alpha158 0.3042±0.00 1.5372±0.00 0.3117±0.00 1.6258±0.00 0.6720±0.00 0.6870±0.00 0.000769±0.00 1.0190±0.00