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@@ -41,7 +41,7 @@ For more details, please refer to our paper ["Qlib: An AI-oriented Quantitative
# Framework of Qlib
<div style="align: center">
<img src="http://fintech.msra.cn/images_v060/framework.png" />
<img src="http://fintech.msra.cn/images_v060/framework.png?v=0.1" />
</div>
@@ -192,24 +192,6 @@ The automatic workflow may not suite the research workflow of all Quant research
# [Quant Model Zoo](examples/benchmarks)
## Run a single model
`Qlib` provides three different ways to run a single model, users can pick the one that fits their cases best:
- User can use the tool `qrun` mentioned above to run a model's workflow based from a config file.
- User can create a `workflow_by_code` python script based on the [one](examples/workflow_by_code.py) listed in the `examples` folder.
- User can use the script [`run_all_model.py`](examples/run_all_model.py) listed in the `examples` folder to run a model. Here is an example of the specific shell command to be used: `python run_all_model.py --models=lightgbm`. For more use cases, please refer to the file's [docstrings](examples/run_all_model.py).
## Run multiple models
`Qlib` also provides a script [`run_all_model.py`](examples/run_all_model.py) which can run multiple models for several iterations. (**Note**: the script only supprots *Linux* now. Other OS will be supported in the future.)
The script will create a unique virtual environment for each model, and delete the environments after training. Thus, only experiment results such as `IC` and `backtest` results will be generated and stored.
Here is an example of running all the models for 10 iterations:
```python
python run_all_model.py 10
```
It also provides the API to run specific models at once. For more use cases, please refer to the file's [docstrings](examples/run_all_model.py).
Here is a list of models built on `Qlib`.
- [GBDT based on LightGBM](qlib/contrib/model/gbdt.py)
- [GBDT based on Catboost](qlib/contrib/model/catboost_model.py)
@@ -219,13 +201,30 @@ Here is a list of models built on `Qlib`.
- [LSTM based on pytorcn](qlib/contrib/model/pytorch_lstm.py)
- [ALSTM based on pytorcn](qlib/contrib/model/pytorch_alstm.py)
- [GATs based on pytorch](qlib/contrib/model/pytorch_gats.py)
- [TabNet based on pytorch](qlib/contrib/model/tabnet.py)
- [SFM based on pytorch](qlib/contrib/model/pytorch_sfm.py)
- [HATs based on pytorch](qlib/contrib/model/pytorch_hats.py)
- [TFT based on tensorflow](examples/benchmarks/TFT/tft.py)
Your PR of new Quant models is highly welcomed.
## Run a single model
`Qlib` provides three different ways to run a single model, users can pick the one that fits their cases best:
- User can use the tool `qrun` mentioned above to run a model's workflow based from a config file.
- User can create a `workflow_by_code` python script based on the [one](examples/workflow_by_code.py) listed in the `examples` folder.
- User can use the script [`run_all_model.py`](examples/run_all_model.py) listed in the `examples` folder to run a model. Here is an example of the specific shell command to be used: `python run_all_model.py --models=lightgbm`. For more use cases, please refer to the file's [docstrings](examples/run_all_model.py).
## Run multiple models
`Qlib` also provides a script [`run_all_model.py`](examples/run_all_model.py) which can run multiple models for several iterations. (**Note**: the script only supprots *Linux* now. Other OS will be supported in the future.)
The script will create a unique virtual environment for each model, and delete the environments after training. Thus, only experiment results such as `IC` and `backtest` results will be generated and stored. (**Note**: the script will erase your previous experiment records created by running itself.)
Here is an example of running all the models for 10 iterations:
```python
python run_all_model.py 10
```
It also provides the API to run specific models at once. For more use cases, please refer to the file's [docstrings](examples/run_all_model.py).
# Quant Dataset Zoo
Dataset plays a very important role in Quant. Here is a list of the datasets built on `Qlib`.