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Add benchmark results

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2020-12-08 11:45:36 +08:00
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@@ -27,10 +27,10 @@ For more details, please refer to our paper ["Qlib: An AI-oriented Quantitative
- [Data Preparation](#data-preparation)
- [Auto Quant Research Workflow](#auto-quant-research-workflow)
- [Building Customized Quant Research Workflow by Code](#building-customized-quant-research-workflow-by-code)
- [**Quant Model Zoo**](#quant-model-zoo)
- [Quant Model Zoo](#quant-model-zoo)
- [Run a single model](#run-a-single-model)
- [Run multiple models](#run-multiple-models)
- [**Quant Dataset Zoo**](#quant-dataset-zoo)
- [Quant Dataset Zoo](#quant-dataset-zoo)
- [More About Qlib](#more-about-qlib)
- [Offline Mode and Online Mode](#offline-mode-and-online-mode)
- [Performance of Qlib Data Server](#performance-of-qlib-data-server)
@@ -199,10 +199,12 @@ Here is a list of models built on `Qlib`.
- [ALSTM based on pytorcn](qlib/contrib/model/pytorch_alstm.py)
- [GATs based on pytorch](qlib/contrib/model/pytorch_gats.py)
- [SFM based on pytorch](qlib/contrib/model/pytorch_sfm.py)
<!-- - [TFT based on tensorflow](examples/benchmarks/TFT/tft.py) -->
- [TFT based on tensorflow](examples/benchmarks/TFT/tft.py)
Your PR of new Quant models is highly welcomed.
The performance of each model on the `Alpha158` and `Alpha360` dataset can be found [here](examples/benchmarks/README.md).
## Run a single model
All the models listed above are runnable with ``Qlib``. Users can find the config files we provide and some details about the model through the [benchmarks](examples/benchmarks) folder. More information can be retrieved at the model files listed above.