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README.md
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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)
@@ -228,7 +228,7 @@ It also provides the API to run specific models at once. For more use cases, ple
# Quant Dataset Zoo
Dataset plays a very important role in Quant. Here is a list of the datasets built on `Qlib`.
Dataset plays a very important role in Quant. Here is a list of the datasets built on `Qlib`:
| Dataset | US Market | China Market |
| -- | -- | -- |

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examples/benchmarks/README.md
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# Benchmarks Performance
Here are the results of each benchmark model running on the `Alpha158` and `Alpha360` dataset respectively. The value of each metric are the mean and std calculated based on 10 runs.
Here are the results of each benchmark model running on Qlib's `Alpha158` and `Alpha360` dataset with China's A shared-stock & CSI300 data respectively. The values of each metric are the mean and std calculated based on 10 runs.
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.