Qlib-Server is the data server system for Qlib. It enable Qlib to run in online mode. Under online mode, the data will be deployed as a shared data service. The data and their cache will be shared by all the clients. The data retrieval performance is expected to be improved due to a higher rate of cache hits. It will consume less disk space, too.

To sum up, Qlib-Server is designed to solve the following problems:

• Manage the data in a centralized way, which makes data management (including cache management, date updating) much easier.
• Reduce the amount of cache to be generated.
• Make the client light-weighted and leverage the powerful computing resources of remote server

• Framework of qlib-server
• Quick start
  • Deployment
    • One-click Deployment
    • Step-by-Step Deployment
  • Using Qlib in Online Mode
• More About Qlib
• Contributing

The Client/Server framework of Qlib is based on WebSocket considering its capability of bidirectional communication between client and server in async mode.

Qlib-Server is based on Flask, which is a micro-framework for Python and here Flask-SocketIO is used for websocket connection.

One-click deployment of Qlib-Server is supported, you can choose either of the following two methods for one-click deployment:

• Deployment with docker-compose
• Deployment in Azure

Deploy Qlib-Server with docker-compose according to the following processes:

• Install docker, please refer to Docker Installation.

• Install docker-compose, please refer to Docker-compose Installation.

• Run the following command to deploy Qlib-Server:

    git clone https://github.com/microsoft/qlib-server
    cd qlib-server
    sudo docker-compose -f docker_support/docker-compose.yaml --env-file docker_support/docker-compose.env build
    sudo docker-compose -f docker_support/docker-compose.yaml --env-file docker_support/docker-compose.env up -d
    # Use the following command to track the log
    sudo docker-compose -f docker_support/docker-compose.yaml --env-file docker_support/docker-compose.env logs -f

Firstly, You need to have an Azure account to deploy Qlib-Server in Azure. Then you can deploy Qlib-Server in Azure according to the following processes:

• Install azure-cli, please refer to install-azure-cli.

• Add the Azure account to the configuration file azure_conf.yaml

      sub_id: Your Subscription ID
      username: azure user name
      password: azure password
      # The resource group where the VM is located
      resource_group: Resource group name

• Execute the deployment script by running the following command:

      git clone https://github.com/microsoft/qlib-server
      cd qlib-server/scripts
      python azure_manager.py create_qlib_cs_vm \
          --qlib_server_name test_server01 \
          --qlib_client_names test_client01 \
          --admin_username test_user \
          --ssh_key_value ~/.ssh/id_rsa.pub \
          --size standard_NV6_Promo\
          --conf_path azure_conf.yaml

To know more about one-click Deployment, please refer to Qlib-Server One-click Deployment.

To know more about step-by-step Deployment, please refer to [Qlib-Server Step-by-step Deplyment]https://qlib-server.readthedocs.io/en/latest/build.html#step-by-step-deployment).

In the Qlib Document, the Offline mode has been introduced.

With Qlib-Server, you can use Qlib in Online mode, please initialize Qlib with the following code:

import qlib
ONLINE_CONFIG = {
    # data provider config
    "calendar_provider": {"class": "LocalCalendarProvider", "kwargs": {"remote": True}},
    "instrument_provider": "ClientInstrumentProvider",
    "feature_provider": {"class": "LocalFeatureProvider", "kwargs": {"remote": True}},
    "expression_provider": "LocalExpressionProvider",
    "dataset_provider": "ClientDatasetProvider",
    "provider": "ClientProvider",
    # config it in user's own code
    "provider_uri": "127.0.0.1:/",
    # cache
    # Using parameter 'remote' to announce the client is using server_cache, and the writing access will be disabled.
    "expression_cache": None,
    "dataset_cache": None,
    "calendar_cache": None,
    "mount_path": "/data/stock_data/qlib_data",
    "auto_mount": True,  # The nfs is already mounted on our server[auto_mount: False].
    "flask_server": "127.0.0.1",
    "flask_port": 9710,
    "region": "cn",
}

qlib.init(**client_config)
ins = D.list_instruments(D.instruments("all"), as_list=True)

For more details, please refer to Qlib-Server Client.

The detailed documents are organized in docs. Sphinx and the readthedocs theme is required to build the documentation in html formats.

cd docs/
conda install sphinx sphinx_rtd_theme -y
# Otherwise, you can install them with pip
# pip install sphinx sphinx_rtd_theme
make html

You can also view the latest document online directly.

This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.

When you submit a pull request, a CLA bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.

This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact [email protected] with any additional questions or comments.