Files
NexQuant/test/utils
Xu Yang e0a24fb46f Several update on the repo (see desc) (#76)
* ignore result csv file

* fix app scripts

* rename taskgenerator to developer and generate to develop

* fix a config bug in coder

* fix a small bug in factor coder evaluators

* remove a single logger in factor coder evaluators

* fix a small bug in model coder main.py

* rename Implementation to Workspace

* move the prepare the inject_code into FBWorkspace to align all the behavior

* fix a small bug in model feedback

* remove debug lines for multi processing and simplify evaluators multi proc

* add a copy function to workspace to freeze the workspace && add config prefix to speed up debugging

* make hypothesisgen a abc class

* use Qlib***Experiment

* fix a small bug

* rename Imp to Ws

* rename sub_implementations to sub_workspace_list

* fix a bug in feedback not presented as content in prompts

* move proposal pys to proposal folder

* reformat the folder

* align factor and model qlib workspace and use template to handle the workspace

* add a filter to evoagent to filter out false evo

* align multi_proc_n into RDAGENT seeting

* handle when runner gets empty experiment

* fix logger merge remaining problems

* fix black and isort automatically
2024-07-17 15:00:13 +08:00
..
2024-07-11 08:49:37 +00:00
2024-07-15 08:31:27 +00:00

🐳 Run Docker & Qlib


📄 Description

This guide explains how to run the Qlib Docker test file located at test/utils/test_env.py in the RD-Agent repository.


🚀 Running Instructions

1. Install the required Python libraries

  • Ensure that the docker Python library is installed:
    pip install docker
    

2. Run the test script

  • Execute the test script to verify the Docker environment setup:
    python test/utils/test_env.py
    

Troubleshooting

  • PermissionError: [Errno 13] Permission denied.

    This error occurs when the current user does not have the necessary permissions to access the Docker socket. To resolve this issue, follow these steps:

  1. Add the current user to the docker group Docker requires root or docker group user permissions to access the Docker socket. Add the current user to the docker group:

    sudo usermod -aG docker $USER
    
  2. Refresh group changes To apply the group changes, log out and log back in, or use the following command:

    newgrp docker
    
  3. Verify Docker access Run the following command to ensure that Docker can be accessed:

    docker run hello-world
    
  4. Rerun the test script After completing these steps, rerun the test script:

    python test/utils/test_env.py
    

🛠️ Detailed Qlib Docker Function Framework

Here, we provide an overview of the specific functions within the Qlib Docker framework, their purposes, and examples of how to call them.

QTDockerEnv Class in env.py

The QTDockerEnv class is responsible for setting up and running Docker environments for Qlib experiments.

Methods:

  1. prepare()

    • Purpose: Prepares the Docker environment for running experiments. This includes building the Docker image if necessary.
    • Example:
      qtde = QTDockerEnv()
      qtde.prepare()
      
  2. run(local_path: str, entry: str) -> str

    • Purpose: Runs a specified entry point (e.g., a configuration file) in the prepared Docker environment.
    • Parameters:
      • local_path: Path to the local directory to mount into the Docker container.
      • entry: Command or entry point to run inside the Docker container.
    • Returns: The stdout output from the Docker container.
    • Example:
      result = qtde.run(local_path="/path/to/env_tpl", entry="qrun conf.yaml")
      

📊 Expected Output

Upon successful execution, the test script will produce analysis results of benchmark returns and various risk metrics. The expected output should be similar to:

'The following are analysis results of benchmark return (1 day).'
risk
mean               0.000477
std                0.012295
annualized_return  0.113561
information_ratio  0.598699
max_drawdown      -0.370479

'The following are analysis results of the excess return without cost (1 day).'
risk
mean               0.000530
std                0.005718
annualized_return  0.126029
information_ratio  1.428574
max_drawdown      -0.072310

'The following are analysis results of the excess return with cost (1 day).'
risk
mean               0.000339
std                0.005717
annualized_return  0.080654
information_ratio  0.914486
max_drawdown      -0.086083

'The following are analysis results of indicators (1 day).'
value
ffr    1.0
pa     0.0
pos    0.0

By following these steps and using the provided functions, you should be able to run the Qlib Docker tests and obtain the expected analysis results.