Quant UX Data Retrieval and Analysis

This Jupyter Notebook demonstrates how to retrieve data from Quant UX and perform data analysis on the retrieved data.

Prerequisites

Before running this notebook, ensure you have the following:

• Python 3 installed
• Required packages: requests, json, pandas, scipy, statsmodels, matplotlib, seaborn
• Quant UX API authentication token and cookie

Installation

1. Clone the repository or download the notebook file (notebook.ipynb) to your local machine.

2. Install the required Python packages using the following command:

pip install requests pandas scipy statsmodels matplotlib seaborn

3. Create a .env file in the same directory based on the '.env.example' file.

• Retrieve your tokens from your browser's dev tools Network tab
• Full in qux_auth=, qux_cookie=, qux_app= accordingly

Usage

1. Open the notebook file (notebook.ipynb) in Jupyter Notebook or JupyterLab.

2. Run each cell of the notebook sequentially to retrieve data from the Quant UX API, perform data analysis, and display the results.

3. Modify the code as per your analysis requirements.

Contributions

PRs (Pull Requests) are always welcome! We appreciate any input, improvements, or suggestions that can enhance the project.

To contribute, please follow these steps:

1. Fork the repository and create your branch from main.
2. Make your desired changes, additions, or improvements.
3. Ensure that your code adheres to the project's coding style and guidelines
4. Test your changes to ensure they function as intended.
5. Submit a pull request, providing a clear and concise description of your changes.

We value the community's involvement and appreciate your contributions towards making this project even better!

References

• Quant UX: link-to-repo

License

This project is licensed under the MIT License - see the LICENSE file for details.