How to Run Open-Source Risk Engine (ORE) Python in a Jupyter Notebook

Introduction

This tutorial will guide you through the process of running the Open-Source Risk Engine (ORE) with Python in a Jupyter Notebook. Whether you are a beginner or someone looking to integrate powerful risk functionalities into your Python projects, this guide will help you get set up quickly and efficiently.

Step 1: Setting Up Your Environment

To begin, you need to create a virtual environment and install the necessary packages.

1. Open Command Line Interface (CLI).
2. Create a Virtual Environment:

   python -m venv venv
   

3. Activate the Virtual Environment:
   • For Windows:

     .\venv\Scripts\activate.bat
     

4. Install Required Packages:

   python -m pip install open-source-risk-engine matplotlib pandas plotly jupyter
   

5. Launch Jupyter Notebook:

   python -m jupyterlab
   

Step 2: Creating a New Notebook

Once Jupyter Lab is running:

1. Open Jupyter Notebook from the web interface that appears.
2. Create a New Notebook by selecting "Python 3" from the options.

Step 3: Running ORE Python in Your Notebook

1. Import the Open-Source Risk Engine:

   import open_source_risk_engine as ore
   

2. Execute ORE Functions as needed within the notebook.

Step 4: Comparing ORE Python and Command Script

To understand the differences:

1. Run the ORE functionalities using Python as shown in the previous step.
2. Run the command script version in a separate terminal to compare results.
3. Document any differences in output or performance.

Step 5: Importing the Utilities.py File

1. Locate the Utilities.py file in your ORE installation directory.
2. Import the utilities in your notebook:

   from utilities import *
   

3. Use the available utility functions to enhance your analysis.

Step 6: Running ORE from Different Folders

To run ORE from a folder other than the default:

1. Change the directory in your Jupyter Notebook using:

   import os
   os.chdir('path_to_your_directory')
   

2. Run your ORE scripts from this new directory.
3. Handle errors that may arise due to missing files or paths.

Step 7: Using ORE Data in Your Analysis

1. Explore example datasets like the ORE Academy Equity Option:

   equity_option_data = ore.load_equity_option_data()
   

2. Perform analyses as per your project needs, leveraging the functionalities of ORE.

Conclusion

You've now set up the Open-Source Risk Engine in a Jupyter Notebook and learned how to run analyses effectively. From creating a virtual environment to importing utilities and running ORE from different folders, you have the tools to incorporate risk management functions into your Python projects. For further learning, check out the ORE Python repository and additional resources provided in the video description. Happy coding!