In the world of portfolio management, effective visualization and analysis are paramount for making informed investment decisions. While the existing software landscape does offer powerful and extensive analysis options, it's impossible to expect all scenarios and use-cases will be covered. For example, powerful features included are robust reporting frameworks, offering users the ability to make informed investment decisions. As users become more savvy and sophisticated, these features often introduce limitations, restricting users to predefined metrics and time granularity. However, with the emergence of portfolio APIs, users now have the power to transcend these limitations and tailor their analysis to suit their specific requirements.
In this package, we explore the potential and power of portfolio APIs using Python and Jupyter notebooks. Specifically, we discuss LSEGs Portfolio and Management (PAM) APIs, and introduce basic concepts related to expanding the capabilities available within LSEGs Workspace Portfolio Analytics application. Whether you're a seasoned portfolio manager looking to enhance your analytics capabilities or a newcomer seeking to gain deeper insights and possibilities, we'll provide some basic capabilities to elevate your portfolio visualization through the power of APIs.
Refer to the Enhancing Portfolio Visualization Article defined within the LSEG Developer Community for more details.
The source code presented in this project has been written by LSEG only for the purpose of illustrating the concepts of creating example scenarios using the Data Library for Python.
Note: To ask questions and benefit from the learning material, I recommend you to register on the LSEG Developer Community
To execute the notebook, you will require a Delivery Platform license to access Portfolio Analytics.
- Nick Zincone