Revolutionising portfolio management with large language model

Abstract

The project focuses on harnessing the capabilities of Large Language Models (LLMs) to enhance the functionality and user experience of Robo-Advisor applications. The primary objective is to develop a sophisticated system capable of providing personalised portfolio recommendations and facilitating fund exploration for investors. Through the integration of advanced natural language processing techniques and the implementation of a Retriever-Augmented Generation (RAG) architecture, the application aims to deliver tailored investment advice based on individual risk profiles and investment preferences. Additionally, the system aims to offer an intuitive interface for users to explore various investment options, analyse performance metrics, and make informed decisions. To ensure users have access to up-to-date information on their portfolios, a robust data pipeline has been set up to continuously ingest and process market data daily.