LLM-Based Real-Time Personalized Financial News Notification System
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Supervisor: Prof. Chow, Ka Ho
Lee Jong Seung (3035555547)
Kim Taehyun (3035741330)
Lee Changjin (3035435840)
Project Introduction
In today’s fast-paced financial markets, which operates in real-time and is highly sensitive to news and sentiments, tracking news in real-time is crucial. Investor often find it challenging to determine the relevance of news to their own portfolios and to track such information efficiently, even in real-time.
To address this issue, we propose developing an LLM-based real-time personalized financial news alert application.
This application offers a website which allows users to input their investment portfolio and receive real-time notifications about relevant news with a summary of the news and an impact to the user portfolio. This ensures that users receive only the most relevant updates, enabling them to focus on news that matters for their financial decisions. Also, the content summary will help users quickly grasp key insights and suggest them which articles worth reading in full.
By providing timely and relevant updates, this project will help individual investors make informed and quick decisions, empowering them to stay ahead in the financial markets with ease.
Project Milestones
Phase 1: Inception (1 Oct, 2024 – 17 Jan, 2025)
1 Oct – 31 Oct, 2024
- Complete system architecture design and review on a full-scale
- Test Refinitiv API and validate any alternative data source if required
- Test embedding search model
1 Nov – 30 Nov, 2024
- Complete UI Design with Figma
- Database Schema Design
- Backend implementation of user authentication and stock keyword input functionality
- Infrastructure setup with AWS
1 Dec – 31 Dec, 2024
- Backend Implementation and integration with stock API
- Implementation of the Keywords Generator LLM
- Implementation of the embedding generator model
- Vector database infrastructure set up
- Front-end baseline setup
1 Jan – 31 Jan, 2025
- Frontend development
- Backend development
- Implementation of polling agent and integration with the message queue
- Implementation of the embedding search model
Phase 2: Elaboration (18 Jan, 2025 – 20 Apr, 2025)
1 Feb – 28 Feb, 2025
- Frontend development
- Backend development
- Implementation of prompt engineering on LLM summarization on news articles
- Implementation of Document Analyzer
1 Mar – 31 Mar, 2025
- Implementation of the backend of the LLM chat function
- Test and experiment with the Stock Analyzer LLM (summary generation LLM & stock price impact analysis NLP model)
1 Apr – 20 Apr, 2025
- Implementation of the frontend of the LLM chat function
- Integration test of the overall system
- Continuous testing and enhancement of the language models of the system
Phase 3: Construction (21 Apr, 2025 -)
21 Apr – 30 Apr, 2025
- Preparation of final presentation and project exhibition