Project Abstract

The growing complexity and volatility of financial markets underscore the importance of advanced, yet user-friendly, analytical tools, particularly for inexperienced traders who often lack the resources and expertise of experienced investors. The primary objective of this undertaking is to promote the democratization of comprehensive trading analytics, thereby facilitating traders of varying proficiency levels in accessing advanced market information. The proposal suggests the creation of a visual analytics system that aims to bridge the gap between complex trading technology and the general user by offering user-friendly interfaces and tools. The process involves doing a comprehensive review of existing financial visualization tools to identify any shortcomings in functionality and usability that could impact inexperienced traders. Following this, a system is developed that operates in real-time and is interactive. This system use machine learning techniques to generate dynamic visual representations of financial data and offer predicted insights. The system will be designed in a manner that allows those without technical expertise to create, alter, and utilize prediction models, all while visually illustrating complex market data patterns. Preliminary findings derived from prototypes suggest that the integration of such a system holds promise in significantly enhancing users’ understanding of market conditions and thereby impacting their trading decisions. The expected outcome is a user-friendly platform designed for inexperienced traders, enabling them to make informed investing choices through the utilization of advanced visualization features and predictive analytics. If this undertaking is successful, it will not only improve the abilities of individual traders but also promote a broader understanding and accessibility of financial analytics. As a result, this will yield advantageous outcomes for the trading community by reducing the barriers that impede efficient market engagement.

Keywords: Real-time Data Visualization, Machine Learning Integration, Interactive Financial Dashboards

 Conference Details

Session: Presentation Stream 11 at Presentation Slot 3

Location: College 127 at Tuesday 7th 13:30 – 17:00

Markers: Nicholas Micallef, Alec Critten (GTA)

Course: MSc Computer Science, Masters PG

Future Plans: I’m looking for work