The rapid expansion of retail participation in global financial markets has created significant challenges in processing and analyzing trading data efficiently. Traditional charting platforms often produce cognitive overload because they lack automated, forward-looking trend synthesis, leaving complex technical analysis to the end-user. This paper presents the design and implementation of PredictX, an AI-driven financial market intelligence terminal that utilizes statistical machine learning and hybrid momentum techniques to enhance the accuracy and rendering speed of short-term price target generation. The proposed system processes monocular time-series data using sequential API integration and generates instantaneous trend projections to better understand the momentum behind the visual market frames. It then applies a 14-day Linear Regression engine combined with a 10-day Simple Moving Average (SMA) algorithm to optimize and render the most realistic 24-hour targets. The system architecture consists of modules for secure authentication, asynchronous data preprocessing, algorithmic synthesis, categorical sentiment classification, and dynamic Glassmorphism rendering. Techniques such as single-page application (SPA) state management and Chart.js hardware-accelerated canvases are integrated to improve visual precision and fidelity. Experimental evaluation shows that the proposed PredictX approach outperforms traditional heavy Python-based predictive frameworks in terms of render speed, server memory usage, and execution latency. The system demonstrates improved algorithmic understanding, faster generation times, and higher user satisfaction. The proposed solution is suitable for applications such as retail trading platforms, educational finance tools, and personal portfolio management. The results indicate that integrating optimized native array mathematics into MVC-based web mechanisms significantly enhances the effectiveness and efficiency of modern financial computing systems.
3D Gaussian Splatting, Spatial Computing, Monocular Video, Structure-from-Motion, Computer Vision, Rendering Algorithms.
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