This project is currently being developed as an experimental platform to help investors make informed, data-driven decisions by combining long-term financial modeling with real-time market intelligence. The solution consists of two core components:
AI-Driven Prediction Engine – Uses a blend of tree-based ensemble learning and sequence-aware neural forecasting techniques trained on multi-year price histories and benchmark index data. The engine produces short-term outlooks, volatility-adjusted risk assessments, and directional signals. Real-time intraday pricing is incorporated to refine predictions so they remain aligned with current market conditions.
Interactive Analytics Platform – A full-stack application enabling users to explore trained models, visualize historical vs. predicted movements, and review AI-generated explanations in English or Korean. The system automatically stores training runs and prediction caches in PostgreSQL, making model updates reproducible and optimized for performance.
As a solo developer taking this project from 0 to 1, I am building every part of the system – from data engineering and modeling strategy to backend APIs and frontend user experience. By incorporating AI-assisted coding techniques throughout development, I’ve been able to accelerate iteration speed and maintain high quality across the entire stack. This approach allows the platform to evolve rapidly while laying the groundwork for a scalable foundation for future hedging tools and investment decision-support systems.
Backend Architecture
Software Stack
Taehun Choi 
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