Developed a scalable, modular financial analytics platform for Alphalytix, a Singapore-based fintech company, to improve stock data analysis and investment strategy evaluation.
The previous system required ~6 months to integrate new financial models. I redesigned the platform using Django, reducing this timeframe to 2–3 weeks.
Technologies used
- Language: Python, JavaScript
- Framework: Django, HTMX
- Charting: ECharts.js
- Data source: Yahoo Finance
Key features
Modular Django architecture
Future-ready system for rapid addition of analytical models.
Strategy-level data visualization
Visualize specific strategy data using pie charts and heatmaps, built with ECharts.js for responsive, interactive charts.
Summary-level data
Summary-level data generated by the financial model.
Residuals & over/under charts
Charts that show model residuals and over/under performance.
Dynamic input table with historical breakdown
A dynamic input table with yearly and quarterly breakdowns of historical, model-recommended and currently used data.
Multi-filtered chart
Dynamic data chart that can be filtered by industry, company, price and metric.
User management
Users can save preferred stock datasets and calculated results for future use.
Automated data integration
Real-time stock data ingestion from Yahoo Finance.
This project transformed Alphalytix’s platform into a faster, scalable, and user-centric solution, improving analyst productivity and decision-making.