Quantitative Finance • Application Systems • Data & Analytics
Building high-performance financial applications and systems, quantitative models, and simulation-driven software with a strong emphasis on correctness, rigor, and real-world execution. Focused on the intersection of markets, technology, and scalable, production-grade infrastructure.
- Designing and implementing quantitative trading strategies and robust backtesting engines
- Developing production-grade financial applications and end-to-end trading systems
- Applying ML-assisted simulations to finance and complex, system-level problems
- Studying market microstructure, risk modeling, and portfolio optimization
- Architecting scalable full-stack systems, APIs, and data pipelines for real-world deployment
- Conducting applied research at the intersection of markets, technology, and regulation
Quant Strategies
Systematic quantitative trading strategies with rigorous backtesting frameworks. Emphasis on signal research, risk control, and performance evaluation.
Python NumPy Pandas Finance Math
Elysian Trading System
End-to-end trading system architecture covering strategy orchestration, execution logic, and system design. Built with extensibility, robustness, and real-world constraints in mind.
TypeScript PostgreSQL Python
MVP90 Terminal (In Progress)
Bloomberg-style venture intelligence and analytics platform. Structured metrics, live analysis, and scalable architecture designed for decision-makers.
TypeScript PostgreSQL
Published author with experience across finance, policy, and applied research. Interested in the intersection of markets, technology, and regulation.
I do it for the thrill. Baaki idk!