Black-Scholes implementation with Greeks calculation and real-time market data.
VIEW PROJECTML-enhanced Monte Carlo achieving 30-55% variance reduction
VIEW PROJECTReal-time portfolio risk analytics with VaR calculations and stress testing.
VIEW PROJECTPursuing dual masters in Computer Science and Financial Engineering, focusing on advanced quantitative methods, distributed systems, and derivative pricing models.
Developed automated reconciliation systems and Timeseries feed / Analysis framework, saving $700k in accounting losses. Built risk-theoretical PnL models.
Developed POC for timeseries analysis platform and fabricated framework for ICE Broker Quotes integration for Valuation Analysis.
Computer Science and Engineering with focus on Machine Learning, Blockchain, and Neural Networks.
Architected C++ frameworks achieving 2-3 μs end-to-end execution for trading strategies. The intersection of mathematical precision and computational efficiency drives my passion for building systems that operate at the speed of thought.
From implementing PEMC algorithms achieving 30-55% variance reduction to developing statistical arbitrage strategies with 1.6 Sharpe ratio, I'm fascinated by translating complex mathematical models into profitable trading systems.
Every line of code is backed by rigorous research. Whether implementing research papers like Li et al.'s PEMC framework or developing novel approaches to market making, I believe in evidence-based innovation.
Engineered production-grade ML-enhanced Monte Carlo system implementing Li et al. (2024) framework, achieving 30-55% variance reduction in Asian options pricing while preserving statistical unbiasedness.
Researched cointegrated pairs across 20 sectors, generating 1.4% ROI on $5M simulated capital with 1.6 Sharpe ratio in backtesting framework.
Engineered cache warming routines, compile-time executions, and lock-free data structures achieving 2-3 μs end-to-end z-score calculation and trade execution.