Quantitative Research
Quantitative researcher focused on systematic trading, market intelligence, and applied financial research
I build research-to-execution pipelines across signal generation, backtesting, risk management, and monitoring — with experience spanning crypto, U.S. equities, derivatives, and NLP-driven trading research.
MSE in Financial Mathematics at Johns Hopkins University. Background in systematic trading system design, alternative data, Monte Carlo methods, and behavioral finance. I have worked across crypto and equity strategies, factor research, structured derivatives, and research-grade execution pipelines.
Focus Areas
Systematic Trading
End-to-end research pipelines from signal design through risk management to live monitoring, across crypto and equity markets.
Quantitative Research
Applied work in behavioral finance, Monte Carlo methods, NLP sentiment, reinforcement learning, and factor-based modeling.
Derivatives & Risk
Exotic option pricing, structured product analysis, and systematic risk frameworks for multi-asset portfolios.
Selected Projects
Four projects spanning systematic trading, derivatives, alternative data, and quantitative research.
Intraday Regime-Aware Watchlist Monitor
Real-time decision-support system for intraday trading on a focused high-beta watchlist
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Exotic Options & Structured Derivatives Research
Monte Carlo pricing and scenario analysis for multi-asset structured products
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Crypto Market Intelligence Research
Alternative data, NLP, and event-driven analysis for digital asset markets
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Financial Bubble Detection with HLPPL
Identifying and quantifying U.S. equity bubbles using the Hyped Log-Periodic Power Law model
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Competitive Distinction
Outside of trading and research, I have competed at a high level in Teamfight Tactics, reaching Rank 1 on the North American server and previously participating in professional competition. The experience reinforced skills that also matter in markets: decision-making under uncertainty, rapid adaptation, disciplined review, and strategic resource allocation.
This competitive background is also reflected in a public Liquipedia profile.