🎓 Dual Master's Student @ UMBC | 📈 Aspiring Quant | 💡 Data-Driven Strategist
Welcome to my GitHub! I'm a Quantitative Researcher-in-training with hands-on experience in building, backtesting, and optimizing algorithmic trading strategies, pricing derivatives, and visualizing P&L scenarios using interactive tools.
- 📍 Based in Baltimore | Originally from Mumbai
- 📚 Dual MS in Statistics and Data Science
- 🧮 Obsessed with market microstructure, execution cost modeling, and volatility surfaces
- ⚙️ Currently building strategy optimizers and interactive risk dashboards
- 🧠 Passionate about blending math, code, and market intuition to solve real-world problems
Languages: Python, R, SQL, C, PL/SQL
Libraries: NumPy, Pandas, Scikit-learn, StatsModels, Matplotlib
Quant Tools: Backtrader, TA-Lib, yFinance, Pyfolio, Amibroker
Data Viz: Power BI, Streamlit, Tableau, Looker
Finance Concepts: VaR, Greeks, Derivatives Pricing, Risk Modeling, Alpha Generation
Stats/ML: GARCH, PCA, Monte Carlo, Regime Classification, Time-Series Forecasting
Developed a Streamlit app to price and simulate multi-leg option strategies
→ Real-time Greeks, vol-adjusted Iron Condors, net P&L heatmaps, and payoff asymmetry analytics
Custom-built Order Flow Imbalance features for high-frequency trading signals
→ Engineered multiple-tier OFI metrics from LOB data, with exploratory notebooks for edge visualization
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💼 Risk Analyst Intern @ Gemini
Designed crypto margin engines, VaR models, and outage-loss dashboards -
🧠 Quant Analyst @ Edelweiss
Built long-short systems (Sharpe up to 1.8), intraday alpha execution models, and NSE-integrated dashboards
“Markets may be noisy, but signal emerges through structure. I build tools to find that signal.”
🏗️ This GitHub is a work in progress—more notebooks, dashboards, and quant experiments coming soon!