Full-stack real-time earnings analysis tool that scans stocks for EPS beats and misses against Wall Street estimates, with ML-powered anomaly detection.

• Developed a live EPS surprise scanner tracking analyst estimates vs. actuals across 10+ equities, deployed full-stack on Vercel and Render.
• Engineered a quantitative drift pipeline classifying quarterly EPS surprises across 5 tiers and 12 quarters with configurable Wall Street estimate alert thresholds.
• Detected anomalous earnings surprises via residual scoring, surfacing per-equity anomaly scores on an interactive dashboard to flag outlier reporting events.

Interactive web app that stress-tests investment portfolios against major historical crises to quantify downside risk and recovery behavior.

• Built a portfolio simulation engine enabling custom allocation across 10+ assets and 3 major market crises with configurable rebalancing and normalization.
• Engineered a stress-testing framework replaying 2008 GFC, COVID-19, and 2022 Rate Shock over 12–36 month horizons, enabling side-by-side drawdown and recovery comparison.
• Designed an analytics pipeline processing 10+ years of historical market data to compute 6+ risk metrics – maximum drawdown, Sharpe ratio, and time-to-recovery – delivering quantifiable portfolio insights.

Data-driven portfolio construction engine that builds a market-aligned CAD portfolio using real-time financial data and constraint-based optimization.

• Implemented a portfolio construction pipeline filtering equities by currency, liquidity, and market capitalization, removing delisted tickers and resolving cross-listed duplicates across 10+ industries.
• Screened equities by annualized volatility to exclude high-risk names per sector while preserving diversification, reducing overall portfolio risk exposure.
• Built a $1,000,000 CAD market-meet portfolio correlated with the S&P 500 and TSX Composite, converting weights into shares net of FX and transaction costs, producing a fully investable allocation.

Interactive financial analytics dashboard for evaluating equity momentum, volatility, and signal-based trading strategies.

• Constructed a quantitative equity analysis tool computing maximum drawdown, total return, and annualized volatility across 50+ equities from historical market data.
• Classified trend direction (uptrend/downtrend/neutral) via log-price regression, identifying momentum shifts for early trend detection.
• Engineered RSI (14) and moving average crossover signals with dynamic visualizations and CSV export, enabling downstream quantitative analysis workflows.

Monte Carlo simulation engine for evaluating portfolio risk-return tradeoffs and identifying optimal risk-adjusted allocations.

• Simulates thousands of randomized portfolio weight combinations to generate return and volatility distributions
• Identifies efficient portfolios (e.g., maximum Sharpe ratio) to compare capital allocation tradeoffs across time horizons

High-performance portfolio designed to showcase my technical projects, leadership experience, and quantitative finance work.

• Built responsive multi-section components with reusable components and clean UI patterns
• Optimized navigation flow, visual hierarchy, and deployment pipeline for performance and accessibility

Object-oriented Java application modeling a basketball roster system with clean data structures and algorithmic operations.

• Designed modular class architecture to manage player creation, search, removal, and sorting using ArrayLists
• Implemented core algorithmic operations to reinforce data structure manipulation and time-complexity awareness

Collection of foundational Python programs demonstrating core problem-solving and programming fluency.

• Built multiple standalone implementations covering control flow, data structures, input validation, and algorithmic logic
• Strengthened software fundamentals through iterative development and modular program design