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

• Screened 50+ equities on currency, liquidity, and market capitalization, resolving cross-listed duplicates by trading volume.
• Filtered the top 25% most volatile names per sector, ranking the rest by correlation to an S&P 500 and TSX benchmark.
• Constructed a $1,000,000 CAD portfolio matching sector weights under 40% and 15% position caps, net of FX and fees.

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

• Engineered a stress-testing framework replaying the 2008 financial crisis, COVID-19, and 2022 interest rate shock scenarios.
• Simulated portfolio performance across US and TSX-listed equities with configurable daily, weekly, or monthly rebalancing.
• Computed 6 risk metrics including maximum drawdown, Sharpe ratio, volatility, and time-to-recovery over 12–36 months.

Full-stack earnings analysis tool that tracks EPS beats and misses against Wall Street estimates, flagging surprises that are unusual for each company.

• Developed a real-time scanner comparing consensus Wall Street EPS estimates against reported actuals across 10+ equities.
• Designed a scikit-learn anomaly model flagging earnings surprises that deviate from each company's own historical pattern.
• Deployed a full-stack FastAPI and React dashboard surfacing 11 quarters of per-equity earnings data and threshold alerts.

Power BI report benchmarking Ontario's primary enrolment against national and global averages, built during my term at Focus on Nature.

• Benchmarked Ontario's 72.05% primary enrolment rate against Canada's 94.75% average, exposing a 23-point shortfall.
• Analyzed 6 Ontario regions, surfacing a 46-point gap between the strongest and weakest regional primary enrolment rates.
• Automated drill-down across 6 Ontario and 8 world regions using DAX measures and bookmark navigation in one report page.

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 (maximum Sharpe ratio) to compare capital allocation tradeoffs across time horizons.

Portfolio site showcasing my projects, experience, and quantitative finance work.

• Built responsive multi-section components with reusable patterns and a live market ticker.
• 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.