A pipeline built in python that inputs a list of tickers, and filters them based on trading volumes, currencies, and valid listing.
Uses statistics like Alpha, Beta, and Sharpe Ratios to pick optimal stocks
Builds an optimal portfolio, starting with $1 million CAD initial investment, based on optimal returns over the S&P 500 index
Achieved a +1.70% return over the S&P 500 index in December 2025 Alone

A movie recommender system that uses TFIDF vectorization and cosine similarity to recommend movies based on plot descriptions.
Uses TMDB API to fetch movie data and display posters in a dynamic, interactive UI built with Streamlit.
Users can search a movie and find similar movies, as well as find similar movies to all previously liked movies

A Flask application built to visualize a stock of the user's choosing
Reflects the stock performance, its metrics (volatility/risk, Alpha, Beta, Sharpe Ratio, financial ratios, etc.)
Grades the current stock out of 10 based on its metrics and how well it's doing.
Provides a Buyer Analysis based off the rating out of 10, as well as professional buyer analysis.