I developed an unsupervised learning trading strategy applied to S&P 500 stock data, with a focus on feature engineering, technical indicators, and portfolio optimization. This project highlighted the practical application of machine learning techniques to identify hidden patterns in financial data and optimize investment returns, demonstrating my ability to work with complex financial datasets and implement sophisticated analytical approaches.
During my internship, I performed an analysis of special orders placed for products, calculating a 40% win percentage that provided valuable business intelligence for the company. This project required me to work with real-world business data and deliver actionable insights that could inform strategic decision-making.
I designed and implemented an investment strategy that analyzed social media engagement and sentiment to rank NASDAQ stocks. This project involved natural language processing techniques to gauge market reaction from Twitter data, with portfolio performance compared to the QQQ benchmark.