Resume

Welcome to my personal page!
The objective of this project was to obtain a portfolio with the optimal result (Highest ROE & Sharpe Ratio). Samples were taken from Virginia Tech SEED’s real energy portfolio. The chosen equities were tested through the Monte Carlos Simulation. Traditionally, themes of portfolio success can be achieved through: diversification, risk-management, setting a goal, and establishing a time horizon. Through trials from the Monte Carlos Simulation, these themes prove accurate. Patterns of the optimal portfolio can be seen through diversifying one’s portfolio and being overweight in companies that have historically performed well. Finally, fibonacci retracement levels were established to determine possible support and resistance levels.
Redesigned website for family owned Chinese website. The current old/current website is "https://www.eastgourmetruckersville.com" . Please click on the source code to see the new and improved website.
For this python project, I used python, pygame library, and the backtracking Algorithm. A random Sodoku board is loaded into the pygames library with various commands. Visit the source code to learn more!
Greetings, I'm Kevin Wang—a first-generation scholar from Charlottesville, Virginia, currently charting my path in FinTech & Big Data Analytics at Virginia Tech (Class of '24). Alongside rigorous academic pursuits, I'm deeply engaged in SEED, Consulting Group, and the FinTech Club, channeling my passion for insightful problem-solving.
Over a transformative summer, I honed my skills as a restructuring intern at FTI Consulting in Richmond, amplifying my practical knowledge in finance.
Beyond academics and professional aspirations, I'm an ardent fitness enthusiast and tennis aficionado. The nuanced strategies of poker also captivate my analytical mind. Amidst it all, treasured moments with friends underscore the value I place on holistic growth.
Dedicated to a purposeful and well-rounded journey, I navigate each endeavor with unwavering commitment.
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Name | Description | Price |
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100.00 |