Quantitative Hedge Fund
Trading Since December 9th, 2019
investing through the lens of theoretical physics
"We balance probabilities and choose the most likely.
It is that scientific use of the imagination."
At Sistine Capital LP the stock market is not about finance, economics, politics, management or value; it's about being in the right place at the right time. While strategies based on those factors have their place in capital allocation they also depend heavily on one thing: a logical progression of events. If events unfold in an illogical manor, the investor is left with nothing more than a guess.
Especially in light of recent events, the stock market is far from logical. Our fully quantitative investment strategy ignores the ever-changing noise and focuses solely on one of the few constants that the stock market has to offer: human nature. Research suggests that humans have always been afraid of missing out and they always will be. What started as a satellite-bound algorithm used to track the flow of ions along magnetic field lines in the Sun's atmosphere now serves as the backbone of our strategy. The algorithm's job is to characterize trends in the market's fear of missing out (FOMO) and maintain a position that corresponds to the highest probability of profit. The strategy only looks at broad sector ETFs to avoid the risks associated with individual stock positions. It narrows its field of view to just two investable assets at a time: one to take advantage of specific levels of FOMO, and one to hedge against the lack thereof. Coupled with the strategy's willingness to sit in cash when conditions aren't easily characterizable, our perspective doesn't feel the need to play any games we don't think we can win.
This sort of approach allows investors to open up a portion of their portfolio to capitalizing on trends in human nature when nothing else seems to make sense. Our goal is to constantly curate an investment portfolio with the highest possible risk-adjusted return. Small, consistent gains paired with the avoidance of large drawdowns is our simple recipe that brings that goal to life.
Born and raised in south-eastern Kentucky I could not have started farther from Wall Street. Luckily a passion for problem solving led me to a degree in Physics with a concentration in theoretical astrophysics. While in college I maintained a paid research position with NASA Goddard working on magnetic field activity in the Sun's atmosphere. In 2017 I was awarded a research fellowship at Harvard University in the Smithsonian Center for Astrophysics, continuing to work on high energy magnetic activity in the solar regime. Through these research projects I developed a knack for characterizing complex systems and building algorithms to better understand their behavior. I used my last year in school to apply the Python programming language and discrete mathematics to an extensive project on the history of military encryption, specifically the Enigma Machines in World War II. While looking into other applications of mathematics I tried to write a simple algorithm for the stock market and immediately fell in love with the process.
After two years of development on an algorithmic investment strategy I decided it was time to start a fund and offer this strategy to others. My background in tracking the flow of energy along magnetic field lines was perfectly applicable to tracking the flow of money between asset classes. Outside of the fund I remain involved in the theoretical physics community. I'm currently working on developing and publishing my Multi-Dimensional Information Gradient Theory alongside a collection of thoughts on the philosophical implications of theoretical physics.