Introduction
Background and Motivation
1.1 The Beginning: High School and the Data Science Dream
My name is Krrish Ghindani. I am currently a sophomore studying Data Science at the University of San Francisco, but my journey began much earlier, back in high school in India. Like many students passionate about math and coding, I initially set my sights on becoming a data scientist. The field seemed perfect: it combined statistical thinking with programming, offered real-world applications, and was experiencing explosive growth. I spent countless hours teaching myself Python, working through machine learning tutorials, and building small projects on Kaggle.
But somewhere along the way, something felt incomplete. While I enjoyed building models and analyzing data, I found myself increasingly drawn to a specific subset of problems: those involving temporal dependencies, sequential decision-making, and quantitative evaluation under uncertainty. I noticed that my favorite projects were always the ones dealing with time-series data, forecasting, and optimization. When I built a simple stock price predictor as a learning exercise, I became fascinated not by the model itself, but by the underlying market mechanisms, the challenge of signal extraction from noise, and the mathematical rigor required to make defensible predictions in adversarial environments.
1.2 The Pivot: Discovering Quantitative Trading
The turning point came during my gap period between high school graduation (May 2024) and moving to the United States for college (approximately three months). Rather than taking a break, I decided to explore what career path would truly align with my interests in mathematics, coding, and systematic problem-solving. This led to one of the most intensive research periods of my life.
Over the course of one month, I reached out to over 500 professionals across finance, technology, and academia through LinkedIn, email, and referrals. My goal was simple: understand what people actually do in different quantitative roles, what skills matter most, and where someone with my background could add the most value. I didn't just send generic messages. I researched each person's background, read their publications or posts, and asked specific questions about their work. Out of 500+ contacts, I received approximately 180 substantive responses.
Through these conversations, I discovered quantitative trading. Multiple professionals pointed me toward this field, noting that it combined several elements I was passionate about: mathematical rigor (probability, statistics, stochastic processes), coding intensity (building and testing systematic strategies), and rapid feedback loops (you know quickly if your models work). More importantly, several quants told me something that resonated deeply: in trading, you cannot hide behind vague metrics or cherry-picked results. The market is the ultimate adversarial test. Either your model generates alpha, or it doesn't. Either your risk management works, or you lose money. This level of accountability and intellectual honesty appealed to me immensely.
I spent the remaining two months of my gap period reading everything I could find about quantitative trading. I went through academic papers on market microstructure, read blogs from successful quant funds, studied time-series econometrics, and worked through problems in probability and stochastic calculus. I read news articles about algorithmic trading systems, studied case studies of successful (and failed) quant strategies, and tried to understand the actual day-to-day work of quantitative researchers and traders. By the time I arrived at USF, I had made my decision: I wanted to build a career in quantitative and algorithmic trading.