My Journey from Engineering to Algorithmic Trading as a Quant Intern at QuantInsti
Hello, I’m Pranjal Tripathi, a recent graduate from IIT Kharagpur. I worked as a Quant Intern at QuantInsti from July 8, 2024 to October 8, 2024. Before my campus placement at Simpl started, I wanted to get a flavor of financial markets and benefit from my analytical and engineering background. I was fortunate to get started at QuantInsti, which is the powerhouse of edtech and fintech solutions in the world of algorithmic trading. I’m excited to share my journey from an engineering background to the fast-paced world of algorithmic trading.
Coming from one of India’s top engineering institutions, IIT Kharagpur, where notable alumni like Sundar Pichai (CEO of Google) also studied, I had the privilege of being exposed to cutting-edge technologies like machine learning, natural language processing (NLP), and robotics.
As a Quant Intern, I’ve been able to leverage my engineering background to dive into quantitative trading strategies, using advanced tools and techniques to explore the financial markets. Today, I’ll walk you through the key steps of my journey, from beginner courses to real-world trading strategies.
From Engineering to Trading: My First Steps as a Quant Intern
At the start of my internship, I began with a few foundational courses on the Quantra platform. Courses like "Getting Started with Algorithmic Trading," "Python for Trading," and "Backtesting Trading Strategies" helped me understand the basics. These were short, beginner-friendly courses that I could easily complete in 2-3 days, giving me a solid foundation in quantitative trading.
As a Quant Intern, however, what truly set my learning apart was the opportunity to apply these concepts to real-world projects from day one. The hands-on experience I gained early on was invaluable in my transition from engineering to trading.
This was possible because of a unique integration between learning and trading platforms offered by QuantInsti. Their LMS, called Quantra, seamlessly connects with Blueshift, their trading platform. This way, without having to learn about python packages, installations, I was able to start working with real-markets data in a cloud based infrastructure.
My first lesson as a Quant Intern: Backtesting results can be misleading
One of my first learning experiences as a Quant Intern was developing a simple scalping strategy based on market volatility. I used the Average True Range (ATR) to capture volatility and set a threshold to determine when to trade. The strategy was fairly simple: buy when the current price was higher than the last three candles, and sell when it was lower.
To test the strategy, I ran a backtest from May 1, 2024, to July 16, 2024, and the results were disappointing. The strategy produced an annualized return of -6% with a negative Sharpe ratio of 0.35. It was a wake-up call, showing that even the simplest strategies can be highly sensitive to market conditions.
Curiously, when I tested the same strategy over a different period (April 1, 2021, to April 30, 2021), the results were much better, yielding a 15% annualized return with a Sharpe ratio of 1.23.
This discrepancy highlighted a crucial lesson for any Quant Intern:
“..strategies that perform well in backtests may not necessarily perform well in live markets. This led me to realise that my strategy had likely overfitted to specific historical data, making it less effective in different market conditions.”
Refining My Strategy: Momentum-Based Trading and Portfolio Diversification
After facing challenges with my initial scalping strategy, I shifted to momentum-based strategies, a more advanced concept for a Quant Intern. This strategy focuses on taking a long position when the short-term moving average crosses above the long-term moving average. While this strategy showed decent results—11% annual return on Microsoft and 24% on Apple—it wasn’t performing as well as I had hoped due to extended periods of inactivity.
To overcome this, I applied the strategy to a diversified portfolio of stocks from different sectors. As a result, the overall performance improved significantly, with an annual return of 29% and a cumulative return of over 100%. The Sharpe ratio also increased to 1.27, indicating better risk-adjusted returns. This was a crucial learning moment for me as a Quant Intern: diversification is key to smoothing out performance and reducing risk.
By applying the strategy across multiple stocks, I could capture momentum in different sectors, allowing underperforming stocks to be offset by those in momentum. This portfolio-based approach helped me better understand how to optimize strategies for long-term success.
If you’re a Quant Intern working on your first momentum strategy, I recommend testing it on a diverse portfolio of assets, such as commodities futures, to further explore uncorrelated trading opportunities. This is something I learned from the "Futures Trading" course by Andreas Clenow, and it has been incredibly insightful in shaping my trading approach.
Continuous Learning: A Never-Ending Journey as My Quant Intern Experience Wraps Up
As my time as a Quant Intern at QuantInsti comes to an end, one thing I’ve realised is that learning in this field never truly stops. During my internship, I was introduced to more advanced strategies, like sentiment-based trading, which relies on indicators such as the VIX and Put-Call Ratios. Initially, these strategies were quite challenging for me, as they require a deeper understanding of market psychology. However, I’ve been refining these models, and it's rewarding to see progress.
One of the things that made this learning process smoother was the seamless integration between learning and trading platforms on Quantra and EPAT. With just a click, I could test strategies on vast amounts of historical data available on Blueshift. All the charts I shared during my internship were created using Blueshift, which also enabled me to dive into detailed trade analysis—such as reviewing winners, losers, and trade specifics.
Throughout this internship, my focus has been on expanding my understanding of quantitative and machine learning approaches, as these will be key to my future growth. I’ve also come to appreciate the importance of cloud-integrated tools that eliminate the need for installing software or manually connecting to brokers. This flexibility allowed me to concentrate on what really matters—developing and optimising trading strategies.
What’s Next After My Quant Intern Journey?
As my time as a Quant Intern at QuantInsti comes to an end, I want to express my gratitude for this invaluable learning experience. I'm incredibly thankful to the entire team for the opportunity. The skills, knowledge, and exposure I’ve gained—through hands-on projects, advanced strategies, and a collaborative environment—have built a strong foundation for my future in quantitative trading.
Moving forward, I’m excited to apply and expand on everything I’ve learned. QuantInsti has been a pivotal stepping stone in my journey, and I’m grateful for the mentorship, support, and growth. Thank you, QuantInsti! I look forward to staying connected and following your continued innovations in algorithmic trading.
Interested in following a similar path?
QuantInsti offers exciting internship opportunities for aspiring quants! If you're interested in pursuing similar quant or strategy internships, reach out to careers@quantinsti.com and explore exciting opportunities to kickstart your career in algorithmic trading.
Don’t miss your chance to be part of an innovative team at the forefront of financial technology!
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