Adarsh Kumar, a commerce student from an educational background, hadn’t envisioned a future in algorithmic trading when he began his academic journey.
Having already made a significant shift from science in 11th and 12th to commerce in college, his academic path seemed set, structured, conventional, and clearly defined.
However, in his First year of college, a TEDx video changed the trajectory of his thinking.
He recalls,
“In my first year, I got a video from TEDx where I saw how AI and ML models are able to make predictions… I got a little fascinated.”
What began as a fascination slowly turned into intent. The idea that machine learning models could predict financial outcomes, even analysing subtle cues, stayed with him. He began researching how he could enter the world of AI, but having already moved from science to commerce, returning to a technical field felt like a bold step.
Still, one thing was clear: if he was going to pursue AI, he wanted it to be applied meaningfully.
Why Trading?
While AI had multiple applications across industries, Adarsh felt drawn toward financial markets and Trading.
For him, trading represented more than a career path. It was a space where intelligence could directly translate into results.
“Instead of applying in some other domain where I can’t directly generate something for myself, trading was a big reason to get into this field.”
The flexibility also appealed to him: swing trading, intraday trading, building systems that could operate even when he was busy. For him, Algorithmic trading felt like the right intersection of independence and analytical depth.
Landing on EPAT
Adarsh explored multiple options. He initially considered FRM, but found that it leaned heavily toward theory and lacked the hands-on practical experience he was looking for.
He enrolled in a general data science course to build his technical foundation. However, it did not connect AI to financial markets in the way he envisioned.
Eventually, while researching online, he came across the Executive Programme in Algorithmic Trading (EPAT).
“When I saw this course content of EPAT, I got to know that it was like a blessing for me.”
At the time, he was already in his second year of college and had spent a year building knowledge in data science. This helped him grasp machine learning concepts faster. However, trading itself was still new territory.
He enrolled in EPAT while continuing his bachelor’s degree, managing academics alongside a rigorous six-month professional program.
From the beginning, the structured nature of the program stood out to Adarsh. He appreciated that the curriculum did not immediately jump into building complex models.
“EPAT was very structured and step-by-step. I was enjoying it a lot. They didn’t step directly into creating models. First, they started with the basics - Python and how OOP works. The lectures on inheritance showed how we can apply the same strategy across multiple stocks. That was the best part.”
The ability to think in systems, to build something reusable and scalable, reshaped the way he approached trading strategies.
The Challenges Along the Way
Despite building his data science knowledge, Adarsh faced two major challenges: understanding trading concepts and handling coding complexity.
“I had no background in trading at that moment. Understanding technical indicators was challenging. Sometimes the code felt complex too.”
The final project added another layer of pressure, especially while being a full-time college student. At one stage, he even faced a rejection during a project review. However, mentorship played a crucial role in helping him push forward. He also turned to additional learning resources to bridge his knowledge gap.
“The mentor was very supportive. They shared resources and helped me achieve it. I started going through the blogs. That was the best remedy at that time.”
By revisiting concepts, understanding derivations, and practicing step-by-step implementation, he gradually built confidence.
Understanding AI in the Real World
Like many aspiring quants, Adarsh initially envisioned building sophisticated AI-driven trading systems. However, once he stepped into real-world trading environments, his perspective matured.
He realized that deploying complex intraday AI models requires infrastructure, continuous data feeds, and significant resources, especially at a personal level.
“That’s why I prefer building simple models.”
This shift wasn’t disillusionment; it was practical wisdom gained through exposure.
Placement and Career Transition
One aspect of the program that stood out clearly to him was Placements.
He was immensely satisfied with the placement Cell and received regular job updates, guidance on improving his resume, and detailed job descriptions that helped him prepare strategically.
“The most satisfactory part was the placement cell. I think I was selected in my second round itself.”
After graduation, his first role didn’t align with his interests. Within a month, he made a bold decision to step away and realign his path.
Today, Adarsh works as a Research Analyst at Friendoc Invest Smart, focusing on High-Frequency Trading models.
“I am working on HFT tick-by-tick models and trying to improve existing models, and I am enjoying my job!”
Adarsh’s path was not linear. It involved changing streams, questioning career choices, and balancing college with professional learning. Along the way came technical challenges and project rejections, followed by the difficult decision to start again when his first job didn’t feel right.
But throughout the journey, one thing remained constant: curiosity. What began with a little fascination transformed into a career in High-Frequency Trading.
Adarsh’s story is an example that sometimes a career begins not with certainty, but with curiosity, followed by consistent action, and that makes all the difference.
If you’re ready to start your own journey, the Executive Programme in Algorithmic Trading (EPAT) offers the structure, mentorship, and content to help you make that leap.
Frequently Asked Questions
Q. Can someone without a technical background learn algorithmic trading?
Yes, it’s entirely possible. Many traders start from non-technical fields and gradually build the required skills. With structured learning and consistent effort, topics like strategy design, risk management, and coding can be understood step by step.
Q. Is prior programming knowledge required?
No, it’s not a prerequisite. Many beginners start with tools like Excel or TradingView, using formulas or basic scripts. Over time, they learn to use Python or other programming languages for tasks like backtesting, automation, and data analysis.
Q. What’s the difference between self-learning and following a structured course?
Self-learning through online videos or forums can be helpful but often lacks depth and consistency. A structured programme offers a clear progression through concepts like market microstructure, probability, and strategy building, helping learners avoid confusion and save time.
Q. How important is probability in trading?
Understanding probability helps traders evaluate outcomes more objectively. It plays a key role in risk management, position sizing, and decision-making, especially in uncertain or volatile market conditions.
Q. How long does it typically take to see consistent results in trading?
There’s no fixed timeline, but many traders spend their initial years exploring, testing, and refining strategies. Consistency often comes after building a foundation in risk management, capital allocation, and strategy evaluation.
Q. Is the EPAT course content still useful after completing it?
Yes. Revisiting lectures and course materials can reinforce understanding, especially when applying concepts to live markets. Traders often return to foundational topics as their strategies evolve.
Q. How can traders continue learning after completing a course?
Ongoing learning can include taking short courses, participating in forums, mentoring others, or experimenting with new tools. The trading landscape evolves continuously, so staying updated is essential for long-term growth.
Next Steps
If you are just getting started with algorithmic trading, begin with the Quantitative Trading Free Learning Track. It includes eight beginner-friendly courses covering data basics, trading strategies, and coding for finance. Once you're ready to dive deeper, you can explore Quantra’s Algorithmic Trading for Beginners Learning Track, which offers hands-on, application-focused modules to build your skills step by step.
For those looking for a comprehensive and guided journey with mentorship, live lectures, and career support, the Executive Programme in Algorithmic Trading (EPAT) provides a complete foundation for launching or accelerating a career in this field.
Schedule an EPAT counselling call
To understand if EPAT is the right choice for you, talk to one of our specialists who have counselled thousands of learners over the past decade and helped them make the right career decision.