For most of his career, Santanu Nandi worked behind the scenes of the financial industry. A backend developer with more than two decades of experience across India and the United States, he spent years building and supporting the banking systems that quietly keep the industry running.
He worked across many domains during that time, from credit card services to retail banking. But one area kept pulling at his attention in a way the others never did.
"I found that trading, or front office, is something which interests me more actually, in comparison to any other like credit card services or retail banking," he recalls. That interest would eventually reshape how he thought about the next stage of his career.
Why Algorithmic Trading?
Working closely with trading systems gave Santanu a view into a part of the industry he wanted to understand at a deeper level. The more time he spent around it, the clearer a pattern became. The future of trading was moving toward algorithmic execution, Python, machine learning, and data-driven decision-making.
These were not skills he could pick up passively on the job. They formed a distinct discipline with its own logic, and he recognised that the gap between building systems for traders and thinking like a quantitative trader was wider than it looked from the outside.
He knew these were capabilities he needed to add to his own portfolio if he wanted to move closer to the front office work that interested him.
The Search for Structure
Like many professionals exploring a new field, Santanu started where most people start, with online resources. The problem was not scarcity. It was the opposite.
There were countless articles, videos, and tutorials available, and sorting the useful from the irrelevant took more effort than the learning itself. "If I go to Google, I can find a lot of links. But what is relevant and what is not, that filtering itself is sometimes difficult," he explains. "You spend a lot of time filtering out the wrong things."
What he wanted was structure. A learning path that connected the dots between trading, finance, mathematics, programming, and machine learning, rather than a scattered collection of tutorials that never quite added up to a whole. After weighing his options and talking them through with peers in the industry, he came across EPAT.
Why EPAT?
What first drew Santanu in was the promise of a strong foundation. What he found once he began was larger than he expected. "I thought the window would be small, but then I found there's a bigger window," he says. "It not only gives a foundation, but it gives a lot of references so that I can enhance my skills."
The value equation mattered too. Having spent much of his career in the United States, Santanu was familiar with the cost of executive education there. Measured against comparable programmes abroad, EPAT offered a way to build specialised skills at a fraction of that cost, which made the decision easier to justify while working full-time.
For him, the appeal was not a single feature but the way the programme joined disciplines that usually sit apart. Before EPAT, he understood trading concepts on their own. What he lacked was a clear picture of the broader ecosystem that surrounds them, and that was precisely the gap the programme helped him close.
Balancing EPAT With a Full-Time Career
Completing EPAT while working full-time in New Jersey was not easy. The six-month curriculum covers a wide range of topics, and keeping pace with classes, assignments, and professional responsibilities took discipline. There were nights spent catching up on coursework after office hours and weekends set aside entirely for learning.
Those constraints also made the wins more satisfying. When his scores came back strong, the effort felt validated. "Some of the tests I scored 100 out of 100," he recalls. "That gives a kind of pleasure. It's like a dopamine. You feel that okay, in the next one also I have to do something like that."
Looking back, he believes the programme's real strength was not teaching individual topics in isolation, but showing how they fit together inside quantitative trading. That connective view, more than any single lesson, was what turned scattered knowledge into something he could build on.
The Topics That Stood Out
Given his software background, it is no surprise that Python became one of Santanu's favourite parts of the curriculum. But his interests reached well past programming.
Machine learning, options trading, statistics, portfolio analysis, and quantitative modelling all stood out as areas he enjoyed exploring in depth. What kept him engaged was how applicable the material felt. A few sessions leaned more on memorisation than application, but he found the large majority of the content directly relevant to the work he wanted to do.
That practical emphasis mattered to someone who had spent years shipping real systems. The curriculum was not an abstract exercise for him. It was a set of tools he could imagine putting to use.
Building Toward Something of His Own
Many learners join EPAT to advance within their current careers. Santanu's ambition points somewhere further out.
His long-term goal is to build something of his own, a trading desk driven by systematic strategies and supported by the quantitative skills he has been developing. "Having something of my own, and creating a company of my own which does trading, that will give me the ultimate pleasure," he says.
The path will not be simple. Working inside a banking environment brings compliance considerations that make any transition more involved, and he is clear-eyed about that. For now, his plan is to revisit the course material, deepen his understanding, refine his strategies, and keep building toward the larger vision. What gives him confidence is that the resources do not stop at graduation. Through project support, alumni communities, mentors, and continued access to material, he sees EPAT as something that extends well past its six months.
For Santanu, EPAT delivered more than a certificate. It provided structure in a field crowded with information, connected concepts that had felt disconnected, and opened possibilities he now intends to pursue. As he puts it, "EPAT is the course which will give me a lot of skill sets. It gives a foundation, but it also opens a much bigger window." The window he once thought was small turned out to be the beginning of something much larger.
Frequently Asked Questions
Can software developers transition into algorithmic trading? Yes. A software background is a strong starting point, since programming, data handling, and systems thinking carry over directly. What developers typically need to add is the finance, statistics, and quantitative modelling layer, which is exactly what a structured programme like EPAT is designed to build.
Is Python enough to get started in algorithmic trading? Python is a core tool and one of the most widely used languages in the field, but it is one piece of a larger picture. Effective algorithmic trading also draws on statistics, machine learning, options and portfolio concepts, and an understanding of market structure. The programming becomes far more useful once it sits on that broader foundation.
How hard is it to complete EPAT while working full-time? It takes real commitment. The six-month curriculum is demanding, and many working professionals rely on evenings and weekends to keep up, as Santanu did while based in New Jersey. The structure and defined timeline can actually help, giving busy learners a clear path to follow rather than an open-ended study plan.
Why choose a structured programme over free online resources? Free resources are abundant, but sorting the relevant from the irrelevant can consume significant time. A structured programme connects trading, finance, mathematics, programming, and machine learning into a coherent sequence, which reduces wasted effort and shows how the pieces fit together.
What topics does EPAT cover beyond programming? The curriculum spans machine learning, options trading, statistics, portfolio analysis, and quantitative modelling, among other areas. The emphasis is on how these disciplines combine within quantitative trading rather than on any single subject in isolation.
Does EPAT support learners after the programme ends? Yes. Beyond the core coursework, learners have access to project support, alumni communities, mentors, and continued resources. Many graduates, Santanu included, treat the programme as an ongoing reference rather than a one-time course.
Can I run my own trading strategies while working in banking? It depends on your employer's compliance policies, which often place restrictions on trading activity for those working in banking or financial institutions. Anyone considering this should review their firm's rules carefully and plan around them. It is worth understanding these constraints early rather than after the fact.
Is EPAT good value compared to other executive programmes? For learners familiar with the cost of executive education abroad, EPAT is often seen as offering specialised, application-focused skills at a considerably lower cost. Value ultimately depends on your goals, but the breadth of the curriculum relative to its price is a common reason professionals choose it.
Next Steps
If you are just getting started with algorithmic trading, begin with the Quantitative Trading Free Learning Track. It includes beginner-friendly courses covering data basics, trading strategies, and coding for finance.
Once you are ready to go deeper, 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, 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.