EPAT Alumni Story
Vaibhav Goel
A Quant Developer at Morgan Stanley (via EY) in Mumbai, Vaibhav spent years moving from back-office code to the risk desk, carrying deep but scattered market knowledge. Through EPAT he tied five years of exposure into a structured, repeatable framework, from statistics to options theory to execution, and found a disciplined edge in option selling. Today he trades positional options with growing consistency and, as he puts it, "the goal is very clear," a prop trading firm of his own.
Most traders spend years chasing a high risk-reward ratio on a low win rate. Vaibhav Goel wanted the opposite. "My mindset was never like a 20 or 30% accuracy," he says. "I wanted something which is much more accurate, much more predictable, and which can be scaled easily." That single preference, held long before he had the tools to act on it, ended up shaping his entire trading career.
Vaibhav completed his B.Tech in computer science from NIT Kurukshetra and began his career as a software developer at Société Générale in Bangalore, decommissioning the bank's legacy middle-office and back-office applications, the very systems that traders and risk desks relied on every day. That work pulled him toward the functional side of finance, from OTC derivatives to options and futures. Three years in, he moved onto the trading floor as a market risk analyst on a one-delta and volatility desk, working with VaR, stress scenarios, and sensitivities like delta, gamma, and vega. Stints at HSBC and, today, a role as a Quant Developer at Morgan Stanley (via EY) in Mumbai followed.
By the time he found EPAT, Vaibhav already knew a great deal about markets. The problem was that none of it was organised. This is the story of how he turned five years of scattered exposure into a structured, repeatable framework, and why he now credits a single philosophy on option selling with changing how he trades.
Why Algo Trading?
Vaibhav's frustration was practical. He had been trading on the side for years, but the results, while often profitable, were never consistent enough to scale. "In the past I have been profitable, but it was not so consistent," he recalls. "The scaling was very difficult."
He worked through the options methodically. In the cash segment he traded stocks like HDFC on longer, monthly timeframes and generated returns of 20 to 30% in a good year. Those were solid numbers, but they did not convince him. "If you have a capital of 50 lakh or 20 lakh, it doesn't make sense to get a 20% return, and then it will take maybe 20 years to reach 10 crore," he explains. "Hardly we have 20 to 25 years where you can be in the market and generate a good capital base." He wanted more from each year.
So he moved into derivatives, where most people begin with option buying and futures. Each avenue had a catch. Intraday option buying was full of noise and, with a steady stream of regulatory changes, hard to manage alongside a job. Futures were costly and inconsistent. Option selling worried him most of all, because of the fat tail. "If there is a 10% shock on the downside and the market opens 10% gap down, then whatever portfolio of options you are holding will be blown," he says. Having built VaR and stress models inside a bank, and knowing those same models failed in 2008, he could not shake the concern. What he needed was not another strategy tip. He needed a framework.
Why EPAT?
The nudge came from LinkedIn. Vaibhav came across a QuantInsti alumnus whose progress caught his attention, and it made him ask a simple question: where had this person learned quant in such a structured way? "In the last three to five years I had already got exposure on multiple aspects of finance and algos," he says, "but they were not structured. It was more bits and pieces."
That was the gap EPAT filled. He had tried assembling an education from free videos, blogs, and finance certifications, but found the material scattered across sources and rarely built for trading specifically. "Things are there, but it's so much scattered around," he says. "Specifically for trading, I did not find much content which was organised, which QuantInsti provided." He also valued that the curriculum kept evolving. "One thing which I liked about QuantInsti was that they keep changing the curriculum based on the requirements."
He enrolled and completed the executive programme, treating it not as an introduction to markets, since he already had that, but as the connective tissue that would tie his existing knowledge into something he could actually build on.
How EPAT Shaped His Thinking
The turning point was options. For years Vaibhav had avoided option selling because of tail risk. What changed his mind was Euan Sinclair's teaching on how a disciplined, repeated selling programme behaves over time. "Even if the 2008 financial crisis occurs, if you are continuously selling that same 20-delta option, over a period of time you will know that implied volatility will always be greater than realized volatility," he explains. There is a persistent fear premium baked into put options, and harvesting it patiently, rather than fearing a single gap, was a philosophy he could commit to. "Out of so many people I have learned from, his philosophy actually hit me hard."
Two other faculty members reshaped how he approached the work. Nitin Aggarwal's sessions on statistics settled a long-running tension for him about how much of the market can really be predicted. "The best thing that you can do is get the data from the historical data, and from that do the back testing. That is the maximum that you can do," he says. "That gave me a boost. The best thing I have is the historical data, not the theories or the predictions that other people are trying to justify." In markets full of subjectivity, that was his anchor to objectivity.
The third was execution. Robert Kissell's module on market microstructure covered ground that was entirely new to Vaibhav. "That was something I had not come across before," he says. Learning about impact cost and timing risk from someone who had run a trading desk at a major bank changed how he thought about getting in and out of positions. "That helped me a lot in understanding how the execution-level things happen." Taken together, the modules gave him what no single source had, a complete frame that ran from statistics to options theory to execution.
The Real Challenge
For Vaibhav, the hard part was never the mechanics of any one trade. It was reconciling how much of the market can be quantified with how much cannot. "Everything in the market cannot be quantified," he says, "because at the end of the day it's the people who are running the businesses. So the behavioral aspect definitely comes into the market."
That belief shaped how far he was willing to automate. He built and quantified his research process and his framework, but deliberately stopped short of full automation. "If I give everything to the system, then it becomes a little difficult in case the situation changes suddenly," he explains. "I have left room for my own experience to get into the model." The initial process runs automatically, while the judgment stays human.
The value of that decision showed up in March 2026, when a sharp, war-driven crash hit the market. His models, built largely around mean-reverting behaviour, were not designed for that kind of shock. "In those scenarios the models usually don't work that good," he says. Because he had kept manual control over his option-selling adjustments, he could respond to the fat-tail event rather than be run over by it. The challenge, in other words, was knowing exactly where the machine should end and the trader should begin.
Life After EPAT
Today Vaibhav trades positional options, working monthly and bimonthly expiries, and combines three lenses he has built over his career: fundamentals, technicals, and now quant. The positional approach solves a problem that had frustrated him for years. "In intraday, the transaction costs are extremely high, especially in India, and I see they are not going to go down, only up," he says. A longer holding period keeps costs low enough for the strategy to scale. "From there I became consistent, and now I'm able to scale my capital size."
The programme also reshaped his day job. He still returns to the curriculum. "Yesterday only I was referring to it," he says, describing how the object-oriented concepts and the NumPy, pandas, and scikit-learn foundations continue to support his quant development work, including on FRTB models. "It's some basic things, but still it helps a lot. The base is still the one that helps me."
And EPAT opened doors. Vaibhav credits the programme with helping him land his current quant developer role, and with connecting him to a network he did not have before. He mentions meeting other alumni who have gone on to build their own firms, and being part of a community where paths that once felt distant start to look reachable. "That networking matters a lot," he says.
EPAT and the Role of AI
Vaibhav uses large language models where they earn their keep, in research and rapid prototyping. "The coding part, mostly I use Gemini or Claude," he says. When he has a hypothesis, the tools let him test it almost immediately. "If you want to do vectorized backtesting, with the use of LLM models it becomes extremely fast. They actually tell you whether it is statistically possible or whether you are making just a hypothetical theory."
He gives a concrete example. Building a screen with a fixed set of rules, he used Python to filter the entire Indian universe down to the handful of names worth a closer look. "Out of 5,000 stocks, you only need to check 10," he says. "It immediately gives me the stocks falling under that criteria, and then you can make decisions." The automation removes grunt work without removing him from the decision.
But he is clear-eyed about what AI cannot replace. Having worked with neural networks, including CNNs and RNNs, back in his college days, he argues that machine learning only helps once the fundamentals are in place. "Unless you know the basic models and how to build them, AI will not be that much helpful," he says. "Even if you know machine learning models, you don't know how to actually use that model in your development." For him, the tools accelerate a trader who already understands the underlying structure. They do not manufacture that understanding.
Looking Ahead
Vaibhav's long-term goal is unambiguous: his own prop trading firm, based in Mumbai. "The goal is very clear. The only thing is the timing," he says. He tried once before, in 2022, and is candid about why it did not work. "I was not able to be successful because of the less market experience, less capital size, and I did not have that kind of networking."
All three of those constraints have eased. His trading has become more consistent and scalable, his capital base is growing, and through EPAT he has built the network he lacked the first time. He is not rushing. "Not immediately, but the road map is in the next five years or so," he says. "That is the ultimate goal, to have my own prop trading firm and set it up in Mumbai."
If he has one piece of advice for people considering EPAT, especially those new to markets, it is to be patient with the material. "It's a good course, but if you are totally new, it will take time to absorb the content," he says. And the payoff, he notes, often arrives later than people expect. "Even when you pass the entire executive program, five or six months down the line you will realize the importance of it, not immediately. Specifically if you are going into your own business, that content is much more helpful in becoming successful."
Vaibhav began his career maintaining the systems that traders leaned on, always one layer removed from the decisions themselves. The arc since then has been about closing that distance, from back-office code to the risk desk, from scattered knowledge to a structured framework, and now toward a firm of his own. The predictable, scalable edge he wanted from the start is no longer just a preference. It is how he trades.
Frequently Asked Questions
Can a risk or back-office professional move into algorithmic trading? Yes, and that background can be an advantage. Vaibhav's years working with VaR, stress scenarios, and derivatives sensitivities gave him a strong grasp of risk before he ever built a strategy. The step that often remains is turning that scattered exposure into a structured, repeatable framework, which is where a programme like EPAT fits.
Is option selling too risky for individual traders? Option selling carries genuine tail risk, and a sharp gap in the market can cause significant losses, as Vaibhav experienced with the March 2026 crash. The approach he adopted treats it as a disciplined, repeated process while keeping manual control over adjustments during shocks. Anyone considering option selling should size positions carefully and understand the fat-tail exposure before scaling.
Why choose positional trading over intraday in India? High transaction costs are the main reason Vaibhav shifted to positional options on monthly and bimonthly expiries. Intraday strategies incur frequent costs that are difficult to overcome at scale, particularly in the Indian market. A longer holding period reduces the drag from those costs and makes a strategy easier to grow.
Do I need to know programming before starting EPAT? A coding background helps, but it is not the only path. EPAT covers Python foundations including object-oriented concepts and libraries like NumPy, pandas, and scikit-learn. Learners without a technical background should expect to invest extra time in the programming modules, while those from a software background can build on what they already know.
How much of trading can actually be automated? Vaibhav's view is that the research process and framework can be quantified and automated, but full automation is rarely wise. Because market behaviour includes a human element that models cannot fully capture, he deliberately keeps judgment in the loop for sudden regime changes. Where the machine ends and the trader begins is a decision each person has to make deliberately.
How useful are AI tools like LLMs for building trading strategies? They are useful for rapid prototyping and vectorized backtesting, letting a trader test a hypothesis quickly and check whether it holds up statistically. They are less useful as a substitute for understanding. As Vaibhav puts it, the tools accelerate someone who already grasps the underlying models rather than creating that knowledge from scratch.
Does EPAT help with career moves and networking? Vaibhav credits EPAT with helping him land his quant developer role and with connecting him to an alumni network he did not have before. Being around others who have built their own firms made ambitious goals feel more reachable. Outcomes vary from person to person, but the community and network are a meaningful part of the experience.
How long before the EPAT content pays off? Vaibhav is candid that the value often arrives later than people expect, sometimes five or six months after completing the programme. He advises newcomers to be patient with the material, since some concepts take time to absorb and apply. The content tends to become more valuable as learners put it to work, particularly for those building their own trading business.
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.