What does the study of the brain have to do with the behavior of financial markets? For Renan Costa Vieira de Paula, the answer is: quite a lot.
Renan's path into finance was anything but conventional. With an academic foundation in neuroscience from the Federal University of ABC, he spent years understanding complex systems, identifying patterns, and tracing cause and effect. That scientific mindset, developed long before he ever stepped onto a trading floor, quietly became the backbone of how he would later approach markets.
His professional journey began in credit, working with large enterprises for four years, before transitioning into fixed income. Today, he works at a firm that trades across the majority of assets in the Brazilian market. His role revolves around price distribution, order book dynamics, and bid-ask movements, ensuring rates on his firm's proprietary platform reflect best market practices. On any given day, he trades roughly 400 assets across Brazil.
Beyond his desk, he continues to explore crypto and prediction markets, not as a side interest, but as an extension of a deeper conviction. For Renan, markets are both a profession and a passion. As he puts it,
"I hope it continues for the rest of my life. I think it's not possible to stop."
Why Algo Trading?
The shift toward algorithmic trading did not come from curiosity alone. It came from necessity. Working in fixed income, Renan encountered practical problems that demanded more than intuition could offer.
"I needed to understand better how the order book influences prices. I wanted to understand how to measure it, how to quantitatively understand the prices I was seeing, how to create indicators and metrics to measure the effects I was facing," he recalls.
He was searching for rigor. For structure. For tools that would allow him to approach trading decisions the same way he once approached scientific questions. As he puts it, the goal was to figure out "how to make some P&L with it."
He wanted a framework that could translate market behavior into measurable, repeatable systems.
Why EPAT?
During his search for structured learning in algorithmic trading, EPAT stood out on two fronts: the credibility of its faculty and the practicality of its format.
Renan was already familiar with leading quantitative thinkers and had read their work. Knowing that these practitioners were part of the EPAT curriculum gave him confidence that the program offered genuine depth, not surface-level exposure. "I was already a big fan. I had read the books of those professors. So it was a very good choice," he says.
The format mattered equally. Working full-time, he needed a learning structure that would not disrupt his career. He recalls:
"The courses being taken on Saturdays and Sundays helped a lot. The pace was very good too. It made me feel very comfortable and very stimulated to continue the journey."
Above all, what convinced him was the chance to learn directly from practitioners who had built real trading strategies. He points to "the structure of the course and the possibility to take courses of the great minds in algorithmic trading" as the most important factors in his decision.
How EPAT Shaped His Thinking
Renan is candid about what EPAT was designed to be, and what it was not.
"I think it wasn't enough, but I think it's not supposed to be enough."
For him, the program was never the final destination. It was the launchpad. EPAT built the theoretical foundations and the systematic mindset required to keep growing independently beyond the six months.
"The program opened my mind for other universes I was not aware of the existence of," he says.
One of the most pivotal experiences came when professors bridged the gap between abstract theory and practical implementation. He describes those moments vividly:
"My favorite moments were when the professors, after laying the foundations and giving their thoughts on the mindset of a trader and the theoretical framework, concluded in a strategy that wrapped all the things they had said before."
Topics like mean reversion, co-integration, statistical arbitrage, and futures trading resonated most, aligning closely with his fixed-income background and statistical intuitions. He describes the full arc of the learning experience:
"You translate the market into a theoretical framework, and then you translate that theoretical framework into an algorithm that will try to capture the phenomena in the market."
The learning environment extended beyond lectures as well. Whenever Renan sought scientific papers, articles, or reading recommendations, the support was readily available. Intellectual curiosity was not just tolerated, it was actively encouraged.
The Real Challenge
With prior programming experience and a strong mathematical background, Renan did not find the technical content particularly intimidating. For him, the real challenge was time.
"The most difficult part was having enough time to explore all the possibilities, because it is a huge universe of areas,"
he explains. Six months is enough to build solid foundations, but far from enough to explore every corner of the field. Balancing a demanding full-time role in fixed income with intensive technical learning required genuine discipline. He viewed this not as a flaw but as intentional program design: broad exposure first, deep specialisation later.
When roadblocks did arise, EPAT's support team responded quickly.
"If I had some difficulties, the team was very ready to help,"
he says, noting that this reduced friction and kept momentum going.
Life After EPAT
The impact of the program became most visible in Renan's day-to-day trading.
Before EPAT, he had ideas about structuring systematic approaches. After EPAT, those ideas became rigorous.
"The program gave me the foundations to be more rigorous, more scientific about the methods and about the implementation,"
he says. He now applies mean reversion algorithmics directly on his fixed-income trading desk, building indicators, measuring deviations from expected behavior, and systematically identifying opportunities where prices diverge from historical patterns.
The shift was not merely technical. Instead of relying on intuition, he now deploys structured algorithms tailored to Brazil's unique market dynamics. As he puts it:
"I took some of the algorithms, tried to modify them for my daily basis, and implemented them. And when I said I was studying it in the program and making it in my daily basis, people knew: okay, he knows what he is doing."
The credentials also strengthened his professional standing. When proposing systematic strategies internally, his structured, evidence-based approach now inspires greater confidence among colleagues and stakeholders.
EPAT and the Role of AI
As AI tools have become more accessible, Renan has integrated them into his workflow, but thoughtfully.
He is direct about what AI can and cannot do:
"For AI to work, you need to know what to search. You need to know how to make the algorithm work and how it improves your day."
Without foundational understanding, no AI tool can replace strategic thinking.
His practical approach today:
"Let's say I'm reading a paper. I understood a mathematical equation and I say, okay, this is what I wanted. I use AI to help me translate those equations into modules."
What once required two or three hours of manual implementation can now be accelerated, without surrendering control over the underlying strategy.
He is clear about the limits:
"I don't think it is for making the whole picture. But helping with specific points, it is very, very good."
For Renan, AI is a tool for scale and efficiency, one that only delivers value when the person using it already knows what they are looking for.
Looking Ahead
Over the next two to three years, Renan plans to deepen his work in crypto and prediction markets.
"I think there is a lot to understand, and as these markets are new for anyone, they have a lot of inefficiencies. Those algorithms are very, very good to exploit these kinds of opportunities," he says.
In parallel, he aims to expand algorithmic applications within Brazil's evolving derivatives and fixed income landscape, noting that "the landscape here in Brazil is evolving, especially in derivatives and fixed income."
The longer-term vision is larger still.
"In five to ten years, I hope to have my own trading shop to exploit these strategies," he shares. He credits EPAT for pointing toward that path: "It is very focused on creating your own shop and making the self-sufficient way. You can talk with friends and say, okay, I know about these strategies, you know about these strategies, what about we start something and work for our own."
He describes algorithmic trading as a lifelong pursuit, a journey that continues to evolve with every new strategy tested and every new market explored. EPAT, in that journey, was not simply a course. It was the framework that helped transform analytical curiosity into systematic capability, and ambition into structured execution.
Frequently Asked Questions
Q. Can someone from a non-finance background learn algorithmic trading?
Yes. Renan's own background was in neuroscience before he moved into finance. What matters most is an analytical mindset and a willingness to learn systematically. Structured programs can guide learners from foundational concepts to practical implementation, regardless of their starting point.
Q. Is prior programming knowledge required for EPAT?
No. Many learners begin with little to no coding experience. EPAT is structured to build programming skills progressively, starting from basics and moving toward strategy development, backtesting, and automation. Prior experience can accelerate the pace, but it is not a prerequisite.
Q. How does algorithmic trading apply to fixed-income markets?
Algorithmic approaches in fixed income can include mean reversion strategies, spread analysis, order book dynamics, and statistical modelling of price distributions. Practitioners like Renan apply these techniques to identify systematic opportunities across large numbers of instruments simultaneously.
Q. How long does it take to apply EPAT learnings in a real trading environment?
This varies by role and market access. For Renan, the transition was relatively direct, as his professional context allowed him to begin applying mean reversion algorithmics shortly after completing the program. Others may spend additional time refining strategies in simulated environments before deploying them live.
Q. What is the role of AI in algorithmic trading today?
AI tools can accelerate research, code translation, and data analysis. However, as Renan emphasises, AI is only as effective as the foundation it sits on. Traders who understand quantitative concepts can use AI to scale their work; those without that foundation may find it difficult to direct AI toward meaningful outcomes.
Q. Is EPAT worth it for professionals already working in finance?
For practitioners seeking to move from intuitive to systematic approaches, EPAT offers structured frameworks, practitioner-led instruction, and a peer network that complements existing experience. Renan credits the program with transforming how he structures strategies and presents them internally.
Q. What markets are most suited to systematic trading strategies?
Systematic strategies can be applied across equities, fixed income, derivatives, crypto, and prediction markets. The key is identifying markets with sufficient data, liquidity, and structural patterns that algorithms can exploit. Renan sees particular opportunity in crypto and prediction markets due to their relative inefficiency.
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.
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