By QuantInsti
Brian’s journey into trading did not begin with algorithms, Python notebooks, or machine learning models. It began in a place that feels familiar to many aspiring traders: curiosity, experimentation, and the hope that markets could offer both flexibility and independence.
Coming from a background in Economics and Finance, he initially approached the markets through manual trading. He explored long-term investing, buy-and-hold strategies, trend following, and technical indicators like moving averages. On paper, the ideas made sense. In practice, something kept getting in the way.
“Emotions involved… sometimes you can get too greedy or too fearful and then it really messes with your decisions.”
That realization became the turning point. What Brian was really searching for was not just a better strategy, but a better way to think, test, and execute.
Why Trading?
Trading appealed to Brian because it felt like one of the few fields where effort, analysis, and independent decision-making could directly shape outcomes. It offered room to think for oneself, adapt quickly, and build something personal over time.
At first, he explored the markets in a conventional way. Like many new traders, he tested different styles and tried to find what suited him best. But over time, he discovered that knowing a strategy and following a strategy were not the same thing.
The challenge was not only market direction. It was discipline.
Manual trading left too much room for second-guessing, hesitation, and impulsive decisions. Even when the setup was clear, emotions often changed the outcome. That gap between theory and execution pushed him to rethink his approach more seriously.
Discovering Algorithmic Trading
The real shift came when market conditions changed, especially during crypto bear cycles, where straightforward buy-and-hold approaches stopped delivering the same results. Brian began looking for a framework that could help him stay systematic even when markets became difficult.
That is when algorithmic trading started to make sense.
What drew him in was not just automation for the sake of convenience. It was the promise of structure. Rules could be defined in advance. Strategies could be backtested on historical data. Decisions could be evaluated with evidence rather than memory.
In other words, it gave him a way to move away from reaction and toward repeatability.
“The idea was compelling because it could remove the emotion part of it and also stick to the trading rule.”
Compared to manual trading, this felt like an entirely different world. Suddenly, he could test ideas properly, experiment with more complexity, and learn without putting capital at risk immediately.
Why EPAT Stood Out
Once Brian knew he wanted to pursue algorithmic trading seriously, he started looking for structured learning options. Much of what he found online felt fragmented. Some resources were useful, but many were limited to pre-recorded videos or occasional webinars without enough depth, support, or continuity.
EPAT stood out because it felt more complete.
It was not just the curriculum that made the difference. It was the overall learning environment: multiple experts, live sessions, a cohort-based format, and the ability to ask questions instead of learning passively in isolation.
For Brian, that support mattered.
“It felt a lot more holistic… you have a whole company with different experts behind it.”
He also appreciated that help did not stop when the lecture ended. The accessibility of faculty and support teams made the process feel more guided and less intimidating, especially for someone building a new skill set from the ground up.
“Very, very good support… compared to other courses where there’s almost no support at all.”
That combination of structure and responsiveness helped turn what could have been an overwhelming transition into a manageable one.
From Non-Coder to Builder
Although Brian was confident in his decision, the journey itself was not effortless. Coming from a non-programming background, learning Python was one of the first major hurdles.
Even with preparatory materials, it took time to become comfortable with writing and understanding code. But the hands-on nature of the programme made a big difference. Instead of only learning concepts in the abstract, he could see them in action.
Jupyter notebooks, in particular, helped him bridge the gap between theory and practice. They allowed him to run code, make changes, observe outputs, and learn interactively. That immediate feedback loop made the learning process far more tangible.
As his understanding deepened, so did his mindset.
Brian began to see strategy development less as guesswork and more as a research process. He started treating strategy testing with the rigor of a repeatable experiment.
“It should be like a science experiment where you have different trials and you record down… the result.”
That shift is one of the most meaningful parts of his journey. He was no longer simply trying to find a winning setup. He was learning how to think like a systematic trader and builder.
Life After EPAT
For Brian, EPAT was never the finish line. It became the foundation.
After completing the programme, he continued building on what he had learned rather than stopping at the curriculum. He specialised in machine learning for trading and developed a project using an LSTM neural network. Later, he expanded beyond that, experimenting with other models such as tree-based boosting algorithms and refining how he approached prediction and strategy development.
This post-EPAT phase is where the deeper transformation became visible.
Instead of depending on fixed templates or looking for ready-made answers, Brian kept exploring independently. He continued trading primarily on his own, adapting methods, improving systems, and testing ideas with greater confidence and structure.
That independent momentum is often what separates a course completion story from a real career and mindset shift. In Brian’s case, the learning kept compounding.
AI and the Future of Trading
Brian believes today’s trading environment is more accessible than ever, especially because of advances in AI tools. Tasks that once consumed hours, whether debugging code, searching for technical explanations, or iterating on ideas, can now move much faster.
But he is equally clear about something important: AI is only as useful as the thinking behind it.
In his view, structured learning still matters because it teaches traders how to ask better questions, frame problems properly, and evaluate outputs critically. Without that foundation, AI can accelerate confusion just as easily as it can accelerate progress.
“The quality of the questions or the instruction or the prompts that you give” makes a huge difference.
That perspective reflects a mature understanding of the modern trading workflow. AI can help with speed, iteration, and support. But domain knowledge, judgment, and systematic thinking still remain essential.
Looking ahead, Brian expects markets, especially crypto, to keep evolving rapidly. New cycles will create new themes, new inefficiencies, and new opportunities. His goal is not to cling to one fixed approach, but to remain adaptive and continue integrating AI more meaningfully into his workflow.
Brian’s story is not just about moving from manual trading to algorithmic trading. It is about moving from emotion to process, from uncertainty to experimentation, and from consuming strategies to building them with intent.
It shows that the most important transformation is often internal. Tools matter. Technology matters. But the real shift happens when a trader begins to think in systems, test with discipline, and learn with patience.
Even now, Brian’s advice remains simple and timely:
“It’s a great time to start now… it will make things so much faster and easier.”
Frequently Asked Questions
Q. Can someone from a finance or non-coding background learn algorithmic trading?
Yes. Brian’s story shows that a non-programming background does not prevent someone from learning algorithmic trading. What matters more is a willingness to build skills step by step and stay consistent through the learning curve.
Q. Why do many manual traders move toward algorithmic trading?
A major reason is discipline. Manual trading often leaves room for emotional decisions, while algorithmic trading helps traders define rules clearly, test them properly, and execute more systematically.
Q. Is Python difficult for complete beginners?
It can feel unfamiliar at first, especially for those without prior coding exposure. But with hands-on practice, guided learning, and tools like notebooks that make experimentation easier, beginners can gradually become comfortable.
Q. How important is backtesting in the learning process?
Backtesting is critical because it helps traders evaluate ideas using historical data before risking capital. It creates a more evidence-based process and encourages structured experimentation instead of relying on assumptions.
Q. How should beginners think about strategy development?
A useful way is to treat it like a research process. Test ideas methodically, record results carefully, compare variations, and focus on learning what actually works rather than chasing shortcuts.
Q. Does AI reduce the need for structured learning in trading?
No. AI can speed up certain tasks, but structured learning remains important because it helps traders understand markets, ask better questions, and use AI tools more effectively.
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.
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