As universities build more programmes around quantitative finance and fintech, a recurring challenge is how to make algorithmic trading practical. Students can learn the theory of signals, automation, execution, and market structure in lectures, but the real educational value often comes when they have to turn those ideas into working strategies.

At Hong Kong Polytechnic University (PolyU), ProfitView is used to support that kind of hands-on learning.

The Programme Context

PolyU’s public programme information for JS3220 Applied Mathematics and Finance Analytics describes a four-year undergraduate scheme with three possible awards. One of those is the BSc (Hons) in Quantitative Finance and FinTech.

The public materials highlight areas such as:

  • algorithmic trading
  • robo-advising
  • deep hedging
  • machine learning
  • reinforcement learning in finance

That is exactly the kind of curriculum where students benefit from practical work alongside formal teaching.

How ProfitView Is Used

In PolyU’s teaching use, students work in teams and use ProfitView’s simulated exchange to build and test Python trading algorithms as part of a practical exercise that includes both a competition and a final presentation.

That structure is important. It means students are not only introduced to the idea of systematic trading, but are expected to implement and defend a strategy in a shared environment.

This approach gives instructors more than one dimension on which to assess student work. Depending on the course context, teaching staff can evaluate:

  • strategy design
  • code quality
  • market reasoning
  • overall approach
  • presentation and explanation

That is a better fit for university teaching than reducing the exercise to a simple leaderboard of returns. In an educational setting, the strongest submission is not always the one that took the most risk; it is often the one that shows the clearest thinking and the most disciplined implementation.

Why This Works Well in FinTech Education

Quantitative finance and fintech programmes sit at the intersection of mathematics, computation, and markets. Students need opportunities to connect those three domains.

Using a simulated environment helps make that possible. Students can build and test algorithms with real-time market behavior in mind, while instructors retain a controlled teaching setup. That reduces operational friction and allows the exercise to focus on learning outcomes rather than infrastructure.

Python also plays an important role here. It is widely taught, relatively accessible, and well suited to strategy prototyping. For students, that means the practical barrier to entry is lower. They can spend more time thinking about how a strategy should behave and less time struggling with overly complex tooling.

Team-Based Work Has Educational Value

The team-based format adds something important beyond the code itself.

In real quantitative and trading environments, strategy development is rarely a purely individual activity. People have to compare ideas, divide technical work, interpret results together, and justify design decisions. A classroom exercise that includes both implementation and presentation reflects that reality more closely than a traditional individual assignment.

It also gives students a visible way to compare approaches. Different teams can make different assumptions, choose different signals, and manage risk differently. That creates a much richer learning environment than a course where every student is solving the same problem in the same way.

Practical Exposure Matters

There is a big difference between knowing what algorithmic trading is and having actually built a trading algorithm, even in simulation.

The practical exposure students get from this kind of exercise can help them:

  • translate ideas into explicit, testable rules
  • understand the limits of a strategy as well as its strengths
  • see how implementation details affect outcomes
  • explain technical work clearly to others

Those are useful skills not only for trading roles, but for broader fintech and quantitative careers as well.

A Useful Model for Universities

PolyU’s use of ProfitView is a useful example of how universities can teach algorithmic trading in a way that is practical, structured, and approachable.

The public programme materials show the academic direction. The hands-on exercise adds the implementation layer that helps students understand what quantitative finance looks like in practice.

For institutions that want students to do more than study market automation at a distance, this kind of model is compelling: teams, code, simulated markets, and a final presentation that rewards understanding as well as results.

Learn More

You can view the public programme and instructor information here:

If you run a university or professional training programme and want to introduce hands-on algorithmic trading projects, contact ProfitView.