Bridging Maths and Commerce: A Cross-Department Algorithmic Trading Project

Some of the best learning happens at the boundary between subjects. When students ask "where will I ever use this?", the most convincing answer is a real project that refuses to sit inside a single textbook.

This year the mathematics and commerce departments ran a joint initiative on algorithmic trading, and it became one of the most engaging things my students have done.

The Idea

Financial markets are a natural playground for statistics and calculus. Prices move, averages smooth out noise, and simple rules can turn raw data into decisions. Working alongside the commerce faculty, we built a unit where students modelled a basic moving-average crossover strategy.

The mathematics side contributed:

Averages and smoothing: Understanding how a moving average filters short-term fluctuations.
Rate of change: Reading momentum as the slope of a price curve.
Statistical reasoning: Discussing variance, risk, and why past performance guarantees nothing.

The commerce side contributed the market context: what a candlestick means, how trades settle, and the economics behind price movements.

Building the Model

Students wrote a short Python script to compute two moving averages, a fast one and a slow one, over historical price data. When the fast average crossed above the slow one, the model flagged a potential buy; when it crossed below, a potential sell.

Seeing their crossover graph line up with actual market turns was a genuine "aha" moment. The mathematics was no longer decoration; it was the engine.

Lessons Learned

Interdisciplinary framing raises engagement. Students who were lukewarm about statistics leaned in when it decided a simulated trade.
Real data is messy, and that is the point. Discussing why the strategy sometimes failed taught more about risk than any tidy example could.
Collaboration models the real world. Working with the commerce department showed students that professionals rarely solve problems inside a single discipline.

Cross-department projects take coordination, but the payoff is students who see mathematics as a living, connected tool rather than an isolated set of rules.

Bridging Maths and Commerce: A Cross-Department Algorithmic Trading Project

This year the mathematics and commerce departments ran a joint initiative on algorithmic trading, and it became one of the most engaging things my students have done.

The Idea

The mathematics side contributed:

Averages and smoothing: Understanding how a moving average filters short-term fluctuations.

Rate of change: Reading momentum as the slope of a price curve.

Statistical reasoning: Discussing variance, risk, and why past performance guarantees nothing.

The commerce side contributed the market context: what a candlestick means, how trades settle, and the economics behind price movements.

Building the Model

Seeing their crossover graph line up with actual market turns was a genuine "aha" moment. The mathematics was no longer decoration; it was the engine.

Lessons Learned

Interdisciplinary framing raises engagement. Students who were lukewarm about statistics leaned in when it decided a simulated trade.

Real data is messy, and that is the point. Discussing why the strategy sometimes failed taught more about risk than any tidy example could.

Collaboration models the real world. Working with the commerce department showed students that professionals rarely solve problems inside a single discipline.

Cross-department projects take coordination, but the payoff is students who see mathematics as a living, connected tool rather than an isolated set of rules.