What Is Quantitative Trading?

Quantitative trading, or quant trading as it is often shortened to, is a trading method in which you buy and sell assets using mathematical models, historical data and predefined rules.

This changes the decision-making process so it relies on probabilities and patterns rather than emotions or news headlines.

Today, quant trading is most commonly associated with hedge funds and institutional firms, but the reality is that almost anyone can use the underlying concept behind it to structure the market observations into rules, and then test to see if those rules actually work.

How Quantitative Trading Works

The principle behind quantitative trading is quite simple. You start with a market hypothesis, for example, you believe that any sharp drop in the biggest tech stocks will lead to rebounds shortly after.

With quant trading, you use this assumption to test it against historical data. If you see that a pattern does appear consistently enough, you can then turn your hypothesis into a strategy.

This is where quantitative trading distinguishes itself from discretionary trading, as the strategy here is to create a clear set of conditions, and then make the rules you set be what makes the call when it comes to making trades.

If you’re a beginner, you might still manually follow the rules, but most advanced traders automate the process using software or code that scans the market and executes trades.

How to Start Quantitative Trading

You don’t actually need a highly sophisticated algorithm to start trading this way. It is much better to start simple, with a strategy involving momentum, mean reversion or moving averages.

These are not only good basics on their own, but can also teach you the mechanics of a rules-based trading strategy. You learn how to define conditions, how to test them, and how to judge whether the results are meaningful.

The next step is where things get a bit more complex, as spreadsheets and historical price data give way to programming.

Python is the most common language for beginners because it is flexible, widely supported, and useful for handling market data.

When you get a hang of the programming basics and actually start to structure your process, you will create a cycle through which you collect the data, test the rules, review the results and make adjustments in your hypothesis.

Advantages of Quantitative Trading

As you can imagine, a system that’s based on clear rules and historical data has some clear advantages over other strategies.

The biggest one, of course, is objectivity. If you’ve set the rules, the strategy either generates a signal or it does not, so there’s no room for second-guessing or panic moves.

This in turn develops better discipline in traders, which is easily among the biggest issues that people that are actively trading face day to day.

Another major benefit is testability. With quant trading, you can evaluate your strategy both through backtesting and forward testing, seeing how your model would have performed in the past, and then determine if it is worth pursuing it today or in the near future.

Quantitative trading also offers scalability, which is why institutional firms are so reliant on these strategies. A well-developed strategy can be applied to many assets, timeframes and market conditions, without you having to analyze each part manually.

Last, but certainly not least, a quant trading approach is inherently a strong foundation for risk management, as all the features like position sizing, stop-loss levels and exposure limits can be built into your system from the get go.

The Downsides and Limitations

For all of its advantages, quantitative trading has a number of potential downsides to consider.

The most obvious one is the learning curve, as it requires a level of technical knowledge and analysis skills that many beginners simply do not possess.

If your data is not properly interpreted, or is incomplete, it can also make your strategy appear stronger than it really is.

Another potential danger is overfitting, as many strategies will perform great on old data when tailored too closely to the past, but then may break down as soon as market conditions change.

Backtesting also does not account for live trading issues such as latency, slippage, fees or liquidity constraints, and most importantly, even a great quant strategy does not eliminate risk, nor it can guarantee profits.

Final Words: Is Quantitative Trading Worth It?

All in all, quant trading can be highly valuable, especially for developing crucial trading skills and habits such as reliance on data, patience and risk management. 

Quant trading helps you deal with uncertainty in a more rational way, and in a market environment that’s increasingly being shaped by algorithms and AI, this is easily one of the most useful skills you can develop as a trader.

The biggest hurdle here comes from the fact that this approach takes a lot of time to learn and refine, but the payoff for your hard work can certainly be worth it.