What is quantitative trading?
Quantitative trading is a type of market strategy that relies on mathematical and statistical models to identify – and often execute – opportunities. The models are driven by quantitative analysis, which is where the strategy gets its name from. It’s frequently referred to as ‘quant trading’, or sometimes just ‘quant’.
Quantitative analysis uses research and measurement to strip complex patterns of behaviour into numerical values. It ignores qualitative analysis, which evaluates opportunities based on subjective factors such as management expertise or brand strength.
Quant trading often requires a lot of computational power, so has traditionally been utilised exclusively by large institutional investors and hedge funds. However, in recent years new technology has enabled increasing numbers of individual traders to get involved too.
How does quantitative trading work?
Quantitative trading works by using data-based models to determine the probability of a certain outcome happening. Unlike other forms of trading, it relies solely on statistical methods and programming to do this.
You may, for example, spot that volume spikes on Apple stock are quickly followed by significant price moves. So, you build a program that looks for this pattern across Apple’s entire market history.
If it finds that the pattern has resulted in a move upwards 95% of the time in the past, your model will predict a 95% probability that similar patterns will occur in the future.
Quantitative vs algorithmic trading
Algorithmic (algo) traders use automated systems that analyse chart patterns then open and close positions on their behalf. Quant traders use statistical methods to identify, but not necessarily execute, opportunities. While they overlap each other, these are two separate techniques that shouldn’t be confused.
Here are a few important distinctions between the two:
- Algorithmic systems will always execute on your behalf. Some quant traders use models to identify opportunities, but then open the position manually
- Quantitative trading uses advanced mathematical methods. Algorithmic tends to rely on more traditional technical analysis
- Algorithmic trading only uses chart analysis and data from exchanges to find new positions. Quant traders use lots of different datasets
Learn more about algorithmic trading, or create an account to get started today.
What data might a quant trader look at?
The two most common data points examined by quant traders are price and volume. But any parameter that can be distilled into a numerical value can be incorporated into a strategy. Some traders, for example, might build tools to monitor investor sentiment across social media.
There are lots of publicly available databases that quant traders use to inform and build their statistical models. These alternative datasets are used to identify patterns outside of traditional financial sources, such as fundamentals.
Quantitative trading example
Let’s say, for example, that you hypothesise that the FTSE 100 is more likely to move in a certain direction at a particular point in the trading day. So you build a program that examines a large set of market data on the FTSE 100 and breaks down its price moves by every second of every day.
The below graphic charts the price movements of the FTSE 100 since 1984.
