Quantitative trading is defined as a type of market strategy that relies on mathematical and statistical models to both identify and execute opportunities.
Also known as quant trading, this strategy uses models that are driven by quantitative analysis, as well as advanced research and measurement to strip complex patterns of behavior into numerical values.
Of note, quantitative trading eschews qualitative analysis, which evaluates opportunities based on subjective factors such as management expertise or brand strength.
Quantitative trading requires a large amount of computational power. This makes it a common tool amongst large institutional investors and hedge funds with access to this level of computing.
However, advances in new technology has allowed for increasing numbers of individual traders to become involved in quantitative trading in recent years.
How Does Quantitative Trading Work?
Quantitative trading works by utilizing data-based models to determine the probability of a certain outcome happening.
Unlike other forms of trading, this strategy relies solely on statistical methods and programming to do this.
For example, you can observe that volume spikes on IBM stock is quickly followed by significant price moves.
As such, you can construct a program that analyzes and looks for this pattern across IBM’s entire market history.
If you find that the pattern has resulted in a move upwards of 95% of the time in the past, your model will predict a 95% probability that similar patterns will occur in the future.
Quantitative trading differs from algorithmic trading, which relies on automated systems that analyze chart patterns then open and close positions on their behalf.
Quantitative traders instead use statistical methods to identify, but not necessarily execute, opportunities.