Quantitative trading uses data, mathematical models and explicit decision rules to evaluate market opportunities. It turns a trading idea into a process that can be tested, measured and applied consistently. A strategy may be simple or complex; using AI is not a requirement.
What makes a strategy quantitative?
Consider the idea that an unusually large price move may reverse. A quantitative approach specifies what counts as unusual, the information available at that moment, the conditions for a signal and how results will be measured. Without those definitions, two people could interpret the same idea very differently.
The important shift is from an impression to a testable hypothesis. A favourable historical result still needs scrutiny: it may reflect chance, unrealistic trading costs or information that would not have been available in real time.
Quantitative vs discretionary trading
A discretionary trader can change a decision through judgement. A systematic strategy applies its defined process. Neither label proves that a strategy is profitable; both require evidence and risk discipline.
AlphaNet’s research combines systematic factor development with numerical modelling and validation. Its quantitative approach is therefore broader than simply asking a neural network to predict the next price.