Quantitative trading is using mathematical and statistical models to identify and execute opportunities. It’s frequently referred to as ‘quant trading’, or ‘quants’.
Quantitative analysis uses research and measurement to strip complex patterns of behavior 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 utilized 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.
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 Reliance stock are quickly followed by significant price moves. So, you build a program that looks for this pattern across Reliance’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 v/s Algorithmic trading
Algorithmic (algo) traders use automated systems that analyses 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:
A quant trader is usually very different from a traditional investor, and they take a very different approach to trading. Instead of relying on their expertise in the financial markets, quant traders (quants) are mathematicians through and through.
Most firms hiring quants will look for a degree in maths, engineering or financial modelling. They’ll want experience in data mining and creating automated systems. If you’re hoping to try out quant trading for yourself, you’ll need to be proficient in all these areas – with an understanding of mathematical concepts such as kurtosis, conditional probability and value at risk (VaR).
As well as building their own strategies, quant traders will often customize an existing one with a proven success rate. But instead of using the model to identify opportunities manually, a quant trader builds a program to do it for them.
This requires substantial computer programming expertise, as well as the ability to work with data feeds and application programming interfaces (APIs). Most quants are familiar with several coding languages, including C++, Java and Python.
Quant traders develop systems to identify new opportunities – and often, to execute them as well. While every system is unique, they usually contain the same components:
Before creating a system, quants will research the strategy they want it to follow. Often, this takes the form of a hypothesis. Our example above uses the hypothesis that the Nifty tends to make certain moves at particular times each day, for instance.
With a strategy in place, the next task is to turn it into a mathematical model, then refine it to increase returns and lower risk.
This is also the point at which a quant will decide how frequently the system will trade. High-frequency systems open and close many positions each day, while low-frequency ones aim to identify longer-term opportunities.
Backtesting involves applying the strategy to historical data, to get an idea of how it might perform on live markets. Quants will often use this component to further optimise their system, attempting to iron out any kinks.
Backtesting is an essential part of any automated trading system, but success here is no guarantee of profit when the model is live. There are various reasons why a fully backtested strategy can still fail: including inaccurate historical data or unpredictable market movements.
One common issue with backtesting is identifying how much volatility a system will see as it generates returns. If a trader only looks at the annualised return from a strategy, they aren’t getting a complete picture.
Every system will contain an execution component, ranging from fully automated to entirely manual. An automated strategy usually uses an API to open and close positions as quickly as possible with no human input needed. A manual one may entail the trader calling up their broker to place trades.
HFT systems are fully automated by their nature – a human trader can’t open and close positions fast enough for success.
A key part of execution is minimizing transaction costs, which may include commission, tax, slippage and the spread. Sophisticated algorithms are used to lower the cost of every trade – after all, even a successful plan can be brought down if each position costs too much to open and close.
Any form of trading requires risk management, and quant is no different. Risk refers to anything that could interfere with the success of the strategy.
Capital allocation is an important area of risk management, covering the size of each trade – or if the quant is using multiple systems, how much capital goes into each model. This is a complex area, especially when dealing with strategies that utilise leverage.
A fully-automated strategy should be immune to human bias, but only if it is left alone by its creator. For retail traders, leaving a system to run without excessive tinkering can be a major part of managing risk.
The biggest benefit of quantitative trading is that it enables you to analyze an immense number of markets across potentially limitless data points. A traditional trader will typically only look at a few factors when assessing a market, and usually stick to the areas that they know best. Quant traders can use mathematics to break free of these constraints.
By removing emotion from the selection and execution process, it also helps alleviate some of the human biases that can often affect trading. Instead of letting emotion dictate whether to keep a position open, quants can stick to data-backed decision making.
However, quantitative trading does come with some significant risks. For one thing, the models and systems are only as good as the person that creates them. Financial markets are often unpredictable and constantly dynamic, and a system that returns a profit one day may turn sour the next.