Last Updated on 13 January, 2021 by Samuelsson
There are different types of traders with different approaches to trading. While some base their trading on fundamental factors, others make use of historical price and volume data to analyze the market. Ever heard about quants in trading and wondered what they are?
In trading, quants refer to traders who make use of quantitative strategies that involves complex mathematical and statistical models run by computer trading algorithms to identify trading opportunities, which could be either be executed manually or automatically executed by algorithms.
In this post, you will learn the following:
- Who quants are
- What quantitative trading is about
- The history
- How it works
- The components of a quantitative trading system
- The pros and cons
- The profile of a quant
Who are quants?
The word “quant” is gotten from quantitative, which basically has to do with numbers, so a quant (quantitative analyst) is someone who works with numbers. By definition, quants are traders who analyze a huge amount of market data using complex mathematical and statistical models to find tradable opportunities in the markets.
With the availability of online data feeds and the advancement of computer algorithms for analyzing a huge amount of data, finding trading opportunities has become more of a quantitative process than discretion, especially among big trading firms who can afford the high computational power needed for such analysis, and quants are the human element behind those analyses. Quants mine price and volume data, research the available data, identify profitable trading opportunities, and create relevant trading strategies to capitalize on those opportunities using self-developed computer programs.
Thus, a quant trader should have a balanced mix of in-depth mathematics and statistics knowledge, computer skills, and some practical trading experience. Quants are usually very different from traditional retail traders and investors, as they use a very different approach to trading — rather than relying on their expertise in the financial markets, quants use algo-based, complex mathematical models to scan the markets for opportunities.
Quant trading is mostly done by large investment institutions, such as hedge funds, banks, and prop trading firms. These institutions often have a dedicated quant team that creates computer algorithms that use specified mathematical models to analyze datasets to find new opportunities and then build strategies around them. When hiring quants, these firms look for a degree in math, statistics, or software engineering, as well as an MBA in financial modeling. They may want experience in data mining and the ability to create automated systems.
Although most quant traders work for the big institutions, which can afford the supercomputers and data needed for the analysis, a growing number of them are now trading on their own. Generally, the required skills to start quant trading on your own are the same as for a hedge fund. Thus, if you’re hoping to try out quant trading for yourself, you’ll need exceptional mathematical knowledge, so you can build and test your statistical models. Also, you will need a lot of programming skills to create your system from scratch. An understanding of mathematical concepts such as kurtosis, conditional probability, and value at risk (VaR) may be indispensable.
Now that many brokerages and trading providers are beginning to allow their clients to trade via API, in addition to the traditional platforms, DIY quant traders can code their own systems that execute automatically. Apart from creating their own strategies, quant traders may also customize an existing one with a proven success rate and then create an algorithm to run it instead of using the model to identify opportunities manually.
Whichever way a quant chooses, quant trading requires substantial computer programming expertise, as well as the ability to work with numerical data and application programming interfaces (APIs). To succeed as a quant, you need to be familiar with several coding languages, including MATLAB, C++, Java, and Python.
What is quantitative trading?
Often referred to as quant trading, quantitative trading is a method of trading that relies on complex mathematical and statistical models to identify — and, in most cases, execute — trading opportunities in a chosen financial market. The models are driven by quantitative analysis — which is where the strategy gets its name from — done by computer algorithms built for that purpose.
Quantitative trading is an extremely sophisticated type of market strategy that uses quantitative analysis to find trading edges in the markets. The analysis uses research and quantitative measurement to break down complex patterns of market sentiment into numerical values. This sort of analysis ignores qualitative analysis, which evaluates opportunities based on subjective factors like brand goodwill or management expertise.
With quant trading, the interest is on historical data, and the two most common data points used in quant trading are price and volume. However, any parameter that has a numerical value, or can be given a quantitative measure, can be incorporated into a strategy. Apart from the price quotes, there are lots of publicly available databases that can be used to build statistical models for quant trading, and these alternative datasets are used to identify patterns outside of traditional financial sources, such as fundamentals. For example, some traders might build tools to monitor investor sentiment across social media.
Quant trading is widely used at individual and institutional levels for high frequency, algorithmic, arbitrage, and automated trading, and it often requires a lot of computational power; hence it has always been exclusively within the purview of large institutional investors and hedge funds. But in recent times, technological advancement has enabled an increasing number of individual traders with the appropriate skills to do it on their own.
The history of quant
Seen as the father of quantitative analysis, Harry Markowitz is considered the first investor to apply mathematical models to financial trading. In his doctoral thesis, which he published in the Journal of Finance, he used numerical value to explain the concept of portfolio diversification. He later helped two fund managers use computers for arbitrage for the first time.
In the late 70s and 80s advancement in computing helped quant trading become more mainstream. One of them is the designated order turnaround (DOT) system, which enabled the New York Stock Exchange (NYSE) to take orders electronically for the first time. Another was the first Bloomberg terminals that supplied real-time market data to traders.
Then, in the 90s, algorithmic systems started becoming more common, and more hedge fund managers embraced quant trading systems. However, it was the dotcom bubble that proved to be the turning point, with quant strategies proving less susceptible to the crazy buying of unknown internet stocks and the burst that followed.
The rise of high-frequency trading in the new millennium introduced more people to the concept of quant, and by 2009, 60% of US stock trades were executed by high-frequency traders, who used mathematical models.
How does quant trading work?
Quant trading works by using models that are based on numerical values to calculate the probability of a trading outcome. Unlike other trading approaches, quant trading relies solely on statistical methods coded into analytical algorithms.
For example, if a volume spike in Amazon stock is followed by a huge price move, a quant may create an algo that scans for such pattern across the historical market action (price and volume) of Amazon. If it finds that the pattern has resulted in an upward move in 95% of the time in the past, the model will predict a 95% probability that when a similar pattern occurs in the future, the price will move upward.
The components of a quantitative trading system
While every quant trading system is unique, a typical quant system consists of these four major components:
- Identifying a strategy
- Backtesting the strategy
- An execution method
- Risk management
Identifying a strategy
All quant trading systems begin with an initial period of research to find a suitable strategy that fits into a portfolio of other strategies you may be running and your preferred trading frequency. This strategy is often in the form of a hypothesis, put forward by academics who regularly publish theoretical trading results. Some of the common strategies employed by quant traders are as follows:
- Mean reversion: This strategy is based on the financial theory that the price of any security has a long-term mean and that any short-term deviation from that mean will eventually revert. Quants who use this strategy will write a model that determines the mean and defines what constitutes a good deviation from the mean. An upside deviation generates a short trade, while a downside deviation will generate a long trade.
- Trend following: Often called momentum trading, a trend following strategy attempts to exploit both investor sentiment and breakouts to ride on a market trend, which can gather momentum in one direction, and follow the trend until it reverses.
- Statistical arbitrage: This strategy is based on a similar theory as the mean-reversion theory, but here, you compare a group of similar securities. The theory is that a group of similar stocks should perform similarly on the markets, and if any stocks in that group outperform or underperform the average, they represent an opportunity for profit.
- Algorithmic pattern recognition: The idea here is to identify when a large institutional firm is going to make a large trade, so you can front-run them — place a trade in the same direction just before their orders come in so that you can ride the momentum generated by their huge orders and selling it back to them.
- Behavioral bias recognition: This model tries to identify and exploit the behavioral biases (loss aversion, for example) of retail traders.
- ETF rule trading: The idea here is to profit when ETF managers have to buy a new stock that has just been added to a market index they are tracking. A quant can buy the new entrant before the ETF managers and sell it back to them when they will be buying it.
After picking a suitable strategy, the next thing is to turn it into a mathematical model by obtaining any data necessary to test and optimize the strategy and then write the rules. At this point, a quant will decide how frequently the system will trade — while high-frequency systems open and close many positions each day, low-frequency ones aim to identify swing and position trading opportunities.
Backtesting the strategy
Once a strategy, or set of strategies, has been identified and used to create a mathematical model, it has to be tested for profitability on historical data. Backtesting aims to provide evidence that the model can be executed and is profitable when applied to both historical and out-of-sample data. However, performance on historical data is not a guarantee that the model will make money in live trading, which is why it is necessary to test it in live trading with a small capital first.
An execution method
Every quant system must include an execution method, which is how the trades generated by the strategy are supposed to be sent and executed by the broker. Irrespective of whether the trade generation is semi-automated or even fully-automated, the execution mechanism can be manual or fully automated. Low-frequency strategies can be executed by manual methods, whereas high-frequency strategies will require a fully automated execution mechanism, which should even be tightly coupled with the trade generating algorithm.
The final piece of a quantitative trading system is risk management. As with any other form of trading, quant trading doesn’t joke with risk management, and risk, here, refers to anything that could interfere with the success of the strategy, including technology risk, such as servers co-located at the exchange suddenly developing a hard disk malfunction.
At the trader’s end, capital allocation is an important aspect of risk management; it determines the size of each trade and, if the trader is using multiple systems, how much capital goes into each model. This is a complex area and relies on some non-trivial mathematics, especially when dealing with strategies that utilize leverage.
A system that is fully-automated should be immune to human bias if it is left alone by its creator, but this is often difficult for many retail traders.
The pros and cons of quant trading
The main advantage of quantitative trading is that it enables you to analyze potentially limitless data points across a large number of markets since it runs as an algo-trading system. While traditional traders will typically only look at a few factors when assessing a market, quants can use mathematical models to break free of these constraints.
Another benefit is that it reduces human bias in trading — by removing emotion from the analysis and execution process, quant trading helps alleviate some of the human biases that can often affect trading. So, rather than letting emotion dictate whether to keep a position open, quants can stick to data-backed decision making.
However, there are some significant risks associated with quant trading:
- The models and systems are only as good as the person who developed them.
- The financial markets are dynamic and unpredictable — a system may return a profit one day and turn sour the next.
- Quant trading is not for everyone, as it requires a high degree of mathematical experience, programming skills, and trading experience.