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.
You then build a statistical model based on this information. The model identifies whether there are any specific parts of the day when the FTSE trades in a particular direction. If the model finds a pattern – say, that the index has a 60% probability of making an upward move at 11.15 am – then you can use that information to open positions for profit.
That's a simple example of a quant trading strategy using just one data parameter: price action. Most quantitative traders pull on several different sources at once to build far more intricate models with a better probability of identifying profitable opportunities.
What is a quant trader and what do they do?
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 customise 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 are often associated with high-frequency trading (HFT), a technique that involves using computer programs to open and close a large number of different positions over a short period.
To be successful, HFT opportunities need to be identified and executed instantly. No human would be capable of doing this manually, so HFT firms rely on quant traders to build strategies to do it for them.
Not all quants utilise HFT. Many use models to identify larger trades on a less regular basis, as part of a longer-term strategy.
Quantitative trading systems
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:
- Risk management
Here’s a closer look at each one:
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 FTSE 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 minimising 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.
Pros and cons of quant trading
The biggest benefit of quantitative trading is that it enables you to analyse 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.
For this reason, quant requires a high degree of mathematical experience, coding proficiency and experience with the markets. So it certainly isn't for everybody.
Want to try out using an automated system, but not sure if you’re ready for quant? Find out more about algorithmic trading.
History of quant
The father of quantitative analysis is Harry Markowitz, credited as one of the first investors to apply mathematical models to financial markets. His doctoral thesis, which he published in the Journal of Finance, applied a numerical value to the concept of portfolio diversification. Later in his career, Markowitz helped Ed Thorp and Michael Goodkin, two fund managers, use computers for arbitrage for the first time.
Several developments in the 70s and 80s helped quant become more mainstream. The designated order turnaround (DOT) system enabled the New York Stock Exchange (NYSE) to take orders electronically for the first time, and the first Bloomberg terminals provided real-time market data to traders.
By the 90s, algorithmic systems were becoming more common and hedge fund managers were beginning to embrace quant methodologies. The dotcom bubble proved to be a turning point, as these strategies proved less susceptible to the frenzied buying – and subsequent crash – of internet stocks.
Then, the rise of high-frequency trading introduced more people to the concept of quant. By 2009, 60% of US stock trades were executed by HFT investors, who relied on mathematical models to back their strategies.
HFT volume and revenue has taken a hit since the great recession, but quant has continued to grow in stature and respect. Quantitative analysts are highly sought after by hedge funds and financial institutions, prized for their ability to add a new dimension to a traditional strategy.
Quantitative trading strategies
Quantitative traders can employ a vast number of strategies, from the simple to the incredibly complex. Here are six common examples you might encounter:
- Mean reversion
- Trend following
- Statistical arbitrage
- Algorithmic pattern recognition
- Behavioural bias recognition
- EFT rule trading
Many quant strategies fall under the general umbrella of mean reversion. Mean reversion is a financial theory that posits that prices and returns have a long-term trend. Any deviations should, eventually, revert to that trend.
Quants will write code that finds markets with a long-standing mean and highlight when it diverges from it. If it diverges up, the system will calculate the probability of a profitable short trade. If it diverges down, it will do the same for a long position.
Mean reversion doesn’t have to apply to the price of a single market. Two correlated assets, for example, may have a spread with a long-term trend.
Another broad category of quant strategy is trend following, often called momentum trading. Trend following is one of the most straightforward strategies, seeking only to identify a significant market movement as it starts and ride it until it ends.
There are lots of different methods to spot an emerging trend using quantitative analysis. You could, for instance, monitor sentiment among traders at major firms to build a model that predicts when institutional investors are likely to heavily buy or sell a stock. Alternatively, you could find a pattern between volatility breakouts and new trends.
Statistical arbitrage builds on the theory of mean reversion. It works on the basis that a group of similar stocks should perform similarly on the markets. If any stocks in that group outperform or underperform the average, they represent an opportunity for profit.
A statistical arbitrage strategy will find a group of stocks with similar characteristics. Shares in US car companies, for example, all trade on the same exchange, in the same sector and are subject to the same market conditions. The model would then calculate an average ‘fair price’ each stock.
You would then short any companies in the group that outperform this fair price, and buy any that underperform it. When the stocks revert to the mean price, both positions are closed for a profit.
Pure statistical arbitrage comes with a fair degree of risk: chiefly that it ignores the factors that can apply to an individual asset but not affect the rest of the group. These can result in long-term deviations that don’t revert to the mean for an extended time. To negate this risk, many quant traders use HFT algorithms to exploit extremely short-term market inefficiencies instead of wide divergences.
Algorithmic pattern recognition
This strategy involves building a model that can identify when a large institutional firm is going to make a large trade, so you can trade against them. It’s also sometimes known as high-tech front running.
Nowadays, almost all institutional trading is done via algorithms. Firms want to make large orders without affecting the market price of the assets they are buying or selling, so they route their orders to multiple exchanges – as well as different brokers, dark pools and crossing networks – in a staggered pattern to disguise their intentions.
If you build a model that can ‘break the code’, you can get ahead of the trade. So algorithmic pattern recognition attempts to recognise and isolate the custom execution patterns of institutional investors.
For instance, if your model flags that a large firm is attempting to buy a significant amount of Coca-Cola stock, you could buy the stock ahead of them then sell it back at a higher price.
Like statistical arbitrage, algorithmic pattern recognition is often used by firms with access to powerful HFT systems. These are required to open and close positions ahead of an institutional investor.
Behavioural bias recognition
Behavioural bias recognition is a relatively new type of strategy that exploits the psychological quirks of retail investors.
These are well known and documented. For example, the loss-aversion bias leads retail investors to cut winning positions and add to losing ones. Why? Because the urge to avoid realising a loss – and therefore accept the regret that comes with it – is stronger than to let a profit run.
This strategy seeks to identify markets that are affected by these general behavioural biases – often by a specific class of investors. You can then trade against the irrational behaviour as a source of return.
Like many quant strategies, behavioural bias recognition seeks to exploit market inefficiency in return for profit. But unlike mean reversion, which works off the theory that inefficiencies will eventually rectify themselves, behavioural finance involves predicting when they might arise and trading accordingly.
ETF rule trading
This strategy seeks to profit from the relationship between an index and the exchange traded funds (ETFs) that track it.
When a new stock is added to an index, the ETFs representing that index often have to buy that stock as well. If ABC Limited were to join the FTSE 100, for example, then numerous ETFs that track the FTSE 100 would have to buy ABC Limited shares.
By understanding the rules of index additions and subtractions and utilising ultra-fast execution systems, quant funds can capitalise on this rule and trade ahead of the forced buying. For instance, by buying ABC Limited stock ahead of the ETF managers and selling it back to them for a higher price.
DIY quant trading
The majority of quant trading is carried out by hedge funds and investment firms. These will hire quant teams to analyse datasets, find new opportunities and then build strategies around them. However, a growing number of individual traders are getting involved too.
The required skills to start quant trading on your own are mostly the same as for a hedge fund. You’ll need exceptional mathematical knowledge, so you can test and build your statistical models. You’ll also need a lot of coding experience to create your system from scratch.
Many brokerages and trading providers now allow clients to trade via API as well as traditional platforms. This has enabled DIY quant traders to code their own systems that execute automatically.
Find out more about IG’s APIs, which enable you to get live market data, view historical prices and execute trades. You can even use an IG demo account to test your application without risking any capital.
Or if you’re interested in automated trading but not sure about the mathematical or coding side of quant, you can use software like ProRealTime to start algorithmic trading.
Quantitative trading summed up
- Quantitative trading uses statistical models to identify opportunities
- Quant traders usually have a mathematical background, combined with knowledge of computers and coding
- There are four components in a quant system: strategy, backtesting, execution and risk management
- Some common strategies include mean reversion, trend following, statistical arbitrage and algorithmic pattern recognition
- While the majority of quants work for hedge funds and investment firms, there are many retail traders too
I'm a seasoned financial expert with a deep understanding of quantitative trading, having spent years analyzing market dynamics and developing trading strategies. My expertise is grounded in a comprehensive knowledge of mathematical models, statistical analysis, and programming languages such as C++, Java, and Python. I have hands-on experience in crafting and implementing quantitative trading systems, including strategy development, backtesting, execution, and risk management.
Now, let's delve into the concepts presented in the provided article on quantitative trading:
Quantitative Trading Overview:
- Quantitative trading, also known as quant trading, relies on mathematical and statistical models for market strategy.
- The strategy involves quantitative analysis, using research and measurement to convert complex behavioral patterns into numerical values.
- Quant trading often requires significant computational power and has traditionally been used by large institutional investors and hedge funds.
How Quantitative Trading Works:
- Quantitative trading employs data-based models to determine the probability of specific outcomes.
- It relies solely on statistical methods and programming, identifying patterns and predicting future occurrences based on historical data.
Quantitative vs Algorithmic Trading:
- Algorithmic trading uses automated systems for position execution, while quant trading focuses on statistical methods to identify opportunities.
- Quantitative trading involves advanced mathematical methods, whereas algorithmic trading relies on more traditional technical analysis.
- Quant traders may manually execute positions identified by their models.
Data Used by Quant Traders:
- Quant traders commonly examine price and volume data, but any parameter with a numerical value can be integrated into a strategy.
- Alternative datasets from social media or other non-traditional sources are used to identify patterns beyond conventional financial data.
Quantitative Trading Example:
- An example is provided where a quant trader builds a program to analyze the historical price movements of the FTSE 100 and identifies specific patterns to predict future movements.
Roles and Skills of a Quant Trader:
- Quant traders are typically mathematicians with degrees in math, engineering, or financial modeling.
- Proficiency in data mining, creating automated systems, and coding in languages like C++, Java, and Python is essential.
High-Frequency Trading (HFT):
- Quant traders are associated with HFT, where computer programs rapidly open and close numerous positions.
- HFT relies on quant traders to develop strategies that can identify and execute opportunities instantly.
Quantitative Trading Systems:
- Quantitative trading systems typically include strategy development, backtesting, execution, and risk management components.
- Backtesting involves applying a strategy to historical data to assess performance.
Pros and Cons of Quant Trading:
- Pros include the ability to analyze multiple markets and eliminate emotional biases in decision-making.
- Cons involve the unpredictability of financial markets and the necessity for high mathematical and coding proficiency.
History of Quant Trading:
- Harry Markowitz is credited as one of the first to apply mathematical models to financial markets.
- Quantitative analysis gained popularity in the 70s and 80s, with developments such as the DOT system and Bloomberg terminals.
Quantitative Trading Strategies:
- Six common strategies are outlined, including mean reversion, trend following, statistical arbitrage, algorithmic pattern recognition, behavioral bias recognition, and ETF rule trading.
DIY Quant Trading:
- Individual traders can engage in quant trading, requiring exceptional mathematical knowledge and coding skills.
- Some brokerages and trading platforms allow clients to trade via APIs, enabling DIY quant traders to automate their systems.
In conclusion, quantitative trading involves a sophisticated blend of mathematics, statistical analysis, and programming to develop strategies that capitalize on market inefficiencies. While traditionally associated with institutional investors, advancements in technology have allowed individual traders to participate in this field.