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 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.

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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. Find out more about algorithmic trading. 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. 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. 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.

Machine Learning

Quantitative traders can employ a vast number of strategies, from the simple to the incredibly complex. Here are six common examples you might encounter:. 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. 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.

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.

To negate this risk, many quant traders use HFT algorithms to exploit extremely short-term market inefficiencies instead of wide divergences. 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. 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.

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 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.

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. 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. 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. 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. 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. You can even use an IG demo account to test your application without risking any capital.

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Related search: Market Data. Market Data Type of market. Indices Quantitative analyst High-frequency trading Mathematical finance Algorithmic trading Statistical arbitrage. Patrick Foot Financial Writer , Bristol. What is quantitative trading? How does quantitative trading work? Next, we will go through the step-by-step procedure to build an algorithmic trading strategy.

You can learn these Paradigms in great detail in one of the most extensive algorithmic trading courses available online with lecture recordings and lifetime access and support - Executive Programme in Algorithmic Trading EPAT.