Interested in quantitative
trading? Discover everything you need to know, including what it is, how it
works and what quant traders do. Plus, a few quantitative strategies to get
started with.
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 behavior into numerical values. It ignores qualitative
analysis, which evaluates opportunities based on subjective factors such as
management expertise or brand strength.
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 analyze 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.
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).
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.
High-frequency trading:
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:
Strategy
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:
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.
Execution:
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.
Risk management:
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.
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：
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.
Trend following：
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：
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
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