What you need to know about quantitative trading: uses, examples and strategies

What is quantitative trading?

Quantitative trading is the use of mathematical computations and statistical models to find opportunities in financial markets. The computational tools used in quantitative trading, or quant trading for short, intake large amounts of data to identify opportunities much faster than you could manually on your own.

In order to use quantitative trading, you must build a quantitative trading system by developing computer models that can identify opportunities in accordance with your trading strategy. Building this model, or even just running it, requires a large amount of institutional knowledge in finance, mathematics and programming.

Backtesting ­­­– a method of trying out a quantitative model on historical market data – is also a big part of developing quantitative strategies. By employing backtesting, quant traders can see how often a data pattern resulted in a certain outcome historically, then build their strategy accordingly.

This method of quantitative analysis is used to forecast across asset classes and industries. Style trends, weather reports and inventory for a business can all be forecasted using quantitative analysis. Of course, different methods and inputs are used for different industries.

Quantitative trading may also make use of high-frequency trading (HFT). The technique uses high-power computing programs to open and close positions faster than humanly possible. High-frequency trading makes possible several quantitative trading strategies that focus on extorting minute changes in a security’s price.

How is quantitative trading different from algorithmic trading?

Quantitative trading is different from algorithmic trading by method of trade execution and the level of technical knowledge required for the different models. While both trading styles use automated computations to determine opportunities in the market, there are important differences between the two techniques.

Quantitative systems use more datasets and advanced mathematical computations compared to algorithmic systems, which typically rely on traditional methods of technical analysis and use only data published by exchanges.

Plus, algorithmic systems always execute trades automatically while quantitative trading systems can execute trades automatically but can also be designed for traders to open and close positions manually.

Why do people use quantitative trading?

People use quantitative trading because it is a highly lucrative field once you’ve acquired the knowledge and resources required. It’s also an extremely demanding job, with many quants becoming burnt out after just a few years.

Because quantitative trading is done by computers, it is also considered a more objective and data-driven method than technical analysis – which is manually done by traders with the aid of data visualisation.

Compared to algorithmic trading which focuses on more traditional technical analysis to create trading models, quant trading can be developed from an array of different datasets and data sources.

Quantitative trading also provides a faster and more accurate way to test different trading models.  Large computational power and the lightning-fast speed at which quantitative trading works is a huge draw for traders looking to experiment and find new opportunities.

Traders may also use quantitative analysis to backtest their strategies on historical data in order to refine their own trading style or see how their ideas would work under different market conditions. Traders who use quantitative analysis in this way may not be pure quants. Instead, they use the computational strategy as one element of many in their trading toolbox.

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What is a quant?

Quants are traders who base their entire strategy around quantitative analysis. These traders are often the ones to develop and backtest quantitative trading systems. Quants typically have a high level of expertise in mathematics, statistics and computer programming which enables them to build quantitative trading systems.

While you can use quantitative analysis as a retail trader, most quants work for large financial institutions which can provide the computational power and resources needed.

Professional quants are divided into traders and researchers:

• Quants who work as traders build and maintain the computation models and algorithms used. They may tweak parameters to get their desired outcome, but if a model stops working as expected, a quantitative researcher might take over
• Quantitative researchers comb through the data used to build a model or trading system and find the exact signals that are triggering the system to execute trades. In this role quantitative researchers tend and repair the models

How do you become a quantitative trader?

To work as a quantitative trader, or quant, you are expected to have expertise in finance, advanced mathematics and computer programming.

A career as a quant trader requires a deftness with statistics and crunching numbers. You also need the trading skills to come up with unique strategies that can be implemented by the models you create. To create the programs, you’ll also need to be fluent in at least one programming language and have access to high computational power.

Many career quants have higher degrees in financial engineering or quantitative financial modelling. Most begin as a data research analyst before becoming a full-fledged trader.

Five strategies for quantitative trading

There are several types of quantitative trading strategies. A specific strategy may be employed by the quant depending on the market data they want to focus on such as price trends, trading volume or trader sentiment.

Mean reversion

Mean reversion is a popular quant strategy based on the idea that price movements have long-term trends, so divergences from the trend will likely self-correct in the future. Quantitative systems built on mean reversions will find markets diverging from long-standing trends and open a trade in the opposite direction.

If a price diverges down from an upward trend, the system will compute the likelihood of a profitable buy position. Whether the system opens a long position depends on if it determines a correction to come soon or not. This is a simple example of mean reversion, but the strategy can also apply to multiple markets with long-term correlations.

Trend following

Similar to momentum strategies, trend-following strategies find specific patterns in price movement. A common trend following strategy is to buy when the price is rising and sell when the price is falling. A quantitative system may monitor price action on a specific asset which moves in correlation to a larger market.

Quantitative traders building systems focused on trend following have many different options to define a trend. The system may be built to recognize the sentiment of traders at influential firms to predict movements from institutional investors. You could also focus on momentum trends, monitoring volatility and trading volume to determine the strength of a new trend.

Statistical arbitrage

Statistical arbitrage models focus on a specific group of assets expected to correlate, such as US beverage companies. Shares of both Coca-Cola and Pepsi trade on the same exchange and are affected by similar market conditions. The statistical arbitrage model would determine an average fair price for these two stocks. Then, using HFT, the model would open short or long positions on both companies depending on whether the current market price is above or below the determined average fair price.

Arbitrage can be thought of as a combination of computer-calculated fundamental analysis to determine the average fair price with rapid HFT executions to capitalize on quick-moving deviations in multiple stocks at once. Arbitrage trading takes mean reversion strategies a bit further by applying them to correlated companies or entire markets. Because arbitrage trading requires heavy computing power over micro-amounts of time, it is mostly done by high-frequency traders and hedge funds with the required technology.

Algorithmic pattern recognition

Algorithmic pattern recognition refers to quant models that identify when a market-making institutional firm is going to make a large trade. All institutional trading firms place large orders via algorithm. They use models which spread their orders across multiple exchanges, brokers, dark pools, etc. all staggered slightly to disguise their orders.

If you build a strong enough quantitative trading system that can interpret these ‘disguised’ orders, you can anticipate the trade. So, if your model picks up an incoming order to buy a large amount of HSBC stock, you can buy the stock ahead of the larger trade and sell it back for a profit once the large order moves the price of HSBC up. Algorithmic pattern recognition is another quant strategy that requires a uniquely high degree of computing power and HFT systems.

Sentiment analysis

Sentiment analysis uses data aggregated from outside of the markets, such as social media posts and research reports, to identify the general market sentiment surrounding specific assets. You can then trade short-term price movements based on this data.

Sentiment analysis models vary from one quant to another, because the classifications of positive and negative sentiment can differ. In most models though, each data piece will be associated to a specific asset and date and ranked on a fixed scale. Different asset classes require different models for sentiment analysis because traders may use different language for each. For equities, for example, the quant system will need to use machine learning to identify bearish and bullish phrasing and rank it appropriately.

How to build a quantitative trading system

To build a quantitative trading system requires following several iterative steps beginning with the formulation of your strategy and ending with the system’s implementation.

Most quant traders will work at hedge funds and other financial institutions which can provide the high computational power needed to build these systems. However, it is possible to build a quantitative trading system as a retail trader.

Many brokerages provide application programming interfaces (APIs) which allow traders to connect their own algorithms and models with their trading platform. Often the data for these models is provided by the brokerage as well. 

If you’re looking to code your own trading strategies, REST API may be more your style. REST API allows you to input your self-built quant models into an automated execution system. Learn more about REST API here.

If you are interested in building your own quantitative trading system, here are four general steps to get started.

1. Identify your strategy

The first step is to identify your trading strategy. This may be one of the strategies listed above: momentum, trend following, arbitrage, etc. Often your strategy will be born from a hunch or hypothesis after you gather and analyse data from market sources you’re looking to trade. You can then use techniques like regression or correlation analysis to analyse the data and determine significance.

Once you’ve got a strategy in mind, and before you move on to the next step, it’s important to determine all of the parameters involved. These include stop-loss and take-profit levels, position sizes, entry and exit points and more.

 

2. Backtest your strategy

Once you have analysed market data and formed a thorough strategy, you can simulate trades with these rules to see how they would perform in past market conditions. Backtesting is useful because you can learn the outcome of your trading strategy instantly across multiple markets and occurrences. Of course, backtesting your model and receiving a high degree of correlating results does not always mean your model will work in live markets. False correlations can occur, and unprecedented events happen all the time across asset classes.

To ensure successful backtesting you must use a solid platform, include all trading costs, use accurate and detailed historical data, and make sure your own bias doesn’t impact results. If the results of your backtesting are bad, you should alter the strategy and try again or reject it all together and start over with a new strategy. If the results are positive, you ‘ll need to continue to thoroughly test your strategy. There might be ways to optimize your strategy further and make it even stronger. Once this is done, your strategy is validated.

3. Implement your strategy

In order to implement your well-tested strategy, you need a system in place that can automatically send trade signals generated by the strategy to the broker. A fully automated system is used in high-frequency trading, but you can also manual or semiautomatic execution methods. However, strategies like arbitrage trading require a fully automated system.

Most quant traders use the systems built by engineers in their organizations to execute trades. Expertise in programming languages such as C/C++, R, Python or MATLAB are required to build these systems. While executing your strategy, pay close attention to an unforeseen trading costs and divergences between the live performance and that of the backtests.

4. Risk management

Risk management is a critical element to any strategy, including quantitative trading. Quant traders must consider every element of the trading system they’ve built or else the entire strategy might fall apart. For example, capital allocation and the size of each trade relative to your total holdings also need to be carefully judged by a quant system.

Human bias, which can be inputted by quant traders when managing their systems, is another huge risk to quantitative trading. Any bias that infects the model has the potential to disrupt it. If you have done rigorous backtesting, you should be able to let your quant system run with minimal tinkering to prevent additional risks.

Quantitative trading FAQs:

What are the pros and cons of quantitative trading?

Quantitative trading may seem like the secret key to successful trading, but the highly complex systems require an advanced level of knowledge and wide skillset to build and manage. Additionally, there is always the risk that unforeseen events can negatively affect the systems quant traders create and ruin entire trading strategies.

Pros	Cons
Quickly analyse large amounts of data for multiple markets at once	The models and systems are only as good as the quant making them
Remove emotion and bias that may negatively impact your trades	Even if you design a sophisticated model, market conditions can change and negatively warp any assumptions the strategy was built on
Automated execution and HFT allows you to make more trades than if you were inputting orders manually	Requires a large base of capital and range of knowledge not accessible to most traders

What is high frequency trading?

High frequency trading (HFT) is a technique that employs automated computer systems to open and close positions over extremely small timespans. HFT occurs so quickly only a computer can identify and execute trades at the rate required. It is often used with quantitative trading, because the quant models provide in-depth guidelines for HFT computers to consult while operating.

Not all high frequency trading systems use quantitative strategies, and not all quant trading uses HFT. Sometimes quant traders execute the trades themselves.

What is the Quant token?

Quant may also refer to the Ethereum-based token, Quant (QNT), used to power Quant Network’s Overledger. Overledger is an enterprise software designed to allow any blockchain-based project to operate across all other blockchains, public and private.