What is Quantitative Trading?

What is Quantitative Trading?

As a retail investor, it can be difficult to identify new market opportunities. Endless factors are at play, and it often seems impossible to stay on top of it all. Quantitative trading can take all of the emotions and guesswork out of the manual trading process. It allows you to efficiently buy and sell stocks using thorough data to back up its methodology. Often, it can uncover trading opportunities that otherwise might go unnoticed. If investors are equipped with the right tools and knowledge, quantitative trading can be a game-changer.

Quantitative trading, or quant trading, is a strategy used to identify trading opportunities. It involves using mathematical computations and statistical analysis to detect potential changes in securities prices. It does not incorporate any qualitative factors, like the perceived strength of company executives or brands, because these are subjective by nature. The logic behind this approach is that since fundamental investing can often be reliant on a cocktail of various factors (such as interest rates, valuations, management guidance, and others), quantitative trading chooses to focus primarily on what truly matters: the stock price.

While it sounds complex, quant trading is made possible through modern technology. This strategy is mainly used by financial institutions and hedge funds, but individual investors are also using it to streamline their decision-making.

How Does Quant Trading Work?

Quant trading involves creating models that are applied to real-time data to identify market changes and opportunities. To achieve an accurate quantitative model, a trading strategy must first be selected. Once the investor decides on a strategy, the model is extensively tested using historical market data to ensure that the algorithm operates as intended. After backtesting, it is time to use the model in identifying time-sensitive trading opportunities and changes in the current market. This is done by applying the model to computer systems that have access to both real-time market data and programming languages.

How Has Quantitative Trading Evolved?

The emergence of quant trading as a mainstream method within the investment world marked a radical change from the traditional marketplace. Historically, markets were all in person, and securities would be exchanged through human interaction. Because of the nature of the industry, it was ideal for traders to be strong communicators.

The methodology behind quant trading emerged in the 20th century with investor Harry Markowitz's 1952 thesis "Portfolio Selection," which intersected mathematics and finance. Despite its long history, quant trading began gaining prominence over the past 20 years. The marketplace has become increasingly digital, resulting in a worldwide expansion of trading opportunities. Because of the mathematical complexity involved, quant traders, or quants, are typically advanced degree holders in quantitative areas such as computer science and finance.

Quantitative Trading vs. Algorithmic Trading

Quantitative trading may sound like it is synonymous with algorithmic trading, but this is not the case. Both are computer-based, but algorithmic trading is a far more automated process. It entails using algorithms that automatically execute trades based on rules established through analysis of historical data. Because it is automated, it is far simpler than quantitative trading. Quant trading involves more human intervention and manual inputs to predict overall trends in the marketplace. It may not fully execute trades automatically. Because of its greater scope, the mathematics involved in quant trading is far more advanced than that of algorithmic trading.

Pros and Cons of Quantitative Trading

You may be wondering why you should use quantitative trading over more traditional techniques. Despite its perceived complexity, quant trading offers several advantages:

• It relies solely on numbers and data, taking human emotion out of the trading process. Emotion can interfere with rational decision-making.

• It can digest far more data than a human, who usually sticks with analyzing the few variables they are most comfortable with.

• It is quick to analyze data.

• Because of its data capabilities, it increases the number of possible trades that can be made.

• It takes out fundamental variables from the decision-making process and focuses on securities price movement, thus making the process more objective and direct.

While there are many advantages to quant trading, it does have its downsides:

• It requires advanced knowledge in mathematics, computer science and finance.

• The models used must adjust to changing market conditions to remain successful.

• It can be risky to use quant trading if its models are not up to date with new patterns in the marketplace.

Core Components of Quant Trading

For a more complete understanding of quantitative trading, let's take a look at the core components involved.

Strategy Identification

The first step in quant trading is to select a trading strategy to use in your mathematical model. A variety of strategies can be found in academic publications, finance blogs or trade journals. The strategy you use will vary based on whether you are conducting analysis for the short term or medium term, and its complexity can vary.

Backtesting

Backtesting is a critical component to ensuring that the mathematical model works as intended. When testing the model, it helps to have as large of a data set as possible. An extensive database is essential to optimizing the model. The data, which often includes price and volume information, is used to train the model before applying it to the real-world marketplace. It's important to keep in mind that backtesting cannot guarantee that your model will be accurate when it is applied to the real-time market. But it is key to increasing the likelihood of success. For effective backtesting, a programming background (such as in Python) might be needed, otherwise no-code platforms like Composer can be very helpful for beginners.

Execution

Once the backtesting process is complete, it is time to apply the mathematical model to the marketplace. How it is executed can depend on personal preference. It can be manual, semi-manual or completely automated. Deploying your strategy is where the action happens and trades are made.

Risk Management

No matter how you go about it, trading is always going to be risky. All trading deals with market risk, which is the risk that losses will happen because of quick price changes. However, quant trading also faces risks inherent to technology. While not all risks can be mitigated, techniques such as scenario analysis and stop-loss orders can be employed for this purpose. 

Examples of Quantitative Trading Strategies

Here are some of the most common strategies used in quant trading:

• Momentum trading: A model detects and invests in stocks that are on their way up and sells those on their way down. This strategy is also referred to as trend following. Read more about momentum trading here.

• Mean reversion: This concept illustrates the idea that prices and returns will follow a stable trend over the long term. Quants can create a model to buy undervalued securities and sell those that are overvalued. 

• Statistical arbitrage: This strategy looks at securities that are similar, such as in regard to market conditions. An average or fair price is determined for each. With the fair price as a baseline, stocks are sold when their price is higher and are bought when lower.

You can check out more in-depth quant trading strategies here.

How to Get Started with Quant Trading

The knowledge and skills needed in quant trading depend on whether you are a professional quant trader working at a firm or a retail investor. Although there are no specific required qualifications, career quants often possess advanced degrees in quantitative fields such as mathematics, computer science, engineering or finance. If you are an individual investor looking to get into quant trading, you may not need to have this educational background, and the software and data are often reasonably priced and available for you to get started. However, you will need to learn advanced concepts in mathematics, computer science and stock trading.

When diving in, it can be helpful to explore top-rated quant trading books.

Regardless of whether you are a professional quant trader or a retail investor, there are some skills you will need to possess. These include a thorough understanding of the marketplace, trading strategies and programming. Some of the most common programming languages used in quant trading include C++, Java, Python and Perl. You will need to know at least one programming language, and it is preferable to know more. Fluency with spreadsheets, MATLAB and advanced data capabilities will help you become a successful quant.

Composer and Quant Trading

If you are interested in exploring quant trading, Composer can be a useful tool. It can be used to conduct the core components of the trading process, such as backtesting. More than 500,000 backtests have been run in Composer by over 25,000 people. 

Composer also classifies risk, which promotes successful trading. 

Quantitative trading is an important tool for today's digital market. Its mathematical models can digest large amounts of data and identify potential trades — all while avoiding the interference of human emotion in decision-making. It involves the intersection of mathematics, computer science and finance, so there is a steep learning curve. Quant trading is no longer only for large financial institutions. By putting in the work to learn its intricacies, retail investors can become successful in quant trading. 

Composer can help you in your quant trading journey. Sign up for a free trial today.