29 July 2022 • 6 min read
If you're interested in trading, then you’ve likely heard of the terms “quantitative trading” and “algorithmic trading.” But what do they mean and what do they entail exactly? And what are some of the key advantages and disadvantages of both?
If you’re not entirely sure, or if you thought that they were actually one and the same, then you’re not alone. While both approaches allow traders to automate their strategies, the models used by each differ significantly, and in the following article we’ll tease out some of these differences as well as highlight the similarities between quantitative trading and algorithmic trading.
Quantitative trading is a type of trading that focuses on using mathematical models and analytics to make decisions and identify trading opportunities for increased profitability. The traders who create and implement these trading strategies are called quant traders.
This type of trading commonly uses price and volume as data inputs to the mathematical models used in developing trading strategies. Quantitative trading is primarily used by financial institutions and hedge funds, though it is also increasingly being adopted by independent retail traders.
When it comes to trading, the decisions that traders make are based on various factors. But when traders use a quantitative trading strategy, the decisions are based solely on numbers and data. This can help minimize the emotional decision-making approach that can happen during trading, leading to more successful trades.
One of the benefits of quantitative trading is that quant traders can analyze various markets by using potentially endless amounts of data. Contrast this with the typical trader, who normally focuses on just a few variables and examines only those aspects with which they are most familiar. When armed with mathematical strategies, quant traders can easily overcome such limitations.
The downside of quantitative trading is that it requires highly specialized knowledge to do it successfully. For example, quant traders must have advanced mathematical experience, proficiency in coding, and extensive experience with markets. Quantitative trading also carries significant risks as markets change or new patterns emerge.
Algorithmic trading is a type of trading that uses computer programs to make trade decisions automatically. These programs follow a set of rules (algorithms) using mathematical models and other market conditions such as price, timing, and volume. Algorithmic trading is also sometimes referred to as “algo trading” or “black-box trading.”
Algorithmic trading uses large amounts of data in order to gain a better picture or understanding of the market, which can then be used to program buy and sell orders for the most opportune times. Large institutional investors, including pension funds and mutual funds, frequently use algorithmic trading to split big orders into multiple smaller parts. Since the data is acquired electronically, major players leverage algorithmic trading to automatically place orders before any other human traders ever know about the data, giving them a significant advantage.
In addition to increasing a trader’s chances for profit, algorithmic trading speeds up order execution and makes trading more organized by minimizing the influence of human emotions. Algorithmic trading also enables traders to automatically place orders, which saves time and may even lower transaction costs. Additionally, it lessens the risk of human error that usually comes with manually executed trades. And even the algorithms themselves can be optimized.
Despite the numerous advantages of using algorithms, it's important to remember that algorithmic trading was largely attributed to the 2010 flash crash and thus carries some important risks. First, investors can be forced to pay a high price if a system fails during trading hours. Consequently, it's crucial to invest in cutting-edge technology and conduct meticulous tests before adopting algorithmic trading.
It's also important for algo traders to be familiar with computer programming, as trading algorithms are extremely sophisticated. And before any algorithmic trading strategy is implemented, it should be rigorously backtested.
While both quantitative trading and algorithmic trading rely on computers to automate the trading process, they are quite different approaches both in terms of the types of trading tools and how those tools are put into practice. Quantitative trading attempts to predict market trends using mathematical and statistical models. In contrast, algorithmic trading attempts to profit from market movements using algorithms that automatically place trades based on predetermined rules.
Algorithmic trading basically boils down to a set of if/then rules based on historical data, which traders then use to enter and exit positions in the future in order to maximize profitability. Quantitative trading entails the use of statistics, mathematical models, and big datasets (previous data related to trading) to project market transactions in the future.
In other words, algorithmic trading is used to automate the entirety of a trading strategy, making it far more convenient, easier, and less specialized than quant trading, which involves a high degree of technical expertise, is often done manually, and relies on mathematical models and statistics. Because of their overlapping areas, they can be considered two sides of the same trading coin, with the aforementioned differences in mind.
Quant traders use advanced mathematical methods, while algo traders often use more conventional technical analysis. Algorithm trading also only analyzes chart patterns and data from exchanges to find trading positions. Quantitative trading, on the other hand, makes use of different datasets and models.
Quantitative trading involves statistical analysis to find, but not always execute, trading opportunities. For example, some quantitative traders employ models first to find opportunities, but then manually open the position. Conversely, algorithmic trading uses automated systems to make decisions based on the analysis of chart patterns. However, algorithms will always open or close positions on the trader’s behalf.
Quant traders are specialized traders, ones who apply mathematical and quantitative methods to evaluate financial products or markets. They create mathematical and statistical models to forecast trade profits or stock price movements, often using algorithms.
Algo traders create and improve their own algorithms and codes to monitor the markets and open or close positions based on market conditions. Algo traders use their knowledge of financial markets and computer programming to place trades at the best possible times by creating trading rules based on technical analysis, fundamental analysis or quantitative analysis.
To put it another way, if you’re an algo trader then your decision-making process will rely on data and trend analysis, while quants rely on mathematics and technical analysis. Another key distinction is that algo traders hone in on historical data, whereas quants will use many datasets simultaneously. And while both use algorithms, transactions in quant trading models are often done manually, unlike algo traders who use (as their name suggests) algorithms to automate their trading.
Closer to home, the trading that can be done on Trality’s platform with crypto trading bots using technical indicators and trends (among other things) is an example of algorithmic trading. Conversely, quantitative trading looks at volatility, reversion trading, or basis trading in which multiple assets are fitted to a mathematical model. The one asset that disagrees with the model will then become the asset that is traded.
Fundamentally, the key difference between the two involves whether the approach is mainly one of using historical data (algo) or if it’s a matter of forecasting using a mathematical model to imply if something is relatively rich/cheap based on price implied by the model (quant).
It’s perfectly possible to combine quantitative and algorithmic trading. Since algorithmic trading is essentially a subset of quantitative trading that utilizes a pre-programmed algorithm, these two methods of trading can and often do overlap. Quantitative analysis is also frequently used in algorithmic trading.
In essence, quantitative trading also uses algorithms and programs, but these algorithms are based on mathematical models that quant traders create. Algorithmic trading uses powerful computers to run the intricate mathematical models created by quant traders and execute the orders. This entails automating every step of the process, from order creation down to execution. The defining factor is that these algorithms fully execute the trade automatically.
In particular, quant traders must be familiar with data mining, analysis, and research as well as automated trading systems. Quant traders are typically proficient with tools such as Python, Perl, C++, and Java. Aspiring traders can also dabble with automated trading by engaging in projects such as building Python trading bots.
If we were to think of a Venn diagram with quant and algo trading, there would be a significant area of overlap. However, as we’ve seen in the article, there are also crucial differences between the two in terms of their theoretical starting points, tools, and practices. Overall, algorithmic trading can be considered a subset of quantitative trading, but several key elements and rationales differentiate the two.
Because of the complexity of mathematical modeling and statistical tools such as Stata or Matlab to download and analyze large datasets, to say nothing of the coding languages that are needed, quantitative trading is for advanced users with the aforementioned skills and expertise. Although private traders can engage in quantitative trading, it is often done on an institutional level.
Algo trading, on the other hand, is widely available to both beginners and experienced traders, with trading platforms such as Trality offering the ideal gateway into the many advantages of automated trading.