Last Updated on 24 November, 2021 by Samuelsson
What Is Quant Trading?
There are different approaches to trading the financial markets. While some use discretionary methods and manual execute trades they feel would work well, others automate their trading processes. Even among the automated methods, some use basic technical analysis methods to develop strategies, while others make use of quantitative (quant) modeling — quant trading.
Quant trading (quantitative trading) is essentially a method of trading that is based on quantitative analysis. That is, it relies on mathematical computations to identify trading opportunities. Price and volume are two of the more common data inputs used in developing the mathematical models used in quantitative trading.
This article tells you what you need to know about quant trading. And to simplify the topic, we will discuss it under the following subheadings:
- What is quant trading
- Understanding quantitative trading
- History of quant trading
- Quant trading example
- Key benefits of quant trading
- Disadvantages of quant trading
What is quant trading
Quant trading, also known as quantitative trading, is the use of computer algorithms and programs that are based on complex mathematical and statistical models to identify and execute available trading opportunities. This trading approach is based on quantitative analysis, which uses research and measurement to break down complex behavior patterns into numerical values. Overall, quant trading is about conducting in-depth market research using historical price and volume data, as well as market and social trends, in a bid to identify consistent patterns that have an edge in the market and then developing models to exploit that edge.
Before the advent of computer-based trading, financial asset trading were conducted in physical locations, allowing traders and market makers to interact, agree on the price and quantity, and then settle the trade on paper. However, as markets become digital with global reach and expansion, tech-savvy traders are increasingly becoming dominant, offering vast expansion, loads of trading data, new assets, and securities. Hence, there came the opportunity for data mining, research, analysis, and automated trading systems.
Understanding quantitative trading
Quant trading makes use of modern technology, mathematical models, and the availability of comprehensive databases for making rational trading decisions.
Quantitative traders take a trading technique and create a mathematical model, and then develop a computer program that applies the model to historical market data. After backtesting and optimizing the model, the system is implemented in real-time markets with real capital if favorable results are achieved.
The way quantitative trading models function can best be described using an analogy. Consider a weather report where the meteorologist forecasts a 90% chance of rain while the sun is shining. The meteorologist derives this counterintuitive conclusion by collecting and analyzing climate data from sensors throughout the area.
A computerized quantitative analysis reveals specific patterns in the data. When these patterns are compared to the same patterns revealed in historical climate data, and 90 out of 100 times the result is rain, the meteorologist can conclude with confidence — hence, the 90% forecast. Quantitative traders apply this same process to the financial market to make trading decisions. In addition, they adopt a risk management approach that factors in the probability of success of their models.
How quant trading works
Quant trading operates 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 requires a lot of computational power to extensively research and make conclusive hypotheses out of numerous numerical data sets. As a result, quantitative trading has been a preserve of top financial institutions and high-net-worth individuals for a long time. To date, quant trading has not lost its relevance as retail clients are also utilizing it.
A popular example of the quantitative trading model is analyzing the bullish pressure experienced on the NYSE during lunch hours. A quant would then develop a program to study this pattern over the entire history of the stock. If it is established that this pattern happens, say 90% of the time, then the quantitative trading model developed will predict that the pattern will be repeated 90% of the time in the future.
Basic components of quantitative trading
Here are the key components of quant trading:
Before creating a system, quants will research the strategy they want it to follow. Strategy identification is when the trader decides the type of strategy that must suit the portfolio that the trader wants to apply. For instance, a trader may implement a medium-term strategy that will seek to take advantage of earnings and dividend reports, whereas another trader may apply a short-term strategy.
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.
Different strategies can be developed, such as mean reversion, trend following, or momentum trading. This phase aims to gather all the necessary data to optimize the strategy for maximum returns and minimal risk in the market. Thus, it is effectively turning a strategy into a mathematical model.
Backtesting the strategy
Strategy backtesting is carried out after identifying the best-suited strategy. It is conducted to qualify the identified strategy. This involves applying the strategy to historical data to determine how reliable it would have performed in the market. During backtesting, the strategy is tweaked and optimized in an attempt to expose inherent flaws. Flaws can be unpredictable or even in their performance levels. Therefore, the historical data must be high-quality to achieve accurate backtesting results, just like the utilized software platform.
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.
Strategy execution system
Every trading system must have an execution element, which is how generated trade signals will be placed in the market. The execution often ranges 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.
The key considerations for execution include reducing trading costs, such as commission, tax, slippage, and the spread. Good execution allows a trading system to operate at its optimal best, with the best prices achieved in the market at all times. Every system will contain an execution component.
Trading financial markets carry many risks, and as such, proper risk management is essential at every stage of the trading process. Risk refers to anything that could interfere with the success of the strategy. In the market, quant traders face different types of risk. First, there is the market risk, which encompasses all the risks involved during rapid and dynamic changes in the market prices of underlying financial assets. Traders often attempt to mitigate such risks using various parameters, such as stop losses, stake amount, trading times, tradable markets, and more.
There is also the capital allocation risk. It is an important area of risk management that covers the size of each trade or, if the quant is using multiple systems, how much capital goes into each model. While this is a complex area, especially when dealing with strategies that utilize leverage, it is the backbone of the trading system because the primary rule for successful trading is protecting the capital.
Using a fully automated strategy helps to remove human bias, but only if it is left alone by its creator. Retail traders should learn how to leave a system to run without excessive tinkering.
History of quant trading
Harry Markowitz is recognized as the father of quantitative analysis because he was one of the first investors to apply mathematical models to financial markets. In his doctoral thesis, which was published in the Journal of Finance, he applied numerical value to the concept of portfolio diversification.
During his career, Markowitz helped fund managers Ed Thorp and Michael Goodkin use computers for arbitrage for the first time. With many technological developments in the 1970s and 1980s, quant trading gradually became more mainstream. Some of those developments were the Designated Order Turnaround (DOT) system, which allowed the New York Stock Exchange (NYSE) to accept orders electronically for the first time, and the first Bloomberg terminals, which provided real-time market data to traders.
In the 1990s, algorithmic systems became more common that hedge fund managers started adopting quantum methodologies. The dot-com bubble was a game-changer, as these strategies proved to be less susceptible to the frenzied buying — and subsequent crash — of internet stocks. In addition, the rise of high-frequency trading has introduced more people to the concept of quant, such that as of 2009, 60% of US stock transactions were carried out by HFT investors, who relied on mathematical models to support their strategies. The Great Recession affected high-frequency trading volume and revenue, but quant continued to grow in stature and respect.
Quant trading example
Let us take an example. Suppose you hypothesize that the DAX 30 is more likely to move in a certain direction at a particular point in the trading day. So you build a program that examines a large set of market data on the DAX 30 and breaks down its price moves by every second of every day. With the information you derive from the program, you can build a statistical model that identifies whether there are any specific parts of the day when the DAX trades in a particular direction. If the model finds a pattern – say, that the index has a 60% probability of making an upward move at 11.15 am – then you can use that information to open positions for profit.
The above is a simple example of a quant trading strategy using just one data parameter: price action. However, most quantitative traders pull on several different sources at once to build far more intricate models with a better probability of identifying profitable opportunities.
Also, quantitative trading algorithms can be customized to evaluate different parameters related to a stock. For example, consider the case of a trader who believes in momentum investing. They can choose to write a simple program that picks out the winners during an upward momentum in the markets. Then, during the next market upturn, the program will buy those stocks.
Key benefits of quant trading
The main essence of trading is to calculate the optimal probability of executing a profitable trade. A typical trader can effectively monitor, analyze and make trading decisions on a limited number of securities before the incoming data overwhelms the decision-making process. The use of quantitative trading techniques removes this limit by using computers to automate monitoring, analyzing, and trading decisions.
Secondly, our emotions often get in the way when we trade, and this has become one of the most pervasive problems with trading. When trading, emotions, such as fear and greed, can stifle rational thinking, which usually leads to losses. Computers and mathematics do not possess emotions, so quantitative trading eliminates this problem.
Disadvantages of quant trading
There are some downsides to using the quant trading strategy though. As you may know, financial markets are very dynamic entities, and as such, quantitative trading models must be as dynamic to be consistently successful. As a result, many quantitative traders develop models that are temporarily profitable for the market condition for which they were developed, but they ultimately fail when market conditions change.
Quantitative trading uses mathematical models to identify opportunities, so quant traders tend to have a mathematical background and are very good with computer coding. A good quant trading system must have a robust strategy, be backtested, be equipped for automated execution, and have a risk management technique. Common strategies used in quant trading systems include mean reversion, trend following, statistical arbitrage, and algorithmic pattern recognition.