01 September 2022 • 5 min read
How do you start quant trading? Are quant traders profitable? Can you quant trade on your own? How long does it take to become a quant trader? If you’re familiar with the crypto space, then you’ve likely come across references to quantitative trading. But you might also have a lot of questions about it and whether it’s right for you.
Quantitative trading can be intimidating to beginners, especially given the complex mathematics and technical charts that are often associated with it. To help new quantitative traders avoid some of the common pitfalls facing would-be quants, this guide will provide an overview of what quantitative trading entails and how to get started in the field.
Quantitative trading is a trading system that uses statistical and/or mathematical models to find opportunities and execute them. It is very often known as simply "quant trading" or "quant.” To predict future results of market transactions, quant trading relies on statistics, mathematical models, and massive datasets of past trading data. Research and measurement are also used to reduce complex behavioral patterns to numerical values.
More generally, quantitative trading can be understood as the application of a scientific approach to financial markets because it involves measuring the probabilities of changes in market conditions and utilizing that data to develop a rules-based trading strategy. It’s important to note that trading using quantitative techniques excludes qualitative analysis, a type of analysis that assesses opportunity based on subjective criteria, including management skill or corporate reputation.
Due to the high computing demands of quantitative trading, large financial institutions and hedge funds have typically used it. A recent CoinDesk article showed just how profitable quant trading has been for crypto hedge funds, particularly in 2018 and 2019. In fact, the most common crypto hedge fund is a quant-based one. However, as with automated trading, in recent years new technologies have made it possible for a growing number of individual traders to explore and use this approach to systematic, rule-based investing.
Quantitative trading works by evaluating the probability that a specific outcome would occur using data-based strategies. It uses only statistical techniques and programming, unlike other types of trading. Additionally, historical data and mathematical models are quite vital in this strategy.
In essence, quantitative trading methods use several technologies, databases, and mathematical concepts. Its foundations are logical reasoning and statistical data analysis drawn from huge data sets, and its mathematical accuracy is precisely what has fueled its appeal as one of the most effective trading strategies in the financial sector.
Two common variables that traders might incorporate into mathematical models are price and volume. Many traders create tools to track public sentiment toward specific assets or industries. Some traders also use alternative or public datasets to discover present and potential trends, ensuring that the mathematical model they created is adequate and advanced.
For instance, traders may see that major price changes are swiftly followed by volume surges on Apple stock. They will then develop a program for this trend that analyzes Apple's market history. If the model discovers that the pattern has caused a move to over 95% in the past, it will forecast a 95% probability of similar patterns occurring in the future.
The world of investing can be quite tribal, with each group asserting the superiority of their particular approach when compared with other approaches. Quants, for example, are pure mathematicians and don't simply rely on their knowledge of the financial markets. Quant traders frequently look at price and volume. However, their strategy can also take into account any other variable that can be reduced to a numerical value. For instance, some traders create tools to track investor sentiment on social media.
Quant traders can develop and inform their statistical models using many freely accessible databases. To find trends outside of conventional financial sources such as fundamentals, they also explore alternative datasets. In addition to developing their own, quant traders often modify an existing strategy with a high success rate.
They create automated software that is designed with mathematical models, which enable them to recognize patterns in historical data so they can make informed trading decisions. Ultimately, the trading concept they choose for a program is determined by their preferences and the scope of their research. As such, quant trading is leveraged by big financial institutions as well as individual traders for a variety of trading approaches, including arbitrage, day trading, and algorithmic trading, among others.
Strictly in terms of job prospects, most companies looking to hire quants prefer candidates with degrees in mathematics, engineering, or financial modeling. Additionally, they will need to have experience building automated systems and mining data. Quant traders must be highly skilled in computer programming and they must also be capable of working with data feeds and application programming interfaces (APIs). C++, Java, and Python are some of the coding languages with which the majority of quants are familiar.
Quantitative trading offers advantages and disadvantages, just like all trading systems. The advantages include not having to manually monitor data and analysis when trading stocks since quant systems are created to be automated or semi-automated. As a result, the amount of data that traders must evaluate to make trading decisions is more manageable in a systematic way.
Additionally, traders can evaluate a wide range of markets using theoretically infinite data points. When analyzing a market, a typical trader will normally focus on just a few variables and examine only those that are familiar to them. Mathematical strategies can be used by quant traders to overcome these limitations.
Furthermore, contrary to human traders, these automated systems do not let emotions such as fear or greed affect investment choices. By removing emotions from decision-making and execution processes, traders can reduce some of the biases that can frequently impact their trading.
Quantitative trading does, however, carry some considerable risks and many quant strategies have been known to fail. As with all things created by humans, they’re only as good as their creators. Since financial markets are constantly changing, often in unpredictable or unexpected ways, a strategy that generates profits one day can lose money the next.
For quantitative trading to be implemented successfully in unstable markets, the planned trading strategy must be sufficiently flexible. Quant traders often only develop profitable short-term quant trading models for this reason. However, due to volatile crypto market conditions, the results from these short-term trading strategies are not always reliable, which can and often does lead to losses.
Quantitative trading also requires specialized knowledge of mathematics and coding, which, quite frankly, many traders simply do not have. Consequently, the effectiveness of a quantitative system reflects the knowledge and experience of its developer. High levels of mathematical expertise, coding prowess, and market knowledge are all part of the quant trading game, creating a particularly high threshold for entry.
Quantitative trading and algorithmic trading both automate their strategies, but they are fundamentally different from one another.
Algorithmic traders, often known as “algo traders,” use automated systems to evaluate chart patterns as well as open and close positions on their behalf. Quant traders, on the other hand, employ statistical tools to find, but not always execute, opportunities.
Usually, algorithmic trading is seen as a subset of quantitative trading. Although they overlap, these are two distinct strategies that should not be confused. Here are some key differences between the two:
There's no doubt that quantitative trading is a complex field that requires extensive training and many years of experience to understand and implement effectively. However, the world of quantitative trading is also an exciting one, with new developments and applications emerging at a rapid pace.
As the future of trading (to say nothing of the future of crypto and the ongoing story of DeFi) increasingly relies on automation, expect to see exciting developments and increased interest in quant-based approaches to investing.