Generous salary, generous bonus and creativity at work make quantitative trading an attractive career choice. Quantitative traders, or quants for short, use mathematical models to identify trading opportunities and buy and sell securities. The influx of candidates from academia, software development, and engineering has made this field very competitive. In this article, we will learn about the job of a quantitative analyst and the skills and education required.
- Quantitative traders use strategies based on quantitative analysis (mathematical calculations and numerical operations) to find trading possibilities that may involve hundreds of thousands of securities.
- An aspiring quantitative trader needs to be very skilled and interested in all mathematical things-if you are not living, breathing and sleeping numbers, then this field is not for you.
- A bachelor’s degree in mathematics, a master’s degree in financial engineering or quantitative financial modeling, or an MBA degree are all helpful for employment; some analysts will also receive a doctorate. In these or similar areas.
- Lack of advanced degrees, candidates should have at least on-the-job training and experience of data analysts; they must have experience in data mining, research, analysis, and automated trading systems.
- Traders also need soft skills, such as the ability to thrive under pressure, stay focused for a long time, endure intense and aggressive environments, and the ability to encounter setbacks and failures in the pursuit of success.
What exactly are quantitative traders doing?
The word “quant” is derived from quantitative, which essentially refers to the processing of numbers. Advances in computer-aided algorithmic trading and high-frequency trading mean that there is a huge amount of data to analyze. Quants mines and studies available price and quotation data, identifies profitable trading opportunities, formulates related trading strategies, and uses self-developed computer programs to take advantage of opportunities at lightning speed. Essentially, quantitative traders need a balanced combination of in-depth mathematical knowledge, practical trading experience, and computer skills.
Quantitative traders can work for investment companies, hedge funds, and banks, or they can be proprietary traders who use their own funds to invest.
An aspiring quantitative analyst should at least have a background in finance, mathematics, and computer programming. In addition, quantitative analysts should have the following skills and background:
- Numbers, numbers and numbers: Quantitative traders must be very good at mathematics and quantitative analysis. For example, if terms such as conditional probability, skewness, kurtosis, and VaR sound unfamiliar, then you may not be ready to become a quantitative analyst. In-depth mathematical knowledge is essential for studying data, testing results, and implementing established trading strategies. Determined trading strategies, implemented algorithms and trading execution methods should be as foolproof as possible. In today’s lightning-fast trading world, complex digital computing trading algorithms occupy most of the market share. Even a small mistake in the basic concept of a quantitative trader can lead to huge trading losses.
- Education and training: It is often difficult for new university graduates to find a job as a quantitative trader. A more typical career path is to start as a data research analyst and become a quantitative analyst a few years later. Education such as a master’s degree in financial engineering, a diploma in quantitative financial modeling, or a quantitative stream elective course during the regular MBA period may give candidates a good start. These courses cover the theoretical concepts and practical introduction of the tools needed for quantitative trading.
- Trading concept: Quants needs to discover and design its own unique trading strategies and models from the ground up, and customize the established models. Quantitative trading candidates should have a detailed understanding of popular trading strategies and their respective advantages and disadvantages.
- Programming skills: Quantitative traders must be familiar with data mining, research, analysis and automated trading systems. They often participate in high-frequency trading or algorithmic trading. Must have a good understanding of at least one programming language, and the more programs the candidate knows, the better. C++, Java, Python, and Perl are some commonly used programming languages. Familiarity with tools such as MATLAB and spreadsheets, as well as concepts such as big data and data structures is a plus.
- Computer use: Quantitative traders implement their own algorithms on real-time data including prices and quotes. They need to be familiar with any related systems, such as Bloomberg terminals that provide data feeds and content. They should also be familiar with charting and analysis software applications and spreadsheets, and be able to use the broker’s trading platform to place orders.
According to the latest statistics from Indeed.com, the average salary of traders is quantified.
In addition to the above technical skills, quantitative traders also need soft skills. Those who work in investment banks or hedge funds may sometimes need to submit their developed concepts to fund managers and executives for approval. Quants usually do not interact with customers, they often work with professional teams, so general communication skills may be sufficient. In addition, quantitative traders should have the following soft skills:
- Trader’s temperament: Not everyone can think and act like a trader. Successful traders are always looking for innovative trading ideas, able to adapt to changing market conditions, thrive under pressure, and accept long hours of work. Employers will thoroughly evaluate these characteristics of candidates. Some people even conduct psychological tests.
- Risk-taking ability: Today’s trading world is not suitable for the faint-hearted. Due to reliance on computer margin and leveraged trading, losses may exceed the trader’s available capital. Aspiring quantitative analysts must understand risk management and risk mitigation techniques. A successful quantitative trader may make 10 trades, face a loss in the first 8 trades, and only make a profit in the last two trades.
- Accept failure: Quantitative analysts are always looking for innovative trading ideas. Even if an idea seems foolproof, dynamic market conditions can bankrupt it. Many aspiring quantitative traders fail because they are stubborn on an idea and continue to work hard to make it work in a harsh market environment. They may find it difficult to accept failure and are therefore unwilling to give up their concept. On the other hand, successful quantitative analysts follow a dynamic separation method. Once they find challenges in existing models and concepts, they will quickly turn to other models and concepts.
- Innovative thinking: The trading world is highly dynamic, and there is no concept that can make money in the long run. Algorithms are opposed to algorithms. Each algorithm tries to surpass other algorithms. Only algorithms with better and unique strategies can survive. Quantitative analysts need to constantly look for new and innovative trading concepts to seize profit opportunities that may soon disappear. This is a never-ending cycle.
Quantitative trading requires advanced skills in finance, mathematics, and computer programming. High salaries and soaring bonuses attract many candidates, so getting your first job can be a challenge. In addition, continued success requires constant innovation, adaptation to risks and long hours of work.