Quantitative trading (also called quant trading) identifies and capitalizes on trading opportunities using algorithms and programs. Quantitative trading also involves research work on historical data to find profit opportunities. Due to the need for advanced tech skills in this evolving field, traders involved in such quantitative analysis and related trading activities are increasingly MBAs and Ph.D. holders in finance, computer science, and even neural networks. Employers include the trading desks of global investment banks, hedge funds, and arbitrage trading firms, in addition to small-sized local trading firms.
Key Takeaways
- Quantitative trading uses computer algorithms based on mathematical models to identify trading opportunities.
- Quants are employed by global investment banks, hedge funds, and local trading firms.
- Quants need advanced skills in finance, programming, data analysis, and risk management.
- A quant trader’s career path often requires a master’s or Ph.D. in a quantitative field.
- Quant salaries can exceed hundreds of thousands of dollars annually, making it a lucrative yet competitive career.
The Evolution of Quantitative Trading
Earlier, markets were physical and floor-based, where traders and market makers interacted, agreed on a security, price, and quantity, and settled the trade on paper. Among other qualifications, a loud clear voice and a good strong build were considered an asset for trading job aspirants because these made them impressive on the trading floor.
As markets became digital with global reach and expansion, the floors emptied out. Traders who had little to offer but a loud voice began to vanish, making way for the computer-savvy techies. Electronic markets offered vast expansion, loads of trading data, new assets, and securities, and there came the opportunity for data mining, research, analysis, and automated trading systems.
In the last two decades, MBAs and Ph.D. holders in finance, computer science, and even neural networks are taking traders’ jobs at reputed trading institutions.
Fast Fact
In the United States, quant trading positions are most prevalent in big financial hubs such as New York and Chicago, and areas where hedge funds tend to cluster, such as Boston, Massachusetts, and Stamford, Connecticut. Globally, quant traders may find employment opportunities in major financial hubs such as London, Hong Kong, Singapore, Tokyo, and Sydney, among other regional financial centers.
What It Takes to Be a Quant Trader
A quant trader may work for a small-, mid- or large-size trading firm for a handsome salary with high bonus payouts, based on the generated trading profits. Employers include the trading desks of global investment banks, hedge funds, or arbitrage trading firms, in addition to small-sized local trading firms.
Today, getting a trader’s job at established firms often requires a specialized master’s degree in a quantitative stream (MBA, Ph.D., CFA), unless one is a seasoned trader with proven work experience. Other less experienced younger quants can start at small-sized firms, or start as junior analysts and work their way up over a long period, although it is a fiercely competitive field.
In addition to having a background in finance, mathematics, and computer programming, quants should have the following skills and background:
- Expertise with computer usage
- Hands-on knowledge of one or more programming languages
- Familiarity with building and customizing trading systems and automation possibilities
- Familiarity with data feeds and usage
- Data mining, research, and analytical abilities
- Risk-taking abilities and trader’s temperament
- An innovative mindset to continuously discover new strategies and opportunities
Essential Tools for Quant Traders
Quants implement their own algorithms on real-time data containing prices and quotes. They need to be familiar with any associated systems that provide data feeds and content. Quant traders typically have access to these tools:
- Systems for accessing market data, like the Bloomberg data terminal, having the necessary technical and quantitative analysis tools available that fit into their stream of trading (like Bollinger bands, charts, etc.)
- Computer systems with programming language compatibility: Perl, C++, Java, Python are the common ones among the trader community
- Historical and/or real-time data availability, to backtest their identified strategies
- Automated access to brokerage/trading accounts usually through Direct Market Access
Daily Responsibilities of a Quant Trader
Using the above, a quant trader typical performs the following activities:
- Identify a trading strategy: It can be based on simple price-volume numbers, or on a complex mathematical model
- Develop and build the working algorithm/program/system based on the trading strategy
- Backtest the prototype to verify practical implementation, and required customization: Once identified, it is very important to backtest the strategy on historical/live test data to assess practical feasibility. Further changes are incorporated as needed
- Include risk management criteria: perform scenario analysis, implement stop-loss mechanisms, capital allocation limits, etc. to make the system as protective as possible
- Implement the system on live feeds for trade execution in the open market: Let the quantitative setup go live, and continued observation on profit-making potential. Further customization for identified enhancements or failures, if any
- Continued efforts on identifying new strategies
- Additionally, works in the background within the research department, and provides trading tips to the traders in the trading department
A quant trader’s job is a continuous and rigorous process with long working hours. Present-day trading seems to have become a computer vs. computer market, where a human trader’s contributions are limited to building computer programs smart enough to trade better than those developed by counterparts. The more automation built in the overall market, the more efficiency is needed as profit opportunities thin out with every passing day.
What Are the Steps to Become a Quant?
Most firms require at least a master’s degree, or preferably a Ph.D., in a quantitative subject (mathematics, economics, finance, or statistics). Master’s degrees in financial engineering or computational finance may also be effective entry points for careers as a quant trader.
If you hold an MBA degree, you will likely also need a very strong mathematical or computational skill set, in addition to some solid experience in the real world in order to be hired as a quant trader.
Alongside their educational requirements, quant traders must also have advanced software skills. C++ is typically used for high-frequency trading applications, and offline statistical analysis would be performed in MATLAB, SAS, S-PLUS, or a similar package. Pricing knowledge may also be embedded in trading tools created with Java, .NET or VBA, and are often integrated with Excel.
What Area of Statistics Is Most Useful for Quants?
Certain aspects of statistics are the backbone of quantitative trading, including regression theory and time-series analysis. Electronic engineering techniques such as Fourier analysis and wavelet analysis are also utilized in quantitative analysis. Most of the statistics concepts you will need to understand to work in quant trading is so advanced that it is not taught at an undergraduate level. For this reason, it is important to pursue advanced study in statistics (namely Ph.D. coursework).
What Programming Languages Do Quants Need to Know?
C++ and Java are the main programming languages used in trading systems. Quants often need to code in C++, in addition to knowing how to use tools like R, MatLab, Stata, Python, and to a lesser extent Perl.
The Bottom Line
Quant trading involves high frequency, algorithmic, arbitrage, and automated trading. A quant trader’s job and associated perks appear very lucrative, but it requires multifaceted skills and a combination of programming, data analysis, and financial expertise. Quant traders usually have a moderate success rate, and many diversify or move to other areas after a few years due to burnout. Apart from all the necessary infrastructure, skills, and knowledge, one needs to have the right mindset to be successful as a quant.
