This article will explain what quantitative trading is, what quant traders do, and the skills and education one needs to become a quant trader.
What is it?
‘Quantitative trading’ consists largely of trading strategies that draw from quantitative analysis. They depend on mathematical calculations and number crunching in order to recognize trading opportunities. The most common data inputs for quantitative analysis as the primary inputs to mathematical models are price and volume.
The techniques in quantitative trading include high-frequency trading, algorithmic trading, and statistical arbitrage. These are rapid-fire methods and usually have investment horizons that are short-term. A large number of quantitative traders are knowledgeable about quantitative tools, which include moving averages and oscillators.
Quantitative trading is something that financial institutions and hedge funds generally use. Because of this, the transactions are typically quite large. They also might involve the purchase and sale of a wide variety of shares and other securities. With that said, quantitative trading is a process that is gradually experiencing more frequent usage by individual investors.
Understanding the trade
Quantitative traders take advantage of a lot of factors for the sake of their trade. These include mathematics, modern technology, and the basic availability of comprehensive databases. With these components, they are capable of making logical trading decisions.
Quantitative traders employ the use of a trading technique and construct a model of it utilizing mathematics. Then, they develop a computer program that administers the model to market data from the past. The model will then go through backtesting and inevitable enhancement. Should there be an achievement of satisfactory results, then the system will experience implementation in real-time markets with real capital.
An explanation of how quantitative trading models operate is best done with an analogy. Let’s say in a weather report, a meteorologist forecasts an 80% chance of rain while the sun is out. How exactly does the meteorologist come to this seemingly absurd and implausible conclusion? The answer is that they collect and analyze climate data from sensors all over the area.
A computerized quantitative analysis exposes specific types of patterns within the data. When there is a comparison between these patterns and the same patterns that the historical climate data reveals (backtesting) – and 80 out of 100 times the result is rain – then the meteorologist can draw a solid conclusion. This is where the 80% forecast comes from.
Quantitative traders use the exact same process in regards to the financial market in order to conduct trading decisions.
About the traders themselves
Quantitative traders, or “quant traders”, have an array of jobs that pertain to the trading process:
- They mine and research the accessible price and quote data
- Identify lucrative trading opportunities
- Cultivate relevant trading strategies.
Moreover, quant traders benefit from opportunities presented to them via exceptionally fast computing speed and by employing the use of self-developed computer programs. A quant trader essentially requires an equitable mixture of factors. These include thorough knowledge in mathematics, reasonable trading exposure, and an appropriate level of technological competency.
An aspiring quant acquires the proper tools and skills before dabbling in quantitative trading. At the very least, they should have a solid background in finance, mathematics, and computer programming. They should also possess the following skills and background.
1 – Mathematics
Quant traders have to be exceptionally proficient with mathematics and quantitative analysis. For instance, if terms like conditional probability, skewness, kurtosis, and VaR do not sound familiar to you, then you might not be ready to try your hand at quantitative analysis. Comprehensive knowledge of math is crucial for researching data, results testing, and implementing identified trade strategies. These trade strategies implement algorithms and techniques in trade execution have to be infallible.
Nowadays, in the fast trading world, intricate number-crunching trading algorithms take up most of the market share. A small mistake in the underlying concept thanks to the quant trader could have enough clout to result in a massive trading loss.
2 – Education
Most of the time, it’s difficult for new college graduates to acquire a job as a quant trader. The more natural progression would be to start out as a data research analyst. After a few years, you are more likely to be able to get a position as a quant trader. A good start would be a master’s in financial engineering or even a diploma focusing on quantitative financial modeling. Alternatively, one might choose electives in quantitative streams during the regular MBA. These courses primarily cover the theoretical concepts and and an introduction to tools that are requirements for quant trading.
3 – Trading concepts
There is an expectation for quants to discover and create their own trading strategies and models from scratch. Furthermore, they often will personally modify established models. The conventional quant trading candidate needs a thorough knowledge of popular trading strategies. What’s more, they need knowledge of each one’s corresponding advantages and disadvantages.
4 – Skills in programming
Quant traders need to have some familiarity with data mining, research, analysis, and automated trading systems. They often have involvement with either high-frequency trading or algorithmic trading. A solid understanding of at least one programming language is also an important factor. The more programs the candidate understands, the better chances they have. A few of the most common programming languages that are useful in this trade are C++, Java, Python, and Perl. An understanding of tools like spreadsheets and MATLAB, plus concepts like big data and data structuring, is certainly a bonus.
5 – Use of a computer
Quants base their own algorithms on real-time data that consists of price and quotes. They have to be familiar with any systems that correlate with it. An example of this is a Bloomberg terminal, which supplies plenty of data feeds and content. Furthermore, they should also be comfortable with charting and analysis software applications and spreadsheets. Also important is having the capabilities and comfort level for using broker trading platforms as a means to place orders.
An investment bank or hedge fund employee will occasionally need to present their developed concepts to fund managers and higher-ups. They do this in the hopes of gaining their approval. Quants don’t usually interact with the clientele and they frequently work with a specialized team. With this in mind, average-level communication skills may be adequate. Below are some additional soft skills that a quant trader should have.
1 – The temperament of a trader
It is not possible for everyone to have the mind of a trader. Traders who experience great success are always seeking out innovative trading ideas and are capable of adapting to market conditions. Moreover, they thrive under moments of stress and accept long working hours. Employers meticulously evaluate candidates based on these traits, with some even giving out psychometric tests.
2 – Risk-taking capabilities
The trading world nowadays is not necessarily for the faint of heart. Losses can potentially exceed a trader’s available capital, courtesy of margin and leverage trading with reliance on high speed computers. Aspiring quants have to understand techniques pertaining to risk management and risk mitigation. A quant who ends up being successful may make 10 trades. Moreover, they may experience losses on the first eight trades and only profit with the last two.
3 – Is comfortable with failure
A quantitative trader will constantly be on the lookout for innovative trading ideas. Even if an idea appears to be foolproof, the dynamic conditions of the market may end up rendering it a complete bust. A lot of quant traders will fail due to getting stuck on an idea and persisting in their efforts to make it work. This is in spite of the hostile market conditions. They could find it extremely hard to accept failure and are therefore reluctant to let their concept go.
Conversely, the more successful quants follow a dynamic detachment approach. They will promptly move on to other models and concepts once they uncover challenges in ones that already exist.
4 – An innovative plot
Taking all of what has been said into account, the trading world is a highly dynamic environment. It is not exactly possible for any concept to continue to make money for long. Algorithms constantly go up against each other and they are all trying to outperform one another. The one that consists of comparatively better and unique strategies has a better chance of survival. A quantitative trader has to keep searching for new trading ideas in order to seize lucrative opportunities that may quickly vanish. On the whole, the cycle never ends.
Four major components
A standard quantitative trading system consists of four dominant components:
- Strategy Identification – This means finding a strategy, capitalizing on an edge, and ultimately deciding on trading frequency. This research process embodies discovering a strategy and checking if the strategy fits into a portfolio of other running strategies. Moreover, it encompasses acquiring necessary data to test the strategy and attempting to optimize the strategy. This is for higher returns and/or lower risk.
- Strategy Backtesting – This means obtaining data, examining the performance of the strategy, and removing any prejudices. Backtesting provides evidence that the strategy via the previous process is profitable upon application to historical and out-of-sample data. This establishes the expectation of how the strategy performance in the “real world.” However, this process is NOT a guarantee of success for the quantitative trader for multiple reasons. It is arguably the most subtle section of quantitative trading.
- Execution System – This refers to connecting to a brokerage, automating the trading, and diminishing the costs of transactions. An execution system is the means by which the broker sends and executes the trades that the strategy generates. Trade execution can be either semi-automated or fully-automated. Regardless, the execution mechanism is capable of being manual, semi-manual (i.e. “one-click”) or fully-automated.
- Risk Management – This means optimal capital distribution, “bet size”/Kelly criterion, and the psychology concerning trading. “Risk” consists of all of the previous biases. It also includes technology risk, such as servers at the exchange suddenly developing a hard disk malfunction. An additional inclusion is brokerage risk, such as the broker becoming bankrupt. Simply put, it covers almost everything that could potentially interfere with the trading implementation, of which there are numerous sources
The pros and cons of being a quantitative trader
The main objective of trading is to compute the optimum probability of lucrative trade execution. The average trader can effectively oversee, analyze, and ultimately make trading decisions on a certain number of securities. That is to say, they can do so prior to the amount of incoming data that overpowers the decision-making process. Utilizing quantitative trading techniques clarifies this limit by employing computers for automation. It will automate the monitoring, analyzing, and the decision pertaining to trading.
In essence, drowning out emotion is probably one of the most prevalent issues when it comes to trading. Whether it is anxiety or greed, emotions in trading only serve to suppress rational thinking. This, in turn, has a tendency to lead to losses. Obviously, computers and mathematics do not have emotions, so quantitative trading sets out to eliminate this particular problem.
A quantitative trader may find that this type of trading, as with most methods of trading, has its fair share of drawbacks. Financial markets are among the most dynamic entities that could ever exist. The crypto market in particular is noted for discrepancies in data as well high, often volatility-driven emotional waves. For these reasons, quantitative trading models have to be dynamic in order to be regularly successful. A majority of quantitative traders construct models that are briefly profitable for the market condition they were developed for. However, they will often ultimately fail upon any changes in the market conditions.