20 Tips for Becoming a Quant Trader
20 Tips for Becoming a Quant Trader
As a result of lucrative salaries, hefty bonuses, and creativity on the job, quantitative trading has become a very attractive career option in recent times as covered over at runrex.com. Through the following 20 tips, this article will look to explain what quantitative trading is, what quant traders do, and the skills and education one requires to become a quant trader.
What is quantitative trading?
As is captured in discussions on the same over at guttulus.com, quantitative trading consists largely of trading strategies that draw quantitative analysis. They depend on mathematical calculations and the crunching of numbers to recognize trading opportunities. The most common data inputs for quantitative analysis as the primary inputs to mathematical models are price and volume.
Techniques in quantitative trading
As outlined over at runrex.com. the techniques in quantitative trading include:
High-frequency trading
Algorithmic trading
Statistical arbitrage
These are rapid-fire methods, usually with short-term investment horizons. A large number of quantitative traders are knowledgeable about quantitative tools, which include moving averages and oscillators.
Usage of quantitative trading
Quantitative trading is something that financial institutions and hedge funds generally use, which is why, according to guttulus.com, the transactions are typically quite large. They also might involve the purchase and sale of a wide variety of shares and other securities. Having said that, quantitative trading is a process that is slowly experiencing more usage by individual traders.
What does a quant trader do?
Quantitative (quant) traders take advantage of lots of factors for the sake of their trade. These include mathematics, modern technology, as well as the basic availability of comprehensive databases. Armed with these components, they are capable of making logical trading decisions.
The workflow of a quant trader
Quantitative traders employ the use of a trading technique and construct a model of it using mathematics. They then develop a computer program that administers the model to market data from the past. This model will then go through backtesting and inevitable enhancement. Should satisfactory results be achieved, then the system will be implemented in real-time markets with real capital.
The duties of a quant trader
Quantitative traders have an array of roles in the trading process as covered over at runrex.com:
They mine and research the accessible price and quote data
Identify lucrative trading opportunities
Cultivate relevant trading strategies
Technical skills required to be a quant trader
Any aspiring quant trader should have, at minimum, a background in finance, mathematics, and computer programming. Also, they should have the following technical skills:
Numbers
Quant traders have to be exceptionally proficient with mathematics and quantitative analysis. For example, if terms like conditional probability, kurtosis, skewness, and VaR don’t sound familiar, then you are probably not ready to be a quant trader according to guttulus.com. Having an in-depth knowledge of math is a must for researching data, testing the results, and implementing identified trade strategies, particularly since even a small mistake in the underlying concept on the part of the quant trader can lead to massive trading losses.
Education and training
As revealed over at runrex.com, it is usually difficult for new college graduates to land a job as a quant trader. A more typical career path is starting out as a data research analyst and then becoming a quant trader after a few years. education like a master’s degree in financial engineering, a diploma in quantitative financial modeling, or electives in quantitative streams during the regular MBA may give candidates a head start as these courses cover the theoretical concepts and practical introduction to tools required for quant trading.
Trading concepts
Quant traders are expected to discover and design their own unique trading strategies and models from scratch as well as customize established models. A quant trading candidate should, therefore, have a detailed knowledge of popular trading strategies as well as each one’s respective pros and cons.
Programming skills
Quant traders must be familiar with data mining, research, analysis, and automated trading systems as they are often involved in high-frequency trading or algorithmic trading. This means that a good understanding of at least one programming language is a must, and the more programming languages the candidate knows, the better. C++, Java, Python, and Perl are a few commonly used programming languages. Familiarity with tools like MATLAB, and Spreadsheets, as well as concepts like big data, and data structuring, is a plus.
Computer usage
Quant traders implement their own algorithms on real-time data containing prices and quotes. They, therefore, need to be familiar with any associated systems, like a Bloomberg terminal, which provides data feeds and content as covered over at guttulus.com. They should also be comfortable with charting and analysis software applications and spreadsheets, and be able to use broker trading platforms to place orders.
Soft skills required to be a quant trader
Beyond the above-discussed technical skills, quant traders also need soft skills. Those employed at investment banks or hedge funds may occasionally need to present their developed concepts to fund managers and higher-ups for approval. Quant traders don’t typically interact with clients and they often work with a specialized team, which means average communication skills may suffice. Also, a quant trader should have the following soft skills:
A trader’s temperament
As revealed over at runrex.com, not everyone can think and act like a trader. Successful traders are always looking for innovative trading ideas, can adapt to changing market conditions, thrive under stress, and accept long working hours. Employers thoroughly assess candidates for these traits, with some even giving psychometric tests.
Risk-taking abilities
As a result of margin and leveraged trading with dependency on computers, losses can reach amounts higher than a trader’s available capital. Aspiring quant traders must, therefore, understand the risk management and risk mitigation techniques as articulated over at guttulus.com. A successful quant trader may make 10 trades, face losses on the first eight, and profit only with the last two trades.
Comfortable with failure
A quant trader keeps looking for innovative trading ideas, and even if an idea seems foolproof, dynamic market conditions may render it a bust. This is why many aspiring quant traders fail as they get stuck on an idea and keep trying to make it work despite hostile market conditions. They may find it difficult to accept failure and are, therefore, unwilling to let go of their concept. Successful quant traders, on the other hand, follow a dynamic detachment approach and quickly move on to other models and concepts as soon as they find challenges in existing ones.
An innovative mindset
The trading world is highly dynamic, and no concept can make money for long. Algorithms are pitted against each other, and with each trying to outperform the others, only the one with better and unique strategies can survive. Therefore, a quant trader needs to keep looking for new innovative trading ideas to seize profitable opportunities that may vanish quickly.
These are the skills you will need, at minimum, to become a quant trader.
Components of a standard quantitative trading system
To become a quant trader, you should also know the major components of a standard quantitative system, covered in the following 4 tips:
Strategy identification
As covered over at runrex.com, 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 it fits into a portfolio of other running strategies.
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, with more on this over at guttulus.com.
Execution system
From discussions over at runrex.com, this refers to connecting to a brokerage, automating the trading, and diminishing the costs of transactions. An execution system is how the broker sends and executes trades that the strategy generates. Trade execution can either be semi-automated or fully automated.
Risk management
This means optimal capital distribution, “bet size”/Kelly criterion, and the psychology concerning trading. “Risk” consists of all the previous biases as well as technical risks such as servers at the exchange suddenly developing a hard disk malfunction. Brokerage risk is an additional inclusion, such as the broker becoming bankrupt. Risk management covers almost everything that could potentially interfere with the trading implementation.
You will need to be familiar with the components of a standard quantitative trading system if you want to be a quant trader.
The main drawback of being a quant trader
Finally, we have to mention the main drawback of being a quant trader as being aware of it will help you decide if this path is for you. Drowning out emotion is probably one of the most prevalent issues when it comes to quant trading. Whether it is anxiety or greed, emotions in trading only serve to suppress rational thinking, which in turn tends to lead to losses.
This article will have, hopefully, provided you with all the information you need to become a quant trader, with more on this topic to be found over at runrex.com and guttulus.com.
