Making a Career in Algorithmic Trading: Roadmap, Jobs, Skills and more

If you've ever been intrigued by the dynamic world of Algorithmic Trading and aspired to turn this into a fulfilling career, you've landed in the right place. Here, we highlight some important factors for job seekers in the domains of High Frequency Trading, Automated Trading, Quantitative Trading or simply Quant Jobs.

Whether you're a finance graduate eager to dive into the realm of quantitative analysis or an experienced trader looking to evolve your strategies, this blog is your go-to guide.

We'll unravel the essentials, share insights, and equip you with the knowledge needed to thrive in your quantitative trading career (HFT, Automated trading, quantitative trading), all with the help of our top algorithmic traders.

This blog covers:

• Academic requirement for algorithmic trading
• Skills required for a career in algo trading
• Jobs and a career in algorithmic trading
• Who are quants?
• Types of quants
• Who employs quants?
• What do recruiters look for in a resume?
• Tips for Algo Trading job interviews
• Salaries for a career in algo trading
• Impact of ML and AI on your algorithmic trading career
• Resources to learn and upgrade your career in algo trading
• FAQs about a career in algorithmic trading

Academic requirement for algorithmic trading

Algorithmic trading involves using computer algorithms to execute trading strategies. The educational requirements for a career in algorithmic trading can vary, but generally, a strong educational background in quantitative fields is desirable.

Here are some common educational requirements and recommended areas of study:

Degree

Most algorithmic traders have at least a degree in a relevant field. Common majors include:

• Finance: Provides a strong understanding of financial markets and instruments.
• Any maths-oriented major like Mathematics or Statistics or Physics: Develops quantitative skills necessary for analysing market data and developing trading strategies.
• Engineering: Helps in developing a problem-solving mindset and learning to program

Quantitative Finance

Some institutions offer specialised programs or degrees in quantitative finance. These programs often cover financial mathematics, statistical methods, and programming, which are essential for algorithmic trading.

Also, learning the risk management principles and compliance requirements in financial markets is essential for developing robust trading strategies.

Master's or PhD (optional)

While not always required, having an advanced degree, such as a Master's or PhD, can be beneficial. It can provide a deeper understanding of quantitative techniques, financial modelling, and statistical analysis.

But before you dive into the intricacies of building a career in the algorithmic trading domain, you must watch this video below in which our industry experts tell you everything that you need to know before getting into quant and algorithmic trading.

Skills required for a career in algo trading

A quant designs and implements mathematical models for the pricing of financial assets/securities, assessment of risk, or predicting market movements.

So, if you're eyeing a journey in Algorithmic Trading, there are some key skills you'll want in your toolbox. These aren't just important—they're the secret sauce to moving up the ladder in your Algorithmic Trading career. Ready to dive into the must-haves that will shape your path?

Then you can take a look at these skills below. Upgrade your approach with a sophisticated Algorithmic Trading Platform.

Analytical skills

Having an analytical bent of mind is a very important quality for any quant trader/developer, and is valued in an interview.

For example, an interviewing candidate may be given a huge data set and asked to find patterns from the data. Candidates get evaluated on how they approach any given problem and their ability to justify their solutions objectively.

Mathematical skills

As the core of algorithmic trading revolves around algorithms, data, and programming, having reasonable programming skills and a basic understanding of statistics and calculus is important for any job seeker in algo/HFT trading.

For example, if a candidate is applying to a firm that deploys low latency strategies, then an expert level of programming would be expected from such a candidate.

Programming skills

Knowledge of a programming language is an added advantage as it enables you to automate many repetitive tasks.

Python is good for conceptualising, backtesting of strategies, and has many libraries for validation and visualisation of results. It can also be used by firms for strategies that are not dependent on low latency. On the other hand, C++ is usually used by firms that trade very low latency strategies.

Data Analysis and Machine Learning skills

Knowledge of data analysis techniques and machine learning can be valuable. Algorithms often incorporate these methods for decision-making and pattern recognition.

Backtesting skills

While devising any strategy, it is important to understand the risks and rewards associated with that strategy in order to determine whether it has an edge in the markets. This is done by backtesting a strategy.

The frequency of trading, instruments traded, leverage, etc. also needs to be taken into consideration before going live with the strategy in the markets.

A single strategy doesn’t guarantee profits year-after-year. One has to formulate and overhaul strategies on a regular basis  to remain profitable in the markets.

To understand various algorithmic trading strategies, you can learn about the algorithmic trading strategies, paradigms and modelling ideas.

The skill of understanding the financial markets

A career in quantitative trading involves dealing with large financial datasets, pertaining to different instruments like stocks, derivatives, forex etc. Hence, even if you are coming from a non-finance technology background, as a developer in a quant firm, you need to have a fair understanding of the financial markets.

Trading firms usually make their new recruits spend time on different desks (e.g. quant desk, trading, risk management desk) to gain an understanding of the markets.

Besides the skills mentioned above, one could also develop the following skills:

Exploring the intricacies of algo trading? If the idea of crafting your own trading strategy from scratch and rigorously backtesting it intrigues you, you might find value in our Learning track that consists of 8 free courses to begin with Algo Trading.

To get an idea of what the course is really about, here’s a comprehensive 3 part video series of "Algo Trading Course".

Part 1 - Learn Algorithmic Trading | Beginners Guide

First part introduces you to algo trading, the industry landscape, pros and cons, building an algo trading python strategy, the benefits of a quant approach, different types of data, and more.

Part 2 - Algo Trading Strategies | Create and Backtest Trading strategy

This part covers a wide range of topics including trading idea generation, alpha seeking, universe selection, entry and exit rules, coding logic blocks, and backtesting.

Part 3 - Python Trading Bot | Python Quantitative Trading

In this 3rd and final part of the video series, "Algo Trading Course" explores how Python trading bots can be used to backtest a trading strategy on the research platform such as Blueshift.

Besides these, one must be equipped with domain knowledge.

Recommended reads:

• Top skills to prepare for a quant interview
• Quant Interview Questions Preparation

Watch this informative video covering the necessary skills needed for landing a job as an algorithmic trader.

Jobs and career in algorithmic trading

The last couple of decades have seen an exponential growth in the algorithmic trading market and it continues to grow at a significant pace.⁽¹⁾ ⁽²⁾

According to the report published by Research and Markets, the global market for Algorithmic Trading estimated at US$14.7 Billion in the year 2020, is expected to garner US$31.1 Billion by 2027, growing at a CAGR of 11.3% over the period 2020 to 2027.

Today, algorithmic trading and high-frequency trading are recognized by companies and exchanges all over the world and have become the most common way of trading in the developed markets. Be it trading in stocks, derivatives, Forex or commodities, trading firms worldwide adopted algorithmic trading in a big way.

Big banks, hedge funds, and other trading firms are now hiring the best talent to stay ahead of their competition and to gain big bucks leading to a surge in algorithmic trading jobs.

Students, engineering graduates, developers and even old-school traders are aspiring to build a career in algorithmic trading.

Developers from non-technical backgrounds (like telecom industries or verticals that focus heavily on programming) are in demand.

Why? They’ve spent years within the same industry and they have enough relevant knowledge about the basics and the nuances of programming which are essential to trading.

Additionally, you can watch this video for a more in-depth understanding of a career in algo trading and the skills required for the same.

How secure is your job in the algorithmic trading domain?

While AI is transforming the financial landscape, traders need not fear redundancy. Yes, some repetitive tasks may become automated, but exciting new opportunities are also emerging which means there is a wide scope for a career in quantitative trading.

Here's how:

1. Enhanced Trading Strategies:

• AI-powered tools can analyse vast amounts of data, identifying trends and generating trade signals humans might miss.
• New job opportunity: Quantitative Trader: Use your programming and data analysis skills to develop and test these AI-driven strategies.

2. Algorithmic Trading:

• High-frequency trading relies heavily on algorithms for faster executions.
• New job opportunity: Algorithmic Trading Specialist: Design, implement, and monitor these trading algorithms, ensuring optimal performance.

3. Beyond Automation:

• AI excels at analysing data, but trading still requires human judgement and intuition.
• Evolving job role: AI-Augmented Trader: Combine your trading expertise with AI insights to make informed decisions and stay ahead of the market.

4. Upskilling is Key:

• Proficiency in programming languages, machine learning, and data analytics is becoming crucial for traders.
• Invest in learning these skills to stay relevant and unlock new career paths in the evolving financial landscape.

Remember:

• Job displacement doesn't equal job disappearance. New opportunities will arise as the industry adapts.
• Focus on developing complementary skills that AI cannot replicate, like critical thinking, creativity, and communication.

The future of trading is about collaboration, not competition, between humans and AI. Embrace the change, upskill yourself, and you'll be well-positioned to thrive in this exciting new era.

You can take a look at this informative video that uncovers the vast career opportunities in algo trading.

Who are quants?

"Quants," short for quantitative analysts, are professionals who use mathematical and statistical techniques to analyse financial markets, assess risk, and develop trading strategies. The quants play a crucial role in the financial industry, particularly in areas such as investment banking, asset management, hedge funds, and proprietary trading firms. Quants apply quantitative methods and computational tools to make data-driven decisions in the complex and dynamic world of finance.

Here are key aspects of quants and their roles:

• Quantitative Methods: Quants use mathematical and statistical techniques, applying computational tools to analyse financial markets and make data-driven decisions.
• Financial Modelling: They create mathematical models to represent financial instruments, market behaviours, and risk factors, using these models to simulate and predict the performance of investment strategies.
• Risk Management: Quants assess and quantify potential risks associated with investment portfolios and trading strategies, aiding in the management and mitigation of risk exposure for financial institutions.
• Algorithmic Trading: They develop and implement automated trading strategies that leverage mathematical models to execute trades at optimal times and prices, taking advantage of market inefficiencies.
• Derivatives Pricing: Quants are involved in pricing complex financial derivatives, developing models to accurately value options, futures, and other derivative instruments.
• Data Analysis: They work with large datasets to extract meaningful insights, analysing historical market data, economic indicators, and relevant information to inform decision-making processes.

Types of quants

People frequently enquire and are curious to learn about various online trading jobs, algorithmic trading jobs, futures trading jobs, etc. in their journey of an algorithmic trading career.

Here we list down a few profiles to understand what types of roles are available in the industry and what type of skills would be required to take them up.

These are just a few of many roles that fall in the algo trading domain. You can also find job roles based on your skill set as these roles would be a better fit being in line with your skill set. Also, one important thing to mention here is that the developers are also sought after in the domain of High-Frequency Trading (HFT Trading). Hence, HFT can be for you if you are a developer.

Who employs quants?

If you’re looking to make a career in algo trading some of the questions that might cross your mind would be ⁽³⁾ ⁽⁴⁾:

• Who will hire Algorithmic Trading professionals?
• Who will give jobs to Quants?
• What companies hire Quants?

List of companies that hire Quants

• Commercial Banks
• Large Global Banks: JP Morgan Chase, Bank of America, Citigroup, HSBC, Barclays, Deutsche Bank, etc
• Accountancy Firm
• Big Four: Deloitte, EY, KPMG, PwC
• Other Major Firms: Grant Thornton, BDO, RSM, CLA, Citrin Cooperman, EisnerAmper
• Software Companies
• Palantir Technologies, Google, Amazon, Microsoft, etc
• Finance Firms
• Investment Banks: Goldman Sachs, Morgan Stanley, Bank of America, Citigroup, UBS, Barclays, Deutsche Bank, etc
• Hedge Funds: Bridgewater Associates, Millennium Management, etc
• Asset Management Firms: BlackRock, Vanguard, Fidelity Investments, State Street, etc

You can find more information about quants, the salary and other main aspects such as job opportunities etc. in the blog on “How much salary does a Quant earn?”

What do recruiters look for in a resume?

Recruiters are always on the lookout to hire the most talented and skilled individuals out there for their organisations. But when hiring for the domain of algorithmic trading:

• What do recruiters look out for?
• What describes an algorithmic trader job description or a quantitative analyst job description?
• What type of job will help one’s algorithmic trading career?

Algo Trading job requirements

Following are some requirements from established companies in the algo trading domain, for selection of candidates that they look out for:

• For the position of Trading Strategy Development, the knowledge of Python & R would be an advantage.
• To become a Python Developer an advanced skill-set in programming languages like Python is largely preferred
• A domain knowledge in stock markets (quant, fundamental, technical, derivatives, macro, etc.) and strong Logical skills are valued
• Those with Master’s in Applied mathematics or statistics, MBA, computer science can become Quantitative Researchers and Traders with the ability of successful implementation of profitable trading strategies (from ideation to execution i.e. research, design, back-test and execution)

Tips for Algo Trading job interviews

There are people who have become successful traders despite being from a commodity background, being Finance & Tech grads, being Technocrats and Engineers, etc.

The below mentioned tips would increase your chances of being selected for an algo trading job. These tips are:

• One should be able to demonstrate a strong understanding of the core areas that are highlighted in their resume.
• Don’t mention skills you don’t have since that would leave a negative comment.
• Recruiters also tend to give positive weight if the candidate has undertaken project work or published any research papers in his/her areas of interest.
• Customise your resume for each application (in case you’re exploring various quant trading roles) by emphasising on skills and experiences most relevant to the specific role you're applying for.
• Also, preparing a cover letter allows you to explain why you're interested in the position, how your skills align with the job requirements, and why you're the best fit for the role.
• Detail your programming skills, especially in languages commonly used in algo trading.

Salaries for a career in algo trading

One of the most commonly asked questions is: How much do algorithmic traders make?

There exist a variety of roles for multiple businesses and companies, depending on the type of knowledge and skills you possess. QuantInsti’s career cell shares these numbers on the QuantInsti website, stating job opportunities & salary packages bagged by the participants of their algo trading course.

Algo Trading salaries

It is a known fact that salaries & bonuses are lucrative in algorithmic trading firms.

Variation in Algo Trading jobs and salaries

They vary:

• With country in which you work
• with different job roles and cadres
• with companies where bonuses get equally split between traders and programmers based on the profitability of a strategy
• with the type of the trading firms (e.g. Family office, or bank, or High Frequency Trading (HFT) firm etc.) and
• the strategies (low-frequency trading strategy / high-frequency trading strategy) that are deployed by the firms

Salaries are based on the posts or designations for which one is hired. Salaries for the following and other posts would be as per the hierarchy of that respective company.

• quant trader salary
• algorithmic trading salary
• quantitative analyst salary

This results in different types of roles and jobs in the Quant or Algorithmic trading space. The Equity market also offers a broad range of career opportunities.

Check out Quant Trader Salary to learn specifically about the salaries in the industry.

Also, take a look at the informative video covering how QuantInsti helps to prepare you for a career in the algorithmic trading domain.

Impact of ML and AI on your algorithmic trading career

The rise of Machine Learning (ML) and Artificial Intelligence (AI) has drastically reshaped the landscape of algorithmic trading, impacting careers in profound ways. ⁽³⁾ ⁽⁴⁾

AI in trading stocks has enabled traders and finance professionals to develop more sophisticated, data-driven strategies. By leveraging AI algorithms, they can efficiently analyze large datasets, spot emerging patterns, and execute trades with greater precision. This technological advancement opens up opportunities for those with programming skills and an understanding of AI concepts to stay competitive and capitalize on these market insights.

Here's a breakdown of the key changes and their implications:

Transformations in Algo Trading Careers

• Booming demand for AI Specialists: With the increasing complexity of AI-powered algorithms, the demand for specialists with expertise in deep learning, natural language processing, and other advanced techniques has skyrocketed. This has opened up exciting new career paths for data scientists, software engineers, and mathematicians with an interest in finance.
• Evolving Skill Sets: Traditional algo traders now need to adapt and embrace data science and programming skills to stay competitive. The ability to understand, develop, and integrate AI models into trading strategies is becoming increasingly crucial.
• Heightened Automation: Many manual tasks like data analysis, signal generation, and order execution are being automated by AI algorithms. This frees up traders to focus on higher-level activities like strategy development, backtesting, and risk management.
• Democratisation of Access: Advancements in cloud computing and the availability of pre-built AI algorithms have made algo trading more accessible to a wider range of individuals. This has created new opportunities for retail investors and smaller firms to compete with established players.

Suggested courses

• Learning Track: Algorithmic Trading for Beginners
• Learning Track: Portfolio Management and Position Sizing using Quantitative Methods

Resources to learn and upgrade your career in algo trading

Let us take a look at the useful resources below, briefly, that can help you take the next big step in your algo trading career.

• Explore the various subjects of study, academic backgrounds and professional backgrounds which match the requirements for algo trading.
• To start exploring this domain, you could refer to the unconventional guide to the best websites for Quants.
• Completely new to Algorithmic Trading? Get an insight into this domain via this candid discussion with an Algo Trader.
• If you’re already a Quant, and you want to further better your skills, you too can become a better quant with the right support and guidance.
• If you aspire for a career in High Frequency trading, get thorough with the whats, hows and whys of the domain.

Success stories

Last but not least, check out these success stories of various individuals (beginners as well as professionals) who successfully kickstarted their careers in algorithmic trading after learning from QuantInsti.

In these success stories, you will even find that individuals with no prior trading experience have managed to make significant strides in the interesting world of algorithmic trading. This highlights the potential for anyone with the right dedication and aptitude to excel in the algorithmic trading domain.

FAQs about a career in algorithmic trading

Here are some of the most commonly asked questions which we came across during our Ask Me Anything session on Algorithmic Trading.

Question: How can a finance guy make a career in this domain?

Reply: As I said, it is a set of three things i.e. statistics & econometrics, programming (which is financial computing) and quantitative trading strategies. So if you have a finance background and you are already good at the 1st and 3rd aspect then you need to pick on the financial computing side.

If someone tells you that you are already a trader and you do not need to learn to program to automate your strategies, they might not be lying, you can do that.

There are some tools available with the ‘drag-and-drop’ option where you can build a logic without actually coding. But, these functions are limited and it will also limit your ability to change or modify your strategy or implement your ideas.

So it really helps if you know of the algo trading course. Even if you are not trading, if you are doing data analysis it will help you a lot. The things you can do with two lines in a python code, you will need a lot more if you are doing it on a non-programmable tool.

Question: What are the skills required to land a job as an Algorithmic Trader?

Reply: The kind of skills that you would need typically would revolve around ‘the three pillars’ of algorithmic trading (like explained before).

If you are looking:

• to become a trader yourself, then you would need to learn all the three domains
• to become an algorithmic developer, then you would need more expertise on the programming side
• to become more of a strategist, in that case, you would need a deeper understanding of the trading strategies (like derivatives, all different types of classes etc.)
• become a quant analyst, then you need to have a stronger expertise in statistics and econometrics

So everyone would need to know all these three but how much expertise you would need to know in each one of them varies depending upon what kind of profile you are targeting.

To develop expertise in these areas, consider enrolling in algorithmic trading courses that offer targeted learning paths to help you build the specific skills required for your desired career.

Question: What are the career opportunities in Algorithmic Trading? How can EPAT help?

Reply: Besides the three aspects mentioned above - including quant analyst, traders and developer there is a list of profiles out there.

This varies from:

• back-office roles,
• front office roles,
• analyst roles,
• development roles,
• management roles,
• network management and much more.

Algorithmic trading is one of the more rewarding streams compared to conventional trading or other career domains and it is much more intellectually stimulating as well.

If you want to do something where you want to contribute and experiment a lot, these kinds of profiles can really help.

QuantInsti's EPAT course has a dedicated placement and career cell that helps you find the right connections and acquire the right information.

It will help:

• in getting you placed,
• seeking guidance to move up the ladder in your organisation in the same or a different role,
• to start a new business,
• if you need a broker connection, vendor connection,
• funds,
• investments, etc.

So that’s where QuantInsti’s career cell becomes much more instrumental. They keep on bringing you different opportunities available in the market in different geographies.

Question: What prospects can someone with 10-20 years of domain expertise and no trading experience expect as an algorithmic trader?

Reply: We do have an interesting case study which is about an EPAT alumnus in his 40s. He was not from a trading background but he was able to make a switch after 20+ years of experience.

So yes, such opportunities do exist. He put in a lot of time, effort and commitment. If these things are there then yes there are a lot of opportunities, opportunities that keep on coming, we share such opportunities with all our participants every week but ultimately it’s you.

We can only guide, you have to go and win the war.

Question: Just how liquid is the job market for someone who has completed QuantInsti’s executive programme?
Or Are jobs in quant trading the reserve of PhDs?

Reply: A PhD definitely helps but not all the firms need just PhDs. You need to know your stuff. That's what is the key requirement which we keep on seeing from different placement partners and not just in particular geography but everywhere.

HFT firms keep a focus of hiring lot of maths and physics PhD guys but not a huge lot of them so it’s an advantage but not like you cannot get into quant trading without a PhD.

Question: Most of the quant interviews check the problem-solving skills of the candidate through puzzles etc.
How is EPAT going to prepare me for such interviews?
Does QuantInsti have HFT firms as placement partners?

Reply: That’s right, even one of our own firms does that i.e. evaluate the candidates through puzzles. We do help you with that; the career cell does help you with acquiring those analytical skills as well as getting the right resources.

EPAT altogether focuses on algorithmic trading strategies, programming, statistics, financial computing (R, Python, MATLAB etc.) and strategy paradigms. We do not cover the analytical part in the EPAT given the kind of course it is but the career cell does help you on that side.

Yes, we do have a number of HFTs as our placement partners.

Question: I have a little experience in C & C++ programming working in a non-finance firm.
Right now I am looking to switch to a finance firm so that I can learn more about quant finance and HFT.
But my main aim is to be an algorithmic trader without going for higher studies.
So I am looking away from a quant developer to an analyst and then to a trader.
Can you suggest something on this?

Reply: We do have some participants in the past who took the same route. I think that can be a logical route considering you already have a strong programming experience.

As an algorithmic developer where you learn a bit about trading strategies and stats a bit to understand what you are coding and developing. But once you do that you can go-ahead as an algorithmic developer and move towards analyst and trader roles.

All in all, I think that’s a good route to go for considering the background you just mentioned.

Question: What is the salary for a Quant and what is the demand for Quants in India?

Reply: It depends on your experience and what background you are coming from. To give you a brief idea, if the range of work experience is between 2-8 years and you are from a decent background then the quant salary typically would be somewhere in the range of 1-5 million INR on the fixed side.

It’s a broad range obviously since it depends on so many different factors hence it is difficult to give a narrow estimation.?

Conclusion

These are some of the important points that aspiring quants/developers should keep in mind as they prepare themselves for a successful career in algorithmic trading.

People often wonder:

• How to prepare for a career in algorithmic trading?
• How to get established in quantitative finance?
• How to shift to a career in algorithmic trading?
• How to start with algorithmic trading?

Opting for professional training to learn Algo Trading is the next step in the journey. You might want to opt for a quant algorithmic trading programme which would largely benefit your skills, professional life and your career in the domain of algorithmic trading.

If you are a trader, a programmer, a student or someone looking to pursue and venture into algorithmic trading then you must explore our comprehensive Algorithmic Trading course. The algo trading course is a course taught by industry experts, trading practitioners and stalwarts like Dr. E. P. Chan, Dr. Euan Sinclair to name a few - is just the thing for you!

Moreover, there are numerous job opportunities as a quant that you can choose from once you learn from the course.

Author: Chainika Thakar (Originally written by By Viraj Bhagat)

Note: The original post has been revamped on 1st March 2024 for accuracy, and recentness.

Disclaimer: All data and information provided in this article are for informational purposes only. QuantInsti^® makes no representations as to accuracy, completeness, currentness, suitability, or validity of any information in this article and will not be liable for any errors, omissions, or delays in this information or any losses, injuries, or damages arising from its display or use. All information is provided on an as-is basis.