Making a Career in Algorithmic Trading

Making a Career in Algorithmic Trading

By Milind Paradkar

The advent of algorithmic trading in the late last century caused a massive tectonic shift in the way trading took place in exchanges worldwide. Be it trading in stocks, derivatives, Forex or commodities, trading firms worldwide adopted algorithmic trading in a big way.

The last couple of decades have seen an exponential growth in the algorithmic trading market and it continues to grow at a significant pace. According to the “Global Algorithmic Trading Market 2016-2020” report published by Research and Markets last year, the global algorithmic trading market is expected to grow at a CAGR of 10.3% during the period 2016-2020.

In order to remain competitive and earn big profits year after year, big banks, hedge funds, and other trading firms have been hiring top talent from various universities and colleges worldwide. This in turn has led to a surge in algorithmic trading/HFT jobs. Scores of students, engineering graduates, and developers want to explore and build a promising career in algorithmic trading today. This said, many of the wannabe quants & developers are unaware of the nature of the work in algorithmic trading firms and the skill sets needed to make a foray into this coveted algorithmic trading world.

QuantInsti recently conducted a webinar on “Career development – Jobs in algorithmic/HFT trading”. The webinar offered a unique chance for attendees to interact with a team of quants & HFT developers on a one-to-one level and ask career-related queries to the esteemed panelists. The panel of speakers included Mr. Sunith Reddy (Head of Technology, iRageCapital Broking), Mr. Puneet Singhania (Director, Master Trust) and Mr. Gopinath Ramkumar (Market Risk Quant)

In this post, we highlight some of the most important takeaways from the webinar for job seekers in high-frequency trading jobs, quant jobs, and algorithmic trading jobs.

Key Takeaways:

Automated trading is not free from human interventionClick To Tweet

Automated trading is not free from human intervention

A very important point that wannabe quant/developers ought to know is that automated trading does not mean it is free from human intervention. Automated trading has caused the focus of human intervention to shift from the process of trading to a more behind-the-scenes role, which involves devising newer alpha-seeking strategies on a regular basis.

Top skill sets for algorithmic tradingClick To Tweet

Top skill sets for algorithmic trading

Analytical skills – Having an analytical bent of mind is a very important quality for any quant trader/developer, and is given a high weightage 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.

Math and programming 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 in a firm that deploys low latency strategies, then an expert level of programming would be expected from such a candidate.

To know more on skills sets required, check out this infographic – Top skills for nailing Quant or Trader interview

Understand the strategy development process

While devising any strategy, it is important to understand the risk and rewards associated with that strategy in order to determine whether it has an edge in the markets. This is done during the backtesting of a strategy. The frequency of trading, instruments traded, leverage also needs to be taken into consideration before going live with the strategy live in the markets. Quants should also remember that no single strategy can guarantee profits year-after-year. Quants are required to formulate new strategies on a regular basis using advanced mathematical models & statistics and overhaul the old strategies to remain profitable in the markets. To learn about various algorithmic trading strategies, you can check out this blog post – Algorithmic Trading Strategies, Paradigms and Modelling Ideas

Techies need to understand financial markets

Quantitative trading involves dealing with large datasets, trading in 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.

Should I learn C++ or Python?

Python is good for conceptualizing and backtesting of strategies. Python offers many libraries for validation and visualization of results. It can also be used by firms for strategies that are not dependent on low latency. See here to know what makes Python the most preferred language for Algorithmic Trading. On the other hand, C++ is usually used by firms that trade very low latency strategies. Thus, if the objective of an aspiring developer is to get into an HFT firm, then irrespective of the language that he starts with, he will have to finally end up learning C++.

Points that recruiters look in a resume

Recruiters appreciate if candidates are honest on their resume, candidates should be able to back any points that they have mentioned. Candidates are expected to demonstrate a strong understanding in the core areas that are highlighted in their resume. Recruiters also tend to give positive weightage if the candidate has undertaken a project work or published any research papers in his/her areas of interests.

Salaries for traders and programmers

It is a known fact that salaries & bonuses are lucrative in algorithmic trading firms. However, there is no common compensation policy followed across algorithmic trading firms. For example, salaries paid to tech guys in similar roles can vary from one firm to another. In some firms, bonuses get equally split between traders and programmers based on the profitability of a strategy. Compensation can also vary depending also on the type of the trading firms (e.g. Family office/bank/HFT firm etc.) and the strategies (low-frequency/high-frequency) that are deployed by the firms.


These were some of the important points that aspiring quants/developers should keep in mind as they prepare themselves for a successful career in algorithmic trading. You can click here to catch the entire webinar – “Career development – Jobs in algorithmic/HFT trading”. In case you need any further guidance, feel free to contact us.

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Next Step:

If you want to learn various aspects of Algorithmic trading then check out the Executive Programme in Algorithmic Trading (EPAT™). The course covers training modules like Statistics & Econometrics, Financial Computing & Technology, and Algorithmic & Quantitative Trading. EPAT™ equips you with the required skill sets to be a successful trader. Enroll now!