By Sushant Ratnaparkhi and Rekhit Pachanekar

It might be surprising to know that a financial institution in the U.S. will easily offer a Quant salary of $100,00 to the right candidate. As we go deep in this article, you might even realise this quant salary is actually on the lower end. Anyway, financial pundits say that we can liken the progress achieved in the world of trading to the human evolution process. As humans moved from hunter-gatherers to agriculturists, so did trading move from manual analysis and gut-feeling to algorithmic trading which involves financial modelling with the help of computers.

To give you an example of the impact of quant trading, Man Group's AHL Dimension programme is a $5.1 billion dollar hedge fund which is partially managed by AI. After it started off, by the year 2015, its machine learning algorithms were contributing more than half of the profits of the fund even though the assets under its management were far less.

Before we give a few lists and figures related to quant salary, let’s understand what the word ‘quant’ really means. Here’s a list of topics we will cover in this article:

• What is a Quant?
• Quant Salary
• The job responsibility of a Quant
• Quant Funds
• A candid conversation with an algorithmic trader

What is a Quant?

According to Wikipedia, a Quant or Quantitative analyst in the financial world is a person who specializes in the application of mathematical and statistical methods – such as numerical or quantitative techniques – to financial and risk management problems ^[1].

To put it simply, a Quant is at an intersection of three domains, Finance, Mathematics, and computer programming. Quants have been in demand in the world of trading as they have the sound financial knowledge to identify a problem statement such as the risk of an investment, develop a mathematical model to solve it, and then develop a computer algorithm to execute it automatically.

In fact, algorithmic trading has led to a spike in demand for Quants, with major financial firms employing more Quants than Equity traders themselves. No wonder companies are lining up and doling out a quant salary so high.

Depending on your skills and interests, you have a plethora of options in the Quant domain. Let’s go to the part we have all been waiting for.

Quant Salary

Following is the compiled list of Quant salaries posted for a Quantitative analyst

Country	Average Base Salary
US	$112,816
India	Rs. 1,036,126
UK	£63,029
Canada	C$76,768
Singapore	S$94,817
Hong Kong	HK$636,632
Australia	AU$105,851

One should note that this is the average base salary computed from leading portals such as Indeed, LinkedIn, Payscale and Glassdoor.

Since a majority are starting as freshers and with a joining bonus and profit-sharing which is not included in the base salary, the actual salaries will definitely be higher.

As you can see, the salaries for a typical quant are on the higher end when compared to skilled professionals in their respective regions.

Interestingly, a compilation of jobs posted on popular job portals such as Indeed, Glassdoor and LinkedIn are shown in the following chart:

You can clearly see the demand for Quantitative Analysts has skyrocketed in the US with UK and India coming second and third respectively.

If you are interested in the figures, you can go through the following table:

	Indeed	Glassdoor	LinkedIn
US	12667	13192	8354
India	1000	1333	706
UK	1416	1181	2460
Canada	590	512	73
Singapore	NA	79	77
Hong Kong	NA	444	33
Australia	248	403	290

But is there more to the story than meets the eye? What type of roles is on offer once you become a Quant?

Well, here is a list of just some of the job roles that are prevalent in the Algorithmic Trading and Quantitative Trading domain along with estimated salary figures.

If this recent survey of the best jobs in banking is to be believed, Quantitative Trading jobs ranks only second in their survey, which is quite amazing.

There is a reason Quants have become sought after in the world of finance, especially trading. Back in the day, manual traders used to apply technical analysis tools as well as fundamental analysis to gauge whether the stock is worth investing or not. Over time, complex mathematical models were built to assess the profitability of a trade.

In the quest to develop a sound trading strategy which avoided human emotions, these models became increasingly complex and difficult to put into practice. Here is where the role of programmers came in, who developed the algorithms for the models. Over time, it was realised that we didn’t need three different people to perform the task when one trained professional could easily perform them alone. With the right skills, a quant analyst is able to do the work of a finance professional, computer programmer and a statistician.

Let’s understand a little more about a Quant’s life in the next section.

The job responsibility of a Quant

This is a job description posted by an HFT trading firm for the role of a quantitative Analyst:

• Trading a vast portfolio.
• Analysing hundreds of gigabytes of data to ascertain micro behavioural patterns (at the microsecond timeframe) to trade such signals.
• At the same time analysing information across time + assets to build macro signals.
• Building automated trading signals.
• Building engineering solutions to manage the complexity.
• Discovering and imitating the logic and thereby automating the process of setting the hundreds of parameters that traders do on a daily basis.

Hold on! While this is a job posting, it does not tell you the true scope of your work. Granted, as a fresher, you will be working in a team and building quant models yourself, as you move up the ladder, you will be able to handle a fund of your own. And guess what they are called.

Quant Funds

At first glance, you might think what is the big deal about a quant fund, it must be just another one in the plethora of options we have today, like index funds, mutual funds etc. But drill down and you will find that quant funds are slowly but surely getting a foothold in the industry. If you look at the US Market, the two big names are Renaissance Technologies and Two Sigma. While Renaissance technologies have an AUM of $10 billion, Two Sigma is reportedly managing around $50 billion. It is also reported that 27% of US stock trading is conducted by quant hedge funds. Even existing funds have started offering quant funds in their product offerings. MDT team at Federated uses Quant models to pick stocks. This has allowed them to beat the market benchmark in recent years.

It is not just the US markets where Quants are making a mark. Take the case of a highly regulated market such as China and you will often hear about the Quant Goddess, Li Xiaowei who has set up her own fund and outperformed the market by a substantial margin. With the Chinese stock market seeing increasing foreign investments in the year 2019, and news of it being highly unpredictable makes her achievement all the more noteworthy.

A few points which differentiate a quant fund from others are as follows

• Reliance on proven Quant models to create a trading strategy
• Relatively low expense ratio compared to actively managed funds
• Human bias is non-existent

In the era of diversification, quant funds have thus created demand from investors who are seeking a relatively structured fund. This has also spiked up the demand for quants who are already fewer in number. I guess this explains the high figures as seen in the quant salary table previously.

So far we have talked about salaries and jobs the world over, but we seem to overlook one important advantage of being a quant. While most of the fund houses would definitely have profit-sharing as a part of the compensation package, there are countless examples of quants who have set up their own algorithmic trading desks.

But how do you go about setting up a trading desk? Well, let’s ask the algorithmic trader themselves.

Candid Conversation with an Algorithmic Trader

How do you set up an Algo desk?

Algorithmic Trading is a process of using a set of instructions to place an order of buying or selling script with volumes and speed impossible for a human being. The set of instructions is based on various market metrics like price, time, volume and any other user preference. The good part about Algo trading is that it eliminates human intervention thereby making trading sans emotions and intuition.

A typical Algorithmic system’s architecture entails three primary components

• Market Data Handler
• Strategy Module
• Order Router

The market data handler, as the name suggests, receives the market data and stores it. Strategies for trading, in a mathematical model, are fed to the strategy module. It also serves as an interface between the market and a trader.

Order Router or manager sends the order back to the exchange for buy/sell.

To set up an Algo desk, as a broker you need to identify your co-location to place the servers in close proximity of the exchange, feed in your strategies to your system after having it backtested and authenticated, have a good internet connection, and I think you are good to go!

What are the steps would you suggest to venture into the field?

Well, the first and most important step is to build a solid base. Learn some programming skills and get a grip on the markets. Being good with numbers always helps.

Begin by exploring core subjects like statistics and econometrics. Some books like Quantitative Trading by Ernest Chan or Trading & Exchanges by Larry Harris can elaborate on setting up a “proper” Algorithmic trading system. 

When you are through with the above-mentioned steps, get your hands dirty in strategy building, modelling techniques, and statistical tools.

Get a hang of various trading strategy paradigms, like statistical arbitrage, execution strategies, bid-ask spread.

There are a few free courses available online on Quantra, Udemy and Udacity which are quite good to test waters. There are other paid and advanced courses available for serious learners.

Which are the commonly used programming languages employed by the traders?

C++ remains the most preferred language as far as High-Frequency Trading (HFT) goes. Reason being that memory leaks and related errors are far less in C++ as compared to other languages.

Python has come up in a major way for coding strategies as well as backtesting, because it is easier to master, and is backed by good scientific libraries like Numpy and Pandas.

A number of forums today discuss investing and trading strategies coded on Python programmes.

How is the retail participation in Algorithmic trading?

The setting up cost is definitely on the higher side, from a traditional trading terminal. Obtaining a co-location can be an expensive affair.

According to recent statistics, nearly 70-80% trade on Wall Street is done using Algos, chiefly by large institutional investors and hedge funds.

However, the scene for retail participants is evolving with the offering of web-based platforms. For someone who is not too concerned regarding latency, it works like a charm. Other than that, firms like Interactive Brokers provide retail clients with API and packages so that traders can code their strategies and trade. Once you get a hang of it, it is like a simple Gmail account. You log in to your account, test your strategy, perform backtesting, and after optimisation, trade in live markets.

Paper trading or testing on a simulator is also highly recommended.

How is the ecosystem for Algorithmic Trading in India? Are companies readily opting for Algorithmic trading given its niche category and involvement of highly skilled practitioners?

Algorithmic Trading was allowed in India by SEBI in 2008. In a span of 8 years, nearly 50% or of the trade by volume or maybe even more than that is Algo-based. That speaks for its popularity. Indian bourses have adapted to the change very well, with a steady increase in the active participants. Both FIIs and domestic funds use the Algo route to place orders.

How does the global future look like?

Very promising actually. It is clear that automation is the future that is driving the world. Be it in any field, automation is making a tectonic shift from a traditional path, and the same applies to stock markets. In US markets, 70-80% of the exchange volumes is happening through automated systems.

Emerging markets like India are witnessing exponential growth in the domain. Of course, the markets are maturing every day, so trading costs would decrease after a certain point. Case in point: Automobile Industry, where after the introduction of robots, it was initially thought that the industry would be unsustainable due to high capital cost.

What are a few things that a beginner should keep in mind when venturing into this field?

The most important one is that it is just not enough to have a good trading strategy, but also to have a competitive edge. It can range from having innovative ideas which disrupt to having a low brokerage or the kind of markets you have access to, but you have to have a killer proposition if you plan to be successful. Treat this like any regular business, where you have to develop a strategy to outwit the competition.

It is important for someone starting new to have the nuances of the trade figured out.

What should be my next step if I want to understand more about this field?

The best way is to find experts and domain authorities to talk and discuss your doubts. Try out freely available tools and resources on the internet. Be prepared to embrace new knowledge and develop new skill sets!

These were but a few questions answered by the trader themselves. What do you think, do you have any questions regarding how to be a quant or set up your own desk? You can always drop in a comment below and we would answer them as soon as possible

Conclusion

If you are or want to be a part of the trading circle or even the finance domain itself, it is becoming imperative to become a quant as a way to future-proof your career as well as become a sought after candidate in an industry which is adopting technology at a frenetic pace. With new models being built and tested around the world, and with trading floors becoming occupied more by quants than traders, it is interesting to see the way the finance world would function when humans will be more interested in the systematic execution of a trade than risk their investment on the infamous “gut feeling”. If you want to learn various aspects of Algorithmic trading and become a Quant 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! 

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