Algorithmic Trading for Technocrats and Engineers

5 min read

Algorithmic Trading for Technocrats and Engineers

by Anupriya Gupta

Being a pioneer in the field of algorithmic trading education, we often get queries like why should I do this certification? How will it benefit me? Is trading right for me? Many who come up with such queries are students from various fields of study and particularly from engineering and mathematical backgrounds. We will try to address some of these frequently asked questions.

Why Quant Trading?

If you have coded before, you would know that the easiest way to implement ideas is through coding. Trading is the most direct approach to making money. If you are able to merge these two aspects, then you essentially have what is called as Quant Trading.

A Chronology – Bazaar

A bazaar or an exchange has existed since times unknown. There is a mix of coinage and barter in a bazaar and there has always been someone who is hedging for example somebody has lot of oil and there are traders who are trying to buy low and sell at a higher price and arbitrageurs who are trying to do it in the bazaar itself. Here the information travelled at the speed of an horse or a bullock i.e physical trading.

Native Share & Stock Broker’s Association

A basic setup of an exchange first came up in 1877 called the Native Share & Stock broker’s Association, today known as the Bombay Stock Exchange (BSE). It started trading in ownership right of firms called as allotments, scrips and shares. This trading however was only allowed for certain brokers. These brokers had clients who would want to buy and these brokers would put their trades through other people in the pit. Very few people had the monopoly rights to the trading exchange.

Post-Depression India 1992

After India came out of the severe depression way back in 1992, Indian exchanges implemented screen based trading. Here you could execute orders without your broker knowing therefore nobody could front run you. Manipulation, costs and errors were considerably reduced. Almost everything became electronic, leaving little to no room for physical trading.

Trading Exchanges Now

Today the development happening in trading exchange is exponential with trades matching microseconds. There is complete transparency and trading volumes are at an all time high. Complex instruments and derivatives are traded at light speed.

Diversified Fields

The state of trading today is such that it combines skill sets of various fields such as: Statistics, Finance, Computer Science, Psychology, Economics, Operations Research, Historical Data, Mathematics to optimize algorithms and lastly strategy. All the above mentioned fields merge to a single point – Strategy. Who has such vast knowledge? In simple single word – Engineers. People with technological background are best placed to use such a vast model. On Wall Street, most traders today are no one else but Engineers.

In Search of Alpha

Alpha is ability to predict future and to make more money than a naive trader. It is also defined as the additional return over a naïve forecast. It’s the job of a Quant Research Analyst to find this alpha.

Alpha is derived from 3 things – Speed, Modelling and Information.

Segmentation of Quantitative Trading

There are four base strategies of quantitative trading as follows:

  1. Market Making: Buy and selling at the same time. You have the buy port and sell port in the same instrument. Get inside the bid-ask spread and buy low and sell high.
  2. Arbitrage: If you can buy a stock at a lower price in an exchange and sell at a higher price on another exchange or through similar instruments, it is called arbitrage.
  3. Momentum: If an exchange is going up, it will keep going up.
  4. Mean Reversion: If an exchange has gone up and it is bound to come back then its mean reversion.

Some Facts about High-frequency Trading

HFT firms buy and sell but they start at zero positions in the market and end with zero positions in the market. An HFT strategy must finish the day flat, HFT firms must exhibit a balanced bi-directional flow. An HFT firm cannot accumulate large positions and cannot deploy large amounts of capital. Most HFT traders have little to no need of any outside capital or leverage hence tend to be proprietary.

HFT takes the opposite side of trades of long-term investors. If a long term investor wants to buy a stock on the other hand we have HFT’s. Long term investors impact many securities besides the ones they are directly trade, because stocks are correlated. Such a situation creates opportunities for Statistical Arbitrageurs. Arbitrage activities keep correlated stocks “fairly-priced”. If the volatility increases and liquidity is in short supply HFT comes in to provide it.

Stress on Tech

For making millions of dollars in this industry, technology being used for trading plays a very important role. Latest in the market servers, chipsets and fastest leased lines. Here is where it gets exciting for people who want to be in the forefront of technological evolution.

Vicious Cycle

When algorithmic trading started in 2008 in India, there were complaints that there were very relative volumes. There is vicious cycle where higher volumes lead to gains in efficiency through the use of technology, leading to lower transaction costs. Technology is the enabler of the virtuous cycle, but cost is the driver. As costs approach zero, volumes will peak as a result.

What are the job opportunities?

Algorithmic trading opens doors to very lucrative and high-paying jobs in the financial sector. Candidates who wish to make a career in this profile can work in different domains in this field. The Algorithmic trading sector has a very broad spectrum of work division; different job profiles in this sector are as following. Know more about career opportunities in automated trading at our placement cell.

Trading Firm Hierarchy

Algorithmic trading consists of a very flat hierarchy mainly because it just 15 years young. It is a closed industry; head of desk, junior traders and the director are all sitting together for trading. You are in close interaction with everyone else on the desk.

Performance Evaluation

The evaluation in an algorithmic trading career is very objective oriented. At the end of the month/year, how much money has your strategy or your team made has would be evaluated. The objectivity of the job makes its ever more appealing; it does not depend or is not evaluated on the basis of team skills or how good the attempt was.

How to get started with algorithmic trading?

In the past, entry into algorithmic trading firms used to be restricted to PhDs in Physics, Mathematics or Engineering Sciences, who could built sophisticated quant models for trading. However, with the growth and demand of the industry for the quant traders and developers has synced with the growth of online education industry. It is now possible to get into this domain without having to go through an 8-10 years long academic route. Refer to our step by step guide to learn algorithmic trading.

Webinar Video

This video is part of a webinar taken by Gaurav Raizada about Algorithmic and High Frequency trading career for technocrats with detailed discussion about this domain and different skill sets requried to excel in this industry. To watch this full webinar video feel free to get in touch with us by filling our contact form.

Next Step

If you’re a retail trader or a tech professional looking to start your own automated trading desk, start learning algo trading today! Begin with basic concepts like automated trading architecturemarket microstructurestrategy backtesting system and order management system. You can also enroll in our Algo trading course EPAT which is one of the most extensive certification programs available in the industry.


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