Algorithmic Trading

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Author : umang08
Published on : October 13, 2016

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Algorithmic Trading

“Automated Trading” shall mean and include any software or facility by the use of which, upon the fulfillment of certain specified parameters, without the necessity of manual entry of orders, buy/sell orders are automatically generated and pushed into the trading system of the Exchange for the purpose of matching. SEBI has allowed Exchanges to extend Algorithmic trading facility to members involving usage of various Decision Support Tools / algorithms / strategies[1].

Algorithm trading is a system of trading which facilitates transaction decision making in the financial marketsusing advanced mathematical tools. In this type of a system, the need for a human trader's intervention is minimized and thus the decision making is very quick. This enables the system to take advantage of any profit making opportunities arising in the market much before a human trader can even spot them.As the large institutional investors deal in a large amount of shares, they are the ones who make a large use of algorithmic trading. It is also popular by the terms of algo trading, black box trading, etc. and is highly technology-driven. It has become increasingly popular over the last few years[2].

High Frequency Trading (HFT) is a subset of algorithmic trading that comprises latency-sensitive trading strategies and deploys technology including high speed networks, colocation, etc. to connect and trade on the trading platform. The growth and success of the high frequency trading (latency sensitive version of algorithmic trading) is largely attributed to their ability to react to trading opportunities that may last only for a very small fraction of a second. Co-location (for brevity, Colo) has provided the vehicle to high frequency traders to capture such trading opportunities[3].

Co-locationis the first and most tangible manifestation of HFT. It refers to the exchanges. Practice of renting space in the facilities that house their computer servers to traders who believe they can benefit from this proximity. Co-location facilitates the practice of .latency arbitrage, trading between markets based on pure speed. To succeed at latency arbitrage, a trader has to be first in line in the price-time order queues used by the exchanges. It is a zero-sum game where the order that is first in line with a bid or offer at an advantageous price wins the competition[4].

Over the last two decades, increased computing power, improved telecommunications infrastructure, and falling processing costs have accelerated the presence of automation in just about every human enterprise – including the securities business.

Today, technology and automation have been brought to bear on nearly every phase of the investment process. However, the impact of this trend on market structure was first felt in the US, and it started over a decade ago. I’d highlight four developments that drove this evolution:
· In 1999, the SEC’s Regulation ATS, which enhanced competition in the US Securities markets by formalizing electronic communication networks and crossing networks as alternatives toincumbent exchanges;
· In 2001, the NYSE introduced decimalization, or the pricing of stocks in penny increments, which tightened the spreads at which stocks trade;
· In 2005, the SEC’s Regulation NMS, which essentially mandated exchanges to provide fast, automated executions in order to be considered part of the “National Market System”;
· And throughout this period, the de-mutualization of the exchanges, which further spurred competition and innovation in the industry.

In the wake of these changes, the size of the average trade fell dramatically, from a couple of thousand shares to a few hundred. Equities volumes doubled. Quoted spreads compressed dramatically. Along with these changes, we began to see Algorithmic and High Frequency Trading strategies deployed in noticeably greater volumes in 2005 – 2007. This transformation has been most pronounced in cash equities and listed derivatives, but it is increasingly evident in fixed income.[5].In 2008, India allowed the first Direct-Market-Access (DMA) and algorithmic trades to go through. Since then, algorithmic trading has taken off and now constitutes a sizeable percentage of all trading activity on the National Stock Exchange (NSE) and the BSE.[6]

Commonly used strategy

Out of these, arbitrage, is by far the most commonly used strategy employed by traders. This gives algorithmic traders a substantial edge — speed. If there is a profitable arbitrage trading opportunity and many traders are trying to grab the same quantity at a certain price, the pre-programmed algorithmic trading engine will reach it in a matter of milliseconds. Human traders, however, can only react in a matter of seconds. Therefore, an automated algorithm tends to outperform human traders at such times. However, with opportunity comes risk. The infamous “flash crash” that occurred in the US in 2010 is the perfect example that shows how terribly wrong a situation can go with algorithmic trading.

Since algorithms generate trades based on signals, you could have a perfect storm brewing if many different algorithms generate signals, back to back, for each other.

In order to prevent such situations, any algorithm must be approved by an exchange. Specifically, risk management system (RMS) checks, such as the maximum traded value, trades per second and total traded quantity have to be within certain bounds. While each stock exchange has its own RMS policies, prescribed RMS checks provide some surety that any single algorithm cannot trigger massive selling or buying. All said and done, algorithmic trading is here to stay. Any profitable trading strategy can be undertaken more profitably through an automated algorithm. The competition is so stiff that most advanced algorithms look to shave microseconds off their trades.Speed can be improved by ensuring that every step in the process from when the signal gets generated by the trading engine to how long it takes for the trade to get to the exchange is optimal. In the algo trading world, speed is referred to as latency.[7]

SEBI‘s Stand

The capital market regulator is planning to impose some curbs in the obscure worlds of algorithmic trading after being blamed for giving some traders a clear advantage over others.

SEBI is considering few measures that could act as speed breakers to this sophisticated system that executes trades at lighting speed. These include artificial speed bumps, restricting tick by tick data , bunching of orders and introducing order randomization , among others ,said a senior regulatory official familiar with the development. Globally, regulators have been debating restrictions on algorithmic trading or high frequency trading (HIFT). SEBI would be the first regulator to take steps to rein in such trading[8].

SEBI vide circulars dated March 30, 2012 and May 21, 2013 has put in place the broad guidelines for algorithmic trading in the securities market. The guidelines, inter alia, include risk management measures/ checks for Algorithmic (Algo) trading. SEBI vide circular dated May 13, 2015 has laid down guidelines

to ensure fair and equitable access to the co-location facility and to ensure that the facility of co-location/ proximity hosting does not compromise integrity and security of the data and trading systems. The stock exchanges are required to provide colocation / proximity hosting in a fair, transparent and equitable manner[9].

[1] Available at visited on 7th August,2016)
[2] Available at (Last visited on 7th August,2016)
[3] Securities Exchange Boards of India,” Discussion paper on 'Strengthening of the Regulatory framework for Algorithmic Trading & Co-location’(2016)
Available at' (last visited on 7th August,2016)
[4] Steven R. McNamara, The Law and Ethics of High-Frequency Trading, American University of Beirut, Olayan School of Business,2016
[5] J. Michael Evans ,Introductory Presentation IOSCO Beijing 2012.
[6] Raghu Kumar, Algorithmic trading is here to stay,The Hindu ,(December 7,2014),Available at
[7] Id
[8] Reena Zachariah,Sebi mulls artificial barriers to algorithmic trading to ensure level playing field, The Economic Times (14th July, 2016). Available at
[9] 'Supra note 3, Pg 2.