Algorithmic Trading In India: History, Regulations, Platforms And Future

14 min read

By Anupriya Gupta

Execution of trades on stock exchanges based on predefined criteria and without any human intervention using computer programs and software is called algorithmic trading or algo trading. While being a subset of algorithmic trading, high-frequency trading involves buying and selling thousands of shares in fractions of seconds.

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While it has its detractors, the general consensus is that algorithmic trading is an inevitable evolution of the trading process and markets around the world have implemented various measures to provide a seamless experience to investors. In the US and other developed markets, High-Frequency Trading and Algorithmic trading accounts for an estimated 70% of equities market share. In India, the percentage with respect to the total turnover has increased up to 49.8%.

In this article we will cover the following points:

Algorithmic Trading in India: Past, Present and Future

On April 3rd 2008, Securities & Exchange Board of India (SEBI), introduced algorithmic trading by allowing Direct Market Access facility to institutional clients. In short, DMA allows brokers to provide their infrastructure to clients and gives them access to the exchange trading system without any intervention from their part. Initially, it was provided only to institutional clients and not retail traders.

Nevertheless, the facility brought down costs for the institutional investor as well as help in better execution by cutting down the time spent in routing the order to the broker and issuing the necessary instructions.

April 29th 2008, this facility had already become popular with some of the top global players signing up for the DMA facility. FI’s & FII’s like UBS, Morgan Stanley, JP Morgan and DSP Merrill Lynch were the entities awaiting approval. Edelweiss Capital, India Infoline and Motilal Oswal Securities were among others who had submitted their request to the stock exchanges. It is worthwhile to note that Foreign Institutional Investors (FIIs) were allowed to use DMA facility through investment managers nominated by them, from February 24th 2009.

By July 31st 2008, leading brokerages along with stock exchanges were preparing the ground for operationalising Direct Market Access (DMA). Brokerages such as Citi, Merrill Lynch, Morgan Stanley, JP Morgan, Goldman Sachs, CLSA and Deutsche Equities had started holding test runs of their DMA software, in an attempt to synchronise it with the systems at the stock exchange.

NSE’s Contribution To The Industry

The National Stock Exchange (NSE) started offering additional 54 colocation server 'racks' on lease to broking firms in June 2010 in an effort to improve the speed in trading.

Deutsche Bank, Citi, Morgan Stanley, Goldman Sachs, and MF Global were among the foreign broking firms which availed of the facility. Motilal Oswal Securities, JM Financial and Edelweiss Capital figured among the prominent domestic firms who signed up for the racks.

Local brokerages like Globe Capital, SMC, Global Vision, East India and iRageCapital had also opted for the facility. Not surprisingly, with a few weeks of offering this facility, there was a long period of waiting up to 6 months to get a space on the server racks!

It was clear to the Indian exchanges and regulatory bodies that Algorithmic Trading is well-received by the institutional clients and banks in the country and its demand would continue to rise. This was the time when exchanges started improving their offerings in the automated trading domain, financial technology companies started offering automated trading platforms and SEBI continued to regulate the markets.

May 12th 2010, NSE moved to enable the Financial Information Exchange (FIX) protocol on its trading platform boosting transaction speed for overseas investors using direct market access.

In simple terms, the FIX protocol helps in converting the language of the orders given by the Foreign Institutional Investors (FII) in the language understood by the NSE, in effect reducing the time taken for the transaction to be executed.

Changes to the Brokerage Industry

Broker commissions had started shrinking as a result of an increasing number of institutional clients warming up to the Direct Market Access (DMA) concept. To keep up with the times, they started offering automated software to the clients.

The new entrants to this space are discount brokers who are essentially brokers who provide facilities at very low brokerage charges. They are able to do this by providing only minimal facilities, unlike full-service brokers who usually provide support as well as training programs for their clients.

Regulations In Indian Stocks Markets

Every year SEBI comes up with regulations to be followed by traders and brokers to keep the trading industry safe and risk-controlled. To read about SEBI’s recent announcement regarding the algorithmic trading industry in India, go to the post here.

Risk management is critical with algorithmic trading. That is why, for any algorithm to be approved by the markets, exchanges require a firm to undergo a series of stringent tests if it intends to trade through algo trading. These tests include the number of orders that would be placed per second, the maximum order value of any order placed, and the maximum traded quantity during a particular trading day.

A brief summary of the latest SEBI circular (SEBI/HO/MRD/DP/CIR/P/2018/62) dated April 09, 2018 is given below:

Managed colocation service

It is suggested that exchanges should change the pricing structure of their co-location renting to make it accessible to small and medium-sized members as the current practice of renting the entire server rack to one entity leads to a high cost.

Latency measurement

In order to provide greater transparency when it comes to reporting the latency for colocation and proximity hosting, it has been suggested that the exchanges should provide minimum and maximum as well as the mean latencies along with the latencies at 50th and 99th percentile.

Tick-by-tick data feed

SEBI has suggested providing tick-by-tick data feed free to the members of the exchanges.

Unique identifiers for algorithms

SEBI has instructed that all algorithmic orders reaching their platform should be tagged with the unique identifier which is assigned when the specific algorithm was submitted for approval.

Future Of Algorithmic Trading In India

With several amendments over the years, India provides a good opportunity for algorithmic trading due to a number of factors such as colocation facilities and sophisticated technology at both the major exchanges; a smart order routing system; and stock exchanges that are well-established and liquid.

Given the rapidly growing trend and demand of HFT and Algorithmic Trading in developing economies & emerging markets, there have been efforts by various exchanges to educate their members and develop the skill sets required for this technology-driven field.

With many different trading platforms and tools available in the market, each claiming to be better than the other, a person who is testing the water in the field of Algo trading may be spoilt and confused by choice. Therefore, we have compiled a list of some of the most popular platforms and algo trading softwares that are being used in the market today (specifically for Indian equity markets), so as to level the playing field and give a clear picture to the users.

Algorithmic Trading Platforms

Omnesys NEST

It is a premier algorithmic trading platform which is capable of executing several strategies like basket trading, order slicing, 2L and 3L spreading. The platform is quite versatile in its operation and empowers brokers to trade across various asset classes like Equities, Derivatives, Currency and Commodities. Although it is quite powerful in terms of its tools, it is mostly catering to institutional brokers and thus, is not used by retail traders.

Presto ATS

Developed by Symphony Fintech, Presto ATS is a versatile algorithmic trading platform for automated trading in India in nearly all asset classes.

Presto ATS can be used in three modes

  • Live training
  • Paper training
  • Backtesting

Presto is FIX enabled and has a host of APIs to be connected to the Indian exchanges. The options are varied for APIs with availability in Java, C#NET, and in HTML. Although it is used by institutional brokers, it is gaining popularity among the retail traders too. The cost of licensing the software for an annual subscription is Rs. 25,000 with the option of single and multi-trading accounts.

Image Source: Symphony Fintech

ODIN

A flagship product by Financial Technologies (now 63 moons), ODIN is a “multi-exchange, multi-segment front-office trading and risk management system”, which features Order Management System (OMS), risk management, as well as third party API integration.

To cater to all types of investors, ODIN has created specific products according to the features needed. They are:

ODIN dealer: For broker-dealers

ODIN Institutional - For Institutional dealers

Diet - For Broker- clients

ODIN Program X Trading - For low latency and high-frequency trading

Image Source: 63moons.com

FlexTrade

FlexTrade Systems makes order management and execution management systems for pretty much everything, including foreign exchange, futures, options, and equities.

Founded in 1996, it is known for introducing FlexTRADER which is believed to be the first broker-neutral system which gives the clients complete confidentiality when it comes to developing and customising their algorithmic trading strategies.

FlexTrade's solutions include:

Buy-side

EMS, OMS, FX, Options, Futures, Fixed Income, TCA, AdvancedAnalytics, Trading Strategies & Algorithms and Market Connectivity Solutions

Sell-side

EMS, OMS, FX, Options, Futures, TCA, AdvancedAnalytics, Retail Broker Platform, Trading Strategies & Algorithms and Market Connectivity Solutions

Image Source: flextrade.com

AlgoNomics

A subsidiary of NSE, NSEIT created AlgoNomics: an algorithmic trading platform which caters to institutional clients, investment banks as well as individual traders. The AlgoNomics platform claims to provide low latency to users as they create multiple trading strategies. As it is with the other platforms covered so far, ALgoNomics also provides support for all market classes, including Equity, Equity Derivatives and Currency Derivatives. 

Source: NSEIT

MetaTrader

While MetaTrader was initially used for forex trading, MetaTrader 5 was created as an all-in-one platform for trading Forex, Stocks, Futures and CFDs.

Supporting both mobile and PC, as well as a web platform which allows you to work from anywhere, MetaTrader makes sure you are always connected.

Additional services expand the functionality of the platform making its capabilities almost limitless. MetaTrader 5 offers built-in Market of trading robots, Freelance database of strategy developers, Copy Trading and Virtual Hosting service.

Image Source: Metatrader4.com

AmiBroker

While AmiBroker is not an algorithmic trading platform in the true sense of the word, we have included it here due to its approach towards automating a trading strategy using AFL. While you do get a technical analysis and charting software so that you can backtest as well as execute a strategy in real-time, it also allows you to code a trading strategy in English. To accomplish this, AmiBroker created the AmiBroker Formula Language (AFL) code wizard wherein you can drag and drop English words to explain how a strategy would work and AFL converts into a code which can be used for execution on the AmiBroker platform.

According to the product description, the AFL language can process as much as 166 million data bars per second on 2GHz CPU.

Image Source: Amibroker

These were but a few algorithmic trading platforms listed above. Nowadays, brokers also provide a way to access markets algorithmically via their programming API. However, they don't provide any ready-to-use software. The API helps in bridging the trader’s preferred development environment for algorithmic strategies to the broker themselves. In India, Zerodha Kite, as well as IBridgePY for Interactive brokers, are an example of APIs provided by brokers.


The obvious advantage is that an individual trader can create their algorithmic trading strategies in another environment but use the brokers API to place live orders in the market. At the same time, one should consider the cost associated with using the API as well as the general downtime, if any, when you use the API.

As you can see, depending on your requirements and level of expertise, you have a plethora of options to choose from. But how do you get started in algorithmic trading? In the next section, we will try to understand this.

How to Start your Algorithmic Trading Journey

Financial knowledge

Gaining an in-depth understanding of the financial market/instrument to come up with a hypothesis on which you can base your trades. You need to have/develop some knowledge-based edge in any market in which you wish to win over the rest of the participants.

Coding your strategy

For this step, knowing an open-source language like Python or R comes in quite handy. Translate your strategy in a set of logical statements and make full use of amazing free libraries available for both these languages.

Backtesting your hypothesis/strategy on historical data

Getting hold of quality data is important and is often not free (especially tick-by-tick data). You can try paid sources like Quandl or can check with your broker if they provide historical data. You can also use a third party backtesting engine to make your life simpler such as the one provided by Quantra Blueshift or Quantiacs.

Parameter optimization

The natural result of backtesting and validating is that it will either lead you to completely discard your hypothesis (90% of the time or more!!) or that you have managed to extract actionable signals from the pool of data you started with. You can then optimize your strategy parameters keeping in mind that your strategy should work well on out-of-sample data as well to avoid overfitting/data snooping bias.

Choosing the right broker and platform

It is very important to do thorough research on this beforehand, as your overall efforts should make business sense after all the overhead costs are taken into account. Make sure you only pay for the features you use to execute your strategy efficiently. In short, keep the trading costs low & operations nimble.

Going Live & Risk management

Once you are satisfied with your algorithm, let it do its job in live markets! Manage risks efficiently using limits, stop-loss and Var/Expected shortfall monitoring. Keep an eye on the larger economy/sector for structural shifts/regime changes in which case you might have to alter or scrap your strategy altogether. Remember that every strategy has a limited lifetime.

Keep learning and developing new skills

As they say the best investment is investing in yourself. Look to enhance and update both your domain knowledge and technical skills required to act on that knowledge/information. For example, pick up a book by the likes of Ernie Chan or do an online course to beef up your coding skills.

Granted, you might have a lot of questions now, with respect to algorithmic trading. Let’s try to preemptively answer them.

Frequently Asked Questions about the Future of Algorithmic Trading

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

Is Algorithmic Trading legal in India?

Definitely Yes! April 03, 2008 is when SEBI allowed algorithmic trading in India, so since then it has been legal.

How tedious is it to get legal approval for any automation? How confidential and secured it will be if it goes to automation after approval, is approval process and infrastructure cost affordable for retail traders?

The approval process is not that costly, but yes the infrastructure, if you are going for HFT can be a big burden if you are a retail trader or individual trader but you can do automation and that would not be a huge cost as such.

Assuming this is from an Indian market perspective, India has a peculiar regulation which says that you have to approve each and every strategy before you take it live. This is different from most of the developed market regulations in which you have to get the platform approved and then you can code any strategy you want to on that platform. Same goes for other developing markets like Thailand where you have to get every algorithmic trading strategy approved before you can automate. The regulation demands that the broker should take the approval on your behalf, you as a retail trader cannot go to the exchange and ask for approval. The cost depends on the broker but technically it’s not that costly.

What are the approvals you need before going algo?

We touched upon this in brief in one of our previous questions but it depends on which geography you are trading into. In case you are trading in the CME, SGX or Eurex then the approval required is more of a conformance test which means that you will be taking approval for your trading platform. Once it is approved you can code whatever strategy on it and send out orders.

In case you are in geographies like India or Thailand then you will need to get your strategies approved and for that what you will be doing is creating a document for each strategy and sending it out to the exchange for approval. If you are a member of the exchange yourself you can send it directly and if you are not a member with the exchange then you send it through a broker. The process in India involves (can vary for different exchanges) to get the strategy signed from the auditor, participate in a mock trading session, then you demo it with the exchange, post that you get an approval from the exchange and then you start trading. That’s the rule you have to follow for each strategy.

How is a strategy confidential if it is going through the approval process?

The exchanges generally do not focus much on the strategy but more on risk management. The focus is that your strategy should not create havoc for the market or for them, which is the key concern for the exchange and not what your strategy does. They would ask you about the strategy at a broad level but I don’t think it goes to a level where your IP is threatened.

How risky is algorithmic trading towards manipulation such as colocation?

Colocation is not manipulation. It’s just a facility provided to you. It’s like saying how risky it is if you are travelling by air by spending more as compared to someone who is travelling by train to a destination, you are reaching faster but you are paying for it and you are getting it so it’s a fair market, you pay for what you get.

For those who colocation matters and for most of the exchanges across the globe it is not that expensive hence the exchanges also have been pretty responsible. Even in India you can get half racks (which is 21 units) you can place a good number of servers in half rack and that comes to around 50,000 rupees a month. I am not saying it’s very cheap but it is not that stringent if you are trading into strategies which are depended upon colocation for which every millisecond matters.

Conclusion

The advent of algorithmic trading has rewritten the rules of traditional broking. With significant volumes on the exchanges now being traded with the help of sophisticated algorithms, it is imperative that traders should be fully aware of the trading platforms and algo trading softwares that would enable them to implement their strategies and remain competitive. Although India was not an early mover into the world of Algo trading, its popularity has been on the rise ever since SEBI allowed the usage of advanced technology to be followed by the equity markets. This has also created a need for algo trading software, tools, and platforms, which are being accessed by traders to perform the financial manoeuvrings.

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