Algorithmic Trading share in total turnover grows to 50% in 8 years



Algorithmic trading in India across the cash and derivatives market as a percentage of total turnover has increased up to 49.8% in eight years from merely 9.26% (average) in 2010. In March this year, 44.8% of the cash market volume and 48.2% of the equity derivatives market was driven by algo, showed NSE data. On the BSE, 37.22% of trade in March 2018 was driven by algo trading.

The daily turnover of equity market is around Rs 25,000 crore to Rs 30,000 crore, and in the F&O market, it is around Rs 3.5 lakh crore to Rs 4 lakh crore on a daily basis. However, as far as awareness of the retail investor is concerned, it is less in India. This is mainly because it requires specialised skills in addition to tools.

Sebi’s recent announcement on steps for strengthening algo trading through shared co-location has boosted the sentiments of algo solutions providers.

Experts believes that in the near future human-machine interaction could go to the next level. Through Deep Learning, AI, algorithms will self-correct and adapt to dynamic markets. Algos will be everywhere, in HFT, mid-to-low frequency, arbitrage, scalping, hedging, market making and anything you can define to a machine.

The efficiency of almost any trading done on the exchanges can be improved by leveraging technology. Automation of the trading process not just improves the efficiency of the trading participants, but also improves the efficiency of the market itself — arbitrageurs use automation to rectify pricing anomalies; and market makers use the power of technology to improve liquidity by providing continuous buy and sell quotes which automatically adjust to events and risks in the market.

Sebi allows options contracts in commodity trading.

Exactly a year after strengthening regulation of the 13-year-old commodity derivatives market, the Securities and Exchange Board of India (Sebi) has taken the first steps towards its growth by allowing exchanges like MCX and NCDEX to launch options in commodities.

Also, it has expanded the list of notified commodities that exchanges can launch by adding to it eggs, diamonds, skimmed milk powder, tea, cocoa, pig iron, biofuels and brass.

Sebi will spell out the details of the type of options and the products on which they can be launched in due course. An advisory committee constituted by Sebi after erstwhile commodity regulator FMC was merged with it on September 29 last year had recommended launch of gold and refined soya oil options initially.

The introduction of new commodity derivatives products is considered to be conducive for the overall development of the commodity derivatives market, attracting broad base participation, enhancing liquidity, facilitating hedging and bringing in more depth to the commodity derivatives market,” Sebi said in a circular. “The commodity derivatives exchanges willing to start trading in options contracts shall take prior approval of Sebi for which detailed guidelines will be issued in due course.”

Other important regulations are allowing equity exchanges like NSE and BSE to launch commodity futures segment and commexes like MCX and NCDEX to launch equities and currency segments.

Sebi will also enable margin fungibility by permitting merger of a commodity subsidiary of a brokerage with itself. In time, other products, like indices, and institutional participants like mutual funds, FPIs etc could be allowed to deepen the market.

Indeed options comprise 75% of NSE’s total derivatives turnover of Rs 404 lakh crore in the fiscal year so far. Average daily turnover of equity derivatives on NSE has been Rs 3.31 lakh crore against just Rs 25,000-30,000 crore for MCX, NCDEX and NMCE, where only futures are traded and institutional participation disallowed.

Since delivery is envisaged, the type of option could be American style though markets have crossed their fingers. “European styled options are being traded in Indian equity and currency derivatives markets, American styled options for commodities are in vogue in developed markets like CME. We are awaiting guidelines from Sebi to decide on the product type,” said Mrugank Paranjape, MD, MCX.

“For farmers, it (options) will be a game changer,” said Samir Shah, MD, NCDEX. “It would help them to sell their produce in the derivatives market and thereby get the benefit of price protection in case the price falls below their cost of production and also derive the benefit of any rise in the price. Options are also a much better hedging instrument as compared to futures for hedgers.”

Source – The Economic Times.(September 29, 2016)


Algo trading turnover at a historic high on popularity

Algo trading turnover at a historic high

With increasing volatility in the equity markets and stiffening competition among brokerages, more and more institutions are adopting technology to use algorithmic and high frequency trading (HFT) to stay in the race.

According to stock exchange data, algorithmic trading accounted for 14.94% and 20.78% of total cash market turnover on BSE and NSE, respectively, in February 2013.

According to BSE, the share of algo trading has never been so high before.

While NSE declined to share the historical data related to the share of algo trading, information on BSE website clearly shows that algos have gained immense popularity in the recent months. For instance, algo accounted for less than 10% of the total turnover in December last year and stayed in the range of 5-8% for most part of 2012.

Algo trading refers to the use of computer programs to execute trades in the stock, commodity or other financial markets. These programs execute trades as and when the pre-defined parameters related to price, timing, quantity are triggered. Complex trading strategies can also be implemented using algos.

Market experts say that technology — by way of algo-based trading and HFT — continues to play a big role in changing the brokerage industry as it helps in executing orders in fraction of a second with utmost efficiency, accuracy and without human intervention.

“Technology is playing a huge role. It might replace human beings one day, although not completely. If the same task could done with the help of a machine in more efficient and time saving manner, why would you not invest in it?”

Market experts said that these software have advanced in such a way that one can do derivatives rollover by a click of a button. “Earlier a dealer had to sit in front of the screen and manually feed the order. It was time-consuming and a costly affair”.

“Proprietary desk of international brokerages the world over wanted DMA into Indian equities so that they could punch their orders using algos without the need of a broker in India…While the concerns raised by the market regulator are appreciated, I do not think the regulator can take a step back.”

The RBI had highlighted the risks attached to algorithmic trading in its June 2012 Financial Stability Report. The report stated that several instances of extreme volatility and disruptions were witnessed in Indian stock markets that could be directly and indirectly attributed to the increased use of algorithmic trading.

Why We Shouldn’t Ban Algorithmic Trading ?

Why We Shouldn’t Ban Algorithmic Trading

On Friday, 20th April 2012, two mysterious events occurred on the National Stock Exchange (NSE). In the morning, Infosys futures crashed over 20% and quickly recovered back to the original level. In the afternoon, just before 2:30pm, Nifty futures crashed 6.7% from the 5,350 level back down to 5,000, and then nearly instantly recovered back to 5200. Both crashes were blamed on algorithmic trading.

Program trading has been blamed for “flash crashes” for nearly 25 years. In October 1987, US markets took a nose dive on a single day and for years, the blame game went on with the primary suspect being program trades. This is understandable. Since a computer can trade with much faster speed than a human, it can set off a spiralling price change by continuously buying or selling with no real control. Why then, should we even allow algorithmic trading?

Program trading can provide for great trading opportunities with less human error. Much of the arbitrage that used to happen in Indian markets was manual. To bridge any potential difference in prices between the NSE and BSE (the two largest stock exchanges in India), arbitrageurs would use two computers, manually entering a buy order on one and a sell order on another. The speed of the operator was his biggest skill, so the dealing room would resound with a cacophony of keyboards when an opportunity arose. The problem? A human can only look at so many opportunities, so many price differences remain. A slight error on that keyboard (F2 instead of F1) can result in a large loss. The operators cost money in terms of computers, real estate and benefits. You could eliminate much of these by using a computer to do exactly the same thing.

Program trading can curtail broker front-running and impact costs. Often, when a fund would have to take large positions, their brokers would put their own buy orders earlier, so that the large purchase from the fund would give them a great profit. In India, much of the volume is made up of the top 100 stocks. After that, stocks trade less than 20 cr. a day. For a mutual fund to buy about 25 cr. ($5 million) in a lesser known stock, its size will immediately drive up the price and a broker is quite likely to front-run their purchase. Using an algorithm instead will allow the purchaser to spread their purchase over several days and several brokers, hunting for volume slowly over time.

Algorithms can also provide liquidity where there isn’t otherwise any. For you to purchase a stock, there needs to be a seller in place. Many stocks don’t have the kind of interest from either investors or traders. Of the near-1,500 stocks traded on the NSE, more than 1,000 trade less than 50 lakh (Rs. 5 million) a day. The spread between the buy and sell prices on the exchange may be too wide; a typical market maker provides the liquidity that allows you to buy or sell at a reasonable cost. Market making operations used to be manual earlier; they are now run through algorithms.

Finally, large orders (greater than a few crores in value) are usually blocked by stock exchanges, assuming there has been a fat finger trade or a mistaken entry. Yet, large deals must take place when they must — if an investor decides to exit a large holding in a stock, they might use an algorithm to send in orders in allowable chunks.

Algorithmic trading, however, comes with its own set of problems. A rogue program can place orders continuously and take the entire system down. During Mahurat Trading in October 2011, such a program created a ruckus in the BSE, so much that the exchange canceled all trades made on that day to avoid a payment crisis.

Even if an algorithm splits large orders into parts that stock exchanges let through, the input itself may be faulty (an extra zero for instance) which means the algorithm does exactly what the order limit was designed to restrict: the fat finger trade. This, they say, is what happened with the Infosys order on Friday, 20th April 2012.

With such large orders, stop-losses can get triggered, creating another spiral. Some traders place a protective stop (in simple terms: “Sell-If-The-Price-Falls-To-X”) way below market prices; such stops get taken out when such large orders come by and those investors that sell feel disappointed when the market rebounds immediately. But that will happen even with large “manual” orders; algo trading is only a convenient scapegoat.

That steep falls are only engineered by automated trading is also suspect. The market has “circuit” limits which shut down trading when the index moves over 10%. After the 2009 elections, when markets moved up 10% in a short while, algorithmic trading wasn’t blamed; neither was it when markets crashed 10% in October 2009. The feeling was that the moves were “justified” since there was news behind it (The election results and a Lehman bankruptcy impact respectively). Since there was no “reason” on Friday, computers must have been to blame.

This is just witch-hunting. Computers do exactly as they have been programmed to do, and there will be large errors if they aren’t monitored properly (as manual traders must be). The regulators and the exchange must investigate each such case, and indeed, it has turned out it was more the human input that caused the error. Every rogue trader or trading program must be found and punished. Surveillance needs to get much more sophisticated to detect misbehaving automated trades. Algorithms already use a different code when entering trades; a series of checks can be run whenever required to see if any rules were violated. Some of this cost needs to be borne by the algo-trading community, by fees like a per-transaction or per-order fee payable to SEBI.

But we can’t go around demanding bans on algorithmic trading just because of a flash crash. Knee jerk reactions like that will hurt legitimate players or put them at the mercy of their brokers, and that is plain wrong.

Algorithmic Trading Gaining Ground

Algorithmi_Trading_gaining ground

India is programmed to go the algorithmic trading way just like rest of the world since it helps to sidestep many ‘human errors’ that trip high volume traders adopting traditional trading methods.

The future of trading is in ‘programmed trading’ and India is heading the same way as other countries have been doing. Most of the day-traders lose money and it is estimated that 95 per cent of trading ended up in losses. While there is no assurance that by feeding the trading strategy into a computer, the success of it could be ensured, the chances of successful trading could go up. This is because many of the human weaknesses, like increasing the size of the order while smelling profit that would result in losses ultimately, will be eliminated and ‘that is the most important thing’.

No Human Emotion

The algorithmic trading is based on a programme written by an analyst or trader with parameters set earlier and it could be a jobbing programme or arbitrage trading etc. The advantage is that ‘human emotions will not play a part’ since the computer would execute the orders according to the programme.

It is a fully-automated process, no human intervention is needed and it is ‘away from sentiments’. The traders could do the trading in cash segments, F&O segment etc and the trading could be done in different scrips and there is the facility of putting stop loss too. The volume buying could be done at different price points giving the buyer the benefit of cost averaging and the buy/sell could be done in micro seconds. The high volatility the markets have been witnessing in the past six months has given greater opportunity for algorithmic trading.

While algorithmic trading facility is available in both NSE and BSE, because of the huge trading volume in NSE, more trades are taking place in that exchange. But one could buy in one exchange and sell in another. The algorithmic trading helped in improving liquidity and the traders could fix their own targets for buy or sell, which could be executed automatically.

Algorithmic trading is more suitable for trading in frontline stocks that had ample liquidity because when a sell order was generated, it should be executable. For HNIs, who have on hand a large volume of shares, they could set different price points for sale to average the selling price; and greater the market volatility, the greater is the opportunity in automated trading.

Algo Trading in India – Statistically Speaking !!!


Algorithmic trading is all the rage in India right now, and across the market the view is unanimous: the only way is up. The question is how just fast it will grow.

By the start of 2012, Algo accounted for some 24 percent of cash equities turnover in India and about 30 percent of equity derivatives. According to figures from the Bombay Stock Exchange, by far the smaller of the two Indian exchanges that dominate equities trading, the share for equity derivatives has already jumped to 45 percent since then.

Algorithms and High frequency trading are the hottest topics in the market – algorithmic trading and HFT itself, and now the regulations around it. This is what the majority of players in the market are focused on today.

India has the building blocks in place for a ramp-up. Co-location has been available from both the Bombay Stock Exchange and its bigger competitor, the National Stock Exchange, for 18 months. Both exchanges, and market observers, say their trading platforms can handle HFT. Direct market access is available. Smart order routing between the two exchanges has also been operating since August 2010.

The Indian regulator, the Securities and Exchange Board of India (SEBI), produced guidelines for algorithmic trading in March which brokers, exchanges and market watchers hail as a sensible response. The new rules, they say, recognise that algorithmic trading is a natural development and are aimed at preventing problems but not blocking growth.

“All the dynamics point to an increase in automated and algo trading in the next few years,” Expect the cash equities and derivatives levels to raise around 50-55 percent within the next year or so.

Learn to live with Algo Trading

Algo Trading

Equity markets have entered an era where machines are rapidly replacing humans. Programme-driven trades account for around 70 per cent of equity trading in the US and for 40 per cent in Europe. We in India are not far behind with one-third of trades in both cash and derivative segments of the National Stock Exchange driven by such programmed orders.

While many of these programmes use algorithms that execute orders through the day, speed is vital in one subset of algo trading known as high-frequency trading (HFT). In HFT, the programme that smells out opportunities and executes them the fastest, scores. Execution time is measured in milliseconds, or one-thousandth of a second. One buy and sell transaction could take just 10 milliseconds and in the race for being the fastest, traders are moving their terminals as close to the exchange servers as possible.

Greeksoft Automated Trading Software (G.A.T.S)


For traders who are in the hunt for arbitrage opportunities in the market, we explain how algorithmic trading works. Algorithmic trading refers to automation of the process of placing orders by using a software that runs on mathematical programs.
One of the key applications of this software that runs on a coded algorithm is — arbitraging. Jobbers, who play on the price differences between the NSE and the BSE or spot and futures market, needn’t stare at multiple screens to identify an opportunity for arbitrage.

Quick spotting, precision in deciphering a trade’s worth and order execution speed are highlights of algorithmic trading. By avoiding human intervention algorithmic trading enables swift order execution.

Let’s now see how the algorithmic software works:

Step 1: As the opening bell goes, price feeds from different exchanges flow into the trader’s terminal.

Step 2: The algorithmic software monitors price ticker and will be in a constant search for opportunities to execute the set orders. On finding matching set of data, orders are triggered and they fly to the exchange in rapid speed.

Step 3: The order hits the exchange’s server and gets executed, sending back a confirmation to the trader.