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.

However, in algorithmic and high frequency trading (commonly referred to as HFT), India still lags the US and Europe. But it is quickly catching up. Algorithmic trades make up around a third of all trades done in India, while in the US that figure is closer to 70 per cent.

In short, algorithmic trading involves the use of algorithms in pre-built platforms to place electronic trades on stocks, futures, options, currencies and commodities on exchanges, with no human intervention.

The algorithms are pre-programmed and make use of information in the market — such as market prices and quantities — to generate buy and sell signals.

Therefore, since no human intervention is involved, algorithmic trades are usually faster and more efficient than manual trades placed by traders.

The algorithms can be relatively simple, such as buying a stock on one exchange and sell it on another where the same stock is priced higher (called arbitrage), — to very sophisticated algorithms that make use of all kinds of information available in the market.

In India, the most commonly used strategies include arbitrage, market making and trend following algorithms.

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.

That is exactly what happened when the Dow Jones Industrial Average (the US equivalent to India’s Sensex) suddenly dropped almost 10 per cent within a few minutes, as algorithm based systems kept triggering each other with “sell” trading signals.

Eventually, the Dow bounced back up, but the 10 per cent drop is the largest single day slide in the history of the Dow.

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.

In fact, in the US some trading firms look to save nanoseconds 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.

The NSE and BSE provide co-location services which allow firms to rent rack space at the exchange data-centres, thereby ensuring that they are connected to the exchange via the lowest latency connections possible.

With the upcoming elections and increased volatility in the markets, we could see many algorithmic trading engines firing off thousands of orders over the next few months.

(The author is co-founder RKSV)

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