Algo trading in India surges but still has room to grow

Algorithmic trading increasingly dominates stock markets, with around 80% of equity transactions globally carried out by this technique.

It is also a well-established component of traders’ toolkits in India, but with algo trading making up around 55% of all trades a couple of years ago, there is still plenty of room for growth in this booming South Asian market.

For technology vendors, some research expects the global market to be an USD 18.8bn commercial opportunity this year, underlying the strength of demand by firms looking to enhance their trading technologies, especially in Asia.

(source — Algorithmic Trading Market Insights, Share, Growth, Industry Analysis, Forecast 2024 | MarketsandMarkets™)

Algo solutions optimized for the Indian market are increasingly in demand due to the growth in passive investing, principally through exchange-traded funds (ETFs). Between 2021 and 2023, the total assets under management (AUM) of Indian ETFs increased by nearly 70%. As passive investors look to cut expenses and brokers seek to expand their low-touch trading capabilities, and slash costs by downsizing trading desks, algo solutions are becoming more widespread.

Algorithmic trading: what is it exactly?

Commonly used by investment banks, hedge funds and other financial institutions to eliminate the emotional component, algorithmic trading is a systematic process of executing large orders using automated pre-programmed instructions, focusing on variables such as price, volume, and time. Relying on complex mathematical models to make high-speed, high-frequency trading decisions reduces the chance of human errors and improves efficiency and market liquidity.

Advantages and drawbacks of algo trading

Although India shares many characteristics with other developed markets, several unique local features significantly impact the application of algorithms there.

F&O trading on the rise

One distinct characteristic of the Indian market is the volume of futures and options (F&O) trading compared to many other nations. The National Stock Exchange of India (NSE) reported a total trading volume of 17.26 billion contracts in 2021, nearly twice as much as the previous year. The massive scale of retail investment has driven interest in F&O markets.

While online brokers provide easy access to F&O trading, the increased market volatility in the wake of the pandemic, the popularity of speculative trading of equity index options and the rapid expansion of commodity futures trading all contribute to the popularity of F&O trading in India.

What the booming F&O market means in terms of algorithmic trading is that there is high demand for cross-asset arbitrage, and for algo strategies that help facilitate it.

Let’s look at some of the commonly used strategies:

• Statistical Arbitrage (StatArb) strategies, which analyze historical price patterns and deviations to identify mean-reverting opportunities
• Pairs trading, where traders simultaneously go long on an undervalued stock and short on an overvalued stock, in order to profit from price convergence
• ETF arbitrage, where traders monitor, and with the help of algo systems, take advantage of price discrepancies between the ETF’s market price and the combined value of its Indian underlying assets
• Index futures vs. component stocks strategies, which track the differences between index futures prices and the prices of individual stocks within the index. Arbitrage opportunities arise when index futures diverge significantly from the combined value of their component stocks.

High OTR and the impact of regulations

Another distinctive feature of the Indian markets is the high order-to-trade ratio (OTR), or the ratio of orders placed to actual trades placed. High OTR, and the regulations governing it, heavily impact the choice of algorithms used in India.

The Securities and Exchange Board of India (SEBI) has issued guidelines on OTR that aim to prevent excessive order placement without corresponding trades. As traders who breach recommended OTR levels now risk penalties or trade restrictions, they prefer algorithms that optimize execution efficiency while minimizing order activity.

Low-latency algorithms that allow for quicker trading than conventional techniques often suit OTR requirements. Other algorithmic strategies are however more impacted by OTR regulations, including:

• Market-making algorithms: they provide liquidity by continuously quoting bid and ask prices. Regulators monitor their OTR closely to prevent quote-stuffing
• Arbitrage algorithms: OTR affects arbitrage strategies that rely on rapid order execution across different markets
• Execution algorithms: Algorithms focused on efficient order execution must balance OTR to avoid penalties

OTR regulations therefore guide algorithm selection in Indian markets, promoting efficiency, fairness, and overall market quality. All algorithms need to be approved for use on the main Indian exchanges, and market participants must attend monthly testing sessions.

Factors to consider in dynamic Indian market

Due to the dynamic nature of the Indian capital markets, in addition to choosing the right algorithm, market players must consider other factors, including:

• Architectural factors, such as distance to exchange, can heavily impact algo efficiency
• Infrastructure: the growth of the wider Indian economy is causing a boom in domestic capital markets investors. The result is high order and trade volumes, and without robust infrastructure, algo solutions may struggle to respond effectively.
• Tools to enhance responsiveness, such as monitoring, alerting and analytics platforms, help users to ensure that their strategies are meeting their defined goals, and delivering alpha.
• Risk management and safety measures that provide security against losses and compliance breaches. These can be used to validate both the orders that are being sent to the market, and the current market conditions. Market participants must have solutions in place get a quick grasp of their risk position: they allow brokers to set precise limits on their order flow, and enable them to establish warnings for parameters nearing their limits, as well as for actual breaches.

As the Indian capital markets continue to grow in size and complexity, algorithmic trading will continue to dominate the landscape. For market participants, choosing appropriate algorithm strategies, tools, and infrastructure is key to meeting their business goals, especially as India leads the move towards same-day and instantaneous settlement.