From Co-location to Kill Switches: Analyzing India’s HFT Framework

[Sachetan and Sachin are students at National Law University Odisha.]

High-frequency trading (HFT), which uses cutting-edge technology to execute deals at extraordinary speeds, has emerged as a disruptive force in the fast-paced world of financial markets. With more than 50% of trading activity now being driven by sophisticated algorithms and computer programs, HFT has completely changed the trading landscape in India. Furthermore, algorithms that favor private desks drove high-frequency trading to generate a revenue of USD 7 billion in the options market.

The evolution of HFT in India started in 2008 when the Securities and Exchange Board of India (SEBI) introduced Direct Market Access, authorizing institutional clients and traders to carry out trades via automated algorithms. According to recent projections, the HFT with a compound annual growth rate of 9.6%, is expected to increase significantly between 2025 and 2030. While bringing a major shift in increasing market efficiency and liquidity, it poses various complex issues, prompting the SEBI to form robust rules and regulations.

Alternatively, there has been criticism over the introduction of HFT. Critics argue that it may place retail investors at a disadvantaged situation and increase volatility during market stress as the retail investors lack sophisticated technology. This argument is buttressed by a SEBI report which reveals that 93% of futures and options traders faced an average loss of INR 2 lakh each between FY22 to FY24. To preserve market integrity and prevent losses, while leveraging the benefits of HFT, SEBI has taken proactive measures such as tightening regulations on order-to-order ratios (OTR) and stepping up oversight retail algorithmic trading. SEBI’s most recent regulatory measures, including severe fines for excessive OTRs and guidelines for safer participation, shall go into effect on 1 August 2025.

In this article, we shall thoroughly examine the effectiveness of HFT in India and global financial markets, along with its effects on market dynamics, evolving regulatory environment and ongoing debates about its place in the financial ecosystem.

Regulatory Development Over the Years

In order to preserve a fair and steady market environment, SEBI has been in the forefront of HFT regulations. SEBI first published instructions on algo trading in the year 2012. The SEBI circular required stock exchanges to have load-management policies, processes, and system capabilities to ensure that all brokers had consistent response times. The stock exchanges were directed, among other things, to use broker servers to process algo orders and to have suitable risk control systems in place to handle any potential risks associated with these transactions and orders. Additionally, it was mandated that stock brokers obtain prior approval from the stock exchange before offering its clients the ability to trade using algorithms.

In order to restrict excessive order submissions, SEBI released guidelines on order-to-trade ratios in June 2020. These guidelines allow stock exchanges to set OTR levels up to 2000, with additional penalties. As a cooling-off measure, traders who exceed an OTR of 2000 three times in a 30-day period will be suspended from trading for 15 minutes the next trading day.  The orders per second (OPS) cap for algorithmic trading in the commodity derivatives industry was revised from 100 to 120 OPS by SEBI in March 2022. This modification was implemented to handle higher trading volumes while ensuring exchanges possess adequate infrastructure capacity.

Significant changes were made by SEBI through a circular dated 4 February 2025 to regulate algorithmic trading, particularly with regard to retail investors. According to the new rules, brokers must authorize and supervise all algorithmic strategies, and also suppliers must register with exchanges. In order to increase transparency and regulatory compliance, retail traders must use permitted static IPs for API trading. These steps aim to lower hazards and steer clear of unfair benefits that are commonly associated with HFT. 

While traders who exceed 2000 three times in 30 trading days may be subject to cooling-off measures, those with an OTR less than 50 are not penalized. Additionally, SEBI has enhanced the OTR computation by using theoretical prices for untraded contracts based on the Black-Scholes model to account for orders outside of 0.75% from the last traded price or 0.50% if applicable. A consultation paper on this issue is likely to be released shortly.

Notable Instances of Misuse of High Frequency Trading

While HFT offers benefits such as rapid execution, increased liquidity, and cost efficiency, past incidents in India and worldwide underscore the risks it poses to market stability.

One such instance is National Stock Exchanges (NSE) co-location scam which exposed the shortcomings in high-frequency trading provided through its co-location facility. Co-location facilities are specialized spaces equipped with infrastructure like power supply and bandwidth, which third parties can lease for high-frequency and algorithmic trading.

The scam was unearthed when a whistle-blower through a letter to SEBI alleged that the NSE had granted certain high-frequency traders and brokers preferential access to its trading platform. Several high-frequency trading firms and brokers benefitted from this. NSE violated its own policy by letting non-ISP (entities without licenses) to lay down dark fibre in its premises which substantially benefitted high-frequency trading firms like Alpha-Grep, OPG Securities. SEBI's Technical Advisory Committee report found that between 2010 and 2014, OPG Securities used the co-location facility to log in to NSE servers ahead of others, gaining early access to market data. SEBI alleged the firm made nearly INR 250 million in profits, while a whistle-blower claimed it executed trades worth over INR 60 billion using this advantage. 

Globally, the flash crash of 2010 unmasked the role HFT can play in fueling instability and unpredictability in the market. In a matter of over half an hour on 6 May 2010, the Dow Jones Industrial Average plummeted by more than 1000 points wiping out around USD 1 trillion in market value. Spoofing was thought to have contributed to the crash. Spoofing is a highly disruptive algorithmic trading maneuver where a malicious trader places a large volume of orders to distort the apparent supply or demand of an asset, only to cancel those orders before execution. Leveraging high-frequency trading tools, the trader exploits the temporary shift in market sentiment to buy or sell at more favorable prices before the market self-corrects.

Later, a report was released by US Securities and Exchange Commission and Commodity Futures Trading Commission which indicated to a major mutual fund, Waddell & Reed Financial Inc., selling 75,000 e-mini S&P contracts having market value of more than USD 4.1 billion. These contracts were initially purchased by high-frequency traders who then went on a selling spree and the resulting wave of selling overwhelmed the market, which led to prices going down as demand dried up. Further, in April 2015, Navinder Singh Sarao was arrested for using spoofing algorithms that caused the flash crash. His company made around USD 40 million from 2009 to 2015 using the same technique.

Further, while the flash crash of 2010 exposed the disruptive trading maneuvers like spoofing, Virtu Financials' case points out that even prominent and successful players can cause risks in the market when lapses in algorithmic controls occur. Virtu Financial, a high-frequency trading firm which claimed to have lost 1 day in 1238 days of trading has faced multiple fines ranging from USD 70,000 by the NYSE and NASDAQ and EUR 5 million by French regulators due to lapses in its high speed trading activities. Major causes are improper price quotes, order errors and failure to comply with market rules. It exposes the HFT firm’s lack of oversight over its automated trading algorithms and business practices.

So, HFT has drawn criticism majorly because of its tendency to cause market volatility, unfair advantages given to traders with advanced technology, and the possibility of market manipulation through techniques like spoofing and layering. Systemic risks are further highlighted by unexpected flash crashes brought on by algorithmic errors. Therefore, these issues require policy reforms to ensure financial stability in the market.

Putting Forward Policy Reforms

Considering the volatility and uncertainty that high-frequency trading can bring to the market, the authors recommend certain measures that India’s securities market regulator can adopt to prevent excessive market fluctuations.

SEBI’s recent circular mandates the empanelment and registration of algo providers with exchanges, which raises concerns given NSE’s past collusion with these providers to grant them unfair advantages. To enhance oversight of algorithmic trading providers, registration should be mandated either with a dedicated regulatory body or with SEBI itself. A similar approach has been adopted in Germany, where high-frequency traders are required to obtain a license from the German Federal Financial Supervisory Authority under the High Frequency Trading Act of 2013. In such case, the supervisory authority can closely monitor HFT activity, identify patterns of abusive trading and enforce penalties. Firms engaged in HFT trading need to ensure that their trading systems are resilient, placing of incorrect orders or malfunctioning of the system which can lead to market disruption should be avoided. Overall, the authority has better control over HFT firms.

Since HFT involves placing large volume of orders using complex computer algorithms, appropriates guidelines should be laid down to ensure independent testing should be done of these algorithms before putting them into operation. In this regard, the regulator can draw from FINRA “Guidance on Effective Supervision and Control Practices for Firms Engaging in Algorithmic Trading Strategies”. Additionally, a centralized algorithm testing sandbox can be established specifically for high-frequency trading to facilitate the effective development, deployment, and oversight of HFT activities.

Another area of concern has been SEBI imposing stricter penalties for excessive OTR. Although SEBI's intentions are not in doubt, several studies have found that imposing higher penalties for high OTRs tends to reduce market liquidity. In this context, the regulator can maximize the OTR thresholds for recognized liquidity providers in Indian market and impose stricter penalties only when excessive OTR depicts behavior of market manipulation.

Kill switches have been acclaimed as a remedy for issues linked to HFT. These mechanisms are used by exchanges to suspend trading whenever the market's value drops beyond a predetermined percentage within a certain time frame. However, the problem is that these kill switches themselves operate using computer algorithms which may start behaving in an erratic manner as algorithms underpinning HFT.

In such a situation, a centralized kill switch system managed by exchanges should be implemented, with oversight extending from its development to deployment. This would help ensure that the algorithms driving the kill switches are of high structural quality and have been thoroughly tested. Exchanges may also periodically carry out simulation exercises to ensure that the kill switches function as intended under various market conditions. Another alternative is to require traders to wait at least 0.5 seconds before executing trades during volatile market conditions, which would provide time to halt malfunctioning algorithms before they cause widespread disruption. European Parliament considered this alternative but never brought it into law because it may lead to less liquidity in the market, wider spreads and even market participants may start going away from exchanges.

Lastly, exchanges should provide co-location services equally to all participants to prevent large investors with significant resources from gaining an unfair advantage and front-running smaller, retail investors.

Conclusion

HFT's contribution to improving market efficiency and liquidity in India is evident. By consistently placing sizable volumes of buy and sell orders, narrowing bid-ask spreads, and guaranteeing more seamless trade execution, high-frequency trading increases liquidity and enhances market efficiency. Industry sources indicate that the introduction of co-location services by exchanges like the Bombay Stock Exchange and NSE has significantly reduced bid-ask spreads and increased trading volumes. However, it has not been without its own flaws, which have led to disruption and instability in the market. Consequently, careful oversight of its development, implementation, and operation is essential to ensure a safer marketplace.