Algorithmic Trading in India: Critical Analysis of SEBI's Framework for Retail Investor

[Aahini and Rudraksh are students at Gujarat National Law University.]

Algorithm trading (algo trading) uses computer programs to automate and execute financial market trades. The system operates on pre-programmed rules that factor in price, timing, quantum, and other metrics. Consider an example of a trader who follows this simple trade criterion: Buy 50 shares of a stock when its 30-day moving average goes above the 100-day moving average. Computer software will constantly monitor the stock price and place buy and sell orders based on this simple instruction when the predetermined conditions are met. The trader no longer has to enter orders or monitor real-time prices and graphs manually. The algorithmic trading system automatically achieves this by accurately detecting the trading opportunity.   

This technology was first introduced in India through direct market access in 2008. This remained mainly in the hands of institutional investors and high-frequency traders for over a decade. A recent study of the Securities and Exchange Board of India (SEBI) revealed that 97% of foreign portfolio investors and 96% of proprietary trader profits stem from algorithmic trading. Recognizing the potential for broader participation, SEBI released a circular titled “Safer Participation of Retail Investors in Algorithmic Trading” on 4 February 2025. Detailed operational standards will be finalised by 1 April 2025, and the new rules will be implemented from 1 August 2025.

As SEBI opens the gates of algorithmic trading to retail investors, this blog critically examines the circular and addresses the key gaps and challenges. The democratization of algo trading could transform how millions of Indians participate in the stock market, but are we ready for this new world of automated trading?

Regulatory Features of the Circular

SEBI released a consultation paper on algo trading by retail investors in 2021, which laid the initial groundwork. However, this circular attempts to address crucial gaps in implementation and accountability. The key features of the circular are as follows:

Broker’s role and responsibilities 

Stock brokers must secure permission before the exchange of algo trades. They are responsible for tagging all algo orders with a unique identifier, and in case of any modifications to approved algos, they require fresh approval. Retail investors who develop their algos must register them through brokers and can extend usage to immediate family members.

API implementation structure

Application programming interface is a code that allows developers to access financial data in real-time. Brokers shall not permit open APIs and allow access only through a unique vendor client-specific key. To ensure security, brokers must implement OAuth-based authentication with two-factor verification. 

Algorithm categorisation

The framework proposed two categories of algos: White Box Algos and Black Box Algos. Execution algos or White Box Algos are based on fully transparent algorithms where the logic is dissolved and replicable, whereas, in Black Box Algos, the logic remains proprietary and is not replicable.

Stock exchange supervision

The exchanges will monitor post-trade activities. They will also be able to use a kill switch, an emergency function used to stop trading activities based on pre-defined rules. Furthermore, the turnaround time to register trades will be decided by stock exchanges based on the type of algorithms.

Registration of algo provider

Algo providers providing the facility to place algo orders with brokers through API shall be required to be empaneled with exchanges. 

Concurrent with this framework, SEBI, in its 208th Board Meeting on 18 December 2024, had also introduced the Past Risk and Return Verification Agency, which is a strategic initiative to address performance reporting gaps in algorithmic trading. By deploying a credit rating agency as the verification mechanism and a stock exchange as the data centre, it aims to validate risk-return metrics for investment advisors, research analysts and algo trading providers. Initially, it will be operational on a two-month pilot program. While the participation is voluntary, this initiative represents a significant step towards standardizing performance claims. 

Key Challenges

Operational risk framework: Technical failures and legal accountability

While algo trading can be efficient, it can pose significant risks due to technical glitches, resulting in massive financial losses. A minor error like a server malfunction or a coding bug could trigger unintended trades or even cause a series of rapid trades that could disrupt the market. More participants could lead to higher transaction volumes with the proposed inclusion of retail investors. Since retail investors may not have proper risk management systems, they are more susceptible to significant losses.

Algo trading can also lead to flash crashes, which occur when stock prices drastically drop and rebound quickly. One of the most famous examples of Flash crashes occurred on 6 May 2010, when the Dow Jones Industrial Average fell by over 1000 points in 10 minutes. Still, the market regained its composure and eventually closed 3% lower. The crash was attributed mainly to a large sell order from a mutual fund combined with high-frequency trading algorithms. These incidents highlight the complexity of algo trading. However, the challenge is determining whether the fault lies with the broker for inadequate oversight or the algo provider for flawed programming.

The kill switch mechanism: Complexities of the automated trading halt protocol

SEBI has proposed putting the “kill switch” mechanism in place, which would bring trading to a halt in emergencies. With the help of this mechanism, malicious algorithms could be prevented from running amok and disrupting orderly conduct. A kill switch is a safety tool in software that lets developers quickly turn off a feature if it’s causing problems. It helps stop further damage or inconvenience to users while maintaining the stability of the broader system. However, the arbitrary and erroneous nature of the kill switch can cause unfair disadvantages to retail investors and disrupt the actual trading process. If an automated kill switch activates at the wrong time, it could make the system unstable. 

Suggestions

To address the challenge of legal accountability, SEBI could develop a dual liability framework, clearly specifying the responsibilities of the broker and algo provider. Brokers monitor the execution of trades to ensure compliance and verify that the trades align with the client’s requirements. Whereas the algo providers are responsible for providing an error-free code. This dual accountability could reduce the risks and enhance investor trust.

Additionally, implementing a robust kill switch mechanism is essential to manage unnecessary disruptions in the market. The governance protocol for this has to be very clear, and there must be clear guidelines on when the kill switch should be activated. Thus, it will leave no room for arbitrary interventions and help retain market integrity without sacrificing the investor’s confidence. Furthermore, having skilled people monitor the systems could help avoid such issues, as human judgment often plays a key role in solving problems. Fast and complex markets need advanced technology and experienced humans to keep things running smoothly.

Despite these preventive measures, disputes arising from algo trading remain a significant challenge. Though SEBI’s circular contains several new regulatory requirements, it does not provide adequate guidelines for settling algo trading disputes. This gap exposes the investors to lengthy and expensive litigation, discouraging them and reducing confidence in the system. A streamlined arbitration process offers a practical solution for quicker and more targeted resolution of disputes. This approach can solve conflicts more efficiently than traditional court proceedings and will provide a streamlined way to address issues related to algorithmic trading.

In other markets, such as in the USA, under the FINRA Arbitration Rules, arbitration is being used to resolve securities-related disputes. They maintain a forum that handles disputes between retail investors, brokerage firms or individual brokers. Similarly, SEBI could also implement a structured arbitration process for dispute resolution. This can be integrated into the existing Online Dispute Resolution framework as stipulated in SEBI’s master circular dated 28 December 2023. This will offer a cohesive solution to the new challenges emanating from the algo trading. A dedicated system will improve market strength and promote the correct form of algo trading that complies with SEBI’s goal of a fair and transparent environment.

Conclusion

SEBI brings a revolution in algorithmic trading integration to the retail segment, providing a broader participation and innovation zone for the market. However, with this shift comes some significant challenges, such as weak liability frameworks, the issue of kill switches and the lack of effective arbitration systems, especially on matters related to algo trading. SEBI’s intent to expand the scope of algorithmic trading is commendable, but its success depends on addressing these challenges effectively. India can build a more effective algorithmic trading system by balancing technology, regulations and market awareness. It will eventually improve market efficiency and help establish India as a world leader in financial innovation.