Between Innovation and Safeguards: Analysing SEBI’s 2025 Algorithmic Trading Circular (Part I)
Introduction
The Securities and Exchange Board of India (‘SEBI’) recently introduced a framework for safer retail participation in algorithmic trading. The SEBI circular dated 4th February, 2025, prescribed clear guidelines for intermediaries such as stock exchanges and brokers who deploy the usage of algorithmic trading software (‘ATS’), recognising the possibility of increased risk, misuse, and manipulation. ATS refers to a computer program that follows a set of pre-programmed instructions to automatically analyse real-time market data, identify trading opportunities, and primarily execute buy or sell orders at high speeds without human intervention. This two-part paper comprehensively seeks to analyse the efficacy of this move, considering potential practical implications and relating it back to SEBI’S overarching objective of investor protection. Part I discusses the guidelines, evaluates the rationale and legal basis, and analyses the positive implications of this introduction. Thereafter, Part II identifies the practical negative consequences and conceptualises a model, arguing that although useful and important, the guidelines would further benefit through the enhancement offered by the model, effectively resolving the identified critiques.
Brief Description of the Stipulated Guidelines
Building upon SEBI’s 2021 consultation paper on algorithmic trading, the use of Application Programming Interface (‘API’), and the automation of trades, SEBI introduced these comprehensive set of guidelines. Firstly, with respect to API, SEBI provided that all ATS orders should have a unique identifier with brokers acting as the principal and the ATS provider their agent. Even retail investors can develop their own ATS which has to mandatorily be registered with the stock exchange, through their broker, if they cross the specified order per second threshold. These registered ATS are permitted to be used only by the requisite developing retail investors and their family, restricting general market access. Secondly, the duties and responsibilities of brokers have been clearly laid down which include the provision of ATS only after obtaining permission of the stock exchange, the establishment of an audit trail through the unique identifier, and efficient handling of investor grievances. Brokers are also mandated to monitor APIs, prohibit open APIs, implement two-factor authentication mechanisms for authenticating access to API through open authentication only, and ensure the presence of systems and procedures to correctly identify algorithmic orders. Moreover, brokers are also required to conduct adequate due diligence before on-boarding the provider. Additionally, the roles and responsibilities of exchanges have been clearly laid down which include the supervision of algorithmic trading through the specification of standard operating procedures (‘SOPs’), surveillance on orders and effective monitoring, specification of turnaround time for registering the algorithms, both on a fast track and normal basis, and defining the roles and responsibilities of brokers and the criteria for empanelment of algorithmic providers. They are also obligated to issue frequently asked questions covering aspects such as the roles and responsibilities of the intermediaries, registration process, measures to enhance confidentiality of retail algorithmic strategies, and data flow between the ATS provider, the broker and the exchange. Lastly, the algorithms have been categorised into two categories: Execution or white box algorithms which are replicable and those where the logic is unknown to the user and thus not replicable, termed as black box algorithms. For the latter category, the provider has to register as a research analyst, maintain a detailed research report and re-register the algorithm in case of any change in the underlying logic.
Evaluation of the Rationale and Legal Basis
Section 11(1) of the SEBI Act, 1992, provides for SEBI’s duties to protect the interest of the securities market through any measure it deems fit, as has been discussed by the Supreme Court in the Sahara India Real Estate Corporation Limited v. SEBI case. This reflects SEBI’s objective of investor protection and implies the existence of discretion to adopt any safeguard solely on the basis of SEBI’s subjective reasoning. It is clear from the phrasing of this provision itself that the power resulting from the corresponding duty of SEBI is broad enough to encompass the passage of the current guidelines. This is because firstly, the circular itself clearly mentions that these guidelines have been furnished in pursuance of Section 11(1) of the SEBI Act. Secondly, the very rationale behind the introduction of this framework is to protect retail investors from misuse and unfair manipulation of ATS, alongside the effective management of the risks persisting in such a system including systemic and technical failures and the possibility of information asymmetry with sophisticated institutional players having better infrastructure and clearly defined proprietary strategies. Since Section 11(1) specifically provides for the mandate of investor protection, it can be correctly interpreted to encompass these guidelines. Additionally, when read with Section 11(2), the mandate is reinforced because this sub-clause specifically empowers SEBI to regulate intermediaries and promote investor education, with a residuary provision enabling SEBI to perform any functions as may be prescribed. These functions directly intersect with the risks posed by ATS such as high information asymmetry and technological opacity which SEBI seeks to regulate through this framework. On a conceptual level too, SEBI’s authority of investor protection becomes clear. Within India’s securities market architecture, SEBI is the only regulator vested with a unified mandate, extending to oversight of intermediaries and the power to mitigate risks cutting across multiple verticals. Exchanges and brokers alone cannot effectively discharge the mandate of investor protection as both carry inherent commercial incentives that might conflict with long-term systemic safeguards. Therefore, SEBI’s authority becomes important to act as a statutory counterweight, prescribing uniform guidelines and preventing fragmented or self-interested norm-making by industry bodies. Additionally, Regulation 30 of the SEBI (Stock Brokers) Regulations, 1992, prescribes the legal basis for issuing such a circular. It clearly provides for SEBI’s power to issue any guidelines by way of circulars, required for the effective implementation of these Regulations.
An analysis of the legal basis makes the rationale behind this introduction clear. As has been mentioned above, the primary aim of SEBI while issuing these guidelines is to safeguard retail investors from the potential detrimental effects and risks of the new and untested ATS technology. Parallels can be drawn with the detailed disclosure requirements stipulated under Regulation 30 of the SEBI (Listing Obligations and Disclosure Requirements) Regulations, 2015, which mandates that listed entities disclose any material event or information promptly to the stock exchanges. It lays down an extensive, illustrative list of such information that has to be disclosed. The objective is very clear, which is to protect unsophisticated, retail investors from unknowingly taking a particular action which affects their pecuniary interests. This risk was clearly highlighted in SEBI’s 2021 Consultation Paper on Algorithmic Trading by Retail Investors. It was explicitly mentioned that unregulated or unapproved algorithms could be misused for market manipulation, falsely luring retail investors by guaranteeing higher returns, even without a dedicated investor grievance redressal mechanism. The intention is to strike a fine balance between financial innovation and retail inclusion. It is based on this logic that the Circular positions brokers as principals who are legally responsible for their ATS providers, reflecting an agency-based liability model. The regulatory architecture of registration, empanelment, and surveillance has been used to create accountability across intermediaries. Lastly, the classification of algorithms reflects the importance of embedding a disclosure mechanism because the additional requirement of registering as a research analyst and maintaining a detailed report in the case of black box algorithms reveals that algorithmic categories not having a discernible logic also have to meet the heightened threshold of keeping investors in the loop and ultimately safeguarding their interests.
Analysis of the Practical Positive Implications
Apart from the enhancement of retail investor protection, traceability and accountability has been introduced in the primarily machine-driven environment. This significantly curtails the risk of identity spoofing, front-running, or the development of unverified third-party codes that could potentially drain investor accounts. The linkage of black box algorithms with the research analyst regime transforms algorithmic trading into a regulated activity with emphasis on documentation, disclosure and information symmetry. The clear specification of guidelines also enhance market integrity and contain structural risk, particularly important to resolve historically posed systemic challenges, the epitome of which is the 2010 United States Flash Crash. This crisis involved an abrupt and severe intraday collapse of major indices such as the Dow Jones Industrial Average which plunged nearly thousand points (approximately nine percent) within minutes before recovering most losses just as quickly. A subsequent investigation conducted by the United States Commodity Futures Trading Commission and the Securities and Exchange Commission revealed that the cause of the crash was a large automated sell order in the futures market that had been executed through an algorithm without regard to price or market conditions. This high-frequency trading algorithm amplified the shock by rapidly withdrawing liquidity and engaging in aggressive and correlated selling, triggering a chain reaction that led to extreme price volatility and temporary market dysfunction across equities and derivatives. This event demonstrated how a single malfunction can trigger cascading volatility, liquidity vacuums, and market dislocations, due to the correlated triggers and opaque operations of artificial intelligence-based trading algorithms. A layered defence system has been built through strengthened regulatory oversight at both, the ex ante stages involving registration, testing, and surveillance, and the ex post processes involving audit trails and grievance redressal. This oversight has been imparted dynamism by the provision of re-registration of black box algorithms pursuant to a change in their logic, since this guideline prevents outdated regulatory approvals from becoming potential loopholes.
The Circular also indirectly professionalises the broader trading ecosystem. Traditionally, brokers have only been incentivised by profits and brokerage volumes but now will be saddled with obligations of due diligence, empanelment checks, handling of grievances, and monitoring and surveillance. This effectively transforms them into gatekeepers of technological trust, consequently narrowing the space for fly-by-night ATS providers who previously operated via Telegram channels or plug and play API hacks. This Circular also institutionalises the algorithmic trading process by moving it from a grey-market ecosystem to a legitimised and transparent marketplace that is systematically and contractually structured. This could eventually lead to the development of formal certification programs, third-party audit systems and the development of professional standards for algorithmic vendors, making this framework similar to compliance standards in investment advisory services. Additionally, while the framework appears restrictive by limiting retail-developed ATS to personal and family use, this provision fosters responsible innovation by ensuring that technically skilled investors do not venture into the domain of shadow portfolio management, a function better reserved for registered intermediaries. In the long run, this can catalyse the creation of a regulatory sandbox culture wherein successful retain innovations tested within family environments could be scaled into broader applications upon receiving approval and clearing the requisite compliance processes. This middle-ground approach balances incentivising retail talent in coding, data science, and finance with respecting regulatory boundaries. Lastly, probably the most under-appreciated implication of these guidelines is on the psychological front. Retail investors are more likely to trust and participate in algorithmic trading under a regulated framework than in unregulated opaque market. Since regulatory legitimacy is assigned immense importance, laying down a structured regime indirectly fosters retail inclusion which is essential to deepen market participation and broaden the receiver-base of the advantages of algorithmic trading. This introduction may reduce over-reliance on speculative trading channels such as derivatives, because retail investors gain structured, technology-enabled avenues to pursue systematic strategies.
Conclusion
Part I of this piece has depicted the content of these guidelines, the rationale for their introduction and legal basis, alongside evaluating the positive practical consequences of this newly introduced framework. Part II continues the analysis by identifying the practical downsides of such an introduction, consequently conceptualising a model to resolve the identified inconsistencies and drawbacks.
*Manav Pamnani is a penultimate-year B.A. LL.B. (Hons.) student at the NALSAR University of Law, Hyderabad, and a qualified company secretary.