From Institutions to Individuals: SEBI’s Push for Retail Algorithmic Trading

-By C K Neeha Reddy

Algorithmic trading, more commonly referred to as algo trading, has changed the face of financial markets as it permits high-frequency and automated execution of trades. Introduced in India in 2008 under the oversight of SEBI, this has developed into a complex mechanism that institutional investors now possess. In a bid to democratize trading opportunities while ensuring greater transparency, efficiency, and precision. On 13th December 2024, SEBI has proposed that this technology be extended to retail investors. This article covers history, objectives, challenges, and potential of algo trading and focuses on the continuous reforms by SEBI that are making this sophisticated tool available to the general market.

History of Algo trading

Algorithmic trading started its history in India as of 2008 by introducing it by SEBI, through the Direct Market Access facility. This facilitates the institutional investor conducting a trade directly with exchanges. Global institutions like UBS and JP Morgan showed interest, and brokerages such as Citi subsequently started testing it. From the year 2009 onwards, FIIs were granted access under DMA, thus expanding its range. In 2010, Smart Order Routing (SOR) was introduced. This ensured the automation of optimal trade execution across exchanges. SEBI's 2012 guidelines addressed risks and market manipulation. In 2024, SEBI proposed reforms to include retail investors. White Box (transparent) and Black Box (proprietary) algorithms were introduced for stricter oversight.

SEBI’s Draft Circular on Algo trading

1. Approval and Registration: Brokers will first seek approval from the relevant stock exchanges for each deployed algorithm. This way, all algorithms are vetted for compliance and safety.

2. Unique Identification: Each approved algorithm would be assigned a unique number, which would help in effective tracking and auditing to sustain market integrity.

3. Types of Algos: The circular makes a distinction between two kinds of algorithms:

   i) White Box Algos: These would be transparent algorithms where a user can see the underlining logic.

   ii) Black Box Algos: These are proprietary, logical undisclosed algorithms and the regulations are more stringent.

4. API Access for Retail Investor: Retail investors will also be permitted to use broker APIs that allow them to automatically exchange trades without having to preregister their strategies. Whitelisting their static IPs by the broker is enough as this simplifies access of automated trading tools.

5. Risk Management and Surveillance: The circular places emphasis on the need for strong risk management practices and improved surveillance measures by the stock exchanges to monitor the algo trades after their execution.

6. Public Feedback: SEBI has sought public comments on the draft till January 3, 2025, to finalize the regulations for implementation in 2025.

Why SEBI introduced Algo trading to retailers

SEBI plans to extend algorithmic trading to retail investors, which are currently dominated by institutional players, so that equal opportunities exist in financial markets. On 13th December 2024, SEBI released a consultation paper on algo trading which outlines regulatory oversight for safe adoption. This move aims to empower individual investors with advanced trading tools, promoting fairness, precision, and efficiency in trading activities.

SEBI's algorithm trading regulation framework will deter unregistered PMS firms that lured retail investors in with such unrealistic returns. It also empowers tech-enabled investors and allows them to write and circulate algorithms around family and friends, thereby developing portfolio management. The advancement of trade technology democratizes in innovation by safeguarding all retail players in the shifting financial service landscape.

Challenges in using Algo trading

Retail investors rely on brokers' pre-built algorithms for algo trading, executed on brokers' servers. Risks including broker manipulation or any loophole in the strategy coded and designed by the brokers can make them suffer huge losses. In addition, unregulated algo providers in the market leave retailers vulnerable, with no proper redressal forums for grievances, making the trading environment risky and opaque for individual investors. 

To counter these risks SEBI is formulating a framework, according to which the brokers should offer an open application programming interface (API) to all their algo clients. All the orders conducted via API will be considered as Algo orders. Moreover, APIs that are used for operating Algo Trades should have unique Identification provided by the exchanges to improve transparency.

However, within this technologically driven landscape, there lies the threat of black swan events - unpredictable and impactful occurrences (like covid 19, Ukraine – Russia war), posing major risks to algorithmic trading by disrupting computational models and causing financial losses. Traders and institutions need to address such challenges, as such events can shift market dynamics unpredictably beyond the capabilities of existing algorithmic systems, and this is where strong risk management strategies come into play.

Algorithmic Trading Market Growth

Algorithmic trading market size has grown rapidly over the last few years. It is growing from $18.22 billion in 2023 to $20.52 billion in 2024 with a compound annual growth rate (CAGR) of 12.6%. The algorithmic trading market size is going to experience rapid growth over the next few years. It is going to reach $33.87 billion in 2028 at a compound annual growth rate (CAGR) of 13.3%. Government support, global population growth and urbanization, growing internet penetration, and increasing algorithmic trading adoption in financial institutions are some of the factors that contribute to growth in the forecast period.

Algorithmic trading has remained one of the most-discussed technologies in recent times. It has given trading houses more power in the highly dynamic markets by removing human faults and changing the way finance markets are interconnected today. Its usage is credited to most markets and even commodity trading as can be evident from the chart below

Conclusion

Algorithmic trading is indeed the next leap in financial service, which integrates technical innovation with market strategy. Efforts by SEBI in integrating retail investors in the ecosystem speak volumes for fairness and inclusivity in the trading process. Though such problems as market manipulation, black swan events, and lack of transparency have existed, regulatory frameworks along with advanced risk management will do much to reduce risks associated with these factors. As the algo trading market continues to grow, it has the potential to redefine the landscape of global finance, fostering a more equitable and efficient trading environment for all participants.