Future of Algo Trading: Retail Gamifications and Market Manipulation

[Annesha and Raveena are students at Symbiosis Law School, Hyderabad. The following article is one of the winning entries in the 2nd Article Writing Competition 2025 organized by IDIA: Increasing Diversity by Increasing Access to Legal Education (Odisha Chapter).]

Algorithmic trading is the practice of executing financial securities like stocks, forex, and cryptocurrency among others, through automated pre-programmed trading instructions accounting for variables. These variables include those of time, price, and volume. This type of trading has become more popular because of its ability to conduct trading with more leverage of speed and computational resources that human traders do not possess. As a matter of fact, a study in 2019 conducted by Robert Kissell in his book, Algorithmic Trading Methods, states that, “92% of trading was performed by trading algorithms rather than humans.” Some examples of such efficient trading carried out by algorithms are as follows. First, algorithmic trading allows better application of the Moving Average Crossover Strategy which entails analyzing the bullish signal for selling securities. Second, it is used in buying an asset in one market and simultaneously sell it in another market where it is priced higher. Third, it is also used in analyzing the mean reversion patterns which entails analyzing the stock value to see if it deviates to the point of reversion. Lastly, algorithmic trading is also used in high frequency trading which involves executing a large number of trades at extremely high speeds, often within microseconds using its powerful computing abilities. 

All of these functional aspects give such trading an edge over human traders; hence, it is essential to consider their prominence in the Indian scenario considering its increasing importance in the financial world. Now, it is especially necessary to consider this subject through the lens of the recent Securities Exchange Board of India (SEBI) guidelines that will become effective from August 2025 onwards. Algorithmic trading began in India with a press release dated 3 April 2008 allowing direct market access which created an eco-system for investors to directly place orders with National Stock Exchange of India Limited. There have been several updates to this press release regarding the position of algorithmic-trading, however, the latest guidelines dated 4 February 2025 appear to stand out for the following reasons.

First, there is the introduction of a Unique Client Identifier which helps trace every order placed by an investor. This can potentially prevent white collar crimes like insider trading or fraud.

Second, if there are registrations that cross the limit set by the SEBI, then such high-frequency algorithms must be registered. This reflects the SEBI’s purpose towards regulating algorithm trading by limiting the spread to only the family members of the investors.

Thirdly, SEBI is also putting a ban on open application programming interface (API). APIs act as a bridge to allow automated systems to connect with trading platforms and execute trades directly. However, with these new guidelines, open access is banned to prevent data breaches and only key-based access only through Open Authentication is allowed.

Lastly, SEBI has categorized algorithms into two types. First category is the ‘white box’ algorithms, where the underlying logic is fully to the investor. The second category consists of ‘black box’ algorithms. The logic is hidden from the investor which further leads SEBI, in these guidelines, to mandate registration of such providers as ‘research analysts.’ These are some of the most essential changes to be brought to algorithmic trading through this circular. Now, it is essential to understand how effective the circular can be and what are some of the possible drawbacks.

SEBI’s 2025 circular claims to facilitate retail production in algorithmic trading. It is supposed to represent a progressionist view towards democratizing access to advanced trading technologies however, the regulatory intent was only to enhance market participation and transparency but the circular falls short on addressing severe core issues associated to algorithmic trading particularly when linked to market stability, liquidity quality, volatility, and manipulative trade practices.

A major concern underscore is that algorithmic trading leads to market fragmentation, reducing entry barriers for smaller firms and increasing short-term competitions, resulting in proliferation of trading strategies. This overwhelms the market infrastructure and fragment liquidity across all multiple platforms making the overall system less cohesive. High-frequency trading focus on micro-level arbitrage which in turn contributes to short-term profit making over long-term gains, potentially disrupting investor’s behavioral science and market fundamentals.

Apart from this, algo-trading has its own potentials to enhance liquidity. While in short term, certain liquidity metrics like narrower bid-ask spreads and improved trade execution show promising future, high level of algorithmic activity often reaches itself to a saturation point beyond which the above liquidity diminishes itself. However, things become more worrisome when in such a saturated market algorithmic strategies decouple prices of real value leading to market ineffectuality. During such events, high information asymmetries like earnings or dividends, trading volumes spike. Thus, the illusion of liquidity shows market inefficiency and breaks the trust and confidence of investors in market, especially when coming to India where the depths and maturity of capital market are still in its transpiration.

Additionally, algorithmic trading has increased the speed at which information is reflected in pricing, theoretically this should increase market efficiency and increase volatility, but simultaneous responses of algorithm to market events trigger widespread reactions, leading to another black swam events like the 2008 Global financial crisis, the 2010 Flash crash and the COVID-19 pandemic market crash. Such non-synchronized activity widens the bid-ask spreads leading to withdrawal of algorithms from the market and feedback loop create large scale withdrawals, escalating temporary market dip into full-fledged crash damaging investors’ confidence on market behaviour and signaling recessionary signals to economies.

In the US, under Securities and Exchange Commission and Financial Industry Regulatory Authority, there is direct regulation for algo providers and broker, market access controls are strict pre-risk oriented and all orders are tagged for surveillance. In European Union, under Markets in Financial Instruments Directive 2014, all trading algos are tested, registered, and documented, high-frequency traders must provide liquidity. In Singapore and Hong Kong, there are mandatory risk checks and kill switches and firms must mandatorily disclose algo strategies and test them for robustness.

It is now essential to ponder on what can be done differently in these circular guidelines to further improve the systema and mitigate risks. To ensure more equitable access and mitigate the above risks a more comprehensive regulatory system must be formulated. First, SEBI should enhance market transparency by mandating real time disclosures of co-location pricing, latency metrics and rack space allocation. Equitable co-location should be mandated so that retail investors have the same access to the data feeds as institutional players. Dynamic volatility should be controlled and we should also impose taxation of algorithmic trading as it could disincentivize innovation. Empowering self-regulation under SEBI to harmonize the controls across all exchanges would ensure consistent market behaviour and protect retail investors from adverse effects of manipulation. 

The key weakness of the circular is the lack of the robust safeguards against the manipulative algorithmic practice, specifically spoofing which is a tactic where large, fake orders are placed to deceive the market. Spoofing is a criminal offence in the US under the Dodd-Frank Act 2010, after the 2010 Flash Crash. India is still on a long way -- while the circular is a step towards financial inclusion and modernization, it appears premature and inadequate for the market which is still building. The circular enables greater participation without creating sufficient structural protection, as the risks of illusionary liquidity, increased volatility, and market manipulation especially in India’s nascent retail investment ecosystem. Hence, these aspects must be taken care of when dealing with algorithmic trading programmes.