ETMarkets Smart Talk| With 87.7% of F&O traders losing money, why systematic trading is becoming more relevant for retail investors, says Prashant Shah

India's retail derivatives market is growing rapidly, but the numbers behind that growth make for uncomfortable reading. SEBI's latest FY26 study found that 87.7% of individual traders in equity derivatives incurred net losses, with aggregate losses touching nearly Rs 91,685 crore. Options accounted for the overwhelming majority of those losses.

For retail traders, the problem may not always be a lack of market knowledge or access to information. In a fast-moving derivatives market, hesitation, emotional decisions, overtrading and inconsistent execution can turn even a sound strategy into a losing one.

This is where systematic trading—and increasingly AI-powered tools—could play a bigger role. By converting a trading idea into a set of predefined rules, automation can take some of the emotion out of execution while helping traders manage positions, risk and decisions more consistently.


But can technology really address the behavioural challenges that continue to hurt retail traders? And as APIs, backtesting tools, cloud infrastructure and AI make algo trading more accessible, is systematic trading poised to move beyond its institutional roots and become a mainstream retail tool?

In an interaction with Kshitij Anand of ETMarkets, Prashant Shah, CEO & Co-Founder of Definedge Securities, explains why the next phase of retail trading could be less about finding the perfect signal and more about building a process that traders can actually stick to. Edited Excerpts –

Q) For decades, algorithmic trading was largely the domain of institutions and sophisticated traders. Why is this suddenly becoming accessible and attractive to the Indian retail trader?

A) Algorithmic trading is becoming more accessible because many of the technological barriers that once kept it largely institutional are falling. Market-data access, APIs, cloud infrastructure, backtesting tools, and strategy-building platforms now allow a retail trader to automate a rules-based strategy without having to build an entire institutional technology stack from scratch. The regulatory environment is also becoming more structured: SEBI issued a framework for safer participation of retail investors in algorithmic trading in February 2025, and the exchange ecosystem has since established procedures for retail algo participation and empanelled algo providers.

The important change is therefore not that algorithmic trading has suddenly become simple. Rather, sophisticated infrastructure is increasingly being packaged into simpler tools. That lowers the technical barrier and makes systematic execution more practical for active retail participants.

There is also a much larger potential user base. NSE reported 12.9 crore registered investors as of March 2026, showing how far India's retail investment ecosystem has expanded. Not all of those investors are potential algo users, but the scale of the underlying market is much larger than it was a decade ago.

Read more: Commodity Talk | Gold isn't the only story: Why copper, energy and other commodities are gaining portfolio

Q) Retail participation in derivatives has exploded over the last few years. Is AI and automation emerging because traders are becoming more sophisticated or because manual trading is simply becoming too difficult?

A) It is a combination of both, but the increasing difficulty of manual trading is the stronger structural driver. Retail traders are operating in a faster and more data-heavy environment, particularly in derivatives, where there are multiple expiries and strikes, rapid price movements, and significant execution and risk-management demands.

SEBI's latest FY26 study found 87.5 lakh unique active individual traders in equity derivatives, down 18% from the previous year. Despite the decline in participation, 87.7% of individual traders incurred net losses. Aggregate individual losses were about Rs 91,685 crore, with around 92% of those losses coming from options.

These figures do not mean that AI or automation guarantees better returns. They do show why systematic processes, risk controls, and repeatable execution are becoming more valuable. A trader can have a sound strategy but still execute it inconsistently because of hesitation, fear, greed, overtrading, or the inability to monitor markets continuously.

Automation therefore addresses a practical problem: how to make a trading process repeatable and less dependent on moment-to-moment human intervention.

Q) What is the biggest pain point that AI and automation are solving for a retail trader lack of time, lack of discipline, lack of data analysis or emotional decision-making?

A) The biggest pain point is discipline and emotional decision-making, followed by time and data-processing constraints. Retail traders increasingly have access to large amounts of information; the challenge is often deciding what matters and then applying a consistent process.

Automation can turn a discretionary idea into a defined process: when condition X occurs, enter; when condition Y occurs, exit; keep position size within a predefined limit; and apply a predetermined risk rule. The system can then repeat those instructions without changing them because of fear, excitement, or short-term market noise.

This is consistent with the broader investor experience highlighted in SEBI's Investor Survey 2025. Among the reported challenges after investment, 90% of surveyed investor households cited lack of knowledge and information, while 60% cited lack of timely and accurate market insights.

These findings support the idea that technology has a role not only in execution but also in filtering, organizing, and interpreting information.

AI adds another layer. It can assist with research, summarizing information, pattern recognition, signal ranking, portfolio monitoring, and strategy development.

But AI and automation are not the same thing: a rules-based strategy that automatically sends orders is algorithmic trading even if it uses no AI. For retail investors, automation's immediate value is consistency, while AI's potential is broader decision support and analytics.

Read more: Commodity Talk| Equities, bonds... and commodities? Why investors may need a third pillar for diversification, Navneet Damani decodes

Q) How large is the AI and algorithmic trading opportunity in India today, particularly on the retail side?

A) First, the overall investor base is already very large. NSE reported 12.9 crore registered investors in March 2026 and said the base was nearing 13 crore. Second, the more relevant near-term market for trading technology is active traders: SEBI recorded 87.5 lakh unique active individual traders in equity derivatives in FY26.

Even a subset of that population represents a substantial addressable market for screening, analytics, backtesting, automated execution, portfolio monitoring, and risk-management tools.

Third, the broader Indian algorithmic-trading market is already a meaningful commercial ecosystem. IMARC estimates the overall Indian algorithmic-trading market at approximately USD 615.61 million in 2025 and projects it to reach about USD 1.35 billion by 2034.

This is an estimate for the broader algorithmic-trading market, not for retail AI alone, so it should not be presented as a retail total addressable market.

Taken together, the evidence suggests that retail AI and algorithmic trading is moving beyond a niche community of technically sophisticated traders.

The larger opportunity is likely to be the wider technology stack strategy development, screening, backtesting, analytics, automated execution, portfolio monitoring, and risk controls rather than simply selling an 'AI trading signal'.

(Disclaimer: Recommendations, suggestions, views, and opinions given by experts are their own. These do not represent the views of the Economic Times)