The Next Decade of Quant in India

From Niche to Mainstream: How Systematic Investing Is Reshaping Indian Markets

February 2026 | Qode Advisors LLP

Source: BW Businessworld / NSE; IMARC Group; PMS AIF World (Oct 2025); AMFI (Jan 2026); Motilal Oswal MF / DD News

A Quiet Revolution, Years in the Making

Five years ago, the word 'quant' in Indian investing circles conjured images of high-frequency trading desks and inaccessible black boxes. Today, the landscape has fundamentally shifted. Quantitative investing strategies now account for a meaningful and growing share of India's volumes, investment products, and asset management industry. It refers to using systematic, data-driven frameworks to make disciplined investment decisions about owning businesses and allocating capital over time.What was once a niche pursued by a handful of specialists has become the most talked-about evolution in Indian capital markets.

The numbers tell a compelling story. Algorithmic trading represented 57% of all equity cash segment trades on the NSE in April 2025, up from below 50% as recently as FY23 (BW Businessworld). In derivatives, algo participation reached 70% in FY25, with stock futures alone at 73% in FY26 year-to-date (NSE Market Pulse, Dec 2025 via QuantInsti). India's algorithmic trading market was valued at $562 million in 2024, projected to reach $1.27 billion by 2033 at a 9.5% CAGR (IMARC Group).

CHART 1: Rise of Algorithmic Trading on NSE (FY15 to Apr 2025)
Source: NSE Market Pulse (Dec 2025), BW Businessworld. Data compiled from NSE disclosures.

This isn't just about trading speed. The deeper shift is in how Indian investors and fund managers are thinking about portfolio construction, factor exposure, and systematic decision-making.

What Accelerated the Shift

Data and Infrastructure Got Better

India's digital public infrastructure, from Aadhaar to UPI to GST filings, has created a data ecosystem that quant strategies can use. Corporate financial data is now more granular, timely, and widely available than ever. The total number of demat accounts in India have been growing at a 23% CAGR (CRISIL / ICICI Pru Industry Report).  We now have reasonably clean and reliable market data available from around 2005 onwards, which earlier created limitations for model building. But as time passes, more market cycles get captured in the dataset. That expanding history allows for more robust backtesting and stronger quantitative models.

More Market Cycles, Better Backtests

India's equity markets have now lived through multiple complete cycles: the 2008 global financial crisis, the 2013 taper tantrum, the 2018 NBFC crisis, COVID-19 in 2020, and the sharp correction of late 2024. Each cycle adds to the dataset. Factor strategies like momentum, value, quality, and low volatility now have 15 to 20 years of Indian-specific backtesting data (NSE Indices). That is enough to start distinguishing signal from noise. 

The Regulatory Push

SEBI has played a defining role. Its February 4, 2025 circular on "Safer Participation of Retail Investors in Algorithmic Trading" did not restrict algo trading. It legitimised it. The framework introduced structured API access, exchange-level empanelment of algo providers, mandatory audit trails, and defined a 10 orders-per-second threshold for retail algo registration (ICICI Direct Analysis). NSE released detailed implementation standards in May 2025 (NSE Circular, May 5, 2025), with the implementation timeline extended to October 1, 2025 (SEBI Extension Circular). The message: SEBI wants retail participation in algo trading, but within a transparent, accountable framework.

Factor Investing Goes Retail

The most visible sign of quant going mainstream is the explosion of factor-based investment products available to everyday investors.

The Nifty 500 Momentum 50 Index, which selects the top 50 stocks from the Nifty 500 based on 6- and 12-month price momentum adjusted for volatility (NSE Indices Factsheet), has spawned multiple index funds and ETFs. Motilal Oswal's Nifty 500 Momentum 50 Index Fund manages an AUM of approximately Rs 13,618 crore (Groww, as of Feb 25, 2026). Nippon India's equivalent manages roughly Rs 7,347 crore (Groww, as of Feb 25, 2026). Similar products now track value (Nifty50 Value 20), low volatility (Nifty 100 Low Volatility 30), and alpha (Nifty Alpha 50).

CHART 2: India Mutual Fund Industry AUM Growth (2016-2026)
Source: AMFI (amfiindia.com). AUM data as of respective month-end dates.

The mutual fund industry itself has become fertile ground. Total AUM crossed Rs 81 lakh crore by January 2026 (AMFI), a 6x increase in a decade. Passive funds, including factor-based products, now account for roughly 17% of total AUM (DD News / Motilal Oswal MF Report), with ETF and index fund AUM jumping 27% in 2025 alone to Rs 14.07 lakh crore (Angel One).

CHART 3: The Rise of Passive / Factor Investing in India
Source: AMFI, Motilal Oswal MF Report (Aug 2025), DD News, Angel One.

The PMS and AIF Front

In the high-net-worth segment, the shift is even more pronounced. According to PMS AIF World's October 2025 report, nearly half of all new PMS strategies launched in 2025 followed quantitative or factor-based models (5paisa / PMS AIF World). A year earlier, PMS launches labelled 'quant' were almost non-existent. Funds like Qode Advisors' All Weather and Tactical models, Capitalmind SmartCore, and ArthAlpha Quant PMS collectively manage around Rs 160 crore, averaging Rs 40 crore per scheme, a sharp rise from sub-Rs 15 crore at launch (5paisa).

The PMS industry has grown to Rs 8.37 lakh crore in AUM by September 2025, posting a 10-year CAGR of nearly 20.75% (Bharat Express). AIFs expanded even faster: total commitments surged from Rs 27,484 crore in 2015 to Rs 15.05 lakh crore in 2025, a staggering 49.2% CAGR (Bharat Express).

CHART 4: Quant PMS Adoption & Algo Market Projection
Source: PMS AIF World (Oct 2025), IMARC Group.

India's Quant Evolution: Then vs. Now

Sources: NSE, SEBI, AMFI, PMS AIF World, BW Businessworld, IMARC Group, CRISIL, Groww, Business Standard, DD News. Individual source noted in each cell.

Where the Real Edge Will Come From

Here is the critical point that many miss in the excitement around quant going mainstream: when everyone has access to the same factors, the factors stop being the edge. The edge moves to implementation.

Consider momentum as an example. Five years ago, simply running a momentum screen on Indian stocks would have given you a significant advantage over most fund managers who selected stocks based on gut feel and management meetings. Today, with multiple momentum index funds, ETFs, and PMS products chasing the same signal, the alpha from naive momentum is compressing.

The differentiation over the next decade will come from several dimensions: how you combine multiple factors without overfitting to historical noise, how you manage transaction costs in a market where impact costs are real (especially in mid- and small-caps), how you handle regime changes between factor cycles (as the Qode factor rotation table in our Value Comeback piece illustrates), and how you integrate sound economic reasoning into your models rather than relying on pure data mining.

The transition is clear: the competitive advantage is no longer having a quant approach. It is having a better quant approach.

This mirrors what happened in the US. Factor investing went mainstream in the 2010s with the rise of smart beta ETFs. AQR, Dimensional, and Vanguard brought factor products to retail. But alpha didn't disappear. It migrated to firms with better risk management, lower turnover, more sophisticated multi-factor construction, and the patience to weather drawdowns. India is entering that exact phase now.

What Could Go Wrong

The enthusiasm around quant carries real risks that deserve honest assessment.

Crowding is the most immediate concern. When too much capital chases the same systematic signals, the signals weaken. Momentum strategies that once earned 8 to 10% annual alpha may compress to 2 to 3% as more capital enters. Rebalancing dates for popular indices become predictable, and front-running erodes returns.

Overfitting remains a persistent danger. India's market history, while growing, is still shorter than that of the US or Europe. Strategies that look brilliant in backtests may simply be capturing noise in a limited dataset. The temptation to add complexity, more factors, more signals, more machine learning layers, increases the risk of building models that describe the past perfectly but predict the future poorly.

Regulatory evolution is another variable. SEBI's retail algo framework is still being implemented in phases, with the full operational rollout continuing into early 2026 (SEBI Extension Circular, Sep 2025). Changes in taxation, transaction levies, or F&O regulations can materially alter the economics of systematic strategies.

The Qode Perspective

Systematic strategies at Qode were developed when quant was still emerging in India. Our experience across multiple market cycles informs our understanding of what works in Indian markets specifically, not global templates applied without context.

As quant becomes mainstream, the edge shifts from having a quant approach to having a better quant approach. Our focus on combining quantitative rigour with sound economic principles, on owning businesses rather than just trading signals, and on building portfolios that survive regime changes rather than optimise for one, is what positions us for the next decade of evolution.

The quant revolution in India is real. The question is no longer whether systematic investing will become mainstream. It already is. The question is who will implement it well enough to generate sustainable alpha in a world where everyone has access to the same data, the same factors, and the same tools.

That is where the next decade will be won or lost.