Algorithmic trading in India has entered a new phase—one defined less by curiosity and more by hands-on execution. As the ecosystem matures, the focus is shifting from learning the fundamentals to deploying real strategies in live markets. Few have witnessed this transformation as closely as Nitesh Khandelwal, Co-Founder & Director at QuantInsti. From pioneering structured education for aspiring quants to enabling real-world strategy development through technology platforms, Nitesh has seen algo trading evolve from a niche interest to a mainstream, innovation-driven domain. In this conversation, he reflects on the journey, the regulatory landscape, and what lies ahead for India’s new generation of quantitative traders.
Q1. How has your role—and QuantInsti’s role—in the algo trading ecosystem evolved over the years?
QuantInsti started with a singular mission: to educate and empower people in quantitative and algorithmic trading through our EPAT certification, Quantra courses, and free learning initiatives. Over time, our scope expanded beyond education. One of the biggest shifts has been Blueshift, our in-browser research, backtesting and trading platform. What began as a tool for learners is now being used by brokers as a plug-and-play, fully compliant algo development and execution environment.
Today, we don’t just train traders—we enable real-world strategy deployment and have become a part of the broader algo ecosystem through education, technology and infrastructure.
Q2. What was the original inspiration behind launching QuantInsti?
The idea originated during our early days at iRage, when we struggled to find talent who understood both markets and technology. We began training internally, and soon realised the demand was far larger. That internal training became QuantInsti.
From there, it grew organically. Today, we have learners across 190+ countries—retail traders, professionals and institutions. The core belief remains unchanged: with the right mindset and access, anyone can participate in algorithmic trading, not just large institutions.
Q3. SEBI has formalised rules around retail algo trading. Do you see this as a positive step?
Absolutely. It brings structure, clarity and accountability without stifling innovation. By defining responsibilities for brokers, exchanges and algo providers, SEBI is ensuring investor protection while allowing tech-savvy traders to operate responsibly. It’s a win for both market integrity and ecosystem expansion.
Q4. What kinds of algorithmic trading strategies remain most relevant for beginners and emerging quants?
I avoid recommending “one-size-fits-all” strategies, because your edge depends heavily on data, infrastructure and risk management. But for those starting out, a few foundational approaches help build intuition:
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Mean Reversion in liquid, large-cap stocks
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Momentum strategies, starting with simple moving-average systems
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Pairs Trading / Statistical Arbitrage with alternate data
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VWAP / Execution Algos for low-impact execution
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Event-driven approaches for earnings or macro events
Q5. What is your outlook for quant & algo trading volumes in 2025–26?
The growth is real and structural. A huge share of derivatives activity today is already quant-driven. Global players are expanding in India, exchanges are upgrading infrastructure, and SEBI’s retail algo framework is unlocking wider participation.
Across institutions, prop desks and tech-savvy retail users, we expect significant expansion in quant and algorithmic trading volumes over the next two years. The ecosystem has momentum, and we’re seeing more people building, experimenting and launching their own quant setups—including many of our EPAT alumni.