Nitesh Khandelwal

About the Faculty

Nitesh Khandelwal is the CEO and Director of QuantInsti and Co-Founder of iRage. He brings over two decades of experience across quantitative finance, algorithmic trading, high-frequency trading, trading infrastructure, and financial education.

An alumnus of IIT Kanpur and IIM Lucknow, Nitesh has worked at the intersection of markets, technology, and education, helping traders, investors, and professionals understand how systematic trading works in real-world market environments. His expertise spans algorithmic trading, quantitative strategies, trading systems, high-frequency trading, execution, risk, regulation, market microstructure, and the role of AI in modern financial markets.

Nitesh has taught and spoken across academic, institutional, industry, and community platforms. His speaking experience includes sessions and appearances with NUS Business School, IIM Ahmedabad, IIT Delhi, SGX / AFACT Singapore, Kenanga Group Malaysia, Moneycontrol Global Wealth Summit, CMT Association, Tradologics Algo Trading Summit, Algo Convention, CodeFest by Pepperstone, and MMML at NSE India.

He has also appeared on podcasts and interview platforms, including Better System Trader, Paisa Vaisa by IVM Podcasts, Fintech Underground by Alpaca, Moneycontrol’s Digging Deeper podcast, CMT Association, and ElearnMarkets Face2Face. Across these conversations, he has shared insights on backtesting, execution, statistical arbitrage, HFT, retail algo trading, systematic strategy development, and the future of quant education.

As a faculty member and thought leader, Nitesh has contributed to structured learning in algorithmic and quantitative trading. He has authored and been featured in leading business and financial publications, including Moneycontrol, ET Edge Insights, Business Standard, Rediff, India Today NE, Outlook India, TechCircle, Entrepreneur India, BusinessWorld, Forbes India, Financial Express, ET Prime, and Business Today.

Nitesh is known for combining practitioner-level market experience with a clear teaching approach, helping learners understand not just the theory of algorithmic trading, but also the infrastructure, workflows, risks, and practical decision-making required to build and run trading strategies in live markets.