The Future of Trading is Quant: Nitesh Khandelwal on CNBC Arabia
In an exclusive interview with CNBC Arabia’s Shifra (host) in the broadcast series “Al-Mal”, Nitesh Khandelwal, CEO and co-founder of QuantInsti, shared key insights into the rapidly evolving world of quantitative trading. A pioneer in the field of algorithmic trading education, Nitesh has helped shape the careers of finance professionals worldwide through QuantInsti’s cutting-edge programs.
About Nitesh Khandelwal, CEO and co-founder, QuantInsti
Nitesh Khandelwal is the Chief Executive Officer of QuantInsti and a co-founder of iRage. With years of experience in the financial markets, Nitesh has been a driving force in the algorithmic trading space. Read more here.
About the Series
The interview is part of CNBC Arabia’s “Al-Mal”, a prominent series that explores the latest developments in business, markets, and financial technology across the MENA (Middle East and North Africa) region. This particular segment focuses on the intersection of AI, quantitative strategies, and the future of finance—making it a valuable watch for both aspiring and experienced professionals in the trading domain.
From demystifying quant strategies to offering actionable advice on skill development, this conversation is a must-watch for anyone interested in the future of trading and investment.
🎥 Watch the full segment here: CNBC Arabia Interview – Nitesh Khandelwal
What Is Quantitative Trading?
“Quant trading is the use of mathematics and statistics to make trading and investment decisions,” — Nitesh Khandelwal
Quantitative trading, often perceived as the domain of elite hedge funds or PhDs, is actually rooted in applying structured logic and data analysis to financial markets. Nitesh explains that at its core, quant trading is about:
- Designing and validating models that test trading ideas using historical data.
- Programming algorithms that can execute trades automatically and efficiently.
- Optimizing performance by removing emotional biases and relying on statistical probabilities.
It's not about making things unnecessarily complicated—it’s about bringing consistency and repeatability to trading decisions. As Nitesh puts it, “It’s about applying structure and logic to the markets.” This structured approach allows even individual traders to manage risk better, test strategies scientifically, and build confidence in their systems.
Can Algorithms Beat Human Traders?
“It’s not man versus machine—it’s man with the machine.” — Nitesh Khandelwal
The rise of algorithmic trading doesn’t mean human traders are obsolete—it means the best traders are those who can work with machines effectively. Algorithms excel at eliminating the psychological pitfalls of trading, like fear and greed. They analyze data objectively and execute decisions based on pre-defined logic and statistical strength.
Nitesh emphasizes that while machines handle execution and speed, humans remain essential in crafting strategies, setting goals, and supervising model behavior. The synergy between human intelligence and machine efficiency creates a trading edge that neither side could achieve alone. It’s not a replacement—it’s an evolution.
Do Quants Outperform Discretionary Traders?
There’s no one-size-fits-all answer to this question, but there is a trend: systematic, rule-based approaches often yield better results, especially over the long term. According to internal surveys conducted by QuantInsti’s million-strong user base, traders who follow a data-driven, structured methodology tend to perform more consistently than purely discretionary traders.
This is particularly evident in high-frequency trading (HFT), where algorithms dominate due to the speed and precision required to exploit minute price differences. In such environments, the edge comes not just from strategy but also from infrastructure, latency optimization, and execution algorithms—all areas where quant traders thrive.
However, Nitesh also points out that the real power lies in the combination—many successful traders today blend discretionary insights with algorithmic discipline.
Are Automated Trading Bots Effective?
Expert Advisors (EAs), trading bots, and plug-and-play strategies have become widespread, especially among retail traders. But the question remains: can these bots generate consistent profits?
Nitesh approaches this question with caution and realism. “If a strategy is so profitable, why would someone sell it instead of using it themselves?” he asks. Many bots being sold lack transparency, overfit historical data, or lose effectiveness in live markets. That said, there are legitimate use cases where traders build and share strategies—particularly when scalability is high or capital constraints exist.
The key takeaway? Automated bots can be effective, but only if you understand how they’re built, tested, and maintained. Blindly relying on off-the-shelf systems can be risky. Developing your own models—or at least understanding the logic behind them—is a far better path to sustainable trading.
What Skills Do You Need to Become a Quant Trader?
Breaking into quant trading doesn’t require a PhD in mathematics—but it does demand curiosity, discipline, and a willingness to learn. Nitesh outlines the three core pillars of a successful quant’s skill set:
- Statistics & Econometrics
You need to understand concepts like probability distributions, regression models, time series analysis, and hypothesis testing. These form the backbone of any data-driven strategy. - Programming & Financial Computing
Languages like Python, R, or C++ are essential. Whether you’re backtesting strategies, cleaning data, or writing execution code, being able to translate ideas into code is a non-negotiable skill. - Market Microstructure & Financial Knowledge
Understanding how markets work—order types, liquidity, slippage, and execution mechanisms—helps you build more realistic and effective strategies.
Even if you're coming from a non-tech background, the tools and resources available today make it easier than ever to acquire these skills.
As Nitesh says, “Learning to code is like learning a language—you get better by doing.”
How AI is Making Quant Trading Accessible
The biggest shift in recent years? The democratization of quant tools through AI and machine learning. Large Language Models (LLMs) like ChatGPT are not just helping traders brainstorm strategies—they're helping them code, analyze data, and streamline research.
Nitesh explains how AI has dramatically lowered the entry barrier. What once took weeks of coding can now be done in minutes with the help of tools like:
- Code-generating LLMs
- Sentiment analysis models like FinBERT
- Voice-to-text transcription tools like Whisper
These tools are making it possible for retail traders and beginners to compete in spaces once dominated by institutions. But he also adds a note of caution: “AI is a tool, not a shortcut.” To use it effectively, traders must still understand the principles behind their strategies—what the model is doing and why.
What Are the Risks of Using AI in Trading?
While AI and machine learning bring tremendous power, they also introduce new challenges. One of the biggest risks? Overfitting.
Many models perform brilliantly on historical data but fail in real-time because they’ve learned noise instead of signal.
Nitesh warns that AI systems—especially complex ones—can be “black boxes” that traders don’t fully understand. During market anomalies or black swan events, these models can behave unpredictably. That’s why risk management, monitoring, and ongoing model refinement are essential.
The bottom line: “AI can amplify your edge—but only if used responsibly.”
The Future of Quantitative Trading
Looking ahead, Nitesh sees quantitative trading becoming more mainstream, powered by advancements in:
- Cloud computing
- Open-source tools like Python, scikit-learn, and TensorFlow
- Real-time data APIs and backtesting engines
Retail traders now have access to platforms and tools once reserved for institutions. Still, Nitesh believes that large players will continue to dominate in areas where speed, data access, and infrastructure offer competitive advantages.
His advice to aspiring traders? “Don’t try to compete on scale—focus on niche strategies, build strong fundamentals, and always think long-term.”
His advice? “Focus on niche strategies, build strong fundamentals, and think long-term.”
Final Words for Aspiring Quants
“Quant trading is not a get-rich-quick scheme. It requires patience, structure, and continuous learning.”
Nitesh’s final message is clear: If you want to succeed in this space, commit to the process. Learn the theory. Practice the skills. Build, test, and validate your ideas. And don’t be afraid to start from scratch.
It’s not about genius—it’s about structure, discipline, and execution.
Get Started
Nitesh emphasized the role of structured learning programs like EPAT in helping aspiring traders transition into the quant world. Whether you're from a finance, engineering, or non-programming background—there's room for you in this evolving domain provided you're willing to learn.
Conclusion
Quantitative trading is more accessible than ever—but success comes to those who invest in learning, practice, and strategy. Whether you’re just beginning or aiming to go pro, the right learning path can fast-track your growth and set you apart in a data-driven trading landscape.
Ready to take the next step?
Next Steps
If you found this conversation insightful, there’s a lot more to explore from QuantInsti and the broader quantitative trading ecosystem that Nitesh Khandelwal highlighted in his CNBC Arabia interview.
To see the real-world impact of QuantInsti’s collaborations, check out our recap of collaborations that shaped 2024 and get a sneak peek at upcoming and what has happened so far in algo trading events and webinars in 2025. These initiatives reflect how QuantInsti continues to drive innovation and education in the global quant space.
If you’re just starting out, we recommend Quantra's Free Learning Track on Quantitative Trading for Beginners, which provides a structured introduction to core concepts, tools, and techniques in algorithmic trading.
For those ready to go deeper, explore focused programs like the Learning Track on Quantitative Trading in Futures & Options Markets—ideal for traders looking to apply quant strategies in derivative markets. You can also browse all Quantra learning tracks to find a course tailored to your interests and skill level.
And for a rigorous, industry-recognized education in algorithmic and quantitative trading, the Executive Programme in Algorithmic Trading (EPAT) remains the flagship choice. Designed for serious learners, EPAT combines practical training in Python, statistics, trading strategies, and machine learning—equipping professionals to excel in today's data-driven financial world.
Looking to explore more topics? The QuantInsti Blog offers expert insights, case studies, and tutorials on everything from machine learning in trading to volatility forecasting and market microstructure. Dive in to continue your learning journey.