Basic Information
- Company Name: NK Securities
- Name: Manav Agrawal
- Role: Quantitative Researcher
- Location: Gurugram
Please elaborate on your internship role and/or any projects that you did?
During my internship, my primary project was to develop and implement a trading strategy within the firm’s existing codebase. The work involved end-to-end ownership—from designing the core logic of the strategy to integrating it into a real-time trading environment.
Each strategy had multiple components where different variations could be tested—ranging from signal generation to execution logic. I also had to define custom evaluation metrics suited to our specific objectives, which helped in assessing and refining the strategy’s performance more effectively.
Overall, it was a mix of quantitative thinking, performance optimization, and hands-on coding.
Please describe the selection process in detail (tests + interviews)?
It was quite different for different firms but the test generally had some MCQs related to puzzles and probability and 1-2 CP questions. Firms like Optiver, DaVinci, IMC had completely different test pattern as they test the speed and intuition of candidate.
Interviews were mostly related to challenging puzzles, probability questions and sometimes CP, OOPs, and other core CS topic.
What was your preparation strategy for Quant roles (trading and/or software)?
I reached out to several seniors who had experience in these roles and followed their guidance.
For trading roles, I focused on building strong problem-solving and mental math skills. I worked through resources like the Green Book, Brainstellar, PuzzledQuant, and also practiced firm-specific mock tests available on the TraderMath website.
For software roles, I primarily focused on DSA. I prepared using LeetCode and participated in Codeforces contests, especially in the 1–2 months leading up to the intern season.
What were the working hours like?
Generally around 9am to 7pm (Although there are no restrictions)
How would you describe the structure, work culture and work-life balance of the company?
The structure was quite flat and collaborative—everyone was approachable, including the senior members. The work culture encouraged asking questions, exploring ideas, and being proactive. I felt that interns were treated as contributors, not just learners.
In terms of work-life balance, it was quite reasonable. While the work was intense and intellectually demanding, the team respected personal time and there wasn’t any pressure to stay beyond working hours without reason. Overall, it was a healthy and supportive environment.
A lot of Quant firms claim they don’t have silos, and use this as a USP. How true is this for the company you interned at?
From my experience, the claim genuinely held true. There was a lot of openness across teams—whether it was traders, researchers, or developers, everyone worked closely and shared ideas freely. I never felt restricted to just my immediate project; if I had questions or suggestions in adjacent areas, people were happy to discuss them.
This cross-functional exposure really helped me understand the bigger picture and how different parts of a trading system come together. So yes, the “no silos” culture wasn’t just a buzzword—it reflected in day-to-day interactions.
Was there any evaluation criteria followed by the company, if yes could you elaborate?
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How did the internship influence your career interests?
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How would you rate your overall internship experience?
It was a great experience overall, I got to learn a lot both finance and coding knowledge.
Any myths or unknown facts you would like to bust/reveal? Feel free to add in any comments you have.
One common myth is that you need to know advanced finance or deep ML to crack quant roles—especially for interns. In reality, most firms value strong problem-solving skills, logical thinking, and coding proficiency over domain expertise at this stage. The learning curve is steep, but they expect you to pick things up on the job.
Another lesser-known fact is how much emphasis is placed on code quality and performance, even for quant roles that seem more math-heavy. Writing clean, efficient, and modular code can often be just as important as getting the logic right.
Final comment: Be curious, ask questions, and don’t get intimidated by how “quant” others may sound. Everyone has their own learning curve—focus on improving yours steadily.
One piece of advice for someone preparing for similar roles.
Focus on building strong logical intuition rather than just practicing questions mechanically. Understanding the “why” behind problems helps you adapt to unseen variants more easily. Also, speed is crucial—try to internalize standard results and shortcuts so you don’t waste time deriving them during tests or interviews.