How a JEE AIR 40 Landed the Highest HFT Quant Job | PRC06 ft. Sharvil Patel (IIT Guwahati)

Main ideas, concepts, and lessons from the subtitles

1) What quant trading companies do (and why they pay extremely high)

• Quant companies trade systematically using data + math-driven rules, rather than discretionary decision-making.

• They distinguish two broad styles:

  • Real-world trading (retail/“retail-like” trading):

    • The company evaluates whether to invest based on company performance and fundamentals, such as:
      • profits
      • sales
      • whether the company’s idea/product is attractive
      • similar financial/accounting indicators
    • Then they invest according to these evaluations.

  • Quantitative (quant) trading:

    • Uses historical numbers and mathematical features (e.g., profit momentum, volume-weighted ideas).
    • These features become signals, which feed an algorithm that outputs trade decisions.

• How signals become trades (simple example logic described):

  • Create a mathematical function producing a quantitative score (called an alpha) from inputs like:
    • x = profit-related number
    • y = investing/position-related number
  • Example mapping described:
    • If x + y = 10 → algorithm says to hold 10 stocks
    • If next instant x + y = 11 → buy more
    • If x + y decreases → sell / adjust position

• HFT (High Frequency Trading) explanation:

  • HFT companies execute trades every millisecond.
  • They aim to act faster than market participants to capture small edges (profits described as being possible from very small price differences).
  • Example scenario described:
    • If someone is buying stocks and HFT detects the order quickly, it places remaining related orders before the other side can fully react.
    • Then it sells at a slightly better price, scaling profit through high volume.

• Why packages are huge (the motivation stated):

  • These firms make significant profit due to speed + algorithmic advantage, so they can justify paying high compensation.

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2) The PRC/JEE + internship-to-PPO journey (Sharvil Patel’s path)

• Initial interest:

  • Sharvil initially knew nothing about quant.
  • He was focused on Competitive Programming (CP) because he assumed companies would reward strong CP.

• How he ends up in quant despite not seeking it:

  • During internship season, he interviews for quant anyway, encouraged by seniors.
  • Seniors emphasize that quant hiring at his institute is competitive, but still a “good opportunity not to miss.”

• Internship procedure described (at least as he experienced it):

  • Total rounds: 3 rounds

    1. Round 1: puzzles

       • Typically 5–6 puzzles involving:
         • math / probability / discrete math
       • He used hints; interviewers evaluate reasoning + progress, not instant perfection.

    2. Round 2: fast math calculations

       • Quick calculations under time constraints (example of square roots like √6, √8).
       • He describes it as very fast (roughly “20 minutes max” in this round segment).
       • Also includes game theory-style puzzles (stone piles / take-away style or “standard game” puzzles) requiring:
         • quick thinking
         • analytical reasoning
       • He felt this round disappointed him during the moment, due to speed.

    3. Round 3: HR round

       • Focused on workload and temperament.
       • They want confirmation the candidate can handle hectic work, without “tantrums.”

• PPO conversion philosophy described:

  • There’s a lot of work evaluation rather than solely “rank-based” hiring.
  • One camp described:
    • day-to-day monitoring by mentors (about an hour each day to check what you did).
  • Sharvil dislikes a model where a 2-month job is judged in a single 1-hour presentation, describing a preference for continuous tracking.

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3) Off-campus vs on-campus quant hiring: what it takes

• Off-campus quant is described as “quite tough” (not impossible).
• Suggested realistic requirement:
  • strong mathematical / international-level achievements, e.g.:
    • Olympiads recognized globally
• Safer/alternate route described:
  • If you’re not in a strong college pipeline or don’t have quant background, prepare for software companies first to build a strong interview profile, then switch.
• Strategic stepping-stone path:
  • Join an on-campus big tech/software role, then later switch to quant/HFT.

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4) CP/Coding strategy as the “core filter” for quant-style interviews

• Main claim: quant/HFT at top firms asks “very hard-core CP.”
• Advice given about how to prepare:
  • Compete in contest CP and build extreme problem-solving ability through practice.
  • Contrast:
    • being “good at CP” vs being exceptionally strong (subtitles mention very high contest/problem difficulty levels by rating).
• CP helps broader technical sharpness:
  • Solving puzzles indicates mental sharpness and analytical strength, which supports success even when you later learn ML/other tech.

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5) “CP vs hackathons” (what stays relevant)

• He argues:
  • Hackathons may be less meaningful now because AI can make them easier to win.
  • Hackathons test more language/knowledge/proficiency and familiarity, which can be gained quickly.
  • CP is a longer-term test of:
    • cognition
    • sustained practice
    • speed + analytics
    • long-term mental state for solving unknown problems
• Key takeaway: CP remains a durable signal of ability.

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6) Example structure for a “software/infra-like quant company” interview (one “OnePlus package” company story)

• He describes a different kind of company (not “pure HFT”) where work is data storage + efficient querying (cloud services and a SQL-like query language are mentioned).

• 4-round interview flow for summer internship:

  1. Round 1: CP

     • ~1700–1800 level CP questions
     • solved on platform or on paper (he says companies may use either; this one used both/allowed both)
     • Interviewer impressed by reasoning quality (“why is this happening?”).

  2. Round 2: CP + real-world data-structure problem

     • Designing/modifying a string/text index so operations are balanced:
       • fast queries (counts/occurrences)
       • fast updates/edits
     • Niche data structure mentioned: square root decomposition
     • He highlights the trade-off between:
       • faster updates vs slower queries (and vice versa)

  3. Round 3: project-based technical questions

     • Questions related to his projects (example: sentiment analysis over news)
     • follow-ups like speeding up processing and adding indexing.

  4. Round 4: HR

     • generic HR questions; selection based on fit and general conversation.

• What this job represents (conceptually):

  • Not building everything from scratch, but improving existing systems by optimizing:
    • efficiency
    • storage
    • query performance
    • speed of data operations

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7) JEE AIR 40 details + personal obstacles (JEE Mains/Advanced story)

• JEE Mains:

  • He describes confidence that he got ~291 marks.
  • He states JEE Mains rank = 40 (his “best moment”).

• JEE Advanced:

  • Expectations based on stats: no one in top 50 of Mains with extremely high Advanced score was found—so his “worst case scenario” intuition was optimistic.
  • He experiences acute eye/stress-related issues near the end of the exam (rotation/vision problems), but says he was still ultimately accurate.
  • He later states JEE Advanced rank = 509.

• Broader lesson stated:

  • His later quant path is influenced by the realization that the JEE-to-IIT pipeline can be an “easier path” than starting quant directly from scratch.

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8) Early life: acting/movie experience and how it shaped his motivation

• His parents are doctors and pushed education.
• He describes being “not interested in studies” earlier on.
• A turning point:
  • School auditions led to a lead role in a Netflix movie involving children.
  • He mentions searching his name/movie and references “Shortcut Safari” and “Chillar Party”-style vibes.
  • Filming (forest/long shoots) became a “dream life,” after which he had little interest in studying for a while.
• This is presented as context for why his discipline later changed and why he became serious.

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9) Rapid-fire answers (compacted)

• After a ₹1 crore package:
  • He chooses “pain of income tax” over pure flex.
• “Actual vibe of IIT?”
  • “Aesthetic heaven” over “toxic rat race.”
• “High package with high pressure vs decent package with peaceful life?”
  • High package, high pressure.

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Speaker(s) / sources featured

• Sharvil Patel (main speaker/guest)
• Host / Interviewer (unnamed in subtitles; repeatedly asks questions and runs the session)
• Referenced third parties (not direct speakers in the video):
  • Shivam Patel (mentioned as inspiration; described with academic/career background)