Career: Hacks for aspiring quants

Questions and answers for quant roles

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Table of contents:

1. Introduction.

2. Quant 101.

   1. Role & responsibilities.

   2. Coding & programming.

   3. Mathematical & statistical analysis.

   4. Strategy development & model improvement.

   5. Data management & market microstructure.

   6. Project management & work process.

   7. Risk management.

   8. Hardware & infrastructure.

   9. Trading execution & market operations.

   10. Collaboration & communication.

   11. Evaluation & performance measurement.

   12. Career advice.

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Introduction

Today's article is going to be a little different...

Me and my buddy, elbows deep in overpriced lattes at Brunchit Malasaña Café, when suddenly—bam—his professional facade cracks. One minute he’s nodding along to my rant about databases formatting, the next he’s staring into his cup like it’s a Magic 8-Ball predicting his demise. “This project’s a dumpster fire wearing a loses costume,” he muttered, like a Shakespearean actor doing a TED Talk.

Look, I’m no therapist, but I am great at sniffing out drama that could double as a case study. So I hit him with: “Okay, but why is this fire dumpster-sized? Did someone toss in a grenade labeled ‘client feedback’?” Turns out, the grenade was actually three managers, two conflicting deadlines, and a partridge in a pear tree (marketing, probably).

Now, here’s where I got sneaky. “Hey,” I said, leaning in like I was sharing nuclear codes, “what if we… document your pain? For science! And by science, I mean my weird hobby of dissecting work disasters in other people’s firms.” He squinted. “Are you recording this?” “Only a little!” I lied. (Spoiler: My phone was already on the table, blinking ominously. Hitchcock-level suspense.)

Prepare for the frame I pulled out of nowhere: don't tell him. We went from absurd questions like: "When did this go from 'damn' to 'call the coroner'? Who's the MVP in this mess? Karen, the financier, or Steve, the out-of-control guy? Is the real villain SQL... or humanity?" To continue with more interesting questions—after all, if you know how to listen, you can find alpha in the strangest places.

His answers zigzagged between genius and unhinged. At one point, he compared the project timeline to a “sourdough starter gone rogue.”—I kept that gem, no regrets.

Twenty minutes in, magic happened. He paused mid-rant about budget sheets and gasped: “Oh. Oh. We’re not building what the client actually wants. We’re building what we think they want because Steve in Sales panicked and—” I cut him off: “Write that down. WRITE THAT DOWN!”—he used a napkin. It’s framed in my desk drawer now.

By the end, he looked like he’d just finished a spin class—exhausted but weirdly euphoric. “So,” he said, “you turned my existential crisis into… a to-do list?” “A spreadsheet,” I corrected. “With color-coding.” He laughed so hard he snorted latte foam. Mission accomplished 🕵️☕

Ah! By the way, I think that if you are interested in this aticle probably you will be in this other one:

The ultimate Roadmap to become a Quant

Quant 101

I know that continuing with a bit more gossip would have been awesome, but I regret to tell you that I’ve already cooked up the questions quite a bit—I want the article to make sense 😇 Also, I grouped them because I think it might help you.

Role & responsibilities

• Q: What does a typical day look like for a quant researcher?
  A: It’s a mix of coding, collaborating, refining trading models, data analysis, meetings, and market monitoring.

• Q: How do you balance quant and non-quant tasks?
  A: By prioritizing core work and delegating or limiting peripheral tasks.

• Q: How do you decide which projects to pursue?
  A: Projects are chosen based on potential impact and feasibility.

• Q: How do you manage multiple projects at once?
  A: I ensure progress on at least two projects concurrently to prevent downtime.

• Q: How do you decide if one model outperforms another?
  A: We rely on rigorous testing and performance comparisons before production.

• Q: What is your overall takeaway about being a quant researcher?
  A: It’s a multifaceted role that blends continuous improvement, and rapid adaptation.

Coding & programming

• Q: How much coding is involved in your role?
  A: I do a lot of scripting, overall for prototyping but there are other processes as well.

• Q: Why is coding essential for quants?
  A: It implements models, processes data, and enables rapid testing and automation.

• Q: Which programming language is most popular among quants?
  A: Python for prototyping and C++ for execution.

• Q: How often are trading models tested?
  A: Usually in daily or weekly horizons to ensure robustness.

• Q: How do you balance theory and practical application?
  A: We use rigorous math to build models, then code and test them in live environments.

• Q: How do you split your time between analysis and implementation?
  A: Both research and practical coding are integral parts of my day.

• Q: How is live model performance monitored?
  A: Through real-time dashboards and automated alerts that track key metrics.

• Q: How do you ensure continuous model improvement?
  A: Through iterative updates, testing, and regular performance reviews.

• Q: How do you manage computational complexity?
  A: We are obsess with algorithmic and code optimization.

Mathematical & statistical analysis

• Q: What role does mathematics play in your work?
  A: Math is foundational. Especially statistics and techniques for iterative improvements.

• Q: What type of math do you use most?
  A: We mainly use statistics, non‐trivial mathematical models, and microstructure analysis.

• Q: What role does statistical analysis play?
  A: It identifies and measures relationships, and validates model.

• Q: How are risk-adjusted returns used in strategy selection?
  A: They ensure returns justify the risks taken.

• Q: Why is the Sharpe ratio significant?
  A: It’s not but helps to compare the efficiency of different strategies.

Strategy development & model improvement

• Q: How much time do you spend on creating new strategies?
  A: Not so much because a significant portion of my time is dedicated to iterating and improving our current strategies.

• Q: How often do you improve strategies?
  A: Continuously and occasional breakthrough ideas.

• Q: How do you adjust your strategies with changing conditions?
  A: We dynamically update our models using real-time market data.

• Q: Is generating fresh ideas a challenge?
  A: Not as much as correctly prioritizing the work is the real challenge.

• Q: How do you balance short-term and long-term objectives?
  A: By focusing on rapid model responses and continuous strategic improvements.

• Q: How frequently do you update your models?
  A: Models are continuously refined and updated as new data and insights emerge.

• Q: How is model robustness measured?
  A: Through extensive testing across various time periods and synthetic scenarios.

• Q: How do you decide when to tweak an existing model?

  A: All systems have a circuit breaker and bail out methods.

Data management & market microstructure

• Q: Do you handle your own data management?
  A: Not directly—the firm’s market data team manages firm-wide data, though desks have specific needs.

• Q: How critical is data quality for your models?
  A: Very—high-quality, well-correlated data is essential for accurate modeling.

• Q: What data sources do quants use?
  A: Historical prices, volumes, order books, and alternative data like news and sentiment.

• Q: What importance does market microstructure hold?

• A: It’s vital for understanding short-term price behavior and refining the execution of systems.

• Q: How do aggressive trades affect the market?
  A: They can alter market dynamics, prompting adjustments in our strategies.

• Q: Are impact models useful for your analysis?
  A: They offer insights, though individual order impact is less frequently studied.

Project management & work process