Shout out to

@Igna. Thank you so much for taking your time to help us out. I learned a lot already reading your answers to other people. Thank you so much.

I’m sorry about the following long post. The next 4 paragraphs are my background. You can probably skip some if you just wanna see my questions.

I am a 6th PhD student studying physics at a non-target university. I also have a PhD minor in statistics, with courses including theory of statistics, machine learning, statistical computing, fundamentals of optimization. Math has always been my favorite subject. I really liked quantitative finance, and want to pivot my knowledge and skills from math and physics to a quantitative researcher.

My research area is condensed matter physics and involves discovering and characterizing novel quantum magnetic materials, that could be useful in both fundamental understanding of physics and application in technologies, such as quantum computing. That being said, my research is mostly experimental (crystal growth, material characterization (XRD, SEM, AFM, Raman), magnetization measurement and has almost nothing to do with quant. We also do some calculations that involves modeling and computations, like band structure and magnon interactions, but we do these by collaborating with other theory groups. I have decent publications, 10 articles so far but I am only afraid my experimental papers are not appealing to quant people. That is, my research topic itself has almost nothing to do with quant finance.

In terms of math and programming, I have pretty good understanding of probability theory, linear algebra and calculus. I have meager understanding and a little experience of implementing machine learning. I have basic programming skills in Python, R and MATLAB. I also know a little bit of C and C++ but I don’t use them in my work. Knowledge of C and C++ is getting rusty. I have never taken a data structure and algorithms course, although I have been exposed to some of those algorithms here and there.

For financial experience, I have traded stocks from 2018 and spent a lot of time on it in 2020. I learned a lot of manual momentum trading. I am good at Mark Miniverni’s momentum trading strategies. I also tried to implement momentum trading strategies in Python and learned a lot. I That being said, I don’t have any formal education in finance. I don’t know much about financial derivatives, pricing theory, or portfolio optimization theory.

I started to know about quant 4 years ago and started doing more research about the quant career from this year. Now, I am aspiring to be a quant, more specifically a quantitative researcher. I started preparing this year from late February and applied 19 positions, all hedge funds. Among them, I got 1 OA which I passed but tanked the interview after two rounds. I realized I wasn’t prepared enough. It’s now mid April so I have about 5 months until mid September before I apply for internship or full time positions. I’m now studying machine learning 4-5 hours a day, mostly working through ISLR and ESL. I want to design a pathway (study & internship plan) to break into the quant career.

I am still working full time in my lab so suppose 4 hours study a day. I have about 5 months, or 600-700 hours. My goals for the next 5 months:

- Enrich my resume (may be personal projects, research projects with a professor, trading competitions, internships in quant research, quant trading, software engineering, or data science) to get interviews.
- Have enough knowledge foundations to pass the interviews and get offers.

My questions:

- Self study plan: if I were to self-study for the next 5 months before I apply for buy side QR roles, what do you recommend me to study, in addition to prob&stat, linear algebra, calculus)? What are absolutely necessary for a quant researcher? What are optional? I have listed possible areas of study based on my research, for your reference.
- Optimization in asset management.
- Stochastic calculus for finance, Steve Shreve volume 1 and 2
- Derivatives markets by McDonald
- Data structure and algorithms
- Python programming
- C++
- Machine learning
- Mining of massive data sets
- Time series analysis

- Off-summer internships: now, I don’t feel I am ready for QR interviews and there are not many positions available. The chance of getting a summer internship is bleak. Or is it just my illusion and there are actually some available and I should apply? Are there many internship opportunities in the off-summer season?
- Other internships: If I can’t find a QR internship soon, what’s the next best internship option I should try? SWE or data science? Which one would better prepare me for future quant job applications?
- Projects: if I can’t find an internship in any of these areas, I would like to work on a project with a professor, which topics do you think would make me stand out?
- internship after graduation: is it okay to look for internships after graduation? Like I graduate in May in 2024 and do an internship during summer 2024? Or should I apply for 2024 full time positions?
- Comparative advantages and niche: since you mentioned before, candidates should have strong core competency, comparative advantages and niche. That’s really enlightening so THANK YOU so much. I am wondering what my comparative advantages and niche are / could be. My comparative advantage could be my statistical / math knowledge but I don’t have much research experience like some PhD have their theses on ML or statistics. My niche might be experimental physics? But I don’t know know how that is exactly related to quantitative finance and could differentiate me from other PhDs in this field. Could you gave me some advice?

I really appreciate your time and any inputs. I also appreciate if you can point me to the right resource, if you or someone else have answered some of my questions. Thank you again!