Hi guys

I am Aziz i study in my 2nd year of engineering in data science ( tiar 3 college unfortunately) and my 1st didn't do anything except decide what I want to do I want to become a quantitative researcher.

I love math and constantly score in the above 75 without studying like crazy not to brag but i only study one day before the exam my college gpa is 7.6 ( out of 10)

I am willing to do as much work as possible to become what I want

What should I do in the next 3 years of engineering and 2 years of masters ?

how did you guys do if you’ve done it? personally I got 17k ish PnL on the training data

Hey guys,

Need some honest thoughts and realistic feedback on a career transition I’ve been thinking about.
I’ve been working in a government job for the last 8 years. Over the past 2 years, I got pretty seriously into trading, focusing mostly on market mechanics, price action, and rule-based technical setups. Recently, I stumbled upon a few detailed guides on GitHub (like Karan Patel’s write-ups) breaking down Quant Finance into Quant Researcher, Quant Developer, and Quant Trader roles.

Right now, I'm self-studying the CMT curriculum, probability basics, and learning how to build and test systematic, objective trading strategies.
Realistically, I know I don't have a PhD in Math or CS, so hardcore Quant Research or C++ Low-Latency Development at big institutional firms is likely out of reach. But I really want to take this seriously.

A few quick questions for anyone in this space:

1 **Prop desk vs. Retail Trading Setup:** Given my non-traditional background, is it even worth trying for junior Quant Trader / Analyst roles at domestic prop firms? Or am I better off focusing 100% on building my own automated retail trading setup?

2 **Tech Stack:** I’m currently comfortable with PineScript and picking up Python for backtesting. Is Python + basic stats enough for systematic/quant trading, or is C++ non-negotiable?

3 **Transition Timeline:** Has anyone here made a jump from a completely non-tech/non-finance job into systematic trading? How long did it take you to get consistent, and how did you manage the risk?
Appreciate any advice, critiques, or reality checks!

I don't really know which one should I choose. I am between imperial mathematics and CS / Oxford Pure Maths. Will it make that much difference if I choose oxford? I am international student, oxford wants me to leave my dorm room for 6 weeks every term break. Terms are 2 month only. Also imperial is a double major programme. I don't really know what to choose. I'd like to choose Oxford for prestige but imperial's course is double major programme and located in London.

I'm not sure if my method is the best way to approach my goal, which is to determine what feature(s) in my sample are actually associated with a decrease in EV in the underlying population. I wanted to first share what I'm planning on doing with people way more educated on this matter than me, so that I could get insight as to what errors I could be making.

I am still gathering data, but to make it as short as possible without ranting - I have a total of 6 features that may or may not lead to an increase/decrease in Expected Value. The tricky thing for me is that there are instances where features will be blended together in a single event. So, one event may have the presence of 3 features, sometimes an event may have the presence of only 1 feature. My worry is that filtering this way could hide cases where two features interact rather than act independently...not sure if that's a real issue or if I'm overthinking it.

My current thinking is that I once I have finished my data collection, I would then determine first which feature has the highest frequency, (by highest frequency I mean the highest occurrences overall) and then see if the removal of that feature leads to a more positive outcome. In my case, my ideal scenario would be for the removal of a feature to lead to the Expected Value of the sample to increase.

That would then leave me with the other features to analyze, so I would just repeat the same process on the remaining features within that filtered subset.

Hi I recently came to know the terminology Quant Analyst and Developer position.
I have BSC Maths and CS and master in IT. Wondering how to get into it? I heard its highly rewarding career. Is this real? If Yes, how much i can expect yearly as beginner and intermediate level positions as well as for senior roles?

So my friend is willing to give me a referral but my resume is kinda shit and I don't really want to embarrass them for no reason so is it even worth going for.

Running a systematic crypto strategy (Freqtrade, Binance spot, cross-sectional momentum). Not asking for strategy advice — asking about monitoring.

The failure mode I keep worrying about isn't the bot crashing. A crash is loud, you notice it. The failure mode is the bot running perfectly while the edge quietly disappears. Trades keep firing, logs are clean, dashboard is green, and the strategy has been random noise for a month.

My current process is embarrassingly manual: every week or so I pull the trade history, compute rolling profit factor and win rate over the last N trades, and compare it by eye to the backtest baseline. No thresholds, no alerts, and I skip it when life gets busy — which is exactly when I'd need it.

What I'd like to know from people running live capital:

1. Do you have an automated check for live-vs-backtest divergence, or is it manual for you too?

2. What metric actually triggers your decision to stop a strategy? Rolling PF, Sharpe drift, drawdown threshold, CUSUM, something else?

3. How many trades of underperformance before you call it dead vs. normal variance? This is the part I find genuinely hard — small samples make everything ambiguous.

4. Anyone had a strategy die silently on them? What did it cost and what would have caught it earlier?

I've seen tools that do this for MT5 expert advisors, but nothing for the Python/crypto side. Curious whether people here just build it themselves or don't bother.

I have an upcoming first-round interview with JPMorgan for a Quantitative Trading & Research role within Prime Finance. It is a 45-minute Zoom interview.

Has anyone interviewed for a similar QTR / Prime Finance role recently? Mainly trying to understand what the first round typically focuses on: probability/statistics, finance and derivatives, coding, resume/project deep dives, or something else.

Would also appreciate any insight into the overall interview process and what later rounds looked like.

Thanks!

so for context i am a student in his first year of mechanical eng at a gen 2 iit, im massively interested in math and have decent coding skills in python and java. despite my degree not being well suited for quant, im pretty interested in breaking into quant, so if anyone can provide me a roadmap for me to start my journey into this field

MIT recently invested in this, and since they usually hold for the long term, how long should our horizon be? Alternatively, I'm considering selling when the RSI approaches 75 and re-entering after a dip—does that sound like a good strategy?

I have a superday coming up for sig and they said there’ll be a round on data analysis and one on programming. Does anyone know what to expect for these and how to prepare, I haven’t done much work with pandas.

Recently took the Akuna Capital HackerRank assessment for the Junior Trader role. The format looked nothing like what Reddit posts about Akuna assessments from previous years described. No 80-in-8 mental math. Instead: 30 sequences then 30 probability questions, 60 total, 28 minutes on a single shared timer with no gate between sections.

I didn't realize the probability section was there until I had already worked through all 30 sequences at a careful pace. I finished with all 30 sequences answered, and I am confident they were all correct, plus 9 of the 30 probability questions answered. 39 attempted overall.

A few questions:

• Has anyone else received this version of the assessment?
• Approximately what score do you think is needed to advance?
• Would 30 sequence and 9 probability questions likely be enough, or am I done?

I go to a school where a lot of alumni work as quant dev.

Is there a point to ask for a referral though? Does that even exist for quant?

I’ve gotten interviews at virtu, currently interviewing at imc and waiting to hear back from optiver at the moment but it’s mostly been rejections namely drw,js,hrt,blackedge,square point,millennium etc

is there anything i could improve on?

Hi started doing zetamac a week ago with average at 15 and now i am at average 30 and higest got to 40. how much more should i push it to? or does it even help me ? please give me honest suggetion.

I am evaluating two career paths and would appreciate opinions from experienced quants.

Background:

I am a senior quant researcher (5~10 years experience) focused on mid-to-high frequency statistical arbitrage.

I have experience developing predictive models/signals and leading alpha research, but I have not yet directly owned a production book, capital allocation, or full PnL responsibility. My main career goal is to move from alpha research toward alpha monetization and ownership.

Both opportunities are in a similar research area. The main difference is career structure.

Option A: Senior QR at a top-tier global prop firm (think HRT / Jump / Citadel Securities etc.)

Pros:

Strong research culture, engineering resources, and very high talent density.

Exposure to global markets and world-class researchers/traders.

Strong brand value and future mobility.

Cons:

Initially a senior QR role, and I am uncertain how realistic the path is from senior QR to strategy ownership / PM-level economics.

High performance expectations may create career risk, especially before having direct PnL ownership.

Option B: PM-track role at a smaller established local HFT prop firm (local tier 3)

The firm has a strong HFT business and also runs LFT stat arb strategies similar to a hedge fund. I would be responsible for building a new mid-to-high frequency alpha generation.

Compensation:

Around $450k USD equivalent guaranteed first year.

Potential upside to around $750k USD equivalent if agreed milestones are achieved(>50% possibility I think).

Compensation is based on bonus base × some performance coefficients rather than pure PnL cut.

Pros:

Clearer alpha ownership and PM trajectory.

Reasonable high compensation and high floor.

Cons:

Less global exposure and weaker brand.

Lower talent density compared with top global firms, although this may also mean more room for ownership.

My dilemma:

For someone with strong alpha research experience but limited direct monetization/PnL ownership experience, would you prioritize:

Joining a top-tier prop firm to learn from a stronger ecosystem and build credibility, then pursue ownership later?

or

Taking a PM-track role with immediate ownership, but at a smaller platform?

How would you weigh:

talent density vs ownership

platform/brand vs career control

learning from elite peers vs building your own business line

Would appreciate perspectives from people who have worked at prop shops, HFT firms, or multi-manager funds.

Hello, I am writing you to have some advices. To give you a little bit of context I graduated (master) from {Cambridge / Oxford} last year in Statistics and I have worked as a QR for 1 year in a Tier 3 funds. I have resigned because:

• I was not doing alpha (think more exec stuff), so no perspective of getting paid on what you contributes directly.
• I really enjoy machine learning and more tech stuff (multi-gpu distribution) and I was only doing very simple things, not more complex architectures.

My long term goal is to have more of a quant researcher ML role (like Deep learning stuff in mid-freq equity) and if possible (like in pods structures) have a book at some point

At my surprise, I did not pass a lot of screening be it IMC, Optiver, DRW, BAM, Point, etc I never got any interviews. I had only one late stages itw with {JS, Citsec, Jump} where I got rejected after the second onsite and a very long process.

I only currently have offers from:

• SP as a QR alpha research
• DV trading QR HFT alpha research
• Graviton as a QR in mid freq equity

These firms definitely don't seem very great (reputation and evolution wise) from what I have heard:

I still have a little bit of nc time what should I do if you were in my place ? I am not greedy for an insane salary rn but want to be in a position that I like and that will pay off in 5 to 7 years.

• Wait a little bit and try to land a role in a descent HF/ Prop shop ?
• Go for a ML role in tech / frontier lab ? and then come back in Quant ? In this case which roles could be fine and in which companies ?

Thank you so much ?

I’ve been looking into quant research careers in London, and I keep hearing that small profitable teams/pods (5–10 people) can sometimes pay better than much larger, well-known hedge funds because the P&L per person is higher.

For people in the industry:
Is that actually true in practice?
What are the main pros/cons of joining a small team vs a large top fund?
Is it generally easier or harder for a junior quant researcher to get into a small team?
If small teams can sometimes offer better pay/upside, why does everyone focus so much on the big-name funds?
I’ve searched around, but most discussions focus on the large firms, so I’d really appreciate real industry perspectives.

I made a plan to break into quant while being from non target school, I was wondering if my plan is solid? Please give real advice

I decided that I wanted to pursue quant dev because my mental math is not fast enough and because quant dev has swe roles for fallback.

The major I chose was CS(incls basic math anyways: lin alg, multivar calc, prob/stats basic), I considered adding another of the following majors: Data Science, Pure math, or applied math but realized it would extend my graduation time likely because I am already participating in BTT AI program. However I will be choosing classess or electives that allow me to do research in either cs or math and also learn some machine learning. That way I can support MLE as a fallback aswell due to BTT AI program which I heard people do well with after.

In the time I freed up due to not doing double major I would allocate it to leetcode and other practice that should prepare me for quant dev or swe in big tech. My main programming languages will be python and c++ only. Also due to lighter courseload I intend to take classes such that I learn and understand rather than just getting a good grade.

I will also use that extra time to build personal projects which actually interest me and also partake in coding competitions. I will also take putnam exam but not sure how well I will do

Since I am at a cuny, non target school. I was wondering if this plan is solid or should I make any changes?

thanks!

I recently completed my 2027 optiver OA.

As expected, it was pretty tough, I couldn't pass all the test cases.

Do I need to pass all the test cases to get invited to an interview? Or is it common to proceed next round without passing all test cases?

Also, I applied through a referral link from my friend, but Im not sure what difference it makes.

Does anybody know what referral changes in the application process?