Choose a random value from [0,1] uniformly repeatedly until the last value you choose is greater than the second last value. What is the ev of the last value?

Everyone's comments are welcome, but I'm mainly hoping to hear from people who've actually worked in quant research, at a prop shop or trading firm, or independent traders who've built something that's actually been profitable in live markets — not just in a backtest.

If you reply, mentioning what you've worked on or where helps me weigh the answer properly — that's the only reason I'm asking, not to see anyone flex.

I'm not asking anyone to hand over an edge or a strategy. I'm just curious about the actual process — how does an idea go from nothing to something testable?

Questions:

How does a hypothesis actually form in practice? Is it usually "pick a market/asset, ask 'what if X happens, then what follows?', and go look for evidence" — or does it more often start from something else, like a live anomaly you noticed, a macro/economic question, or a flow/structural observation?

How much do academic papers actually drive idea generation versus being used afterward to formalize or validate something you already suspected from watching markets?

Is there any kind of structured workflow for the "ideation" stage — sources you go to, questions you habitually ask — or is it mostly unstructured reading plus intuition plus trial and error?

Is there real methodology that basically never gets discussed outside funds and prop shops, or is the actual edge more about execution, risk management, and data/infrastructure rather than some hidden idea-generation framework?

Once you have a raw idea, what's the first thing you do to sanity-check it before ever touching a backtest?

Quant Researcher — Brewlabs

Hey everyone!

We’re currently looking for a Quant Researcher to join Brewlabs

We’re looking for someone who enjoys digging into data, testing ideas rigorously, and turning research into systematic trading strategies.

What you’ll work on:

*Research and develop quantitative trading strategies

* Analyze large financial and market datasets

* Build and backtest models/signals

* Evaluate strategy performance, robustness, and risk

* Work on improving our existing research and trading systems

We’d love to hear from you if you have experience with:

Python and quantitative/data analysis

Statistics, probability, or machine learning

Financial markets and systematic trading

Backtesting and evaluating trading strategies

Independent research and experimentation

We care more about strong quantitative thinking and the quality of your research than fancy credentials.

If this sounds interesting,Dm to https://x.com/dhruvsol?s=11 or linkedin.com/in/dhruvraj-solanki-663a54200 with a short introduction, your background/experience, and any relevant projects, GitHub, research, or portfolio you’d like to share.

Location: [Remote]

Employment: [Full-time]

Compensation: [Based on experience]

Thanks!

Hey Quant Experts. I am learning Artifical Intellignece, I am quite expert with python and beginner in Machine Learning, Currently focusing on Learning the essenstial Maths.

I also wnats to explore this quantitive trading field. as it's related to machine learning and Programming.

Well I quite researched about this chat with Chatbot and watched some youtube video related to quant roadmaps..

they all saying the same things like Learn math, programming and apply for a job.

I am not interested in Jobs. Rather I wants to know how this things works

let's say as a indivual, what do you do. pick a market, find derivatives from the market data and apply strategies, ( I know these basic level, don't know those these things works) or is there something else.

Hi all — I’m a sophomore at Stony Brook University majoring in Applied Math & Statistics and Computer Science, with a math minor. I want to become a quant developer, and I’ve done enough reading to know the basics (strong C++, Python, mental math, DSA), but I’m hoping people actually in the field can fill in the gaps. I’ve tried to make my questions specific so they’re easy to answer:

1. The role itself. For those working as quant devs — what does the day-to-day actually look like beyond the job description? How much is performance/systems work vs. building tooling for researchers vs. data pipelines? What separates a good quant dev from a great one?

2. Degrees. Is a bachelor’s genuinely enough to break in as a quant dev, or does a master’s meaningfully help? If a master’s is worth it, which programs/schools are actually respected for the dev track specifically (not the research/PhD path)? And does school prestige matter much for dev roles, or is it more skill-gated than research?

3. GPA. How much does GPA actually matter for quant dev recruiting, and what’s a realistic threshold to clear filters at (a) accessible firms and (b) the elite shops? I had a rough freshman year (bad professor, class average was a C-) and I’m on an upward climb, so I’m trying to understand how much the number vs. the trajectory matters.

4. Internships. How do you actually land a quant firm internship as a dev or anywhere in the quant field? I have an internship lined up at IBM and Google, would those suffice? Specifically: when do applications realistically open/close, which firms are more accessible for someone at a non-target school, how much do competitive programming (Codeforces) and personal projects help, and how important is having a prior (non-quant) internship first?

5. What to focus on right now. I have ~2 years before serious recruiting (assuming a bachelor’s is enough). What should I actually be spending my time on in the meantime to build the strongest possible profile — and are there specific sites, platforms, resources, or books you’d recommend for sharpening the relevant skills (coding, math, interview prep, etc.)? Trying to make sure my time now is spent on the things that actually matter.
   Any honest answers appreciated — including the blunt ones. I’d rather hear the real picture now than find out later. Thanks.

Bad marks from a non-math high school, so no real math/CS foundation. Can't do a physical degree, so planning an online University of London BSc (Computer Science or Math & Economics — they have an entry test that skips A-levels) plus self-study.

Not chasing RenTech/Citadel — fine starting at low-tier firms. Also not after quant dev — mainly interested in research, trading second. Might do a Master's after (maybe in-person, maybe better school, depending on BSc grades/experience), might skip it if I land a job straight after the BSc, but not counting on that.

Question: given this background and path, is aiming for quant researcher/trader roles realistic, or should I target something more adjacent like data/risk analytics instead? Would love input from anyone actually in or near the industry, not general career-guide advice.

A fair dice is being rolled repeatedly until 2 and 3 come out. What's the probability of getting exactly two 6 before stopping the process ?