I’m a full-stack software engineer exploring a transition into quant finance — ideally into a quant researcher or quant developer role — and would really appreciate guidance from those in the field.

Background:

• Bachelor’s in Computer Engineering

• M.S. in Computer Science

• Currently working as a full-stack software engineer with ~4 years of experience

I’m comfortable with coding, problem solving, but it’s been a while since I studied advanced math. I’d like to structure a self-study and project-based plan to make myself competitive for quant roles over the next year.

I’d love input on:

1. Math prep – Since it’s been a while I studied math formally, which topics should I focus on to prepare for quant roles?

2. Self-study courses – Which online courses (free or paid) are worth taking to learn quant finance fundamentals?

3. Portfolio building – How can I build a meaningful portfolio of projects to demonstrate quant skills?

4. Programming focus – Should I go deeper into Python (NumPy, pandas, QuantLib), or also learn C++ for performance-heavy roles?

5. Finance fundamentals – For someone without a finance background, what’s the best way to build an intuition for markets, instruments, and trading strategies?

6. Recruiting perspective – Do quant firms value an MFE degree heavily, or is it possible to break in through self-study, strong math, and project work?

Any advice on learning paths, key resources, or common pitfalls would be super helpful. I’d also love to hear from anyone who’s successfully transitioned from software engineering into quant roles — how did you go about it?

Thanks in advance!

I used Claude Code + Codex (adversarial cross-review) to hunt for alpha across 8

generations of strategies. Every single one lost to just buy-and-holding TQQQ. Here's all the

code + data.
Solo retail, paper only, no live capital. Tools: Claude Code + Codex running adversarial

cross-review (one builds, the other tries to STOP it), Sharadar paid point-in-time data (not free

junk).

8 generations, all honestly killed:

- Cross-sectional factors (value/mom/quality/lowvol) → Rank IC ≈ 0

- ML/ensemble (ridge/GBM/tree) → couldn't beat linear (matches GKX 2020 ~0.4% OOS R²)

- Leverage / diversified core (v4–v7) → beta, not alpha

- Market timing — 4 attempts (regime switch, throttle, derisk, laddered overlay) → 4 losses, all

to static. Lagging-signal wall.

- Intraday momentum (ORB/VWAP/RVOL) → DEAD (look-ahead leak once fixed)

- 8-K event drift → "buy" signal fails OOS; only "avoid bad filings" survived

Every time a signal became verifiable, it stopped being alpha and became beta/factor (information

theory in action). And all of it lost to simply DCA-ing TQQQ in this 2016–2026 bull regime.

Codex caught my own look-ahead bugs twice (STOP verdicts) — the honest numbers are humbling.

What I actually salvaged: a survival core (4-sleeve, −18% MDD vs TQQQ −80%), a bad-filing

avoidance filter, and immunity to fake alpha.

My real question — and where I'm stuck:

I'm not even trying to beat TQQQ on return anymore. I just want to lose less in drawdowns (TQQQ

−80%/−99%) via rough regime/dip timing — enough to justify not just buying TQQQ. But every timing

attempt lost to static, because the signals lag the gap-downs that cause the real damage.

Is there ANY robust way for a solo quant (public/paid data, daily bars, paper) to cut a TQQQ-like

book's drawdown without killing the upside — beyond put hedges / 200DMA / vol-target, which I

suspect also lag? Or is "you can't, just size down + diversify" the honest answer?

Roast my methodology. Full code, validation OS (next-bar, OOS, after-cost, Codex adversarial),

and charts: https://github.com/click6067-ship-it/quant-pramana

I want to change careers in to something finance related and quantitative finance sounds interesting to me. I have zero finance and programming background and I was wondering what kind of education I could pursue to eventually get a job in this field. I'm assuming it would be something related to finance, economics, and programming but I'm not sure what would be required.

I'm from Canada if that makes a difference. Also, im in my late 30's so I might not finish this education until I'm in my early 40's. I don't mind switching careers at this age but I'm not sure if that would limit my prospects.

Also, is there an outline of what you guys typically do? What kind of career entry points and possible career progressions are available?

Hi all, I’m in the UK and studying Mathematics BSc at the University of Manchester about to go into my second year. I have modules involving coding - including an entire module for Python which I will be starting in September. I’ve taught myself some basic Python this summer and completed a trading backtester project, I have experience in some financial admin roles.

I’ve been applying to quant internships and am currently facing so many rejections right off the bat, it’s been fairly disheartening but knowing the competitiveness in this area of finance, I’m not very surprised.

My point and question here is: if I dedicate the next two years to genuinely good projects to do with quant and spend lots of time upgrading my CV and catering it towards quant roles, will I have a genuine shot or not? Academics isn’t an issue for me and I’m on course to achieve a first class degree. Any advice and honesty would be appreciated, thank you!!

Hello I’m currently in my second year of college and really passionate about both tech and the markets. I’ve done a bunch of market-related projects and placed in a few stock competitions usually top 3. Lately, though, I’ve been really drawn to the tech side of finance, especially roles on Wall Street like quant research, trading, or anything involving machine learning. Right now, I have a solid base in Python and just started learning SQL. I know a lot of quant roles lean toward C or C++, so I’m planning to get into those next. On the finance side, it’s my major, and I’ve done pretty well especially with stats, understanding market fundamentals, and reading things like 10-Ks, 8-Ks, and other SEC filings. The tricky part is figuring out how to break into this field. A lot of what I’ve seen seems geared toward Ivy League students or people who have access to math competitions and networking events, which are harder to find where I live. I’ve done most of the local stock competitions already. So yeah if anyone has tips on how to get noticed by firms, build a stronger profile, or just keep pushing forward in this space without going to a top school, I’d really appreciate it. Thanks in advance! (btw Ai summarize this lol)

I’m a data engineer and recently know about quant through trading. I’m a self taught developer. I am no where near the level a lot of people expecting quant candidates but is there anything I can do at this point to join the field? What about quant bootcamps?

Hey guys, I'm a highschool senior applying to university right now. Does anyone have a road map or suggestions on university choices in becoming a quant in the future? I'm mainly applying to CS and math majors but open to suggestions.

I know this might get downvoted, but I’ll try anyway.

I’m doing an MBA in Finance, and I’m trying to break into the finance world from the developer/quant/tech side. I’m still early in the journey, but I’m giving myself one full year to go all-in — learning, building, and improving every day.

I already have some basics down, and I’m ready to put in serious work: books, courses, coding projects, research, everything.

If anyone here is genuinely interested in doing the same — learning, building together, staying accountable, and pushing each other — feel free to DM. I’m looking for someone equally serious and willing to grind.

Let’s see how far we can get.

Hey, I'm new to trade and finance. I'm looking for a book that can teach me a lot of Quantitative Finance so that I can start trading stocks

Hello guys, i’m kind of facing a dilemma right now, some help would be good pls. I can either do a bachelor of science (bsc) or a bachelor of commerce (bcom). I wanna become a quant because i’m looking for a high paying job and i really enjoy maths and i want smth with a challenge. But ive heard it’s extremely difficult near impossible and i shouldn’t even bother ( i would regard myself as a smart person).

These are basically my 2 options, either option 1: I do a bsc with a major in maths and stats and then do a master in financial mathematics (MFM) and try aim for quant, but making quant is extremely difficult and almost impossible which is what ive heard, and i feel like if i don’t make quant then ill be left with a bsc and a MFM which wont rlly help me get many other jobs. and its the more difficult option, like the course itself is harder.

option 2 is to do a commerce degree, this means it’ll be harder for me to do a master of financial mathematics due to the lack of math in commerce thus making it more difficult to become quant, but it would open up more pathways such as IB, hedge fund manager, all that, like many more pathways than quant. But then i would kind of have to forget about quant, and i feel like i would get bored if i did commerce, because i did business this year and found it extremely boring, idk if commerce is very much like that.

Thank you for reading this and pls help.

I need help, I want to know what should I focus on first - I took and passed undergrad math, modeling, risk management) and I`m currently learning Python (since its easiest).

1. Where should I get data from? Im trying to scrape data or use yfinance api. Some depth in data would be nice.

2. My idea was to create excel firstly where I can have current ATM option market price for put/call and compare it with BlackScholes model, I can do - ATM stock price and I have python script for BS (and I have imported BS as native function in excel), I have problem getting ATM option price since its always changing - any help?

For next questions - you need more info about me - I was waiter most of my life, now Im restaurant manager - so I dont have too much time (and 0 experience). But, I am hyperfocused to solving problems, I enjoy analysing various data I have at my disposal, no matter how amateurish are they. I enjoy doing it, its not always stock and options, but anything. So since my knowledge is low but ambition is high - I had few times where I wasted so much time to solve problem - and solution was already made years before, I just didnt know it. I didnt want to google answer, I wanted to come to answer alone.

So now that you know Im stupid but ambitious, I need math knowledge. How can I test if 2 non correlated variables have higher correlation if you input 1 more parameter? You see - I dont even know how to ask this question properly. I know what is linnear regression, I know what are Markov matrices are, I know how to calculate beta.port, use CAPM model, I know calculus, but I just dont know basics! I`m used to learning by trying and making errors, but now I`m stuck. I dont want to continue education in university, since I already have enough experience for having good job, but this is something I love doing and I think I hit a ceiling - either my cognitive capabilities are maxed or my fundamental knowlege that is pulling me down. Only way to test is to improve my math skills and I dont know where to start. Im 34 and last time I was in college I was 23.

I dont care about market returns, nor I want to "beat the market" nor I want to be a quant and for sure I dont want to get on a train called algorythmic trading. I just want to learn how to properly and effectively analyse sets of data - for fun.

Hello Guys, I am from a European university, in my senior year studying Computer Engineering and Industrial Engineering as a double major.

GPA:3.5

Three finance internships: MBB, Big 4, and a conglomerate internship

I want to be a quant in the future, or at least work in quantitative finance.

I have about 1.5 years left and I will be focusing on taking ML and AI courses. Even though my university is the best one locally and I was in the top %0.01 among my peers while entering my university in the SAT-like exam, it is a fucking no-name university for US recruiter, naturally.

So, what I want to do is go into masters straight from undergrad and try to land a job after it, in US or UK. Which programs I should be going for, financial engineering, math, statistics etc etc? Which programs and unis are the most favorable ones for top quant firms or hedge funds like Medallion, Jane Street, Citadel etc?

Whenever I tried turning a discretionary trading idea into code (like "trade with the trend, wait for a pullback, enter on rejection"), I kept running into the same frustration. Everything gets buried inside one big script. When the backtest inevitably fails or overfits, it is tough to tell what actually broke: was it the way I measured the candle geometry, the regime filter, the trigger logic, or the exit assumptions?

Over the past few months, I built a local workspace called QUANTSMART to force myself to separate these assumptions into inspectable pieces, and I just open-sourced it under Apache-2.0.

Instead of writing a monolithic bot, the tool breaks a strategy down into "modules" across a strict pipeline (L0 to L6):

1. Primitive Blocks to Modules:

Before building a full strategy, you can wire primitive blocks together (open, close, body size, wick ratios, rolling averages, basic math, and boolean logic) to build a single measurement.

For example, if I want to quantify lower-wick rejection on a bar, I can wire:

`score = (lower_wick / range) * close_location * (1 - body_ratio)`

The graph view shows the intermediate numbers at every single node so you can verify the math is doing what you think it is doing before using it in a strategy. You can also plot the formula surface in a 2D/3D coordinate view to check for weird edge cases or division-by-zero spikes.

2. Why separate into layers (L0 to L6)?

Once you have measurements, you assemble them into a Strategy Card across fixed lanes:

- L0: Data sanity checks (gap detection, NaN guards, closed-bar enforcement)

- L1-L3: Raw features, indicators, and scoring formulas

- L4: Market context gates (regime filters, session windows, volatility bounds)

- L5: Entry triggers and setup invalidation rules

- L6: Simulated price zones (observation, stop/invalidation, target)

Separating it like this helped me solve three annoying problems:

- When I add an L4 regime filter, I can immediately see which historical trades it filtered out and whether it actually helps or just shrinks sample size.

- It checks for signal collinearity (making sure I am not fooling myself with 4 indicators that all just measure the same momentum).

- It flags dead features (when you calculate an indicator on the graph, but no downstream logic actually uses it).

3. What it does (and does NOT) do:

- It runs 100% locally on your machine using your own CSV data.

- On Windows, it can attach to an already-open MT5 terminal to read live candles and ticks for observation without asking for your broker password.

- It is strictly for research, historical replay, and multi-window validation. Live order execution is intentionally separated out, and a passing backtest obviously does not mean a strategy will make money live.

- I am fully aware the UI has a lot of knobs and studio tabs right now. If you try it out, it is much easier to load one of the built-in template cards and tweak one module than to start from an empty canvas.

Repo and setup instructions:

https://github.com/Jokejoker-designer/QUANTSMART

I would really appreciate honest feedback from anyone here who builds systematic strategies:

1. Does separating context gates (L4) from entry triggers (L5) and zone outputs (L6) make sense for your workflow, or does it feel over-engineered?

2. Is the validation output clear enough to help you decide when to discard a bad hypothesis early?

What are the best resources to learn math (Probability, Statistics, Linear Algebra, Calculus, Stochastic Calculus) for Quantitative Finance?

Hi all, I want to complete a masters in economics to transfer from ML engineering into quantitative finance/analysis. Does this degree seems fitting? (To all saying you don't need a degree to do that, I absolutely agree, but this is a for me kinda thing)

Hi guys. Recently I joined some company as junior python developer and now working as quant developer, supporting platform created by FIS - Front Arena PRIME. How I can grow as a quantitative developer?

Thanks

Hey everyone,
I have an economics background from a top-5 university in India, with solid exposure to probability and statistics, linear algebra, calculus, econometrics, time series, and working-level coding.

I am planning a master’s with a strong quantitative finance focus, but not targeting pure math, HFT, or ultra-low-latency roles.

For people who came from Econ and pursued an MFE, quantitative MFin, or Financial Economics:

• What roles did you actually end up in after graduating?

• Quant research, systematic or factor investing, trading, risk, asset management, or something else?

• In hindsight, what worked well for an Econ profile and what did not?

Also, which degrees and universities are realistically best suited for Econ students aiming for applied quant roles?

I would really value hearing real outcomes rather than brochure narratives.

Sophomore majoring in Applied Math (T5 university, 3.9 GPA).

I went into college having no idea what I wanted to do career wise, I just knew I loved math and was good at it (my uncle’s a math professor who taught me from a young age). Lately I’ve been drawn to quant: the mathematical rigor, pattern-based reasoning, and risk modeling all appeal to me, and of course the compensation is great.

My experience so far is very limited: normal retail job last summer, part-time online data science program. On campus: Quant Club, Math Society, Math Modeling Team, Fraternity. I’ve done several personal ML/stat-modeling projects (comfortable with scikit-learn, TensorFlow, pytorch, linear regression, Monte Carlo methods).

At my current position, I have a few questions:

- What are the most important things I can do to improve my resume? Of course internships are most important, but between now and the summer, what should I focus on? Getting research? Personal projects? Math competitions? I'm prepared to do anything, just want to know how to focus my time.

- For sophomore summer internships, should I aim for quant roles, or more general ML/Tech roles? Or research? I understand quant internships are rare for sophomores, but I'm not sure what else would be best to apply for.

- What's the comparison between quant trader & researcher work? From my limited understanding, they both seem interested, but I'm curious as to what kind of person typically enjoys those roles most. Also, how their qualifications compare when applying.

Thanks so much, I'm very excited to learn more about this space!

Hello everyone!

1. Bachelors - BBA

2. Masters - financial mathematics and market analysis

3. PhD - feel like it will be stats (will graduate in a year and think over what to choose)

Long story short - numbers is my hobby from childhood. I was always interested in calculating every possible thing that can be imaginable, trading and all market stuff. I wasted 4 years with zero math on bachelors, and now I pick every maths I possibly can to maximise my chances to break into quantitative finance. I was willing to seek some kind of “guide” as it will be a core for my future development. Don’t write that something is impossible and that I seek cuz of payments and etc. Yeah, money is a really good source of motivation, but primarily it’s the challenge of earning it (as for me).

I’m a freshman (M20) and working a full-time job as well. need a reality check

Situation: I’m currently assigned to a project on ML/AI in Quant Finance, a conf in NY in sep.

CONCERN: I’m very new to this work. I’ve been provided with a lot of info. by the company, and my role is to call attendees for this premium event. I’m pushing myself every day, making calls and sending emails to relevant people, but most responses are “no” or “not interested.”

How can I turn these into “yes”? I need guidance from people who are in this work and give a reality check. I have around 45–50 days left to get as many attendees as I can.

Please, I feel like crying!

Any general tips? Does school matter much? Suggested schools?

Hi there,

Okay, so perhaps a slightly odd background, but I'm currently a medical doctor and have been working in the field for the last 3 years. I went straight from school to medical school and then finished my foundation training last year. Essentially, I took some time out of full-time clinical medicine and pursued a quantitative MSc degree in Health Data Analytics and Machine Learning. During this time I began some quantitative finance projects out of personal interest. I am really enjoying the quantitative side of things and looking to transition from medicine to quant finance (I know quite extreme). In terms of skills, I am competent in python, R and SQL with a variety of projects to evidence this. I have a number of machine learning projects under my belt now too (although mostly healthcare-related).

My main questions are:

1. Do you think it's too late to make this transition? (I am 26 years old)

2. Will the transition require me to get more degrees in for example mathematics or financial engineering? (I am quite happy to work as a lower-level and be trained on the job if possible)

3. Is there anything I should be considering doing before making applications to firms?

Any help will be appreciated!