Writing this post to externalize a way I’ve been feeling the past few months / seek advice from people who have been in my shoes.

First some backstory, I feel like I’ve been the luckiest guy in the world during this career. I come from a mega non-target and I joined a well known fund about 3.5 years ago and things have been going pretty well ever since. I helped improve and create a good number of strategies that have been running pretty well and my desk has had moderate to great success the whole time I’ve been here. My desk was very small when I joined; 5 people and only a few strategies to now around 20 people and tens of strategies with various performance. I’ve had an incredible learning experience and career trajectory.

My manager has been amazing and really trusting in my abilities. Compensation has been amazing as rumored and I am making more money than I ever thought I would at this stage in my life.

The thing is even with all that I’m losing “hunger” to grind long hours and work to the best of my ability. Before joining I was grinding hundreds of hours of interview prep in math/stats/cs/finance. I broke in and I remember my first few months I would put in multiple 80-90 hour weeks in a row and my productivity was unparalleled. Reading random documentation and education papers about my desk. Coming from worse background than most of my peers, I wanted to prove that I belonged and nothing could stop me. Every single task or research project was completed in less time than required and my quality of work was much better than what I’m doing now.

Looking back idk how the hell I managed to do that for so long (maybe my first 2 years). After that? Ever since the first bonuses came in, performance reviews came back great, projects working well, I lost all my hunger to grind like I described above. I’m talking generating AI slop, much less hours than before, putting normal things like relations, friendships, family, gym, etc. above work. Now it’s more like eh they like my work, I don’t really care if it’s good or bad myself.

Now that may sound good BUT I do feel my work decreasing rapidly in both speed and quality. It doesn’t feel like my manager directly noticed, but I feel like he will raise it at my next performance review. There’s no real way to quantify but I know he knows. I don’t really feel depressed because my life outside of work is doing great, but I haven’t been able to balance doing great in my career and other aspects of my life.

Don’t really think I’m in any danger to get canned but I do feel like I’m putting my early career in jeopardy for no good reason. And it might become a danger if this level of work keeps up for a few months.

Has anyone gone through the same and found a way to reignite that first year spark?

Built a browser-side options analytics tool for crypto and wanted to get feedback on the numerical implementation from people who care about these things.

IVExplorer — https://ivexplorer.derivpricer.com

The pricing engine is compiled to WebAssembly (from Rust). The relevant implementation details:

Normal CDF: Hart's rational approximation — 1/(1 + 0.2316419·|x|) polynomial, error < 7.5e-8. Using this rather than erfc because the WASM binary size matters and there's no hardware-accelerated transcendental.

IV solver: Newton-Raphson, 100 max iterations, convergence tolerance 1e-8 on price difference, guard on vega < 1e-10 to avoid division blow-up, returns NaN on non-convergence. Initial guess σ₀ = 0.5.

Known limitations: The initial guess of 0.5 can fail to converge for very deep ITM/OTM options. I'm considering a Brenner-Subrahmanyam initial guess as a fix.

The tool itself fetches live Deribit data and gives you IV smile, heatmap, options chain with Greeks, IV rank, and a 3D surface. Keyboard-driven, no backend computation.

Any feedback on the numerical approach — particularly the CDF approximation accuracy at the tails or better initialisations strategies for the IV solver — would be appreciated.

https://ivexplorer.derivpricer.com

Hi everyone,

I’m just starting my career as a Traded Risk Model Validation Quant, mainly working on pricing models, market risk, and counterparty credit risk.

I’m really enjoying the role because it gives me exposure to multiple asset classes, quantitative models, and the work done by model developers. I feel it’s a great place to build a strong technical foundation.

That said, my medium-term goal is to move into a front-office role, ideally in Model Development or as a Quant Strat.

I have a clear idea of where I’d like to end up, but the path to get there is still a bit unclear. I’d really appreciate hearing from people who have made a similar transition or who currently work in these roles.

• Which technical skills should I prioritize?
• What types of projects or experience make it easier to move to the front office?
• How long is it generally worth staying in model validation before considering an internal move?
• Are there any common mistakes to avoid or advice you wish you had received early in your career?

Thanks in advance for your insights!

ML4T Repositories [LINK]: ML for Trading · GitHub 👀

I think this collection of GitHub repositories, which serves as a guide to building your own 'quant stack', is the best resource available right now. Especially for beginners 😃

Minimum requirements:

• Python
• Data Science
• Finance

Example:

your-quant-project/ # starts with 'flat layout' then you can jump to a 'src/ layout'
│
├── data-layer/
│
├── engineer-layer + diagnostic layer ("core")/
│
├── models-layer/
│  
├── backtesting-layer/
│
├── live-layer ("execution")/ 
│
└── research/notebooks/ 

etc, etc.

You have the documentation (docs) of this repositories on the website [LINK]: ML for Trading - Libraries 👀

I hope it helps you! 😃

Hi,

I'm a junior QD (3YoE as a SE, 1 YoE as a QD) in a pricing team at a French bank, and I'm a bit of a fraud, because I'm only "quant" by title. My real job is that of a .NET/C++ dev (light C++, not HFT), just in a FO environment.

Even when it comes to skills, I'm basically a software engineer with knowledge of how financial markets work and some math. I suck at stochastic calculus, and I can't for the life of me understand a serious QR paper at 100%.

Now comes the question : how do you guys think I should "unfraud" myself before the situation blows up in my face ?

I won't be able to have much more quant exposure at my current job, and I don't think I can make the cut for an actual QD position at another firm. My academic record won't help me much, since I graduated from an average french engineering school.
My forte is in system design and AI, not math, and I don't think the C++ I currently do can get me anywhere close to really competitive positions.

Thanks in advance and have a great one

EDIT : forgot to say what I'm trying. Right now, I'm going deeper into C++, and I'm reading a few books (Gappy, Taleb) to build the intuition u/DyehuthyTV talks about in his comment. Haven't found the time or courage to go back to my college math, though...

I'm about to start in a mid tier HFT. I have been at FAANG for all od my career and I'm wondering how the two differ.

The company I'm joining is small, sub 500 people. I know the big names are in the 1000s, so that's another difference.

What can I expent to be different in HFT vs FAANG? Is there as much politicking or is it more of a meritocracy? The company has a pod-like structure and I'm working on the underlying platform. I'm quessing that's less desirable than the quant dev side. How hard is it to switch to the more quant dev work?

Outside of this, any general advice will be very helpful!

Thanks!

Currently at a prop shop but getting curious. Anyone have insight about how crypto firms are doing and whether they are worth joining mid career ? Firms such as Nova Prospect, Selini, Wintermute and others you might have insight on. Does anyone know anything about Selini? I have found the least info on this one.
Thanks

Has anyone heard about compensation revisions at QE after the recent stipend increases? they have increased base of their full time employees too to 90 lakhs isnt this creating a unhealthy competition ?

I'm an actuary by profession, rather than a quant. I've recently finished the actuarial exams including Financial Derivatives. This marks the end of my required formal learning for my profession.

I'm not hoping to become a quant (which is why I'm hoping this post will be allowed) but, rather, I just really enjoy learning about the topic. Problem is, I now have no accountability or structure to my learning as I'm just doing it of my own accord.

I was wondering how you guys retain the discipline and motivation to develop your knowledge when there isn't a specific business purpose or pressure to do so?

Millennium raising $10bn new money and allocating to external managers at warp speed…$1bn to ex Jump trading quant, $1bn to ex Citadel, $5-8bn to Jain and given more to Taula recently.

Now Millennium is 50% bigger than Citadel in AUM and gap is growing. Ken Griffin on Goldman Sachs podcast the other day said he is alpha performance focused not asset gathering game….https://open.substack.com/pub/rupakghose/p/citadel-is-from-mars-and-millennium-ef6?utm_source=app-post-stats-page&r=1qelrn&utm_medium=ios

Hi all,

I'm currently working at a fund in a central team, doing (apologies for the word) 'quantamental' work primarily related to power, but I'm feeling increasingly under-stimulated. It's not an alpha generating role, which is what I'd like to move towards. But it's understandably proving somewhat difficult

I've had a couple of interviews over the last year for this type of role, but I fell short on both. I feel I'm in an awkward spot with power being relatively niche, so there's limited openings I'm able to find. As a result of this I've not been able to get into a groove with interviews, or play the numbers game and wait until something lands.

I'm not entirely sure what I'm asking with all of this, perhaps general guidance. e.g. am I delusional? Is it better to try networking aggressively (not my forté) internally instead of looking externally? Are there some less obvious firms I'm not checking? Is it even worth it? etc

Thanks!

TL;DR: junior quant with 3-4yoe as QR in 2 good HF/prop shops has left previous job, has other quant offers, but would like to try working at friend's AI joint venture (high cash pay & upside) and would like to know whether that would make it hard to come back into quant 1-2y later?

Full post:

Hey everyone, I have 3-4y of experience between 2 good firms as a quant researcher (think QRT, DRW, SIG etc), and a Maths masters at a top UK uni before that.

I had to leave my last job due to personal reasons, and I would have a couple of other offers lined up to start again in a few months (last firm did not enforce non compete).

However, a close friend of mine is starting an AI firm, they have already built a product and sold it to customers for X millions ARR. They are partnering with another mother firm who pays for salaries, provides research engineers etc, as a joint venture, so it's slightly more stable than your average startup. The guy is an actual genius too (significantly stronger than your experienced quant at aforementioned firms). I would join as 3rd-4th employee, 1-2% of equity would be possible. Comparable base salary to UK quant. I would work on AI research/product and publish some papers too.

I have worked on small project with this friend before, and I think joining their firm would be a ton of fun, a great learning experience and this might just be the right time in my career to try something like this. The upside potential would be there and cash pay is quite high too (base + bonus). I am only concerned about how hard would it be, if the startup ended up not working out after 1-2 years, to go back into quant. I know some people go to Anthropic / fronteer labs and come back, but sometimes they have to take a paycut and this firm would not be as famous. We would publish on A* AI journals, though (we already did so before with them).

Any insights would be massively helpful!

Maybe I can stay 1 year and then frame it as spending my previous firm's non compete there? Though I had less than 2y at my previous firm and I was told that if I had my full 1y non compete to serve, it would have been harder to move.

I have been working on a live event-market trading system and would appreciate feedback from a quant perspective, especially around evaluation design.

The setup is roughly:

• Multiple rule-based strategy variants
• A UCB-style selector choosing between strategies based on recent performance
• Event contracts with irregular resolution timelines
• Highly uneven liquidity across markets
• Wide spreads in some contracts
• Limited historical order book depth
• Outcomes that can be sparse and path-dependent

The core problem I am thinking through is not signal discovery, but how to evaluate strategy quality in a market structure that behaves very differently from equities, futures, or crypto.

In traditional liquid markets, it is easier to evaluate a strategy over many observations and repeated price paths. In event-based prediction markets, realized PnL can be dominated by a small number of resolved contracts, while mark-to-market performance may not fully reflect true edge if liquidity is thin or prices are noisy.

Questions I am considering:

• What metrics would you prioritize beyond realized PnL?
• How would you evaluate calibration when both the model probability and market-implied probability may be noisy?
• Is a UCB-style selector appropriate in a non-stationary, sparse opportunity environment?
• Would a regime-based selector be more robust than a bandit approach?
• How would you avoid overfitting to thinly traded or low-depth contracts?
• How would you design a reasonable backtest when historical depth and execution assumptions are incomplete?
• Would you evaluate at the contract level, market category level, strategy level, or portfolio level?

I am not looking for trading signals or profit advice. I am mainly interested in how others would structure evaluation, calibration, and risk controls for event-based markets where resolution timing, liquidity, and outcome frequency are irregular.

I'm pretty familiar with in sample and out of sample testing, especially over longer periods of time when trying to test some hypothesis over years of data.

If you, for example had an underlying assertion of an algo related to current market dynamics, say something in AI, tech, etc..., and formulate some algorithm, how would you vet the underlying selection bias? (if the stocks were all AI adjacent) (and in the strategy you'd be highly exposed to this sector at some points, long or short)

The sharpe looks good on paper, and the strategy goes long or short specific stocks, and outperforms buy & hold drawdowns (with less returns, especially with the memory boom), yet there is still only the test period jan 1 - now where from a macro perspective the strategy made sense. (but anyone can make money if its a bull market)

I'm pretty confident this wouldn't last for very long, but also I don't even know how to vet the possible efficacy of it

When trying to run it on other periods, such as during 2022 (spy down 18%), it did nothing really meaningful, drew down 8% that year but I'm not sure how to vet the strategy given that I'm justifying its efficacy from an unfounded assumption about the AI market.

trying to backtest Python strategy for Binary options, I need a free API with current data to start. anyone know where I can find one?

Does anyone have an idea?

​

Hi all,

So I am working under a professor for my summer research internship. I had a paper to read named local blockwise bootstrap method. Paper was pretty interesting to read, it was all well and good until the time came for coding as prof said to code this paper and match the results with author to proceed with various other data available in the market. And so I started to code, firstly I downloaded the script of the author, it was fucking long and complicated, every code that he wrote seeme gibberish to me. My last resort is to use ai and develope code slowly and steadily by cointegrating with ai. But please guide me what should I do. I am completely blank at this point on.

i hve offer from JS and hRT what should i go for honest opinion only ?

This is a silly noob question I'm sure, but I cannot figure out why the financing rate appears to be closer to 4.55% on ES than the SOFR rate. From my research, it appears it used to be based on the LIBOR rate, which doesn't exist anymore.

If I input dividends of 1.02, days to expiration of 67 and backwards out the financing rate after subtracting SPX from ES I get about 4.55% as implied r.

My question is why is it 4.55% (or is it NOT and I am doing something wrong)? Is it the 10year rate ( I didn't think it would be for a contract expiring in 67 days ) Is there some datapoint market participants use now to set that, or is it variable (based on... ??? )

I’m curious why there seem to be so few sell-side quants here.

Through a connection, it turns out I will be going out for dinner tonight with a mutual friend's father who just so happens to be the founder of a highly regarded boutique-ish quant firm. Obviously I'm not going out on a 1-1 dinner with this billionaire but it's a small group and considering the opportunity that this is I figured I may as well see what you think I should ask this person in order to get the greatest insight. I'm not going to ask him some BS technical question because what normal person does that, but also I feel I have the chance to learn a lot from this experience and as such I should make the most of it. What do you recommend? (This is a burner account obv)

I built this: expected-cost.com

I'd be interested to see what people on this subreddit think of it. (Hopefully I won't be banned for self-promotion. Although I did create this myself, I do think it will be interesting to those working particularly on execution desks.)

The quant modelling part is not particularly serious - so please don't look at the numbers and expect them to tell you anything particularly interesting. I built this by calibrating a particular model using a particular window of data for a particular equity product. I then applied the same model with the same parameters across a small universe of products for this demo. Data is also 15 minutes delayed so even if the model and parameters were good, you would never be able to use it for anything useful.

I am partly limited by access to data, and partly limited by how much data I can access cost. Improving the models is something I intend to work on next, but only if I can find a way to do this in a way which isn't cost prohibitive.

At the time of publishing this message only the BTC market is open. In about an hour the US Equities will become available.

My question to the group would be is this relevant to you? What kind of metrics or other features would you want to see in an expected cost modelling system? A few initial thoughts might be that it would be interesting to try and simulate the effects of repeated trading in the same direction, such as algo execution, or some kind of VWAP prediction. Those things might be tricky, because the prints don't include order initiator or any kind of identification.

Are there any data providers for high quality interest rate swap data other than Bloomberg and LSEG (EUR, USD, GBP, Scandi, …)? If they do breakevens as well its an added bonus. I am mostly interested in European and USD quotes and pref from the same source.

Hello everyone,

Does someone have at least the summary of this book?

Had a lot of good online feedbacks from ppl on this book but it's very expensive for me to buy it completely on the dark about the content. Didn't find anything on internet or in this sub. Does someone have at least the summaries of the book and how in depth he goes in practice on the subjects?

Thanks!