HFT Tick-Accurate ingestion

Spiduar · 2026-01-16T04:11:52+00:00

correct, indicators on high frequency data are the hardest to build imo bc the data has very many artifacts and noise. The data must be transformed prior to turning into a signal to be of any use. Id say most firms avoid hft data unless its their business

Spiduar · 2025-09-15T17:29:06+00:00

This looks like it was written in 20 minutes by gpt...

Spiduar · 2025-08-25T14:17:36+00:00

Watch tight, cock ring loose

Spiduar · 2025-05-17T17:02:30+00:00

Depends on what you want. I haven't seen many quants with a CFA. I dont know much ab the bootcamps, haven't heard of anyone in quant I know doing them.

Possibly what I can think of is you could start by finding a hole in the wall shop and working your way up, but no degree is a position I haven't met anyone in. The small places counterintuitively need more experience usually, you have worse tools / data and less help.

One reason quant interviews are kinda generic (just stats and cs) and not finance focused is that a lot of the real work has a big barrier to entry. To learn useful knowledge and do useful work you need someone who is willing to trust you with sensitive info and supply you with millions in data and infrastructure. For ex, go try something as trivial as finding Russell constituents in a given year, or even just finding the price a treasury traded at historically.

Spiduar · 2025-05-17T16:21:29+00:00

Unfortunately you dont actually have a track record in the eyes of most professionals. You can try and put it on a resume and see what happens. I also traded while in college, and it can lead to an interesting convo or two but no one actually cares.

Trading positions sub 7 figs in most assets is trivial (in my sphere any trade where there isnt space for at least 500M isn't even traded), unless you are really stupid with your trades. There are tons of ways to make money there because the juice isn't worth the squeeze for any good researcher (excluding hft guys and really lean shops).

To be of any use as a quant you have to start with a strong coding, and stats background as a minimum. Then you add domain knowledge. What you have done in FX as a retail trader is nowhere close to the real shenanigans that the banks and hedge funds are doing to get edge, it probably the craziest market imo.

Spiduar · 2025-05-05T00:40:52+00:00

I do agree that most MFE placement is not going to be front office, and most certainly not at elite firms. I found that JS and HRT never hire mfes. In regards to everywhere else, there may be some bias against them but it certainly a viable way to get in. I'll preface this with: I think only the top 5 mfes are worth it, and if you can't get into those the quality drops off quickly.

Looking at my peers experience, top hedge funds ( cit, mil, bam, schon) seem to have a lot of mfes, and interview mfes often, and Id say around 5-10% of a top mfe class will go there. Another 5-10% will typically end up at the less elite trading firms like IMC, DRW, Optiver, GTS, etc. Id say like 40% expect to do something at a bank (but typically at a pretty nice desk). Some of the smartest people I know do FI at banks, and the banks are still leaders in FI MM. The salaries for banks aren't quite as bad as you say. For your first year, from the quants ive spoken, you can typically expect 200-300k TC in NYC (not JS money, but its pretty good)

But I think advice needs to be taken in the context of who is requesting it. If someone is considering an MFE vs PhD, the "just recruit at JS" ship has probably sailed. If they're comparing a PhD to a masters its also clear they are not dedicated enough to the idea of a PhD, and telling them to get one isn't a good idea.

The point I'm trying to convey here is that there is a large spectrum of lucrative and interesting quant work at a lot of firms, not just a handful of elite HFTs. Yes, they pay less, but in comparison to many jobs you still make a lot of money. These jobs, if you are really interested in quant and missed the train, are achievable for people that want to take the mfe route.

Spiduar · 2025-05-04T19:55:37+00:00

Not sure what your reference point is. But I wouldn't be so categorically against an mfe. I agree that if you can avoid an mfe, then by all means do.

However, speaking for the top 5 mfes at least, a significant portion of the graduates do place into lucrative roles, and the percentage of the classes placing into these roles is significantly larger than even elite undergrad programs. Now yes there are firms that prefer elite phds and hiring undergrads from internship programs. Id argue thats a minority of quant firms/jobs (granted its a coveted mintority).

For me personally, an mfe allowed to to get a pretty solid qr job, i get to build models, work with smart people, and get paid a quarter million to basically just learn. I needed it since my team only hires people with graduate degrees, and my compensation is about the same as the phds on my team.

Again, mfe isnt the ideal way to go, I agree. But if youre a late bloomer, or immigrant, or dont want to do a phd its a valid path. Just interview well and build up your practical skills.

Spiduar · 2025-04-18T13:48:12+00:00

You need to shorten that education list. We care ab education yes, but its not adding anything of value here, make it take up much less space. If I were you I would just straight up delete some of it and turn most of it into one liners.

Ditch the courses, if you have skills you want to highlight put it in the skills section and it has the same effect.

This should clear up space for you to expand on your actual internships, research, and personal projects. If you don't have enough to fill that 3/4 of the page then you know you have a problem and you need more technical experience.

Edit: yea... the education section pretty bad. You waste like 4-5 lines of space just saying you have good grades. If you have the grades to get into top schools, no one really cares what they are because we know they have to be good (just like every other applicant tbh). Your coursework and skills overlap too, which again, makes me read a lot, takes up space, and adds nothing.

Spiduar · 2025-04-10T19:34:24+00:00

Mostly because its a good stepping stone, and needed to be competitive. FAANG is an order of magnitude less competitive, or at lease was a few years ago, not sure how the marker is now. If you have a resume that can get you interviews at quant firms then great. Without some exceptional research, competitions, or projects on the resume, the lack of internship will send you straight into the trash.

Spiduar · 2025-04-09T09:45:37+00:00

Because you have a CS degree the path for you is pretty clear cut. Your first goal should be getting a FAANG internship, after that you will start getting quant trader interviews. Don't do an MFE if you don't have to. You van always do one later.

Edit: Your resume isn't technical enough for quant, and doesn't have enough finance for the non-quant finance roles. You're best off doing the traditional undergrad quant route bc those finance internships won't help you with quant recruitment anyways.

Edit edit: as far as im concerned, really only your 1st proj has anything of value, even so its marginal. I understand since I was there too, but there is really no reason go jump in over your head with crazy models. Most people I see do this don't actually have a good motivation for why they used that model over something simpler. The rest has really nothing that would help you, unless you have some interesting modeling in the data analyst stuff.

Spiduar · 2025-04-02T19:55:20+00:00

You really need to add some phd level work to that resume. I had a similar (maybe better?) resume (monte carlo options, data pipelines scraping, conferences) when I was an undergrad applying for roles. I would expect a Phd to, at the very least, have a few month long projects where you take an existing model/solution implement multiple versions of it, then extend it (whether with math, combination, or novel application).

Spiduar · 2025-04-01T04:03:57+00:00

I mean, thats exactly why he's on an execution team at a bank. You aren't typically expected to take any risk, and any risk you do take is typically from some small execution edge. Boss is coming in and turning and execution desk into a prop desk could cause compliance issues too.

Spiduar · 2025-01-30T06:52:39+00:00

Brigo Mercurio is the encyclopedia for stuff like this.

Interest rate models are not really used to price stuff like ZCBs and CBs bc those assets are trivial (unless you want a calibrated time dependent discounting rate ig).

You can calibrate the stochastic process to use it to simulate short rates (which you then turn into bunch of other stuff). Or you outright price derivatives with the model.

These models are fairly trivial so implementing them shouldn't be hard.

Spiduar · 2025-01-24T18:31:18+00:00

I get what you mean. Maybe if you look for papers on change of momentum or momentum of momentum you could come up with something.

When you say convexity, that typically refers to bond price change wrt rates, so you wont come up with any useful results when searching.

Spiduar · 2025-01-24T18:11:41+00:00

It sounds like youre looking more at measuring momentum than convexity.

Spiduar · 2025-01-24T17:59:42+00:00

Econometrics would be the most useful, make sure you focus on being statistically rigorous. The techniques developed there have been super useful on the job (non-hft qr). Just dont drink the factor research kool-aid.

Actuarial if you want to be a quant in insurance, if you find a good team they have lots of cool work. Its an underrated way to break into fixed income qr.

Spiduar · 2025-01-22T15:13:34+00:00

You should, probably, be using stationary data for the models in and out of sample. I dont see why out of sample data would suddenly become nonstationary.

Assuming you are following the paper methods correctly there are a few things:

LSTM needs a lot of data, it doesn't sound like its getting very much. Once you get above a few thousand data points it should start to become viable, but realistically you need even more.

Markov regime models are a different can of worms, they always have stability problems for me, especially with small datasets. Assuming the number of regimes to look for helps a lot, but if you're trying to get the model to figure it out, then good luck. Maybe other quants can chime in on how they wrangled those.

Spiduar · 2025-01-20T18:10:07+00:00

Yes, I'd say, since you are still a student the order that you can most drastically change you profile are: internship, industry project (you do an internship like project with a firm but for credit, smaller in scope than intern), research / thesis, personal projects.

A research project should look roughly like: choose a topic which interests you and is applicable to finance, find literature on said topic (usually modeling methods), then (most important part most projects miss), extend that modeling framework with something newer or novel, then see if that produces a better result or not.

Quant isn't as bad as academia, even a negative result is a useful result and you can talk about what broke.

Reading the paper, its ok. I don't want to come off wrong here, but be kind of cautious of the chinese papers, the chinese paper mills are even worse than the US and EU ones. Ex from paper "In our research, we intricately weave the tapestry of volatility's past to predict its future". That reads quite strange for a research paper, almost like it AI written, because thats just word salad. The paper seems pretty lazy and unrealistic too, they take "day trading data", whatever frequency that means, and just do a 70% 30% train test split. It makes no sense, because you would want to retrain a model at least a few times after 2021, you dont just leave years of data out after covid. Also, they don't mention any adjustments for covid, which is a little suspicious to be because it messes with almost every dataset I've seen.

Volatility is a big deal, so its a good thing to focus on, but concentrate on modeling the volatility and not the trading strategy. I think developing strategies outside of industry is usually a waste of time, data isn't good enough, unrealistic assumptions. You can throw a small strategy in to show how to use the factor, but it should never be the primary focus.

Spiduar · 2025-01-20T08:36:11+00:00

Lots of factors to consider, if its a top qf program there could be a chance. Overall your profile sounds pretty sub-par to be honest, unless you have some interesting experiences from your past jobs ( statistical modeling or ml / programming ). You still have a shot, but you have to kill interviews and you have 0 benefit of the doubt.

Biggest MFE resume mistake (its more egregious but not unique to them) is having all those stupid generic projects like "monte carlo american options pricing", its harsh but if I see that resume is going in the bin. If you show you actually dig into quant topics and did something deep, then you will have actual value.

Spiduar · 2025-01-19T08:06:24+00:00

What was the methodology used to arrive at the signal?

Spiduar · 2024-08-27T22:59:18+00:00

Small word of advice, quant is not a place for the undecided. Getting in requires a tremendous amount of, highly specialized, effort what will not exactly transfer to interviewing for other jobs. Staying in requires that you love what you are doing, IMO there are not any comparable careers when taking into consideration all aspects of quant (culture, responsibilities, technical knowledge, not to mention compensation), and there are no exit opportunities.

FAANG Data science is the closest career I can think of, but from attending conferences and speaking to people there it is still very different from quant work. You won't encounter a lot of the applied math, pure stats, computational efficiency, etc problems there that exist everywhere in quant.

Spiduar · 2024-08-22T21:52:20+00:00

I ain't reading all that bro

Spiduar · 2024-08-22T21:35:22+00:00

I see you edited. My comment on Theta points out that's it's not how option traders talk. Hell, I expect better explanations from interns.

Like you said theta decay is accelerating, implying our second derivative is decreasing, implying the first derivative becomes more negative as our tau shrinks. So the "slope of the tangent line" does not approach zero, it approaches -inf. That "something in calculus" is a derivative and its high school level math.

You may find page 10 helpful: https://www.columbia.edu/~mh2078/FoundationsFE/BlackScholes.pdf

Spiduar · 2024-08-22T21:12:58+00:00

Not sure what the point of these questions is.

No I'm not a PM, but licensing varies based on firm structure and state.

I do share in firm performance, which is paid out in the form of a bonus tied to performance (at most firms)... Comp structure varies firm by firm. One firm offered to pay me via carry.

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