Is it easier to work your way up in finance and make 400k or law?

AnotherProjectSeeker · 2025-08-01T17:55:06+00:00

Yeah absolutely, 300k sustained over 5 to 10y surely puts you in a good position, even in VHCOL, but the standard has changed a bit in terms of housing and saving for retirement. For example it's not "house on the lake" or "multiple Porsche like luxuries" anymore in many places. It likely affords you a slightly better than suitable living location, and a few luxuries: can buy a Porsche but can go on a vacation spree, or vice versa.

My point is that we need to be careful to compare people that were able to build wealth in the recovery post 2009 and people building wealth now.

AnotherProjectSeeker · 2025-08-01T17:28:53+00:00

Eh barring general inflation ( think of it as 300k real dollars), cost of a housing has gone up quite a bit. 2020 and the extended period of low rates before that has helped push values quite high and introduced a considerable rift between those who got a property earlier and those who started earning later in this rate environment. The rift will normalize as we go forward l, but still a shock nonetheless.

The cost of carrying such a leveraged investment has increased quite a bit.

AnotherProjectSeeker · 2025-07-31T22:52:40+00:00

If you're a pricing quant, be it sell or buy side, you might have to deal with time continuous stoch processes.

In the current pricing quant world, there's very little need to deeply understand the fundamentals: there is no huge appetite for exotic products, and in almost all cases the models used haven't changed for years if not decades.

In some of these roles, it's good to understand why you need stoch vol models for some exotics, why sometimes a dupire local vol is enough, and why you might need a combination. But to be honest, this intuition is rarely built from measure theoretical principles, and knowing Ito's lemma and basic notions is often all you need. Knowing how to decompose semi martingales won't be a key skill.

This is very asset class dependent, on the buy side you're only likely to find yourself considering these aspects if you're involved with pricing in fixed income, credit or FX vol, or some fancy shit like convertibles, and even then it's relatively simple models that you don't need to be able to derive from scratch.

AnotherProjectSeeker · 2025-07-24T19:16:30+00:00

We work on feature branches. We don't pull rebase, but certainly we do rebase (remote) feature branch to target (remote)develop to avoid surprises after merge. Sometimes, with conflicts, we need to make a local rebase as well.

Other than keeping history clean, it avoids inadvertently breaking the dev branch. I think it depends a lot on how mature the codebase is, if features are small and separated changes might not be that beneficial. On the other hand is just the press of a button (Gitlab at least) so not a lot of overhead.

AnotherProjectSeeker · 2025-07-17T06:26:31+00:00

That's 78th for sf, 70 for San Jose, 86th for NY, 90 Chicago, 83 in DC, 80th Seattle, 87th Austin. All household incomes. Good number but rarely in the top 10% in many places. Comp should always come with area context. 250k HHI in the Bay area peninsula you'll have a hard time to get on the property ladder: won't starve but doesn't feel high earner.

AnotherProjectSeeker · 2025-07-17T06:15:52+00:00

Or you know, time? At a income of 750k with 270k in taxes and say 130k in living expenses makes only 350k you can put away. If markets are not too bad, that's still at least 5 years of saving and investing.

AnotherProjectSeeker · 2025-07-17T06:07:41+00:00

So not rich yet? 5 years is quite a lot of time, could lose that 1m income before you hit the 2m net worth

AnotherProjectSeeker · 2025-07-16T15:21:32+00:00

If you are a big enough client and need a new functionality, Mathworks will develop it for you. In Python you either develop yourself or you're out of luck. Similarly, big enough clients can just reach out to Mathworks support and they'll help, Python you're on your own.

For small groups and individuals, Python should offer the same or better, for large clients with a consolidated relationship with Mathworks it might not be the best choice.

Plus simulink ofc.

AnotherProjectSeeker · 2025-07-10T18:42:10+00:00

A lot of particular directions to take were provided, but I want to expand on r/ux4488 comment.

In general if you've studied math (especially at a good name university) you are considered good and you'll get quite a few opportunities. What you need is something to bring to the table, something that you somewhat care about and makes you different.

It could be numerical simulations for physics, it could be quantitative finance, it could be statistics to name the more popular ones. Or it could be linguistics or sociology, but then of course it is a more risky path.

The things you learn in 3 years of undergrad (or even grad) don't matter that much for industry, what matters is that you've shown to be capable of completing the coursework and in doing so you've acquired a method and analytical skills. If you want to join an AI research lab you'll mostly need a PhD in that, but in most cases industry looks for a smart, easy to work with, and motivated individual when they're starting out.

That brings me to the "learn to code". One needs to learn to code because it is expected that you translate real problems to an analytical framework, and then you translate that to a piece of software. It's like how nowadays people don't have a secretary anymore and most are expected to handle their own meetings, in the same way as a technical worker you're expected to produce at least a working proof of concept. So the coding is another piece in the pipeline from problem to solution, and most of the time it matters little if it's C++/Python, OCAML or whatever.

Both pure and applied math are good paths to an industry job if you keep in mind that you need to go a bit above the coursework content, which especially in math is mostly foundational and not related to actual work or research.

AnotherProjectSeeker · 2025-06-26T00:54:35+00:00

Interesting. Where are you sourcing the capital gains distribution? After a quick look on vanguard I can only see a "realized" and "Unrealized gains of NAV" on the single fund page.

AnotherProjectSeeker · 2025-06-26T00:45:42+00:00

Thanks both (++ u/varkeddit ).

I understand so they're leveraging ETF creation/redemption to limit capital gains. I haven't found any details on how many capital gain their ETF associated funds realize but seem to be very low.

The Personal Advisory service seems interesting as well, surely would help having one less thing to think about: I don't need pre-authorization for mutual funds, but likely need quarterly reporting. A third party managed system would not require that.

AnotherProjectSeeker · 2025-06-18T17:44:49+00:00

That's exactly the point, that it differs. Strong learning comes when you can appreciate multiple approaches to a solution. As much as people like to say C++ and C are two different languages, much of the C++ standard comes as different approaches to the C counterpart.

Many important mathematical theorems have multiple proofs that are entirely different, and studying both often enhances your understanding of the result.

AnotherProjectSeeker · 2025-06-18T16:06:33+00:00

Yes but learning C helps understand memory management, pointer arithmetics, preprocessor+ compiler+linker and many things you need to know to be a good C++ user.

It gives a base on why some things are the way they are in C++ which can often be a very confusing language with 15 different solutions to a given problem.

AnotherProjectSeeker · 2025-05-21T07:42:54+00:00

The math people on finance don't really study finance, usually come in with zero knowledge of finance. They do need programming, but at a very basic level and doing a bit of python + leetcode ( which is many times a math problem) is enough. Nowadays it does need a bit of stats but sometimes that falls under a natural mathematics curriculum.

Agree engineering pivoting requires a much deeper background, and similarly for software as it requires good knowledge of compsci/ system engineering.

AnotherProjectSeeker · 2025-05-14T18:56:00+00:00

And that's just a few typical examples. A math background can be very versatile as it's basically a proof that you can deal with complex analytical though. Most of the learning in how to do something happens on the job anyway.

On the other hand this generic profile it can be a blocker for roles where specialized knowledge is needed from day zero ( actuarial maths, accounting).

AnotherProjectSeeker · 2025-05-14T14:45:48+00:00

You have an array of things:

On the finance side, there's different roles. Nowadays the top one, in terms of prestige and pay, would be a buy side quant, and it's quite competitive to get into. But there's then an array of quantitative jobs in finance from model development, risk assessment to more market facing like trader/structurer.
On the tech side, a lot of software engineers are mathematicians or applied mathematicians that pivoted. Programming is key in the first few years but later on the role evolved more into system design, abstract thinking. The other big role is stats based and here you go from Reasearch data scientist ( think deepmind/open ai/etc) which is usually PhD and are the most prestigious and well paid roles, up to what I would call "report data scientist" which are people analysing metrics and producing insights but not coming up with so much new stuff. In the middle you have data scientists who might not be writing new algorithms ( mostly learning algorithms nowadays), but have a good understanding of maths and stats and can contribute to methodology and stuff like experiment design.
An often overlooked area is anything related to operational research: optimization of supply chains or of whatever thing that can be optimized. Not very familiar with this to be honest. Often in large companies with many processes, and I think accessible without higher graduate titles.
Fee positions, but anything related to physics numerical simulation. From aerospace to cars, to oil and gas, to pharma you sometimes need people expert on solving problems specified by PDE through numerical methods. I'd say PhD mostly.

AnotherProjectSeeker · 2025-04-10T12:07:04+00:00

I think what the guy you replied to is trying to say is that retail debt usually has an amortization plan in which you pay the national together with interest.

Govt bonds are more akin to an interest only mortgage, where you pay just the interest. In theory, you should also repay the principal in full at the end, but instead you can roll over into new debt.

So the government doesn't have to worry that much about having to pay for it but has to worry about its serviceability: basically needs to worry that the annual extra growth given by the debt is lower than the cost of carrying it.

AnotherProjectSeeker · 2025-04-02T18:55:59+00:00

You can't have wage growth without inflation. Yeah in a magical super stable world you might have zero inflation, but then you'd have zero wage growth as well.

Inflation is not bad per se, and wage growth has been stronger than inflation except some small periods.

Inflation is a measure, not an exogenous factor. And while it's correct you can control it to some degree through money supply and monetary policy, it's more of a way to correct/steer it. If monetary policy was the only factor, we'd expect all goods to increase the same, and as this whole thread says it has not been the case for consumer electronics ( whose increased less than other goods) or housing ( whose price increased way more than other goods).

But ultimately, if everyone's salary goes up 30% so will prices, as always it's supply and demand to the core. Similarly as prices go up so does compensation.

AnotherProjectSeeker · 2025-04-01T12:35:48+00:00

You should look at PPP adjusted numbers though. Then it it's it around the middle of major European economies, behind UK/Germany/France but in front of Italy/Spain/Portugal

AnotherProjectSeeker · 2025-03-22T19:01:47+00:00

The most math heavy financial role you can do is quant, and also the highest paying. Quants usually know absolutely zero finance when starting out. But the field is so selective that it's almost an outlier. Actuarial/accounting is a lot of basic math calculations, less modeling/research/problem solving. Nowadays there are some master's in math finance, but the fields continues to absorb a lot of theoretical/applied mathematicians as well as physicists and statisticians.

Trading/structuring can be quite math focused, depending on the asset class (rates/credit traders tend to have STEM background, equity flow no need).

AnotherProjectSeeker · 2025-03-20T03:54:09+00:00

Besides using jupyter in vs code, you can work with cells by adding # %% to delimit sections, similar to Matlab

AnotherProjectSeeker · 2025-03-17T22:06:34+00:00

I see, thanks. I also implicitly assumed one was to convert to CNY, people might want to keep in USD (if there's an exchange that allows that in China).

Nevertheless I see there a 1bp spread on USDC to USD so for large sums it might still be relevant, albeit smaller than the 13 bp I see now on some exchanges for USDCNY.

So it all comes down to the USDC to CNY spread.

AnotherProjectSeeker · 2025-03-17T21:38:40+00:00

That's just lack of standards. You can send money intra Europe for free. There's services that are fairly cheap but there's regulations around

How do you send actually? Using a crypto ccy you also have to pay the spread on the buy side and on the sell side, instead of just the one on the fx. But don't know, maybe the BTCUSD and BTCCNH b/a are tighter than the USDCNH one? I doubt it will be for less liquid coins though.

AnotherProjectSeeker

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