u/Wonderful-Bunch-3343

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There are a lot of you that keep asking how to break in, what step does it take, and even the body answers it the same way: it's a hard field so study harder and get smarter. People act like it's some impossible secret club, but the difficulty is honestly pretty simple to explain, it's a tiny number of seats, absurd money, and almost no barrier to applying, so the funnel is brutal. 

But for this reddit post I’ll dive deeper into why this is the system we work with. A top firm might take a few dozen new grads a year and get tens of thousands of applications from basically every strong math, CS, physics, and stats student on earth, plus PhDs, plus people leaving other finance and tech jobs. When the comp is $300-500k out of school, everyone smart enough to have a shot takes the shot, so the bar isn't just are you good. It's that you’re better than everyone that is good. 

However, I think it's understated how good you have to be. The skills actually are demanding, you need real probability and stats, fast quantitative reasoning under pressure, and for the dev side serious low-latency engineering, and the interviews are designed to be hard to fake your way through. Brainteasers, mental math, market-making games, and live coding all exist specifically to filter hard. 

But here's the part that gets lost in the doom posting: hard to break into is not the same as gated or rigged. There's no required pedigree, no secret handshake, the application is open to anyone and the interviews are pretty meritocratic, they mostly test things you can actually study and get better at. The reason most people don't make it isn't that they were locked out, it's that the prep is long and specific and most people start too late, apply too few places, or treat it casually. The ones who get in usually started early, drilled the green book and other prep for months, did a competition or two, and applied to posting weekly rather than waiting. 

So all in all, it's hard, but it's actually-hard, not gatekeeping-hard, which means the difficulty is something you can attack with time and deliberate prep rather than something you're either born into or not.

As I said before, I don’t want this to be a doom post, but just speaking my mind of what is required to get into this field. You gotta do project and network like crazy. But other than that you just gotta optimize resume screens to get the shortlisted.

Finally, leaving you with an exercise, put yourself in the shoes of the employer, would you hire the person sitting across the table. If that answer is not yes with your eyes closed then… you get the point.

It seems to me that there are many misconceptions around what is quant trading all about, which may cause someone to either go into the field for the wrong reasons or completely pass it by simply because they think they don't have what it takes. So, here I would like to clarify some things.

First off, the most common misunderstanding is that traders spend their entire day actively trading. Well, at least, it's true at many top trading firms, most of the strategies run there are automated, with only around 5% of strategies requiring any human interaction. Basically, traders monitor dashboards and the performance of various models. And yes, this job consists a lot of waiting, especially during the mornings before market opening. Again, it might sound boring but it's all about being prepared for situations when the model stops working correctly or the market behaves in an unusual way.

Another important thing to highlight is that a lot of work is done before the market opens. Specifically, before market opens, traders read Bloomberg, FT, overnight research reports, and everything else that happened in the market during night time, such as any statements from the Fed made during the night. In addition, traders review their current positions, think of risks and exposure they face and plan ahead to be ready to act fast and efficiently once the market opens.

On certain trading desks at Jane Street and SIG, however, trading can be more discretionary. For example, traders there do a lot of desk research, analyze new products coming to market and help quant researchers improve trading strategies. Here, lines are blurred since traders work very closely with researchers, and thus the interviewing process is also more geared towards evaluating research skills.

As mentioned above, the last 30 minutes before market closing are typically the busiest of all. With expiring options and possible issues with pinning, traders need to carefully manage their positions and unwind their books before leaving work. After market close, traders are required to reconcile their positions and check P&L. This is when the best traders are those who are honest with themselves and can pinpoint the mistakes they made throughout the day, e.g., cutting something too early or missing an opportunity.

Typically, a workday starts at 6-7 am and ends between 5-7 pm. Trading firms based in Amsterdam, such as Optiver and IMC, tend to have earlier hours compared to, for example, Citadel. Weekend days are completely free, and although the workday isn't long like at banks, it still goes till late afternoon. Again, this doesn't mean it's easy, traders should be able to stay sharp and ready to trade even after midnight.

Lastly, while traders certainly require solid knowledge of math and prob/stats, it's equally important to be able to control emotions, take risks wisely, and make decisions under pressure. The truth is, those traders who are the best at doing their jobs are usually calmest. And yes, it's more important to be emotionally stable than extremely intelligent.

Takeaways, traders should be comfortable acting with incomplete information and making decisions without knowing exactly what's happening on the market at the moment, be willing to accept losses sometimes as long as they win in general, and be able to focus even when it's boring. The thing that actually separates good traders from average ones is risk management and emotional control.

This is one of the most common questions I see from people trying to break into Quant finance. 
And you’ve probably seen it (or asked it) too: should I do a PhD? After being in the industry for  a little while my answer is almost always the same. It depends on what role you're going for, but probably not.

Let me explain why I say this. 

If you want to be a quant researcher at a place like Two Sigma, Citadel, DE Shaw, or RenTech then yeah a PhD matters. Most QRs at those firms have one. The interviews are brutal and they're basically testing whether you can think like a researcher, not just someone who passed hard exams. This is an important caveat. That kind of depth usually takes 4-5 years of actual research to build. Can you get a QR seat without a PhD? Some people do, but you're making your life way harder.

For everything else though? A PhD is overkill and honestly might hurt you.

Quant dev roles want engineers who can build stuff. Clean C++, low-latency systems, production-quality code. My friends have literally over-heard hiring managers say that PhDs are a yellow flag for dev roles because they associate it with academic code that works in notebooks and may not translate well in production. A CS Masters or even a strong Bachelors with good systems skills is the sweet spot here. 

Trading is similar. Look at Jane Street's roster on LinkedIn sometime. A huge chunk of their traders have Bachelors degrees. Optiver, SIG, IMC, same deal. They're testing how fast you think, how you handle risk, whether you stay calm when things get weird. A PhD doesn't help with any of that. Some firms have literally hired esports or chess players as traders because the skills overlap…

The thing nobody wants to say out loud is the opportunity cost. While you're doing your PhD making $35K a year, the person who graduated with a Masters alongside you is pulling $300K-$400K at a top firm. Over 5 years that's easily $1.5M in earnings you just left on the table. And by the time you finish, that person is now a senior with a real track record. I've seen fresh PhDs come in at the same level as people who started with a Masters 5 years earlier. Same comp, same title, except one of them has $1.5M in the bank and the other has a dissertation nobody outside their committee has read (it's sad but true).

The PhD wins if it gets you into a room you literally cannot enter otherwise. A QR seat at RenTech, a research-heavy PM track at a multi-manager, stuff like that. But those are a small fraction of all quant jobs.

One more thing since I know it'll come up are MFEs. The general sentiment among people who actually hire for quant roles is that MFEs are not very helpful. The programs teach derivatives pricing and risk management but not the kind of original thinking that top firms care about. A Masters in stats, math, physics, or CS will serve you better at basically every hedge fund and prop shop. MFEs place fine into sell-side bank roles and risk management if that's what you want though.

The other thing worth mentioning is AI. A lot of the grunt work that junior researchers and analysts used to do, data cleaning, initial exploration, writing boilerplate code, summarizing documents, is getting eaten by LLMs. Firms are already using AI tools internally and some have started reducing junior hiring because of it. That changes the calculus on a PhD even more. If the entry-level work that used to justify hiring fresh PhDs is getting automated, the bar to justify 5 years of lost earnings gets even higher. The people who'll thrive are the ones who can do the stuff AI can't, creative research, judgment calls, building real intuition about markets. And you can develop that through experience just as well as through a PhD program.

Keep in mind this is just my opinion; if you're sitting there trying to decide whether to spend 5 years on a PhD or go work, really think about what role you actually want. If the answer is quant researcher at a top hedge fund, the PhD is probably worth it. If the answer is anything else, maybe weigh your options. Build systems, work with real data, develop market intuition. That stuff compounds fast.

This is something that's always been interesting to me. On paper you'd think pure math PhDs would be the most natural fit for quant. The field is built on probability, stochastic processes, optimization. But if you look at the backgrounds of quant researchers at top firms there are a disproportionate number of physics people relative to what you'd expect. Not saying physics dominates, firms hire from math, stats, CS, and engineering too. But physics punches above its weight and I think the reason why says a lot about what these firms actually value.

The short version is that physics trains you to solve messy problems with incomplete information. Pure math trains you to solve clean problems with complete information. And quant finance is almost entirely messy problems with incomplete information.

In a pure math PhD you spend years proving things rigorously. Precise definitions, agreed upon axioms, the goal is establishing something provably true. That builds incredibly deep thinking skills but the mindset can actually work against you in quant. Because in quant nothing is provably true. You're working with noisy data, approximate models, and signals that decay over time. The "right answer" is whatever makes money and that changes constantly.

Physics people spend their training building approximate models of messy real world systems. You learn which variables matter and which to ignore. You learn to say "this model is wrong but it's useful" and be comfortable with that. Someone who studied stat mech or condensed matter has spent years modeling systems with millions of interacting particles using simplified statistical descriptions. That's not far from modeling a market with millions of interacting agents. But more than the math itself, it's the approach. Start with data, build a model that captures essential behavior, test it, throw it out if it doesn't work, try again. That iterative loop is basically what alpha research looks like day to day.

I've seen pure math people in quant who struggled not because they weren't smart enough (often the smartest on the floor) but because they couldn't let go of the need for rigor. They'd spend three weeks on a theoretically perfect model when a rough approach would have captured 80% of the value in three days. In quant the 80% solution on Monday beats the 100% solution next month.

There's also the coding angle. Physics PhDs almost always come out as decent programmers. Monte carlo sims, data analysis, numerical solvers, you can't do modern physics without writing real code. Pure math is more mixed on this. A lot of programs don't require much programming and those people have a gap to close.

To be clear this isn't a pure math vs physics thing as much as it's an applied vs theoretical mindset thing. An applied math PhD doing numerical methods or an econ PhD doing heavy empirical work has that same "build, test, iterate" instinct and does just as well. It's specifically the pure/theoretical backgrounds where the adjustment is harder.

If you're coming from a pure math background and thinking about quant, the gap is totally closable. Work on real messy data without trying to prove anything. Get comfortable with "this seems to work but I'm not sure why" because that's most of quant research. And make sure your coding is sharp.

Curious what other people have seen. Especially if you came from a pure math background and made the switch, what did you have to change about how you approached problems?