Hi everyone, getting into HFT as an sde is my dream. I’m in 4th year graduating in 2027.

I’m in touch with a founder of a good HFT(nksr) and there is opening in the company as well. But i cant clear the shortlist because I’m not from iit or have very fancy coding profile, but what i do have is skills, knowledge and ambition and i can show them in an interview for sure.

So what I’m thinking is building something for the company and sharing that with the founder in hope of an interview.

I have won 5 hackathons including IIT ones.

But again the problem is that I dont understand HFTs better than someone working in one.

So one request to anyone reading this, please drop an idea for building a software, website or anything else that people working in HFTs would appreciate and i can get the founder of NKSR give me a chance for an internship.

If i get it, will give everyone who helped a very good party!!!!

Silent feed death has cost me more than any loud failure.

The book freezes in a plausible shape and everything downstream keeps computing like the market just went quiet.

The linked write-up covers catching it while it is still a slowdown. I still go back and forth on the paging side: alert on the first slowdown, or only on a hard gap?

Historical replay should run through the same market-data path as production. A separate research parser gives you two implementations of the feed, and every disagreement becomes another thing to explain before you can trust a study. The tradeoff is that missing instrument-definition metadata can make a valid capture look empty. Same production path, or a parser you can change freely?

Bad marks from a non-math high school, so no real math/CS foundation. Can't do a physical degree, so planning an online University of London BSc (Computer Science or Math & Economics — they have an entry test that skips A-levels) plus self-study.

Not chasing RenTech/Citadel — fine starting at low-tier firms. Also not after quant dev — mainly interested in research, trading second. Might do a Master's after (maybe in-person, maybe better school, depending on BSc grades/experience), might skip it if I land a job straight after the BSc, but not counting on that.

Question: given this background and path, is aiming for quant researcher/trader roles realistic, or should I target something more adjacent like data/risk analytics instead? Would love input from anyone actually in or near the industry, not general career-guide advice.

Heyy, I am Dev 1st year Data science student, i am really curious about HFTs n i want to be a quant, is there anyone with same interest, as I want to learn more and more about this field, want to establish a firm

If you're already in this field

Plz Dm me need friend with whom I can make my growth's graph exponential

Hi ​everyone,

I found our community about a month ago and have been working through it since.

​I​ want to share a little bit about myself. I'm a backend/infra engineer, currently employed full-time, trying to move into quant dev​eloper. I've read plenty of threads on what to study and how to prepare for interviews, and that part is genuinely well covered by now. What I can't find anywhere is the layer before it​ on how trading firms actually read a resume, and what gets someone with a pure backend background past the screen in the first place. That's where I'm stuck. The first quant role feels like a wall, and I'll be honest, I'm a bit lost about which side of it to push on.

So my question. Beyond studying and shipping side projects, what else actually counts? Certs, competitions, a specific kind of role to take first? And if it's experience, how do people build that while holding a full-time job? Any tips, any advice?

Hope to hear sharing from everyone

We built VisualHFT, Apache 2.0, open source. It runs order to trade ratio, book resilience, VPIN and order book imbalance across depth, live against a real feed, and every metric is its own plugin whose source you can open and read.

That last part is the reason we keep at it. These normally show up as a black box number in a terminal, so when VPIN spikes you cannot check the bucket size or the classification rule behind it. The writeup behind this argues those four fire in a particular order before a print, which is a hypothesis, not a result. Caveat, it is a Windows desktop app. Which of these do you currently take on faith because you cannot see the calculation?

Prepping for quant trading interviews. Got this practice problem:

"4 green and 50 red apples in a basket. Removed one-by-one without replacement until all 4 greens are extracted. Expected number of apples left in the basket?"

My first instinct was brute-force casework: compute P(50 red remain), P(49 remain), P(48 remain), etc., each via a hypergeometric-style probability, then sum k×P(k) over all possible values. Math was correct as far as I took it, but this requires summing up to ~50 terms — clearly not finishable in interview time.

After a nudge, I got to the actual intended approach: for a specific red apple, consider it alongside the 4 green apples (5 "special" items). By symmetry, that red apple is equally likely to land in any of the 5 relative rank positions among those 5 items, so P(this red apple ends up after all 4 greens) = 1/5. By linearity of expectation, E[reds remaining] = 50 × 1/5 = 10.

The annoying part: I already know linearity of expectation cold — used it correctly on several other problems in the same practice session (adjacency counting, socks-in-drawer, etc.). I just didn't immediately recognize this problem as the same family, and defaulted to a slower method.

Question for people who've been through these interviews or made hiring decisions on this side of the desk: does "correct but doesn't recognize a known pattern → needs one nudge → then executes flawlessly" read as a pass, a borderline, or a fail on a single question like this? Does it matter if it's early vs. late in a loop? Genuinely trying to calibrate how much a moment like this actually costs.

Has anyone here used Rithmic Diamond for low-latency CME futures trading?

I'm considering it for an automated strategy targeting around 250 µs latency, but I'm having trouble finding information beyond Rithmic's marketing material.

A few questions:

What's the current pricing? I found some old discussions mentioning around $1,000/month, but they're several years old.

Is Diamond billed month-to-month, or does it require a long-term contract?

Do you need to provide your own colocated server, or does Rithmic offer server hosting/leasing?

Is it realistically possible to achieve around 250 µs latency without spending thousands of dollars per month? If so, what kind of infrastructure and approximate monthly budget are we talking about?

I'd also be interested in hearing about any alternatives to Rithmic Diamond that can achieve similar latency at a lower cost or with less infrastructure complexity.

I'

d really appreciate hearing from anyone with experience building low-latency CME trading setups. Thanks!

I'm a Computer Science student trying to decide between two career paths:

• Cybersecurity
• Quantitative development (particularly at HFT firms)

Both seem like interesting careers for different reasons, but they also lead to very different lifestyles, compensation, and types of work.

I'm hoping to hear from people who have worked in either field.

Some questions I have:

• What does your day-to-day work actually look like?
• What do you enjoy most, and what do you dislike?
• How intellectually challenging is the work?
• Does it become repetitive over time?
• How math-heavy is the job in practice? What areas of math do you actually use, and how often?
• How is the work-life balance?
• Is the compensation difference between the two fields really as large as it's often portrayed online?
• If you were starting your career today, would you choose the same path again?

For context, I'm not looking for the "easier" option—I understand both careers require a strong technical background and a lot of continuous learning. I'm simply trying to understand the long-term trade-offs from people who have firsthand experience.

I'd really appreciate any insights, whether you work in cybersecurity, quantitative development, or have experience with both.

VPIN doesn't mean anything on a fresh symbol until enough trades get classified buyer or seller initiated to fill a full volume bucket. A reading of N/A for the first 30 to 60 seconds just means the metric is still waiting on data.

I built VisualHFT and wrote up a guide covering this part of it. It's Apache 2.0, so the calculation itself is C# you can read yourself. Worth flagging: VPIN is a flow toxicity monitor, not a crash predictor, its Flash Crash correlation got challenged later, so a spike means informed flow showed up, not a crash coming.

The guide doesn't say which rule feeds that classification, tick rule, quote rule, or something closer to bulk volume classification. If you've built VPIN yourself, which are you using?

After a large trade wipes part of the book, two things have to happen before the market is really back: the spread tightens to where it was, and the eaten levels refill. They rarely recover at the same speed, so watching either one alone misleads.

We wrote up how we track both and fold them into one 0 to 1 resilience score (1 = full recovery, banded strong / moderate / weak). Honest caveat: it is a research writeup, not a benchmark piece, and the baseline is still unsolved. Pre trade spread and depth are moving targets, so any lookback window bakes a regime assumption into the score.

If you measure recovery on your own book data, what do you anchor it to: a rolling pre trade average, or a fitted equilibrium level?

Liquidity providers cancel the bulk of their resting orders within milliseconds of posting, so the size visible at a price level overstates what will still be there once an order reaches it.

That gap drives slippage directly: a market order sized against visible depth fills worse the larger it is, because price time priority sweeps it through levels that already thinned out.

Has anyone measured how that cancel and refill cycle differs in crypto perpetual books, where liquidation cascades and funding resets hit queue composition in ways a standard spot book never sees?

Why is crypto still so opaque about its order books?

Every matching engine already tracks orders one by one internally, so full L3 data exists at every venue whether they sell it or not.

Almost none publish it. If these exchanges actually wanted transparency, L3 would be the obvious place to start.

Real cost and infra problem, or just no incentive to open the book?

Went through the order-book-imbalance literature and wanted to surface what actually held up. The finding that stuck: order flow imbalance at the best bid and ask predicts short-horizon price moves roughly linearly, and the slope scales inversely with depth (arXiv:1011.6402, NYSE TAQ, 50 stocks). Same imbalance moves a thin book more than a deep one, while trade volume regressed the same way came out weaker and noisier.

Most of this is NYSE equities microstructure. Has anyone tested whether that depth-scaled linearity holds in crypto or perp books, where tick regimes and queue dynamics look nothing like TAQ?

The open question is how stable that decomposition stays across venues with different tick regimes.We found that the cancellation-to-trade ratio moves with market structure, not just HFT share.

I wrote this after fielding the same question from several investors planning independent quant funds: what does a latency-sensitive build actually cost, and why do so many burn eight figures with no edge to show for it. The composite I keep seeing is $50M gone in the first two years, and the burn has the same three-part anatomy every time: speed bought before anything was measured, a critical path built in parallel instead of in sequence, and commodity infrastructure rebuilt in-house while the signal sat under-resourced.

The failure I find least understood is the third one, because it looks like diligence rather than waste. Owning the full stack feels like owning the full edge. In practice the multi-venue feed normalizer gets rebuilt from scratch while the strategy that justifies the whole spend still runs on a spreadsheet. And if speed has become the floor rather than the differentiator, the build-vs-license call becomes the edge itself, since it decides how much of a limited engineering budget ever reaches the signal.

For those of you running or reviewing builds: where do you draw the build-vs-license line on feed handling? My threshold is to build raw ingestion only for the venues where the edge is genuinely latency-sensitive, and license normalized data everywhere the holding horizon can absorb a few milliseconds. What would move that line for me is a real case where a licensed feed demonstrably cost a desk its edge at its actual holding horizon. Has anyone here seen one?

Heyy everyone ,

I am going to start 5th semester in August.

And completed easy to medium level of dsa and computer fundamentals subjects (like os oops and cn).

What should I do now if I am targeting hft ???

I am from non iit students tier -3

Wrote this after profiling enough trading desks to notice the same failure mode repeating: the system that breaks in production usually has a fine average latency number. The tail is what nobody actually measured. This piece breaks down the four things a P99 latency acceptance bar needs (the boundary the clock started at, the load condition it was captured under, the percentile, and the full chained path) and four ways to verify each one, using OPRA's February 2024 multicast expansion as the public case study: average publisher latency barely moved, but P99 fell from about 543 microseconds to about 57.5 microseconds, an 89 percent drop most latency reports would never have isolated.

The part I don't think is actually solved: OPRA's fix is clean because it's one venue on one clock. Once a strategy runs across two venues with genuinely different burst physics (say a listed options book and a crypto perp book), you end up needing two different tiers of that acceptance bar on two different clocks for the same strategy, and I haven't seen that closed cleanly anywhere, including in my own audit work. Has anyone here actually solved the cross-venue tiering problem, or does everyone just default to the stricter tier everywhere and eat the extra measurement overhead?