HFTUSDT
HFT / Tether USD
crypto Composite

I've talked to a few people on here that could verify accounts in the green and they ranged across different macro strategies (HFT, lft, options, futures, crypto, etc)
The common ground I found anecdotally is that they all had an actual statistical understanding of their trades and models. Half (probably more) of the people in this sub have about as much understanding of their model as the LLM they used to write it.

**Background:** \~1 year ago I joined a very early-stage HFT shop as a systems engineer. I built a large chunk of the core platform — learned C++, networking, NIC tuning, kernel bypass, polling systems, shared memory, etc. We got p90 latency to a low single-digit µs. Loved this work — hard, rewarding, learned a ton. Happy to share specifics on the low-latency stack in comments if useful to anyone building similar.
The quants on the team recently left. Management reassigned me from engineering to quant research, with a mandate to produce a working (even small but consistent) strategy in 6–8 months or the team isn't viable. I didn't choose this. I've spent the last couple of months finding candidate signals in cash equities.
**The problems:**
* No senior quant on the team anymore. My manager has strong market-microstructure knowledge, but for the last \~2 months I've mostly been implementing his ideas and running backtests without time to fully understand the underlying theory — I rarely get to study before the next task.
* If the work fails, I'll have spent a year building skills that may not transfer, while my former teammates deepen their infra/FPGA expertise and stay more employable.
**What I do have:** full access to tick data, complete visibility into the infra, and uninterrupted time to research.
**My question:** Given a real systems background plus forced-but-genuine research exposure, does it make more sense to:
1. Commit hard to the quant pivot for the next few months and aim for a pure quant researcher role — if so, I'd love pointers on what to read and how to build the skill from a beginner base — or
2. Switch back to a pure low-latency engineering role now, while my infra skills are sharp (though switching firms is also hard right now)?
How do people view a "built the platform + did signal research" profile when hiring? And realistically, for someone \~1 year in from a non-target college, how achievable is a pure research seat vs. staying in low-latency engineering?
Appreciate any candid takes.

For HFT's its a no brainer, $100k is pocket change for them

after spending loads of time on it i have concluded that HFT is like playing against the house in the casino (you never have the winning edge) and migrated to longer term swingtrading/investing

I've talked to a few people on here that could verify accounts in the green and they ranged across different macro strategies (HFT, lft, options, futures, crypto, etc)
The common ground I found anecdotally is that they all had an actual statistical understanding of their trades and models. Half (probably more) of the people in this sub have about as much understanding of their model as the LLM they used to write it.

**Background:** \~1 year ago I joined a very early-stage HFT shop as a systems engineer. I built a large chunk of the core platform — learned C++, networking, NIC tuning, kernel bypass, polling systems, shared memory, etc. We got p90 latency to a low single-digit µs. Loved this work — hard, rewarding, learned a ton. Happy to share specifics on the low-latency stack in comments if useful to anyone building similar.
The quants on the team recently left. Management reassigned me from engineering to quant research, with a mandate to produce a working (even small but consistent) strategy in 6–8 months or the team isn't viable. I didn't choose this. I've spent the last couple of months finding candidate signals in cash equities.
**The problems:**
* No senior quant on the team anymore. My manager has strong market-microstructure knowledge, but for the last \~2 months I've mostly been implementing his ideas and running backtests without time to fully understand the underlying theory — I rarely get to study before the next task.
* If the work fails, I'll have spent a year building skills that may not transfer, while my former teammates deepen their infra/FPGA expertise and stay more employable.
**What I do have:** full access to tick data, complete visibility into the infra, and uninterrupted time to research.
**My question:** Given a real systems background plus forced-but-genuine research exposure, does it make more sense to:
1. Commit hard to the quant pivot for the next few months and aim for a pure quant researcher role — if so, I'd love pointers on what to read and how to build the skill from a beginner base — or
2. Switch back to a pure low-latency engineering role now, while my infra skills are sharp (though switching firms is also hard right now)?
How do people view a "built the platform + did signal research" profile when hiring? And realistically, for someone \~1 year in from a non-target college, how achievable is a pure research seat vs. staying in low-latency engineering?
Appreciate any candid takes.

For HFT's its a no brainer, $100k is pocket change for them

after spending loads of time on it i have concluded that HFT is like playing against the house in the casino (you never have the winning edge) and migrated to longer term swingtrading/investing

For QD you want to master the python ecosystem specifically or do low level systems (HFT).
Its different from say, SaaS that’s focused on non-technical product features you’d ship at a pure software company

Built an event-driven options statistical vol-arb strategy and tested it walk-forward OOS with fixed-dollar sizing and Monte Carlo simulations (bootstrapped different fill assumptions and post-event paths).
At midpoint fills, annualized Sharpe is \~3.7 (0.50 partway slippage), and at \~0.575 partway slippage the strategy starts to be around break-even. At \~0.25 partway slippage Sharpe goes above 9, which is clearly unrealistic to achieve in the real market.
A few live tests have filled around mid or slightly better, but the sample is still too small. There is very little room for error. At this point the backtest has hit its limit. I need real fill data to determine whether the edge is actually tradable, but I think it is. The strategy is currently running on small capital with IBKR, fully automated and a suite of risk parameters set up.

My background is in math, data science, I've worked at 3 different HFT quant shops in Chicago, and I’ve spent roughly two years building the research, data pipelines, and execution stack. At this point, additional backtesting almost certainly has diminishing value. The main unknown is the actual distribution of live fills.
The strategy does not have significant tail risk on any single trade, since we're trading defined-risk partially hedged option spreads (betting on IV). I've attached some charts from the backtests.

TLDR; at fills worse than \~0.575 partway slippage, the strategy EV is around breakeven (negative after commissions). At around mid, the Sharpe is above 3. I'll deploy small capital and capture real fill data over the next few weeks and slowly scale up if i can consistently get fills at or better than mid.

As a retail trader you are not competing with Citadel/HFT firms. Reason is because those companies trade with millions. We trade with thousands. This being said, those companies cannot trade same strategies because of the liquidity effect

For clarification, this project is purely for educational purposes. I am not trying to compete with any trading or financial company. I understand that modern HFT firms often use technologies such as Solarflare nic s, FPGA acceleration, and other specialized hardware. However, I believe DPDK is still an important technology for students to learn, as it provides valuable hands-on experience with kernel bypass, packet processing, and low-latency systems, which are fundamental concepts in high-performance networking.

When are you actually going to get a server grade NIC and run your code there? Then benchmark it properly.

Not sure how relevant AF\_XDP is for low latency trading systems. I mentioned last time HFT firms use Solarflare `ef_vi` as the kernel bypass.

Without a real NIC how can you do "Tail-latency analysis under burst traffic" ?

Question about pivoting from SWE to HFT firms

Ah, I see. I was mainly worried about the usefulness of quant skills if I didn't land a role at an HFT firm. I also realize that may have come off as if I'm only shooting for JS or Citadel, but that's not the case; I'd happily work for most firms. Basically, I planned to aim for a QD role and settle for a job in SWE or embedded systems if that didn't work out. A better question that I would like to ask is how different QD is from software engineering, and what kind of courses, certifications, or external resources I should utilize to improve my odds?

I've been interested in becoming a quant dev since high school, and was wondering if I'm too stupid to break in, for lack of a better term. I'm currently a sophomore majoring in Comp E at a T20, but it's not exactly known for STEM, and we only send 1 or 2 kids to Jane Street or Citadel every year. Theoretically, if I worked really hard from now on, there's a slim chance that I may be working at an HFT firm after school, but more realistically, I would just end up speccing into skills like stochastic modelling or probability theory that I never actually get to use. I've heard that in order to break into quant, you need to be some child prodigy with multiple USACO or USAMO top placements, double major in CS and Math at a school like Stanford, Chicago, MIT, Columbia, Berkeley, or CMU, and graduate top of your class. Obviously, I'm sure they're being hyperbolic, but it makes me wonder if dudes like me, who are smart but not geniuses, have a place in quant finance?

If you want something focused on a crypto 'low-latency' system for market-making and HFT, one project worth considering as a “guide or source of inspiration” would be **Hummingbot \[**[Hummingbot Github](https://github.com/hummingbot/hummingbot) \- Python/Cython\] 👀 You should also take a look at their QuantLab/Research Jupyter notebooks. Of course, if you need higher performance, C++ and Rust are the languages to use, while Python serves as the data science and research layer.

That’s what I would recommend.
Hummingbot includes the **Avellaneda-Stoikov model**, which is the approach used by serious Market-Makers and HFT desks 😃