r/highfreqtrading

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Quant Researcher — Brewlabs

Hey everyone!

We’re currently looking for a Quant Researcher to join Brewlabs

We’re looking for someone who enjoys digging into data, testing ideas rigorously, and turning research into systematic trading strategies.

What you’ll work on:

*Research and develop quantitative trading strategies

* Analyze large financial and market datasets

* Build and backtest models/signals

* Evaluate strategy performance, robustness, and risk

* Work on improving our existing research and trading systems

We’d love to hear from you if you have experience with:

Python and quantitative/data analysis

Statistics, probability, or machine learning

Financial markets and systematic trading

Backtesting and evaluating trading strategies

Independent research and experimentation

We care more about strong quantitative thinking and the quality of your research than fancy credentials.

If this sounds interesting,Dm to https://x.com/dhruvsol?s=11 or linkedin.com/in/dhruvraj-solanki-663a54200 with a short introduction, your background/experience, and any relevant projects, GitHub, research, or portfolio you’d like to share.

Location: [Remote]

Employment: [Full-time]

Compensation: [Based on experience]

Thanks!

Hello, I have been learning some low latency programming in C++ for some time now and have built some very basic things like an SPSC lock-free queue and an Object Pool. I want some suggestions as to what else I can build that is along the same lines as the above mentioned projects (like the very basics or building blocks of low latency programming projects).

PS: I was thinking of writing my own benchmarking framework/profiler from scratch in C++, would that be a good idea?

Reddittors who’s already working at one of the HFT companies, I have a few questions for you.

How is it to work for that company? How is it working in HFT general?

Mostly I’m interested at the stress level you are facing every day and how frequently you had a critical error that required surreal effort from your side.

Background, I’m halfway through the interview process at a HFT company, and I’d like to compare to other fields. I have experience in marine technologies and healthcare technologies. They were kind of chill, the healthcare was a bit annoying because it is heavily regulated.

Cheers!

I’m a fresher(cs grad) currently working at a very well known HFT firm. I joined around Dec 2025, and my current role is monitoring l under a broader Quant, Infra and Monitoring setup.

My current work is mainly monitoring systems and pipelines, identifying errors or issues in code and processes, and informing the respective developers. It has given me good exposure to how things work in an HFT environment, but I want to move towards a more engineering focused role.

Before joining, I had already worked on backend development during college. In my third year, I did a 3 month internship where I worked on production grade backend systems. So I do have some actual development experience, although I’m still a fresher overall.

Long term, I want to move into roles like low latency engineering, trading systems, performance engineering, or other core HFT engineering roles. I’m also considering these roles because the salaries and growth are generally much better, so I want to start preparing for them early rather than getting stuck in monitoring.

For people who have worked in HFT or quant firms, I wanted to ask:

1. How realistic is it for a fresher to move internally from monitoring and support into low latency or trading systems engineering?

2. What should I focus on learning and building over the next 1 to 2 years to become a good candidate for these roles?

3. Would it be better to try for an internal switch within the HFT firm, or build the required skills and eventually apply outside?

Would really appreciate advice from people who have gone through a similar transition, especially from monitoring, support, infra or other non core engineering roles into low latency or trading systems.

I’m not looking for the usual “learn C++ and DSA” answer. I’d like to understand what a realistic path would look like and what I should actually be doing over the next couple of years.

I have a basic knowledge of C++ want to master it and deep dive with HFT related core topics .. currently I am living near Bellandur social / new horizon collage bus stop.. if anyone from hft background can help me I only get free on the Saturday 8pm to sunday 10pm .. can meet and sit with learning and topic discussions on weekends

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

I wanted to answer what I thought was a fairly simple question:

Does order-book microstructure contain enough information to beat Polymarket's 5-minute crypto markets after transaction costs?

Turns out the harder part wasn't training the model.

It was getting the data.

As far as I could tell, there wasn't any public historical dataset with tick-level quotes, trades and order-book updates for these markets, so at the end of June I started collecting everything myself.

The archive now covers BTC, ETH, SOL and XRP 5-minute markets.

For every trading day it contains best bid/ask quotes, trade prints, spot prices, and (from 20 July onward) full L2 depth. Everything is stored as daily Parquet files with manifests and measured uptime for each day.

Once I finally had enough history, I trained a regime-routed LightGBM ensemble using only information available at the decision timestamp.

The disappointing part is that the model actually behaves reasonably well.

Calibration is good.

Out-of-sample Brier scores are consistently around 0.06–0.10 depending on asset and market regime.

But the strategy still loses money.

Across roughly 4,300 simulated trades it finishes around -$277.

The hit rate is about 47%.

After spread and fees you need something closer to 49% just to break even.

That two-percentage-point gap turned out to be the entire problem.

Looking back, most of the important work wasn't machine learning at all.

I found that my simulator had been using fees that were 3.5× too low.

One validation gate wasn't actually filtering anything because of a bad threshold.

At one point the trading system quietly stopped opening new positions because several expired markets hadn't been marked as resolved yet, while every monitoring dashboard still showed the process as healthy.

Those bugs changed the conclusions far more than another week of feature engineering.

I'm curious whether anyone here has managed to build a strategy on prediction-market microstructure that survives realistic execution costs.

If you have, I'd genuinely like to hear what worked.

And if anyone wants to reproduce the experiment or try a completely different approach, I'm happy to share the dataset. It now contains a little over a month of tick-level history and keeps growing every day.

Hello Family,

I’d value the perspective of experienced investors and traders on a decision I’m currently considering

Over the past three months, my strategy generated approximately 400% in May, 280% in June, and 250% in July

I understand that numbers of this magnitude naturally raise questions which is exactly why I had no hesitation in putting the strategy on Myfxbook with verified trading results. I prefer transparency over marketing claims

The Myfxbook account has now been running for approximately two weeks and is already up more than 200%, generated across only 4–5 actual trading days. Every trade is there to be tracked and evaluated

At this point, the question for me is no longer whether I can promote the strategy. The verified performance can speak for itself.

The question is whether I should continue trading exclusively with my own capital, or selectively open the strategy to outside capital through copy trading

If I move forward, I’m not interested in building a mass-market signal service. I would rather keep it selective and work with a limited number of serious investors.

For those who understand capital allocation and systematic trading:

Would you keep an edge like this private, or leverage the verified track record and open it selectively to outside capital?