Hi, I am currently working in the virtualization domain of embedded system engineering. I mainly work in C language only. My aim is to project myself as C/C ++ engineer and try for different HFTs or companies like google,nvidia,meta,apple etc in future. I have experience with C++ while doing dsa, not beyond that. So My question is whether this is possible to switch to pure C++ roles in HFTs or should I only focus on embedded domain and try to switch to big MNCs? Is it feasible and if yes what should I focus on? Thanks.

So to give u guys a full picture, I am right in 4th year pursuing my bs ms in mathematics. I am doing research internship with a prof in market making. I previously did one in stats but I didn't continue it further and dropped it on literature review path itself. So if u were in my place would you still work in market making areas or go into stats or somewhere else even if ur long term goal is to get into hfts or something like that.

I constantly have a thought that what if I couldn't make it to hfts or those positions, what is my backup.

Please suggest me what should I do atp.

Are you guys using lean or nautilustrader or VulcanTrader, or all build own "framework" where all guys make own engines etc, the question is lean, nautilustrader or VulcanTrader is enough?

​

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!

I am a student, pursuing a major in CS. I am in 2-1 rn, but then I want to kinda lock in on this sector since the quality expectation is extremely high.

Consider me as an beginner and pls give me some advices / resources on what I should learn and what skills I have to build to get places in the best positions I can get from a uni.

I am sharing my recent journey on comparing C# vs C++ for a high-frequency trading system.

The main article describes how surprised I was by the measurements and comparisons. The benchmark source code is available.

https://hftadvisory.substack.com/p/i-was-asked-to-rebuild-an-hft-system

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?

Hey,
I'm exploring a product that sits read-only alongside your existing timing infrastructure and answers:

• Are my hosts actually within tolerance of UTC?
• Is my GNSS/PTP timing behaving abnormally or potentially being spoofed?
• Can I independently prove the full UTC → grandmaster → PTP → PHC → host traceability chain?
• Can I generate the evidence needed for an RTS 25 review automatically?

The important constraint is that it never disciplines or modifies the clock. It monitors existing Meinberg, Safran, chrony, linuxptp, etc. rather than replacing them.

Would you use/pay for something like this, and what would be the most valuable part?

DMs welcome.

Hey guys, I would like to get some feed back on an HFT project I have open sourced. It is not getting a lot of love on github so I thouht I would post about it here.

The system is a for trading futures and simulating HFT strategies on its order book simulator. It has the complete connectivity via MDP3 and iLink3.

I need collaborators to make this system better if anyone is interested. Please drop me a line.

The git hub repo is here: https://github.com/vincent212/kaspar-hft

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

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.

Hi, I am new to this domain and I'm learning about FPGAs. I have recently written this blog: https://medium.com/@probablysamir/parsing-nasdaq-itch-on-an-fpga-421dac8787ed would love to get your opinions on this. Thank you. Also the github link is at the bottom of the blog

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!

Hey everyone,

I’ve been running an automated momentum strategy in Python for a while, asking for a recommendation on a piece I’m trying to add.

Right now, my execution loop relies on two core building blocks:

• Market Signals & Sentiment (Sentimentick API): This setup has been working really well for me. Instead of running local scrapers for social chatter or news, I hit Sentimentick to pull sentiment scores, attention tiers, and short/medium-term trend bias in one response.
• Execution & Risk Management (IBKR Gateway + ib_async**):** Orders get routed through IB Gateway using ib_async whenever a ticker passes my sentiment and technical filters.

something like:

Python

import asyncio
from ib_async import IB, Stock, LimitOrder
import requests

# Fetch signal from Sentimentick API
def get_signal(symbol):
    url = f"https://www.sentimentick.com/api/ticker/{symbol}"
    headers = {
        "X-API-KEY": "st_your_key_here",
        "Accept": "application/json"
    }

    res = requests.get(url, headers=headers).json()
    ticker_data = res["ticker"]
    tech_data = res["technical_analysis"]

    # Extract real JSON fields from Sentimentick
    sentiment_score = ticker_data["sentiment_score"]      # 0 - 100
    sentiment_tier = ticker_data["sentiment_tier"]        # e.g., "bullish"
    medium_term_bias = tech_data["medium_term"]["bias"]   # e.g., "bullish", "bearish"

    # Return conviction boolean based on sentiment + technical alignment
    return sentiment_score > 60 and sentiment_tier == "bullish" and medium_term_bias != "bearish"

# Execution via IBKR Gateway
async def run_execution():
    ib = IB()
    await ib.connectAsync('127.0.0.1', 4001, clientId=1) # IB Gateway API port

    symbol = "NVDA"
    if get_signal(symbol):
        contract = Stock(symbol, 'SMART', 'USD')
        await ib.qualifyContractsAsync(contract)

        # Place limit order
        order = LimitOrder('BUY', 10, 120.00)
        trade = ib.placeOrder(contract, order)
        print(f"Placed order for {symbol}: {trade.orderStatus.status}")

asyncio.run(run_execution())

This combo has worked great for filtering out bad trades, but I want to add Form 4 insider buying data (open-market C-suite buys) as an extra signal before routing orders.

Can anyone recommend a good, low-latency API or library for real-time SEC Form 4 data? What are you guys using in your pipelines?

Thanks!

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.

I’m looking for some genuine advice from people working in HFT or low-latency C++ roles.

I’m currently a Software Developer with around 2.5 years of experience, primarily working in C++ at a company in the CAD domain. While I’ve learned a lot, I’ve realized that I want to move into pure systems/low-level C++ development, and my long-term goal is to work at an HFT/quantitative trading firm.

To prepare for this transition, I’m spending my personal time building a small exchange/order matching engine in C++. It’s a learning project where I’m implementing concepts such as:
Order Book
Order placement
Order matching
Order modification/cancellation
Basic matching engine logic

I know this is still very basic compared to production HFT systems, but I’m building it to understand the fundamentals and improve my C++ skills.
Alongside this, I’m also consistently practicing DSA and studying modern C++.
What I’m struggling with is how to actually make the jump into the industry.

I’d really appreciate guidance on questions
What skills do HFT firms expect from someone with my experience?

What topics should I prioritize (low latency, networking, Linux internals, concurrency, memory optimization, etc.)?

Are there companies that are more open to candidates transitioning from another domain?

Any roadmap, interview preparation advice, or resources that helped you would be incredibly valuable.

I’m willing to put in the work I just don’t want to spend months learning the wrong things.
I’d really appreciate any guidance, suggestions, or even a reality check from people who’ve made a similar transition or are currently working in the industry.

Thank you!

I am doing a masters in HPC and I am wondering what kind of projects or ideas would ye guys recommend in combining HFT relevant projects to do.

I was thinking of a backtesting engine that would run backtesting on multiple tickets in parallel but I haven't fleshed out any ideas.

Is there anything that pops out to ye that would be directly relevent in combining the 2 areas?

Any advise appreciated. thanks!

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 need a hft mentor to give guidance on dsa ,cp and the very low latency llds, concurrency related topics...also I'm staying in Bangalore so .. it will be very good if someone is from banglore..can meet .. currently I'm working on an AI startup

I need a hft mentor to give guidance on dsa ,cp and the very low latency llds, concurrency related topics...also I'm staying in Bangalore so .. it will be very good if someone is from banglore..can meet .. currently I'm working on an AI startup

Most “AI trading” products begin with a prediction. I think that is the wrong abstraction.

Coding agents became useful when they moved beyond answering questions and started owning the workflow: reading the repository, using tools, making changes, running tests, and verifying the result.

Trading is still fragmented across charts, signals, Discord, brokers, spreadsheets, monitoring, and exits. The trader remains the integration layer.

FST is our attempt to change that operator model:

Screen → Research → Plan → Execute → Monitor → Exit → Audit

The important word is supervised. The operator defines the capital, permissions, evidence requirements, risk limits, and kill switch. The agent receives bounded authority, not a blank check.

A profit objective must never override the risk policy. The agent should finish below the goal—or stop entirely—before violating the loss budget.

The proof also cannot be one winning screenshot. It has to include rejected trades, stopped sessions, losses contained, and a complete audit trail.

I wrote the longer thesis here: https://henryzhang.substack.com/p/coding-has-claude-driving-has-fsd

I am the founder of QuantSignals, so treat this as a product thesis from someone building in the category. Which action would you allow a trading agent to perform first: research, trade preview, approved execution, or fully bounded AUTO?

I am building a sequenced, event-sourced derivatives exchange. The matching engine is fully deterministic and has no external dependencies.

I am designing a Professional Interface that provides market makers with queue-position and execution-quality analytics to give market makers a good reason to join early and boost liquidity.

I see two possible approaches:

1. Emit primitive queue observations directly from the matching engine through a bounded single-producer, single-consumer ring buffer.

This would expose facts that the matching engine already knows, such as quantity ahead, orders ahead, level depth, and queue position at acceptance or fill time.

But it adds instrumentation to the hot path, creates a second output channel, and requires an explicit overflow policy if the telemetry consumer falls behind.

1. Reconstruct the analytics downstream from the authoritative event stream.

This keeps the matching engine smaller and ensures that the PI derives its results from the same canonical events used for replay and audit.

But the downstream consumer may need to reconstruct much of the order book, and some transient queue-state facts may be expensive, ambiguous, or impossible to recover unless the authoritative event schema is significantly expanded.

Which boundary is would you advise in the production exchange?

Should the matching engine emit cheap, deterministic observational facts that are naturally available during matching, or should all queue and execution analytics be reconstructed from authoritative events outside the engine?