Before we start: I’ll be in New York, on 15 September, presenting the keynote at LDX3 New York, doing a book signing, and hanging out with attendees. The focus of the conference is engineering leadership at a time when things are moving very fast. See the full agenda and get tickets. If you’ll be around – hopefully catch you there!
The Pragmatic Engineer is back from our summer break. We resume with a detailed deepdive about the trading industry, and interesting engineering challenges that come when working at a company that has no external customers, but where a single, unfortunate enough software bug could wipe out the whole company.
In tech recruitment, proprietary trading companies have a particularly high bar and typically offer compensation on a par with, or even exceeding, Big Tech; right at the top of the market. That’s because for these market makers, success is all about gaining a competitive edge over rivals. Such competitive advantages today includes software that is superior to that at their competitors.
**Software engineers tend to know little about trading companies – and this piece aims to change that. **Trading companies build bespoke hardware stacks and have larger platform engineering teams than most workplaces. For software engineers, it’s a lucrative niche in terms of compensation, full-stack (hardware to software) work and for engineering challenges, and so we decided to go deeper in this interesting area.
In order to find out more, The Pragmatic Engineer sat down with a leading proprietary trading firm, Optiver. Headquartered in Amsterdam, they also have a large engineering presence in the US and globally. We met engineers and engineering leaders to learn in depth how engineering works in a modern trading business, with contributions from:
Alex Itkin: CTO, Optiver USPat Cooney: Head of Global Platform EngineeringDavid Gross: Technology Lead, Options
Thanks to everyone at Optiver for taking part in this report which lifts the lid on how software engineering is done when even nanoseconds can count. In this article, we look into a software engineering environment that’s distinct from what you expect at most startups and Big Tech. For example:
**No external customers.**Usually, companies have consumer customers (B2C), business customers (B2B), or both. But not trading houses like Optiver, where their own business isthecustomer. This is a different reality: there’s no external deadlines and related pressures, but personal motivation to improve is highly valued.**Latency: “enemy number one”.**Nearly every major engineering decision at Optiver is made in the interest of minimizing latency – the amount of time between a request and response. This approach is present across the software stack and in kernel-level work. It’s why Optiver manufactures its own hardware.**Today, latency is the floor, and AI models are becoming a differentiator.**Gone are the days of having lower latency than the competition allowing for arbitrage opportunities to make risk-free profits. Instead, information models are becoming a differentiator: slow models with a fast trigger sending signals to execute trades, and fast models running at the edge of the network making trade decisions realtime.**Haunted by a bug that nearly killed a business.**Among trading houses, there’s a cautionary tale of when a peer company,Knight Capital, nearly went bankrupt after a single bug in a high-frequency trading system triggered a $440M loss.**Different incentives.**The business is incentivized to move very fast, but with a high premium on caution in order to avert potential financial disasters on the market. This cautious attitude to risk in concert with chasing speed feels pretty distinct in tech.
I this deepdive, we cover:
**Overview of trading & hedge funds.Categories of trading companies, high-frequency trading (HFT), plenty of ML & math, and AI labs poaching HFT talentEngineering organization.**How trading-specific roles work together, platform engineering, the “build and own” culture, and more.**Software tech stack.**The three-layer tech stack, languages and tools, CI/CD stack and the data layer.**Hardware engineering, FPGAs and Silicon.**Latency progression, custom FPGAs, custom hardware, AMD hardware partnership, and more.**Network & physical infrastructure.**Physical infrastructure, dedicated fiber & wavelength leasing, optical cable, radio, data centers & co-locations, and why AI models matter more than ever before.**Engineering practices.**Risk vs speed, knowledge-sharing culture, testing culture, monitoring & incident detection, risk management.**AI at Optiver.**AI tooling stack, future of agentic coding, details about adoption, and how it all looks in practice.**Hiring, career development & culture.**Engineering levels at Optiver, goingfrom hiring mostly juniors to hiring experienced engineers today, competition during hiring, and the onboarding feedback loop.
We’re delighted to publish this report, including details never shared before. Let’s dive in!
Here’s a summary of the world of ‘prop shops’; another name for firms like Optiver that invest their own funds in trading financial assets. Below are some useful mental models for understanding the sector.
**Buy side:**companies invest money and earn returns. Examples: hedge funds, asset managers, pension funds.**Sell side:**firms sell services or products such as advice, underwriting, research, execution, etc. These are usually investment banks and broker-dealers.
Optiver is on the “buy side”, as a prop shop.
Based on whose money is being traded, there are three main capital sources:
Investment banksserve corporate and institutional clients by raising capital, advising on deals, and executing trades on their behalf. Examples: Goldman Sachs, JPMorgan, Morgan Stanley.Hedge fundsraise money from external investors and trade it on their behalf, charging management & performance fees. Examples: Citadel, Millennium, Two Sigma, Bridgewater.Proprietary trading firmstrade only their own capital, with no clients or external funding. Examples: Optiver, Jane Street, Jump Trading, DRW, Hudson River Trading.
Optiver’s CTO US Alex Itkin pictures the evolution of trading as having unfolded across four eras to date:
**Pre-electronic (pre-1990s).**Trading was done face-to-face on noisy trading floors and by phone. Prices were shared on reels of ticker tape and printed in newspapers. Investors contacted brokers to place orders.**First wave of electronification (early/mid 1990s).**Financial markets moved onto computer screens but orders were still entered manually.**Automated trading (late 1990s to ~2015).**Computers did the same as human traders, but faster and at scale. This was the “mechanical” automation era of building automated workflows without data-driven decision-making.**Quantitative trading (~2015 to present).**Data-driven decision-making with machine learning models and inference compute, with human decision-making in some key areas.
**Each era “weeded” the market. **Some companies excelled at automated trading but never made the leap to quantitative trading. According to Itkin, competition has got tougher over time, while the number of serious players has decreased. Today, there are only a handful of really big firms, and one reason for this is cost: investment in research clusters – which serious prop shops all do – requires hundreds of millions of dollars.
Optiver turned 40 years old in March 2026, launching in 1986 at the European Options Exchange. Today, the company has:
~2,200employees**~950engineers and ~1,000 traders and researchers11****offices**: Amsterdam (HQ), Chicago (US HQ), Austin, New York (2025), London, Sydney, Shanghai, Hong Kong, Singapore, Taipei, and Mumbai.10M+trades executed per day, across 100 exchanges€4.5B($5.1B) in trading income, and €1.7B ($1.95B) profit, as per2025 financial results
Optiver is a mix of:
Market maker: providing liquidity on exchanges by quoting ‘buy’ and ‘sell’ prices of financial products and earning the spread between the two.**High-frequency trader:**executing automated trading strategies at very low latency
High-frequency trading involves placing high volumes of orders at lightning speed in an effort to take advantage of extremely rapid market movements. In this domain, speed is the biggest advantage, and achieving it obviously involves high-performance computing. The basic trading loop is run millions of times a day. It’s made up of three steps:
Watchthe market for new information like price changesDecidewhat the information means and the right trade to makeSenda trade to the exchange before competitors do
In trading, timing is everything, and for some types of trade even nanoseconds count. Optiver’s fastest trading system operates in the realm of sub-nanosecond, where measurement noise becomes a challenge in itself. Software, hardware, and physics are all involved, along with microwave and shortwave links between data centers, and custom-manufactured chips.
We go deep into this in the “Hardware Engineering” section below.
However, in this niche, even ultra-low latency is no longer a competitive moat in itself. As competitors have squeezed performance out of their systems, focus has shifted towards fine-tuning of trading strategies. Today, Optiver invests substantially more in building better models than it does in lowering latencies.* More on this in the “Network and physical infrastructure” *section below.
**HFT evolves faster than other industries. **Profitable strategies don’t last long, opportunities are fleeting, and innovation is a constant. In this environment, a tool like AI is relatively straightforward to implement because trading houses like Optiver are well used to change in their daily business environment. More on this topic in the ‘Optiver & AI’ section.
There’s a big role for machine learning (ML) and mathematics in quantitative trading. A good chunk of Optiver’s business is the buying and selling of options, and the pricing of these rests on mathematical theorems like the Black-Scholes model. Traders, quants, and even software engineers building option-pricing strategies must understand the math of this problem space.
Over time, machine learning is becoming more important than math models, but it’s worth keeping in mind that trading is not purely an ML pursuit.
**AI infra providers are heavily involved. **NVIDIA, Groq, and Cerebras are actively courting trading firms, due to how much money they spend on GPUs. For example, see Hudson River Trading discussing Blackwell deployments at NVIDIA’s GTC conference, or Jump Trading being among the first to deploy next-gen Vera Rubin systems. HFT companies have very clear monetization paths for GPUs and spend large sums on hardware, hence why NVIDIA and other suppliers are keen to partner with them.
One new trend is AI labs like Anthropic and OpenAI recruiting from prop shops, defying the assumption that AI labs mostly recruit from Big Tech. There are a few reasons why AI labs seek out talent from the trading world:
**Infra expertise.**Prop shops like Optiver have spent decades operating their own data centers and deploying on-prem hardware at co-location facilities.**Custom, high-performance hardware.**Prop shops also often build their own hardware and their kernel stacks achieve very low latencies. That’s a talent AI labs seek!**Skillsets.**The highest-paying destinations for CS majors out of standout colleges are often prop shops, paying top-of-market compensation for standout talent.Outside of select colleges prop shops recruit from, however, there tends to be little awareness about these companies for new grads, or across the industry.
Optiver’s history can be seen as two distinct ages:
**Regional systems (“unblock yourself”: 1986-2020):**internal systems and platforms were built to serve local needs, such as building support for a market. Systems built exclusively for the US, Europe, or Asia were common.**Global platforms (“build for the whole company”: 2020-present):**Optiver recently started to build new systems to work globally across their platform. This global focus is also why the company is investing a lot more in its platform engineering arm. A globalization push started around 2023, and its momentum has been growing.
The benefit of the old “unblock yourself” approach of local teams building whatever they needed, was that it enabled them to move fast and not get held up by dependencies. But this became problematic because of fragmentation and duplication, and the downsides became more visible over time:
Fragmentation: different teams use different technologies, frameworks, and infrastructure
Duplication: teams in different parts of the business independently build the same or very similar services
The career trajectory of Pat Cooney, Optiver’s head of platform engineering, mirrors the shift to a global platform: he was the CTO of Optiver in Europe in the mid-2010s when the business was split by region, and was appointed head of platform engineering in 2025 when that approach was replaced.
Optiver’s approach to continuous integration (CI) has also evolved. Previously, the company had several regional CI services, but from 2025, it started to rebuild its CI system with two new goals:
**Build for scale:create a CI system built to scale across regions and stand the test of timeUse from any region:**standardize deployment pipelines, so that code built in one location can run anywhere without friction
At Optiver, there are three main areas for tech roles:
Engineering: build and own the full trading-platform stackResearch: quantitative scientists who build models and predictive signals to create and improve trading algorithms. Typically, their background is in math, physics, economics, and statisticsTrading: quantitative traders who watch live markets, adjust trading system parameters in response to conditions, and build tools to automate decisions
**In reality, the boundaries between these areas are porous. **Yes, people do the job they were hired for, but it’s common to also see researchers roll up their sleeves and take part in implementing a trading strategy, or software engineers conducting research.
**Cross-functional collaboration between roles is very common. **For example, when developing market signals and associated trading strategies, it’s normal for engineers, researchers, and traders to collaborate on most, if not all, projects.
**End-to-end ownership, plus autonomy, is a given. **Engineers have autonomy in how they get things done, and they own and solve problems from the ideas stage through to implementation. There is a limited amount of guidance for trading, and it’s down to engineers to find the right solution.
In many ways, this approach to software engineering is pretty similar to startups’: software engineers get limited guidance and lots of autonomy. In order to succeed at tech startups, engineers typically need to understand the business, as well as being excellent at building production-ready software. It’s the same at Optiver, where understanding the business means understanding markets.
Before Optiver’s globalized platform efforts started seriously in ~2023, regions duplicated effort:
Multiple implementations of iden…
