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👋 Hi, this is Gergely with a subscriber-only issue of the Pragmatic Engineer Newsletter. In every issue, I cover challenges at Big Tech and startups through the lens of engineering managers and senior engineers. If you’ve been forwarded this email, you can subscribe here.
Software engineering at a proprietary trading company: Optiver
A shift from a focus on latency to building better AI models, owning the full stack from applications to building custom hardware, very different incentives to most tech companies in play, and more
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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 US
Pat Cooney: Head of Global Platform Engineering
David 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 is the customer. 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 talent
Engineering 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, going from 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 i