On Trading Tech and Quant Development. Interview with Jad Sarmo
Forecasting financial time series is one of the most complex tasks in data science.
Q1. You’ve been working in the Trading Tech and Quant Development space for the last 20+ years. What are the main lessons you’ve learned through this experience?
Jad Sarmo: Back in 2004, I deployed the first automated trading system (ATS) for foreign exchange at a top-tier bank. We had to build software directly on traders’ workstations to send algorithmic orders—latency was measured in hundreds of milliseconds.
Since then, the landscape has evolved dramatically: the proliferation of low-latency submarine fiber-optic cables, high-frequency signals bouncing off the ionosphere, the emergence of cloud computing, AI-assisted development, the rise of blockchain, and nanosecond-level FPGAs.
Despite this, the core principles remain unchanged: a solid grasp of systems and markets, clear business objectives, and the ability to assemble the right experts to solve the right problems. Equally important—especially as firms face increasing external scrutiny and apply for new licences—is a commitment to compliance with applicable laws and regulations from the outset.
A personal lesson I’ve come to value is this: if you’re comfortable, it’s time to take a risk, learn, and repeat. That cycle is essential in such a fast-evolving landscape.
Q2. What is your role at B2C2?
Jad Sarmo: B2C2 are a global leader in the institutional trading of digital assets, serving institutions such as retail brokers, exchanges, banks, and fund managers. We provide clients and the market with deep, reliable pricing across all market conditions.
I joined B2C2 in 2021—during a pivotal year for digital assets—to build our global Quantitative Development desk. My team works closely with traders, researchers, and engineers to improve client pricing, trading strategies, and automated risk systems.
I also lead our Exchange Squad, which manages trading from market data ingestion to algorithm optimization across more than 30 AWS regions globally.
Q3. What are the main challenges in this industry when it comes to data management? Specifically, since you are handling liquid assets, what is the main challenge you’ve seen when an asset can be “easily” converted into cash in a short amount of time?
Jad Sarmo: Like in any asset class—FX, equities, rates—crypto trading involves massive volumes of market and trading data. But crypto adds a unique layer of complexity.
It’s a 24/7 market, with both on-chain (blockchain-logged) and off-chain (centralized exchanges) activity. A significant share of volume also flows through DeFi protocols using smart contracts.
We face challenges like inconsistent exchange APIs (REST, WebSocket, etc.), cloud-native environments, and the need for extremely low-latency systems that handle massive data bursts. Meanwhile, newer or illiquid tokens present formatting hurdles, with decimals occasionally extending to 10+ digits — far beyond what many traditional systems were designed to handle.
Real-time hydration and normalization of incoming data streams are therefore critical to support both research and trading effectively.
Q4. You mentioned in a previous presentation that managing a “Crypto ecosystem” is not an easy task. What is a Crypto ecosystem, and what is it useful for? What are the specific challenges you face, and how do you solve them?
Jad Sarmo: By “crypto ecosystem,” I mean the global, interconnected infrastructure where digital assets are traded: exchanges, OTC counterparties, and all supporting systems.
Each participant may be located in a different —Virginia, Tokyo, London, and beyond. Our system ingests high-frequency data from across the world, unifies it, and processes it with both low latency and high throughput.
The hardest part is normalizing inconsistent feeds so they’re useful across trading and research. Historically, AWS prioritized reliability over low latency, but in recent years, the biggest players—including B2C2—have worked closely with AWS to re-architect the cloud to meet the latency needs of crypto trading.
Q5. Let’s talk about the use of AI and Machine Learning in the financial services industry. You cannot predict the market by training an AI model on historical data, because things change rapidly in the financial markets. How do you handle this issue? Does it make sense to use AI?
Jad Sarmo: Forecasting financial time series is one of the most complex tasks in data science.
A picture of a dog from 10 years ago is still useful to train an image classifier—but financial data ages fast. Market structure, participants, and behaviour shift constantly, so models need regular recalibration.
Ensemble learning is particularly powerful in finance; rather than relying on a single predictive model, we combine many models that each perform slightly better than average. AI is not a crystal ball, but it provides meaningful signals that enhance traditional pricing and risk systems.
Q6. You have been leveraging a vector-native data platform at B2C2. Could you please explain what you do with such a data platform?
Jad Sarmo: We use KX’s kdb+ platform to support our real-time and historical time-series data needs. It enables global ingestion across AWS regions, persistent storage, replay of massive tick datasets, and complex event processing.
The consistency of this platform means researchers can focus on analysis without worrying about where the data lives. PyKX, a Python–Q hybrid notebook interface, allows heavy computations to run in Q, while using Python for exploratory analysis and ML.
KX also provides high-performance dashboards for quick data visualization—even by non-technical users.
Q7. Why not use a classical relational database or a key-value data store instead?
Jad Sarmo: Traditional relational databases are too rigid and slow for high-frequency time-series analytics. Key-value stores are great for quick lookups but lack native analytics support.
Vector-native platforms like kdb+ are designed for exactly this use case. They let us run complex queries over billions of rows in milliseconds—without reshaping the data or creating indexes.
As data volume grows to terabytes per day, traditional databases become engineering bottlenecks. In contrast, vector platforms scale naturally, with each column and date efficiently mapped to files.
Q8. Let’s go a bit deeper. If you start with “FeedHandlers,” how do you end up processing this complex data at scale, in real time and without losing some data?
Jad Sarmo: Our architecture begins with Java or Rust feed handlers that convert raw exchange data into kdb+ format.
A ticker plant then routes data to three layers:
1 A real-time in-memory database
2 A persistent on-disk database
3 A complex event processor
This setup ensures we can act on data instantly, store it reliably, and support deep analytics—all with complete transparency for end users, whether they’re consuming live or historical data.
Q9. What about data quality? How do you ensure data quality in the various phases of data processing?
Jad Sarmo: Data quality starts with ingestion. Exchange feeds vary in reliability and format, so we normalize and hydrate the data immediately to remove inconsistencies.
We maintain constant feedback loops between research and production teams to monitor and improve quality. Clean, consistent data is the backbone of everything—without it, even the most sophisticated models won’t perform.
Q10. You decided to integrate AWS FSx for Lustre with kdb+. What are the main benefits of this design choice?
Jad Sarmo: AWS FSx for Lustre has been a major improvement. It offers virtually unlimited horizontal scaling and high-speed access. We can connect dozens or hundreds of nodes, each with fast local disk and compute, to form a massive high-performance network file system.
It compresses files efficiently, offloading that work from kdb+. We can spin up isolated research environments on demand without affecting production, and there’s no downtime. Auto-scaling lets us right-size our infrastructure at any time.
Compare that to traditional datacentres—provisioning takes weeks and usually leads to overbuying hardware. In the cloud, it’s a five-minute job.
Q11. How is industry regulation affecting this complex data management?
Jad Sarmo: Regulation is advancing quickly. This means we must store data in auditable, retrievable formats. End-to-end traceability—from ingestion to storage to downstream consumption—is non-negotiable.
This adds operational overhead, but it also emphasizes the need for trustworthy systems that meet both performance and compliance standards. We see this reflected in regulatory initiatives like the EU’s MiCA regulation, the approval of Bitcoin ETFs in the U.S., and the UK’s FCA Discussion Paper DP25/1, which explores regulating crypto asset activities.
……………………………………………………………
Jad Sarmo, Head of Quantitative Development | Expert in High-Performance Trading Systems, B2C2.
Jad Sarmo is a technology and trading infrastructure leader with over 20 years of experience building high-performance trading systems for FX and digital asset markets. He is currently Head of Quantitative Development at B2C2, a global leader in institutional liquidity for digital assets, where heoversees a global team delivering real-time pricing, exchange trading, and analytics infrastructure across 24/7 markets.
Prior to B2C2, Jad ran Technology at Dsquare Trading, a high-frequency proprietary FX trading firm that rose to prominence through cutting edge algorithms, low-latency engineering, and a world-class team. There, he designed ultra-fast trading systems and led cross-functional teams through years of continuous innovation in a high-stakes environment.
Jad specialises in bridging the gap between trading, quant research, and engineering — turning complex ideas into reliable, automated, and profitable systems. His expertise spans real-time architecture, algorithmic trading, market data, and risk management, with deep technical fluency in Java, Python, KDB+/q, and AWS.
Based in London, Jad is dedicated to designing robust systems under real-world constraints and mentoring the next generation of technologists.
………………………..
