Background: Stock Trading in the 1990s

In the mid-1990s, U.S. stock trading was dominated by human intermediaries and fragmented systems. On the Nasdaq, market makers manually updated quotes and could even ignore incoming electronic orders if it didn’t suit them (An Introduction to the Sequencer World – Electronic Trading Hub). Datek Securities – where a young Josh Levine was a technologist – had traders known as “SOES bandits” exploiting Nasdaq’s Small Order Execution System, but they constantly hit limits and faced inflexible market makers.

Levine and his colleague Jeff Citron built a tool called Watcher that tapped directly into Nasdaq’s data feeds and allowed faster electronic order entry. In 1995 they added Jump Trades, letting Datek traders bypass Nasdaq entirely and trade with each other directly (Island ECN – Wikipedia).

This was revolutionary: it hinted that a fully automated trading network could cut out the middlemen. Frustrated by Nasdaq dealers who “routinely ignored Datek’s orders…leading to missed opportunities” (An Introduction to the Sequencer World – Electronic Trading Hub), Levine set out to create a new electronic market. In 1996, on the 6th floor of 50 Broad Street, Island ECN was born – an electronic communication network designed to upend the status quo with speed, transparency, and automation.

Distributed Architecture and Fault Tolerance

Island ECN’s system architecture was designed from the ground up for reliability and high availability. Rather than a single monolithic exchange computer, Island ran a distributed, shared-nothing architecture.

In practice, this meant there were always at least two independent matching engine servers running in parallel on separate networks (Island ECN 10th Birthday Source Code Release!). Each server maintained an identical copy of the order book state by processing the same stream of incoming orders in lockstep. A custom sequencer mechanism assigned every incoming message (order, cancel, etc.) a monotonically increasing sequence number and broadcast these events to all matching engine nodes using UDP multicast. Because each node received the exact same ordered event stream, their order books stayed in sync, achieving fault-tolerance through redundancy rather than through complex distributed transactions. If the primary server failed, a backup server – already up-to-date with all order flow – could seamlessly take over, continuing from the last sequence number with minimal disruption.

This approach eliminated single points of failure: as Island’s founder later noted, many so-called “fault-tolerant” systems simply “move the single point of failure somewhere else,” whereas Island’s active-active redundancy truly had no single point of failure.

To handle the heavy order flow, Island eschewed the hub-and-spoke model of traditional exchanges and instead embraced a high-throughput event pipeline. Orders from many clients (broker-dealers, market makers, etc.) flowed into Island’s network where they were time-stamped and sequenced in the order they arrived. The sequencer – essentially a single-threaded event dispatcher – ensured strict time-priority ordering of messages across the whole system.

This deterministic event stream fed directly into the matching engine logic. Island’s design was event-driven: each new order or cancel triggered an immediate matching cycle in the engine. Thanks to the sequencer bus and a lean architecture, Island could process messages extremely quickly and in the correct order without heavy locking or coordination between servers. In the late 1990s, this was cutting-edge; Island’s engineers had effectively created a mini distributed system that behaved as one logical order book, with built-in fault tolerance and real-time replication.

In-Memory Matching Engine for Low Latency

At the heart of Island ECN was its low-latency in-memory order book – a matching engine optimized for speed. In a bold (and unconventional) choice, the earliest version of Island’s core matching logic was written in Microsoft FoxPro for MS-DOS. FoxPro, a xBase database language, offered a reliable ISAM storage engine with B*-tree indexing, which Island leveraged to keep track of orders by price and quickly retrieve them. All open orders were stored in memory, and FoxPro’s index allowed constant-time seeks to the top-of-book or to specific orders. For example, the matching engine could instantly find the best available bid or ask using an index lookup instead of scanning a list. This index-based order retrieval was critical for speed. The system assigned each order a sequence number and timestamp on entry, then inserted it into the in-memory order book at the proper position. Matching was then performed against contra-side orders in the book, starting from the best price and walking down until the incoming order was fully executed or no more matches were possible. Any remaining unfilled quantity was immediately reported back as available (“U R Out” in Island’s terminology). These matching operations all took place in memory with no disk access, and FoxPro’s engine took care of efficiently updating B-tree indexes as orders were added, executed, or canceled.

Surrounding the FoxPro core, performance-critical components were written in C and x86 assembly for maximum speed. Whenever the FoxPro code needed to do heavy lifting – such as sending out messages or handling networking – it would call into optimized C/ASM routines (indicated by calls like INT97 in the code). This hybrid approach exploited FoxPro’s high-level simplicity for matching logic while outsourcing compute-intensive tasks to lower-level code. The entire matching engine was event-driven and ran on a single thread, avoiding context switches and lock contention.

Island’s engineers even discovered low-level tricks to squeeze more speed out of the system. One famous optimization was to reuse recently freed order records for new orders. When an order was canceled or completed, its record in memory would likely still reside in CPU cache; by recycling that record for the next order, the engine could avoid expensive memory allocation and leverage the cache warmth. According to Joshua Levine, this simple tweak was “hugely important” – it significantly reduced latency for entering new orders. Such attention to memory locality and minimal copying presaged techniques now common in high-performance systems.

Another key to Island’s speed was its lightweight messaging. All updates – trades, order confirmations, quote changes – were disseminated via UDP multicast, meaning a single packet could reach many subscribers simultaneously. This design choice slashed the latency of distributing market data because the engine didn’t have to establish a separate connection or copy data for each client. Instead, Island’s software would package each event (e.g. “Order X added to book” or “Trade executed at $Y”) with its sequence number and send it out on a multicast address. Any trading client or market participant listening on that feed would receive the message at the same time. If a recipient missed a packet (a downside of UDP), the sequence numbers made it obvious – a gap in the sequence could trigger the client to reconnect or request a refresh. In practice, the multicast feed, combined with the in-memory engine, enabled end-to-end low latency from order entry to market data dissemination. By the standards of the late 1990s, Island’s latency was phenomenal – measured in milliseconds or less, at a time when traditional systems might take seconds to execute a trade.

As Island grew, the team eventually reimplemented the matching engine on more powerful platforms (migrating from FoxPro on DOS to a Java-based engine in the early 2000s) for even greater performance. The Java version, engineered with careful object pooling to avoid garbage collection, ran orders of magnitude faster than the FoxPro system. Still, the core principle remained an event-driven, memory-resident order book – a design that has proven so effective that modern exchanges still echo this architecture.

Market Microstructure Innovations: Maker-Taker and Priority

Island ECN didn’t just innovate in technology; it also introduced new market microstructure concepts that changed trading behavior. One of its most influential moves was adopting the maker-taker fee model – a pricing scheme that rewarded liquidity providers (“makers”) and charged liquidity takers. In 1997, Island became the first venue to implement maker-taker fees. Traders posting standing limit orders would earn a small rebate (for example, Island paid about $0.01 per 100 shares for adding liquidity), while those who hit those orders paid a corresponding fee.

This was a radical idea at the time. By paying electronic market makers for supplying liquidity, Island incentivized tighter spreads and deeper order books. It attracted volumes by essentially sharing some of its trading revenue back with participants who improved market quality. The maker-taker model gave rise to a new breed of “rebate traders” who sought to profit from earning these rebates on high-volume strategies. In the years that followed, this model swept across the industry – eventually every major U.S. stock exchange adopted maker-taker pricing, fundamentally altering how orders are routed and how market makers compete.

Island’s matching engine was built to enforce price-time priority with strict fairness, which was another cornerstone of its microstructure. In traditional markets, human market makers could delay or “ignore” incoming orders, undermining time priority. Island’s automation made such practices impossible – the software would always execute against the best available orders in the book, in the exact order they arrived. If a new order arrived at a better price than existing ones, it would immediately jump ahead in priority and potentially execute, while orders at the same price were kept in FIFO (first-in, first-out) sequence. The system was constantly recalculating the order queue in real time as orders entered, were modified, or canceled. In essence, every time an event happened, the matching engine would recompute the top of book and the queue rankings on the fly. Thanks to efficient data structures (the B-tree indexes) this reordering was very fast, ensuring that even in a fast-moving market the book was always properly sorted by price and time priority. Island even allowed hidden orders and reserve size (iceberg orders) and other advanced order types, yet still maintained real-time priority rules for the visible portion of the book. Traders quickly learned that on Island’s electronic book, getting priority was crucial – and thus began to adapt strategies accordingly (for instance, splitting orders or constantly updating quotes to maintain priority). The result was a more dynamic, competitive marketplace. Island’s fast, automated priority matching meant that if you blinked, a faster participant’s order might step in front of yours. This environment arguably set the stage for the algorithmic order-placement tactics that define modern high-frequency trading.

Another microstructure innovation was sub-penny pricing. Island was an early proponent of decimalization – it became the first U.S. marketplace to quote and trade in increments of $0.01, in July 2000. At a time when other markets were still transitioning from fractions (e.g. 1/16th of a dollar ticks), Island demonstrated that finer price granularity could benefit traders by tightening spreads. Decimal pricing, combined with the maker-taker model, often reduced the effective spread to mere pennies or less, undercutting the wide quotes of traditional exchanges. This transparency and price competition forced many old-school market makers out of business or into modernization.

In summary, Island’s technology enabled a market structure where fees and fast priority updates incentivized participants to improve the market – a sharp contrast to the static, dealer-centric model of the past.

Unprecedented Transparency: ITCH, OUCH, and Real-Time Data

From its inception, Island ECN championed a transparency model that was unheard of in the 1990s. Island believed that all participants should have equal access to market information, breaking the monopoly that exchanges and insiders had on data. To achieve this, Island published a real-time feed of its entire order book called ITCH, and offered a fast order entry protocol called OUCH for direct connectivity.

Every order placed, every cancellation, and every trade execution on Island was broadcast on the ITCH feed to anyone who wanted to listen – originally, access was even provided for free. This was a radical departure from traditional exchanges, where full order book data was typically available only to privileged market makers or via expensive data subscriptions. Island’s feed provided real-time bid/ask updates and trades, enabling any subscribing firm to see the market’s depth and activity as it happened. In fact, Island displayed its live limit order book on a public website for anyone with an internet browser to view, ensuring that even individual investors could see the same information as Wall Street traders.

The OUCH protocol, meanwhile, allowed institutional traders and broker-dealers to hook directly into Island’s matching engine with minimal latency. Unlike the text-based FIX protocol commonly used for electronic trading, OUCH was a lean binary protocol purpose-built for speed. Traders could enter, cancel, or replace orders via OUCH and receive instant acknowledgments and execution reports, all while the ITCH feed simultaneously streamed out the resulting market data. This combination of open access and efficient, bespoke protocols meant algorithmic trading firms flocked to Island.

Automated trading systems require both rapid execution and a rich stream of market data; Island was the first platform to truly deliver on both. By providing a firehose of live data in computer-readable format, Island effectively provided the market data infrastructure for AI and algorithmic strategies to flourish. Firms like Automated Trading Desk (ATD) – one of Island’s early customers – used Island’s real-time feed to run quantitative models that adjusted orders in milliseconds.

Other emergent high-frequency traders like Getco and Tradebot gravitated to Island in the early 2000s, because its transparency and speed gave them an edge unavailable on slower venues.

Island’s radical transparency also had cascading effects on market structure. Competing ECNs and exchanges felt pressure to open up their own data to avoid losing order flow. Eventually, the SEC mandated better public disclosure of best bids and offers (Regulation ATS and Reg NMS were influenced in part by the kind of market environment Island created).

Island’s ITCH feed became the blueprint for modern direct market data feeds – today, protocols named ITCH (and its relatives) are used by Nasdaq and others, carrying enormous volumes of real-time information to trading firms. What Island pioneered – the idea that anyone can receive the full depth-of-book data and that fast electronic access is available to those who seek it – is now standard in modern markets. The result was a shift from a market where information was asymmetric to one where sophisticated algorithms could observe and respond to every tick in real time. In short, Island ECN shone a light into the black box of trading, and in doing so, it empowered a new generation of electronic traders.

Legacy Architecture vs. Modern Trading Systems

Island’s technical architecture was pioneering, and its influence is evident in today’s electronic trading systems. Modern exchanges and high-frequency trading (HFT) platforms have, in many ways, built upon Island’s core principles – though scale and technology have advanced dramatically. The sequencer-based design that Island popularized (single-threaded event processing with global sequence numbers) is still a common approach for building matching engines that require absolute determinism. In fact, Nasdaq’s current INET trading platform, a direct descendant of Island, still uses a variant of the Island sequencer architecture, reliably processing billions of messages per day on modern hardware. Similarly, many investment banks and proprietary trading firms have adopted the “sequencer” pattern in their internal matching or messaging systems, often crediting Island’s design as the inspiration. This architecture ensures consistency and fairness – every order sees the same state – which is crucial for markets, and it simplifies fault tolerance via state replication.

However, modern systems have had to evolve for extreme scale and ultra-low latency. Island’s matching engine might have processed on the order of tens of thousands of orders per second at peak in the late ’90s. Today’s equity markets see hundreds of millions of orders and quotes per day, and top-tier trading firms demand that each order be acknowledged and executed in microseconds or faster. To meet these demands, contemporary trading engines typically run on powerful Linux servers with multi-core CPUs and specialized network cards. Instead of DOS-based FoxPro, exchanges now use highly optimized C++ or Java code running on 64-bit architectures, often with techniques like busy-wait loops, NUMA-aware memory management, and lock-free queues to shave off latency. Some systems even bypass the kernel network stack using technologies like DPDK or custom FPGA hardware to send and receive packets with minimal delay. The result is astonishing performance: modern electronic exchanges can handle millions of transactions per second with end-to-end latencies well below 100 microseconds. In some reported cases, single-threaded matching engines achieve under 10µs per order with over 2 million messages per second throughput on readily available hardware. This is orders of magnitude beyond what was possible in Island’s era, yet the lineage is clear – they are refining the same basic approach of an event-driven, in-memory book.

Another evolution is parallelization and horizontal scaling. Island’s original design ran essentially on one core (per matching engine instance) and later partitioned by stock symbol when load grew. Modern exchanges deploy many matching engine instances in parallel, each handling a partition of the market (for example, a subset of symbols or a particular asset class) to distribute the workload. They also employ more sophisticated load balancers and failover mechanisms – for instance, using hot/hot replication where multiple engines actively process and cross-validate each other’s output in real-time, or spinning up new engine instances on the fly in cloud-based environments. Moreover, the network and data distribution layer has grown into its own complex subsystem. What Island did with UDP multicast on a local network, modern markets do with high-bandwidth fiber, redundant data centers, and dozens of proprietary feed channels. The ITCH protocol has evolved into versions that carry every detail of market state with minimal encoding to save bandwidth, and subscribers now use fast fiber or microwave links to get data a few microseconds sooner than competitors. Co-location services (where traders place their servers in the same data center as the exchange matching engines) are now standard, reflecting the reality that even light-speed network travel time is a competitive factor in modern trading – a concept that was far less prominent in Island’s day.

Despite these advancements, the fundamentals of Island’s architecture remain front-and-center in modern system design. The idea of keeping the order book in memory for instant access, of processing events in a strict sequence, of using distributed messaging (now possibly with more reliable multicast or redundant unicast streams) to fan out market data, and of building fault tolerance through redundancy rather than complex failover logic – all of these are hallmarks of today’s high-performance trading platforms. Modern systems have added layers of optimization on top, like faster hardware and refined algorithms, but they still owe a debt to Island’s blueprint.

Lasting Impact on Markets and Engineering

Island ECN’s innovations left a permanent mark on both financial markets and the design of trading systems. On the market side, Island’s success forced the incumbents to modernize or fall behind. By the end of the 1990s, Island was handling roughly 10% of Nasdaq’s trading volume and executing over a quarter-million trades per day, up from just tens of thousands a few years earlier. This explosive growth, driven by superior technology, pressured Nasdaq to respond – eventually leading Nasdaq to acquire Island’s successor (Inet) and adopt its engine. The maker-taker fee model that Island introduced in 1997 is now the prevailing pricing structure at virtually all U.S. stock exchanges (). That model has arguably improved liquidity and narrowed spreads for investors, though it also introduced new complexities (like rebate arbitrage and complex order routing strategies). Island’s early move to decimal pricing accelerated the industry’s shift away from fractions, directly benefiting investors through smaller price increments. And of course, the concept of an ECN – an all-electronic competitor to stock exchanges – paved the way for dozens of alternative trading systems that exist today, fostering competition and innovation in market design.

For engineers and technologists, Island ECN became a case study in building high-performance, resilient systems. Its sequencer architecture has been emulated in trading firms, banks, and even crypto exchanges two decades later. Many of Island’s alumni went on to lead technology teams elsewhere, spreading its engineering philosophy. It demonstrated the importance of simple, robust design: Island’s matching core was less than a page of code, yet it handled massive throughput on modest hardware by focusing on efficient algorithms and minimal overhead. This “mechanical sympathy” approach – deeply understanding hardware and operating system behavior to inform software design – was ahead of its time and prefigured modern efforts like the LMAX Disruptor and low-latency Java/C++ frameworks. Island also showcased how transparency and openness can catalyze an ecosystem: by opening its data and access, it became the hub for early high-frequency trading development, which in turn drove demand for further technical improvements.

In retrospect, Island ECN can be seen as the prototype for the modern electronic marketplace. It took the stock market off the trading floor and put it into code running in a data center, proving that such an approach could be faster, fairer, and more scalable. Today’s markets, with their blinding speed and complex microstructure, trace a direct line back to the innovations of Island. Senior engineers looking at Island’s story can appreciate how a small team in the 1990s solved problems of distributed consensus, real-time processing, and fault tolerance in ways that are remarkably similar to what we strive for now. In short, Island’s legacy endures not just in the market share of its successor systems, but in the very DNA of electronic trading technology. It permanently raised the bar for what trading systems could do, and its spirit of innovation continues to inspire the architectures that power global markets today.

I hope you enjoyed reading this article.

Ariel Silahian