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Catalyst thoughts on efficient Trading infrastructures, scalability and latency challenges:Executive Summary

The current increasingly fragmented market environment is forcing market participants to seek low latency access to liquidity from multiple venues and compete to deploy low latency architectures that can handle the explosion in data volumes, provide best execution and run algorithmic trading strategies. This becomes a difficult balancing act between long-term business goals and the massive investment required into low latency trading infrastructures.

An efficient trading infrastructure with consistently low and reliable latency can enable traders to exploit market conditions. Estimates suggest that just a millisecond improvement in latency could be worth $100 million a year to a large investment bank.

Key to the success of the overall optimisation of the trading environment is to understand occurring latencies from an end-to-end perspective and to translate this understanding into efficient actionable steps to gain an advantage over competitors.

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BackgroundThe current trading environment is increasingly characterised by trading strategies in which decisions are typically not taken by human beings, but by computers that are programmed to make decisions autonomously and in time periods that are measured in micro seconds. The race for speed is more critical than ever. Price arbitrage opportunities exist for small fractions of a second and it is during these short periods – when the fastest market players sweep up the best prices leaving less profitable trades for others.

To maintain competitive edge, financial institutions must optimise trading speed to the micro second. This coupled with the fact that current market forces are pushing unprecedented volume growth.

Automated trading applications have to deliver against a demanding set of criteria:

1. High Performance, Low Latency: The time it takes to reach to market movements is critical. Micro seconds matter.

2. Scalable: Market data volumes are skyrocketing. Trading applications have to have the capacity and scalability to handle it.

3. Flexible, Rapid Deployment: The market changes fast and your applications need to change just as fast.

4. Market Fragmentation: Advances in trading technologies and new regulations such as MiFID and Regulation NMS have opened the door to market fragmentation.

“ To maintain a competitive edge, financial

institutions must optimise trading speed to the micro

second ”

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Bottlenecks can be caused by a number of factors, such as MTF’s/traditional Exchanges, inefficient legacy trading applications and infrastructures, memory and I/O access, network interfaces (i.e. Serialisation delays, Propagation delays, etc.), caching, raw CPU processing, and the inefficient flow of data traffic throughout the server platform.

An efficient trading infrastructure with consistently low and reliable latency will enable traders to exploit market conditions. With estimates suggesting that just a 1 millisecond improvement in latency could be worth up to $100 million a year to a large investment bank, the size of the prize should not be underestimated. The market as a whole is moving towards lower latency. Failure to act will not leave firms standing still; their competitive position will be compromised by those who invest wisely.

Challenges

In our experience the challenge areas that effect scalability and performance within trading environments can be split into four different categories:

• Project/Deployment • Trading Application • IT Infrastructure • Connectivity and Network

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Project/Deployment challenges

Latency Measurement Challenge: Effective measurement requires data to be captured from many points across the trading platform. A conceptual measurement architecture consisting of network, operating system and application level measurement points, and a tool for consolidating and analysing the captured data can be defined. Specialist vendors provide parts of this measurement solution, but few come close to providing an end-to-end measurement toolset. An accurate and detailed measurement solution will always be a bespoke, trading platform-specific implementation.

Reducing time to market: Flexibility to adapt to change and for reducing time to market is a key ingredient in today’s rapidly changing trading environment. The emergence of new trading venues and electronic markets, mergers and consolidations of traditional markets, physical movement of data centres and introduction of new order routing and extranet services has put market participants under pressure to adapt quickly or to loose out.

Risk Management: In order to mitigate market risk and counterparty risk, and enforce trading policy compliance, applications have been supplemented with limit checking and other risk management functions. However, a side-effect of these measures is that they also increase latency levels. Advanced information intelligence analytics can be applied to trading and other data to detect behaviour indicative of fraud, market manipulation, insider trading and other proscribed activities.

Regulations: In the past few years several major regulatory initiatives have been introduced, such as Reg ATS/NMS in the US or MiFID in Europe, which has led to increasing fragmentation in the region. Regulations set the rule by which market participants leverage the new market landscape. The effective implementation of new regulations can translate into a significant advantage for a market participant.

Keeping an eye on the goal: Juggling important BAU deliveries against deliveries on a more strategic level can make prioritisation of projects, which will help to improve the banks overall long-term offering, difficult. This affects available resources, budgets and invested efforts. It is important to keep an eye on the overall goal and to apply sensitive prioritisation.

Trading Application Challenges

Shared Execution Platforms: Generic Order Management systems are not designed for latency sensitive trading and add unnecessary delays to the roundtrip execution time. Many banks use the same Order Management solution for the execution of latency sensitive (DMA / Algorithmic) as for non speed sensitive trades. It is paramount to separate execution platforms within a trading environment and route latency sensitive orders through a lightweight, independent EMS to the market.

“ It is paramount to separate execution

platforms within a trading environ-ment and route la-

tency sensitive orders through a lightweight,

independent EMS to the market. ”

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Best Execution technology: The best execution component of the used trading platform is of significant importance when routing orders to the vast variety of execution venues, such as MTFs, traditional exchanges or Dark Pools. The timeframe for the decision making process in establishing the best price amongst all venues is crucial in taking advantage of price arbitrages. Build vs. Buy decisions can considerably impact the success of a trading area.

Component independence: In most banks, development and testing of a singular trading component becomes difficult through the interweaved front-office environment and its constrained release cycles. It is important to ensure the independence of the trading components from each other to add delivery flexibility.

Messaging Middleware: Messaging Middleware is the essential communications glue that binds trading applications together, and has to address the need for low latency and high throughput, while guaranteeing reliable delivery and structured sequencing. The effective deployment and use of the middleware is critical.

Caching Methodology: It is important to select the caching methodology appropriate to your application environment. Each cache technology has its own performance characteristics. And applying an inappropriate caching methodology to your applications will not ensure scalability.

Recovery Time: Often overlooked, recovery time for trading systems frequently causes trading firms to lose valuable additional time after a failure. Many firms have not moved key trading applications to an architecture using hot backups.

IT Infrastructure Challenges

Shared IT infrastructure: A typical bank’s wide area network carries all kinds of traffic, such as voice, video and e-mail, which can contribute to delays or unpredictable performance in trading applications. Because the majority of a bank’s systems do not require the highest level of performance, low latency remains a niche requirement for automated trading.

High Latency Transactions: Persistence models that rely on a relational database and/or underlying disk persistence are at least an order of magnitude slower than memory based models. Algorithmic, DMA and program trading systems all depend on ultra low-latency transactional updates to achieve both performance and resiliency targets.

Scalability: Most of today’s systems require extensive hardware and/or software configuration work to scale up to greater volumes. Adding capacity dynamically during the trading day is almost unheard of.

Data: After deploying the latest technology, including clustered servers or grid computing, results still may be disappointing. Now the throughput limitation for most applications is the rate at which they can be fed the data they

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need to complete their processes. Market demand resulting from program trading, statistical arbitrage, risk and regulatory requirements have driven the requirement for applications to analyse vast amounts of market data and many people have found that mainstream database technology is not a viable solution for tick data, time series data, static-data and real-time analysis.

Database Contention: Database connections cannot scale cost effectively to service a large number of active concurrent connections. When trading systems are scaled-up by adding CPU horsepower and parallelisation, database resource contention quickly causes I/O bottlenecks.

High Availability Data Replication: Disk-based vendor solutions are slow (file system replication) or expensive, and custom solutions are difficult to build and maintain. No serious trading operation can forgo these requirements, so this problem generally ends-up taking a large part of budgets (for hardware, vendor software, and development time).

Priority of Data: Analysing the priority or urgency of data frequently highlights delays introduced by non-critical ancillary systems. IE Operation run queries on the Primary Database or risk reporting can be throttled to once a minute.

Connectivity and Network Challenges

Data Location: Inherent in network latency is that it exists as a result of the distance between the trading firm and the execution venue, and the type of telecommunications media. Data centres are traditionally based in locations outside city centres. Low latency trading requires data servers (and execution platforms) to be hosted in close proximity to the execution trading venues, as this reduces latency by reducing distance. Most network providers and trading venues now offer hosted proximity solutions in their own data centres.

Hardware: Gateways or routing devices take time to examine and either change or move the packet along to what could be multiple hops in the process. The network hardware should consist of ultra low latency switches or Infiniband infrastructure which will provide sub-microsecond switching. Also low latency security products will be required, such as firewalls for interconnects between DC/Exchange.

Connectivity: The choice of connectivity vendor, their fibre optics network, their maximum available bandwidth at any time of the day coupled with the high resilience of the network for an optimum uptime can significantly affect transmission latency.

The vendor selection and the Integration of the chosen vendor into the Network architecture becomes critical for the Trading participant.

“…optimisation of the trading environment

is key… ”

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Conclusions In 2010, the challenge faced by many trading firms will be to optimise their IT and connectivity infrastructures to support diverse business models within a fragmented, multi-market execution landscape. The Trading environment in a single company might be driven by different business models and different strategies, which have their own low latency and connectivity requirements. In some areas, i.e. to serve Agency clients, latency of 10-15 milliseconds, might be acceptable. In other areas, which execute high frequency trading strategies however, the company seeks latency in the low single digit micro seconds.

By and large optimisation of the trading environment is key to ensuring the best overall latency profile, so that requisite latency is achieved to each liquidity venue. Beating the latency goal at one venue, while missing it at one or more others is a net fail for a trading firm.

In order to assess the existing trading environment to reduce latency requires both a broad understanding of its causes and a detailed understanding of each of the technologies involved in the end-to-end trading infrastructure. The goal

is to gain an understanding of where significant levels of latency occur and which parts of the chain offer the best opportunities for latency reduction.

Once latency is understood from an end-to-end perspective, target “market data-to-execution” times can then be set for each trading venue. Some will be easier to achieve than others and the acquisition of market data is paramount.

The internal trading environment can be split into five major application or infrastructure components. Four of these – Data Acquisition, Trading Application, Market Access and Support Functions – must be tied together with the fifth, a Data Messaging Backbone, that can distribute vast volumes of market data and other messages with consistently low latency. Underpinning this architecture is a network that comprises the data centre LANs and wide area network links, supported by scalability of the entire software and hardware architecture.

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The performance of each individual application that is in the critical data flow path must be evaluated.

The difficulty lies in the fact that no two trading operations are the same, and that therefore there is no one solution to fit all. The latency profile of a major sell side firm engaged in trading in global markets and across multiple asset classes is going to be very different from a specialist hedge fund focused on trading a subset of securities on one or two exchanges.

For each firm, very different trading applications and IT infrastructures would be deployed, with different requirements in terms of market data, telecommunications, inter-firm networking, data centre hardware, storage, and operating software. And, of course, very different applications software and support requirements.

Very few IT departments have a holistic view of the end-to-end picture and are not organised nor prepared to provide the services needed to change the existing status quo. The key is the collaboration between the various teams, which provide the end-to-end service. The facilitation and the ongoing management of this collaboration is where real value can be added to achieve high levels of efficiency in the trading environment and significantly reduce latency.

Ultimately the differentiation between a successful and a failed trading area will lie in who can enhance their existing trading environment most efficiently and profitably without spending the gains on infrastructure.

“ The facilitation and the ongoing

management of this collaboration is where

real value can be added to achieve high

levels of efficiency in the trading

environment and significantly reduce

latency. ”

For more information please contact:

James Morgan T: +44 (0)870 901 4155 M: +44 (0)777 618 0836 E: jamesmorgan@catalyst.co.uk

Shareque Husain-Syed T: +44 (0)870 901 4155 M: +44 (0)789 446 6600 E: sharequehusain-syed@catalyst.co.uk

Catalyst 167 Fleet Street London EC4A 2EA www.catalyst.co.uk