Thoughts on Efficient Low latency Trading Infrastructures, essential components required to compete in the market.

1. 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.
Catalyst: helping you to
significantly enhance the return
on your technology assets.
If you’d like to hear more, contact
us on: +44 (0)870 901 4155.
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2. Background
The 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
“ To maintain
a competitive
less profitable trades for others. edge, financial
institutions
To maintain competitive edge, financial institutions must optimise trading speed
to the micro second. This coupled with the fact that current market forces are must optimise
pushing unprecedented volume growth. trading speed
Automated trading applications have to deliver against a demanding set of criteria:
to the micro
1. High Performance, Low Latency: The time it takes to reach to market
second
”
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.
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3. 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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4. 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.
It is paramount to
Reducing time to market: Flexibility to adapt to change and for reducing time separate execution
to market is a key ingredient in today’s rapidly changing trading environment. platforms within
The emergence of new trading venues and electronic markets, mergers and
consolidations of traditional markets, physical movement of data centres a trading environ-
and introduction of new order routing and extranet services has put market ment and route la-
participants under pressure to adapt quickly or to loose out.
tency sensitive orders
Risk Management: In order to mitigate market risk and counterparty risk, through a lightweight,
and enforce trading policy compliance, applications have been supplemented independent EMS
”
with limit checking and other risk management functions. However, a side-
effect of these measures is that they also increase latency levels. Advanced to the market.
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.
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5. 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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6. 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
“
…optimisation
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). of the trading
environment
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. ”
is key…
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.
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7. Conclusions
In 2010, the challenge faced by many trading firms will be to is to gain an understanding of where significant levels of
optimise their IT and connectivity infrastructures to support latency occur and which parts of the chain offer the best
diverse business models within a fragmented, multi-market opportunities for latency reduction.
execution landscape. The Trading environment in a single
company might be driven by different business models Once latency is understood from an end-to-end perspective,
and different strategies, which have their own low latency target “market data-to-execution” times can then be set
and connectivity requirements. In some areas, i.e. to serve for each trading venue. Some will be easier to achieve than
Agency clients, latency of 10-15 milliseconds, might be others and the acquisition of market data is paramount.
acceptable. In other areas, which execute high frequency
trading strategies however, the company seeks latency in The internal trading environment can be split into five major
the low single digit micro seconds. application or infrastructure components. Four of these –
Data Acquisition, Trading Application, Market Access and
By and large optimisation of the trading environment is key Support Functions – must be tied together with the fifth, a
to ensuring the best overall latency profile, so that requisite Data Messaging Backbone, that can distribute vast volumes
latency is achieved to each liquidity venue. Beating the of market data and other messages with consistently low
latency goal at one venue, while missing it at one or more latency. Underpinning this architecture is a network that
others is a net fail for a trading firm. comprises the data centre LANs and wide area network
links, supported by scalability of the entire software and
In order to assess the existing trading environment to reduce hardware architecture.
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
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8. 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
The facilitation would be deployed, with different requirements in terms of market data,
and the ongoing telecommunications, inter-firm networking, data centre hardware, storage,
and operating software. And, of course, very different applications software
management of this and support requirements.
collaboration is where
Very few IT departments have a holistic view of the end-to-end picture and
real value can be are not organised nor prepared to provide the services needed to change the
added to achieve high existing status quo. The key is the collaboration between the various teams,
which provide the end-to-end service. The facilitation and the ongoing
levels of efficiency management of this collaboration is where real value can be added to
in the trading achieve high levels of efficiency in the trading environment and significantly
reduce latency.
environment and
significantly reduce Ultimately the differentiation between a successful and a failed trading area
will lie in who can enhance their existing trading environment most efficiently
latency.
” and profitably without spending the gains on infrastructure.
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 8
www.catalyst.co.uk