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New Approaches to Modern Trading Room Design

Download new 8-page briefing from Wey Technology and A-Team Group

In today’s financial markets, trading firms face constant pressures in a fiercely competitive environment. Providing best execution and superlative customer service is fundamental to attracting order flow. Running trading operations efficiently is also an imperative, to minimise costs internally and to offer best value to customers. Despite the increase in recent years of automated and algorithmic trading, the human touch is as important as ever in financial trading. Traders bring a wealth of experience to the art of trading, even when it is driven by computer-generated indicators. Their experience and relationships are vital to seeking out liquidity and efficient execution.

Trading operations overall need to be agile and flexible in order to meet ever changing demands and to rapidly exploit business opportunities. The regrouping of traders to react to – even pre-empt – market demand, requires trading positions that can be immediately configured to individual trader’s preference and information needs. At the same time, management and operational costs of trading operations need to be driven down in reaction to the ever downward pressure on margins. Whether costs are reduced through sharing of information services, through a reduction in maintenance costs, or change management costs, it all adds to the bottom line.

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22 Oct 2009
 
Special Report: Market Data - Methods for Managing Costs

Download this special report for FREE now! Click the link below.

market-data-methods-for-managing-costs_150x150It’s said that market data is the lifeblood of financial markets. Today, there’s more of it than ever before, and it’s delivered at sub-millisecond rates. It’s an important resource that needs an increasingly important resource to manage it.

But it’s also said to be the second-highest cost item for market participants after payroll. Needless to say, with costs everywhere under scrutiny, even market data can’t escape the attention of the bean-counters.

Market data managers are facing an impossible trade-off: how to do more, with less. Or are they?

This special report examines how to keep the lid on market data costs, even as timely and comprehensive information becomes more important to a market practitioner’s ability to perform.
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01 Oct 2009
 
Market and Reference Data Converge: A United Front for Data Management

Download new 8-page briefing from the GoldenSource and
A-Team Group

A new industry briefing paper prepared by A-Team Group for GoldenSource looks at the inevitable convergence of market and reference data.

There has been, to date, a real disconnect between the management of market data in all its forms and reference data used in post-trade support functions. But this is changing as banks scramble to get their houses in order. And it has to. Without a holistic approach to data management across the enterprise, financial institutions will continue to struggle to obtain an accurate picture of their investments and their true risk position, or to fulfill the associated requirements of regulatory compliance, client reporting, and other applications reliant upon accurate data from across the enterprise. Without a consolidated view of their data, the businesses are unable to expand product lines or adapt existing products quickly enough for client demand.

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18 Sep 2009
 
MARKET DATA PLATFORMS: Infrastructure to Handle Your Data Challenges

Download new 12-page white paper from the NYSE Technologies and A-Team Group

An exponential expansion of market data volumes in recent years has been caused by the confluence of a number of technology and business drivers – from the emergence of electronic communication networks (ECNs) to the advent of smart order routing systems and algorithmic trading engines. Coupled with the increasingly divergent data sets needed to support complex decision systems, there is now a requirement for high performance data management infrastructures throughout the enterprise supporting high frequency automated trading as well as traditional market data processing functions like securities master file updates, risk analysis, and back-office trade support.

The time is ripe for change in the mechanisms financial institutions use to deal with data, long regarded as the lifeblood of the trading environment, but increasingly seen as an asset that needs to be collected and managed properly – and efficiently – if it is to deliver on its promised benefits. Find out how by downloading your free copy now.

Sponsored by:

NYSE Technologies

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25 Jun 2009
 
Special Report: Complex Event Processing

Download this special report for FREE now! Click the link below.

Over the past couple of years, Complex Event Processing has emerged as a hot technology for the financial markets, and its flexibility has been leveraged in applications as diverse as market data cleansing, to algorithmic trading, to compliance monitoring, to risk management. CEP is a solution to many problems, which is one reason why the emerging marketplace is growing, with many vendor options to choose from.

But CEP is not a solution in itself, and so technologists need to concern themselves with how to integrate it within a complete application architecture, how it can best be leveraged, and how to streamline development and deployment. And with multiple vendor choices, choosing the right CEP approach for the job is an important, and early, activity for firms looking to adopt it.

View the full article on Low-Latency.com

18 Jun 2009
 
BACK TO THE FUTURE: Historical Data in High-Frequency Trading

Download new 12-page white paper from the London Stock Exchange and A-Team Group

The adoption of algorithmic trading by the mainstream has created a requirement for high-quality historical data for development, testing and maintenance of trading strategies.

Until recently the exclusive remit of Tier 1 investment banks, algorithmic trading is becoming democratized as smaller brokerages and boutiques implement increasingly affordable high-performance trading platforms. This gives them the opportunity to differentiate their offerings to buy-side clients.

Key to success here is the quality of data. Nowhere is the adage ‘bad data in, bad data out’ more true than in the area of algorithmic and quantitative trading, where the use of highly granular tick and order book data is crucial to producing trading strategies that perform.

Furthermore, increased regulatory scrutiny means firms need to recreate market conditions current during their trading activities, so as to demonstrate due process in meeting their best execution obligations. This all points to the need for a considered approach to sourcing and managing historical data in support of high-performance trading activities.
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11 Jun 2009
 

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