Speeding trade analytics

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1 Reuters Tick Capture Engine Past, present and precise, tick-by-tick. Whether you are looking to capture and analyze streaming real-time and historical market data for algorithmic execution, bring tested trading strategies to market faster than the competition, provide your traders with pre- and post-trade analysis, or ensure best execution compliance, Reuters Tick Capture Engine is the easy-to-implement solution. Reuters would be delighted to invite you to of our labs located in a major city near you. Come in and test your analytics using Reuters data on the Reuters Tick Capture Engine or simply see a demo of this product in action. For more information: Send us a sales enquiry at Read more about our products at Find out how to contact your local office Access customer services at Speeding trade analytics Reuters uses your data in accordance with Reuters privacy policy in the privacy footer at Reuters Limited is primarily responsible for managing your data. As Reuters is a global company your data will be transferred and available internationally, including in countries which do not have privacy laws but Reuters seeks to comply with its privacy policy. If you wish to see or correct data held on you or no longer wish to receive information about developments in Reuters Group products and services, such as free trials or events or you wish to change your preferred method of receiving a communication, please esupport.global@reuters.com writing Personal Details in the subject title. Reuters All rights reserved. Reuters and the sphere logo are the trademarks or registered trademarks of the Reuters group of companies around the world. Published by Reuters Limited, The Reuters Building, South Colonnade, Canary Wharf, London, E14 5EP. IM-Apr Powered by Vhayu Technologies

2 Analytics for competitive advantage - Analyse ever-increasing volumes of market data in milliseconds - Ensure accuracy and low latency with Reuters quality data and reliable feeds - Plug in Reuters DataScope Tick History for 10 years of global cross-asset, tick-by-tick data - Leverage Reuters Market Data System for content integration, permissioning and publishing streaming analytics - Send trading alerts to order management systems and execution platforms - Publish streaming analytics [e.g. Volume Weighted Average Price (VWAP)] to trading desks and applications - Query historical data to develop and test new trading strategies - Plug in existing content, analytics and execution platforms. Data storage for analytics, compliance and other uses - Capture and store every tick of data from minutes to years - Capture and store streaming real-time ticks, tick histories and reference data - Resiliency built in for zero tick loss - Store and apply corporate actions to tick-by-tick data - Store raw data formats and/or derived analytics - Apply data compression to reduce total cost of ownership. Reuters Tick Capture Engine (RTCE) provides you with a competitive edge by calculating your customised or pre-configured analytics at the speed of the market so you can react in milliseconds to changing market conditions and execute your strategies even more quickly. It facilitates pre-trade and post-trade analysis in search of best execution by analysing the market data update stream and, in parallel, storing massive amounts of tick-by-tick, real-time and historical data. It captures real-time, historical and corporate action data in a central location, making it possible to query massive quantities of data for quantitative analysis and compliance. Reuters data quality, integration and support Finding the right technology is only part of the challenge our customers face. Maintaining data quality throughout the trading lifecycle is also a major hurdle. Reuters can pull it all together for you. We have decades of experience integrating and distributing market data. And we have applied this knowledge to integrate our content into RTCE, ensuring data quality out-of-the-box. Streaming real-time feeds and tick histories Reuters Data Feed offers the broadest set of global, cross-asset data. We integrate this global tick-by-tick content into the Reuters Tick Capture Engine and apply Reuters DataScope corporate actions to it. Our new Reuters DataScope Tick History service provides 10-year, tick-by-tick history-- fully integrated into RTCE. This enables you to run quantitative analysis on deep histories and spot market correlations that you can profit from. RTCE pulls all this data from 10 years ago to the last second and makes it all available for programmatic analysis. Ultra low-latency exchange feeds Reuters Data Feed Direct delivers ultra low-latency (total latency less than one millisecond) exchange feeds for automated trading environments. You can apply the analytic power and storage efficiency of RTCE to Reuters Data Feed Direct order books. What about internal and third-party data sources? Many of our customers are already using Reuters Market Data System (RMDS). RMDS is an ultra-low latency market data integration platform providing resilience, permissioning and publishing infrastructure to banks, brokers and hedge funds worldwide. RTCE integrates with the RMDS platform to access internal and thirdparty data. RMDS permissioning also enables tracking and compliance. RTCE leverages RMDS distribution to publish streaming analytics to all users. Reuters 24x7 service Reuters global presence means that our support is world-class. Reuters global service centres can meet your need for 24x7 support and Reuters account teams are local and available to you to respond rapidly to any of your concerns. We can provide one-stop problem resolution for RTCE as well as the associated data and platforms. Faster, smarter enabling best execution RTCE seamlessly integrates into your trading environment, supporting realtime distribution of streaming trade signals to multiple in-house or thirdparty applications simultaneously. For example, RTCE can generate trade signals to your Order Management System (OMS), facilitating program trading based on algorithmic strategies so you can respond very quickly to short-lived market discrepancies and opportunities, and then accept executed trade data back into the service. Example of smarter, faster execution: A new strategy is validated in RTCE before being deployed in production. A measurable market profile (e.g. volatility) is detected in real-time by RTCE, triggering an alert to the OMS/execution platform to deploy a particular strategy that is typically successful in a volatile market. A proprietary benchmark developed in RTCE is matched by real-time market data; RTCE sends a FIX message directly to your OMS, which then forwards the FIX message for execution. 1 Reuters Tick Capture Engine Reuters Tick Capture Engine 2

3 Streaming analytics More and more applications require reliable streaming analytics to play their part in the trade lifecycle. At the same time, update rates are exploding, making it more difficult for individual applications to continue to perform analytics locally. Our customers need a platform with error-free acceptance of high update rates; and one that can be used to write or plug in existing analytics, and publish streaming analytics to multiple users and applications. RTCE and RMDS provide that platform. RTCE handles the high-end analysis. And because RTCE integrates with RMDS, you can publish those streaming analytics into all the applications that subscribe to RMDS today. In addition to being able to stream any analytics you create in RTCE, Reuters has also released a Volume Weighted Average Price (VWAP) analytics package including a pre-configured Reuters Tick Capture Engine to calculate VWAP based on industry standards. RTCE VWAP Module Use RTCE and low-latency distribution via RMDS to send streaming real-time VWAP analytics to your traders and sales desks, to monitor executions and adjust trading strategies in real time. VWAP is calculated using streaming market data from major market centres and is the most widely used performance measurement of best execution and the most prominent algorithmic trading strategy. An easily customizable, white box VWAP module comes with RTCE. Easy integration with RMDS allows these calculated VWAPs, based upon streaming market data, to be used by any application on RMDS including the Reuters 3000 Xtra desktop. Beyond streaming VWAP, customers can request VWAP for particular time frames (e.g. 10am until 1pm) to mirror the timeframe during which a trade was targeted for execution. There is a lot of complexity involved in creating a VWAP module each exchange reports trade types and times in different ways, across multiple logical fields or sometimes not at all. This is important because in measuring VWAP certain trades (e.g. block trades) are excluded. The RTCE VWAP module is pre-configured to capture the trade type and exclude trade types, like block trades, that are typically not used in VWAP calculations. If you need to set your VWAP to your customers benchmark or create your own measure, you can amend these parameters to create a proprietary VWAP based on including or excluding particular trades. Stay ahead with new, tested, trading algorithms RTCE captures and stores real-time trades and additional historical and proprietary data in one place, allowing you to build historical market(s) views for detailed analysis and testing when designing and vetting new strategies. Trade and quote history from the Reuters DataScope Tick History service is integrated into RTCE, complementing streaming market data. This creates comprehensive market views, allowing you to develop and test strategies without time-consuming queries of traditional databases that are not optimised for market data and the update rates that we see today. This allows historical trade data to be queried and analysed on the fly, uncovering historical trends. Using these trends, you can build trading strategies that react to discrepancies in current markets. Building-block analytics allow you to get started or quickly create your own proprietary approach. With accurate market data captured and easily accessible, you can build new algorithmic trading strategies choosing from our library of application programming interfaces (APIs) and scripting and interfaces to statistical packages including Excel, MATLAB and S-PLUS. And with easy integration to third-party or proprietary customerdeveloped analytics, you can back-test these strategies as well. A deeper perspective RTCE Order Book Analyser RTCE Order Book Analyser examines the entire depth of book by bid, ask, volume and market maker for every exchange. Use this market-sweeping algorithm to seek out the best price across different market centres with integration to order books from Reuters Data Feed Direct. Real-time order book analysis supports smart order routing. The Order Book Analyser also provides a query-based, intra-day snapshot of where the markets were at a given time and answers the question: Did I get the best price? It is an accurate and costeffective means of producing execution performance analytics for compliance and customer-reporting purposes. You can examine the depth of book over time, discerning trading patterns, and develop corresponding trading strategies. RTCE offers the following functionality and integration: - Reuters consolidated and direct feeds (i.e. Reuters Data Feed) - Reuters DataScope Tick History 10-year, tick-by-tick history - Sources content from open, lowlatency Reuters Market Data System (RMDS) integration platform including access to over 50 exchange, broker and vendor data sources, as well as internal content - Process streams of real-time market data updates for real-time analysis, including concurrent analysis of real-time and stored historical data - Streaming analytics published to Reuters Market Data System - High performance capture and store of real-time and historical data in a central location, available for analysis with fast response to queries across massive amounts of data - Reuters Tick Capture Engine support for capture, storage and analysis of order books - Industry-strength application programming interfaces (APIs) and scripting to build ultra low latency in-process analyses (including customized trading algorithms) - Alerting capability to enable real-time decision-making for trading applications - Graphical user interface tool to view the data, build customized trading strategies and backtest those strategies - Interfaces to standard statistical packages (Microsoft Excel, MATLAB and S-PLUS) - Dynamic customizable VWAP module - Resiliency: Reuters Tick Capture Engine fault tolerance & stream persistence, as well as RMDS no tick loss source mirroring 3 Reuters Tick Capture Engine Reuters Tick Capture Engine 4

4 Reuters Tick Capture Engine in action Reuters Tick Capture Engine Collection Storage Analysis Reuters real-time data feeds including direct exchange feeds Pre-configured VWAP Reuters Market Data System for third-party and inhouse data Real-time data (e.g. trade data, market data, order books, etc) Custom analytics Third-party and in-house analytics libraries Trade data from Order Management Systems Ad-hoc query Reuters DataScope Select for corporate action, end of day prices etc End-of-day and reference data Event analytics (e.g. trade signals) Streaming analytics to RMDS Tick histories including: - Reuters Datascope Tick History (10-year history) - Exchange histories - Customer databases Historic data (build years of tick-data history) Microsoft Excel MATLAB and S-PLUS Fault tolerance, heartbeat monitoring, Automatic failover, DACS permissioning 5 Reuters Tick Capture Engine

5 Brokers: Head of trading, head of proprietary trading, head of digital markets, quantitative analyst. Hedge funds: analyst, head trader, quantitative analyst. Buy side institutions: head of trading, portfolio manager, quantitative analyst. Quantitative analysis is a body of mathematical and analytical techniques to query (often massive volumes) of historical trade and market data. It is employed to identify investment opportunities (known as idea generation ) and to optimise trade execution. RTCE benefit: Provides an analytics platform to crunch through massive amounts of historical data and bring new trading strategies to market more quickly (see section How RTCE meets these needs ). Examples of quantitative analysis Quantitative analysis is usually quite complex and is often a multi-step process. It is carried out by quantitative analysts or financial engineers whose background is highly mathematical (e.g. PhDs) and who are applying those mathematical techniques to financial markets. The following are very simple examples of quantitative analysis to illustrate this use case. Index correlation Correlate every constituent of the NASDAQ 100 for the last five years against the NASDAQ 100 index. Identifying the typical market conditions or times (e.g. market open or market volatility etc) when the correlation is strongest between the constituents and the index. Based on these results, build trading models that can anticipate price movements based on correlation and market conditions. Liquidity/market impact analysis: The full historical depth of order books from selected market centres are analysed over time to discern patterns. For example, trading from the bottom of the book has less of a market impact at a given time in the trading day. Analysts then develop strategies reflecting this unique market perspective. The following are examples of trade strategies that use quantitative analysis: - Best execution - Statistics arbitrage - Distressed securities - Index or sector correlation - Basket covariance - Volume forecasting models - Timing risk modeling. Quantitative analysis requires both an analytics platform (RTCE) and a history of tick data. Reuters can provide customers with tick data and order books from our real-time feeds (RDF and RDF-Direct) as well as 10-year, tick-bytick histories (DataScope Tick History) seamlessly integrated in RTCE. Reducing time-to-market with new strategies provides a crucial competitive edge. RTCE helps by analysing massive volumes of data quickly. With accurate historical market and real-time data captured in RTCE, customers can build quantitative analyses using RTCE s library of application programming interfaces (APIs) and scripting as well as interfaces to standard financial statistical packages including Microsoft Excel, MATLAB and S-PLUS. And with easy integration to third-party or proprietary analytics libraries, customers can continue to use tried and trusted analytics and can get up and running with remarkable speed. What is strategy validation? Strategy validation aims to test a new strategy before it is deployed to see if it behaves as expected in real market conditions. RTCE benefit: Enables a customer to analyse a new strategy against real-time data and analytics before deploying that strategy in production. This avoids costly errors that were not anticipated in analysing the historical data. Two or more algorithmic strategies could be run side-by-side to see which is best. Quantitative analysis and strategy validation user scenarios

6 Brokers: head of trading, head of proprietary trading, head of digital markets. Hedge funds: analyst, head trader. Buy side institutions: head of trading, portfolio manager. Pre-trade analysis involves analysing potential trade opportunities by scanning market data in real-time for conditions that meet a trading strategy. Specifically, software and applications scan major market centres for real-time prices and unusual trading activity which can signify an important trend or event. Examples of pre-trade analytics which address this use case include Bid/Ask Spread, Volatility, Benchmark (e.g. VWAP), Liquidity, Optimal Horizon. Or, Pre-trade analysis determines how to execute a selected strategy in real-time. It identifies the things that would get best execution, give the best price or have the least impact on the market: which market centre, what size trade, trading from the top of the book or the bottom. RTCE benefit: Use RTCE to analyse trade opportunities in milliseconds by scanning market data in real-time for conditions that meet a trading strategy. And get your order to market more quickly that the competition. Examples of trading strategies that rely heavily on pre-trade analysis - Statistical Arbitrage An attempt to profit from pricing inefficiencies that are identified through the use of mathematical models. Statistical arbitrage attempts to profit from the likelihood that prices will trend toward a historical norm. Unlike pure arbitrage, statistical arbitrage is not without risk. - Index arbitrage A strategy designed to profit from temporary discrepancies between the prices of the stocks comprising an index and the price of a futures contract on that index. By buying either the stocks or the futures contract and selling the other, an investor can sometimes exploit market inefficiency for a profit. - Basket trading A basket is a group of several securities created for the purpose of simultaneous buying and selling. Baskets often play a role in index arbitrage, program trading and hedging. - Transaction Cost Analysis Make better decisions by referencing the expected total cost of trading (e.g. price, commissions, market impact etc) of a prospective transaction. This is achieved pre-trade by comparing that transaction against previous market behavior for similar trades. - Algotithmic Execution Pre-trade analysis requires high-speed analysis and high-speed data handling of streaming real-time market data and historical data. Because of its proprietary technology, RTCE can process all this data (which is updated in milliseconds), calculate pre-configured algorithms (such as VWAP) or customers own algorithms to do the pre-trade analysis, and then shoot out trade signals to traders or orders directly to an OMS system. RTCE enables you to react in milliseconds to changing market conditions and execute your algorithmic strategies more quickly. Market trends Brokers are developing pre-trade analytics using their proprietary algorithms to help buy-side customers determine the best trade strategy to use. These tools will help traders calculate the expected market impact of potential trades. Buy-side traders want immediacy. Pre-trade analytics are tools that can be used at the trader s desktops rather than having to call a sales trader for the information. These analytics determine, for example, that a particular trade can be executed with less market impact. In the past, post-trade analytics were all that were available: analysts and portfolio managers would look at past trends to determine potential future outcomes. There is more emphasis on pre-trade analytics today because of the increase in electronic trading; instead of sharing assumptions on the market impact of a trade with a sales trader, buy-side firms have to interact with a black box. Pre-trade analysis user scenarios

7 Brokers: head of trading, head of proprietary trading, head of digital markets, Hedge funds: analyst, head trader Buy side institutions: head of trading, portfolio manager Algorithmic trading is a machine-driven approach to trading, with the goal of reducing commission and other costs and, ideally, improving the time of execution by reducing latency. At its simplest, it means placing a buy or sell order of a defined quantity of a given asset into a quantitative model that automatically generates the timing and the size of the order based on goals specified by the parameters and constraints of the algorithm. Algorithmic trading is used as an execution pipeline. Brokers design trade strategies that they then offer to their buy-side customers. These algorithms are designed to meet a number of possible goals such as best execution, best price, mid price, minimal market impact, VWAP and iceberg. The buy-side selects the trade algorithm he or she deems to be the best to meet a given goal and places the order directly into the market through their broker s account. This eliminates the issue of latency. Large customers have direct access to markets, but trade through their broker s account. Brokers can then focus on building relationships and doing exotic trades, not being mere execution engines. Algorithmic trading requires lots of streaming market data to be compared in real-time against historical data, as well as high-speed analysis and data handling. Because of its proprietary technology, RTCE can load all this data (which is updated in milliseconds) into its persistence database, calculate pre-configured algorithms (such as VWAP) or customers own algorithms, shoot out trade signals to traders or orders directly to an OMS system, and store the market data as well. RTCE comes pre-loaded with standard analytics (VWAP) enabling a customer to get up and running quickly. We deliver a white box VWAP giving customers the easy ability to re-configure time frames and include or exclude any instrument type. Easy integration with RMDS allows these calculated VWAPs, based upon streaming market data, to be used by other applications in a low latency environment. With accurate market data captured and easily accessible, customers can also build new algorithmic trading strategies choosing from RTCE s library of application programming interfaces (APIs) and scripting and interfaces to statistical packages including Excel, MATLAB and S-PLUS. And with its easy integration to third-party or proprietary, customerdeveloped analytics, customers can back-test these strategies as well. Market trends Algorithmic trading promises to cut costs, eliminate human error, and boost trading efficiency and productivity. Nevertheless, successful implementation of algorithmic trading is no easy task and unbiased expert advice is needed. Algorithmic trading has become a part of the mainstream in response to buy-side traders need to move large blocks of shares with minimum market impact in today s complex institutional trading environment. Algorithmic Trading user scenarios Increased liquidity in the electronic marketplace is key. The acceleration of exchange consolidation, and growth in program and algorithmic trading all are contributing to a dramatic change in how firms find and tap liquidity. An array of technologies, including RTCE, support this continuing shift. Event-driven architectures and applications will draw on real-time, integrated market data to identify liquidity sources. Increasingly sophisticated buy-side firm (especially hedge funds) are looking to build their own algorithms and are a target for RTCE as well.

8 Brokerage: head of trading, head of digital markets, head of trading sales, head of proprietary trading desks, director of compliance Hedge funds: head trader, quantitative analyst Buy side institutions: head of trading, portfolio manager In order to improve execution performance it is necessary to measure the actual outcome of your trades against your expectations of that execution. Simply put post-trade analysis helps you make better trading decisions. Most post-trade analysis is focused on Transaction Cost Analysis (TCA) to uncover the total cost of trade decisions. An holistic approach to TCA includes not only commissions paid, specified benchmarks (e.g. VWAP) against realized execution and market impact, but also a comprehensive analysis of whether the execution ultimately meets the strategic goal of the portfolio manager. And the results of post-trade analysis are fed back into future trading decisions to fine-tune the trading process. TCA may be undertaken by the broker to see how well he or she is performing; it is also increasingly undertaken by the buy-side to compare broker performance. A broker who isn t performing well will lose business. Examples of post trade analysis Real-time correction to trading based on posttrade analysis Post-trade analysis is being used in real-time by the sell side. The result immediate results on how you did; did I obtain best execution; instantaneous feedback while working an order allows an order to be re-worked if a strategy is not working. During the hours it can take to complete the order, the strategy can be switched or a wholly new approach used. Transaction Cost Analysis Transaction Cost Analysis isn t simply a matter of comparing trades against where the market was when the trade was execution. RTCE can certainly enable buy side and sell side to monitor executed trades against the market; but it also delivers a platform where you can store and analyse trade and market data to provide an holistic view of TCA based on your strategy objectives. Reuters VWAP Module RTCE comes pre-loaded with standard analytics (VWAP) enabling a customer to get up and running quickly. We deliver a white box VWAP giving customers the ability to reconfigure time frames easily and include or exclude any instrument type. And with RTCE s patented technology for high performance industry strength application programming interfaces (APIs) and scripting, customers can build their own ultra lowlatency in-process analyses, making it possible to develop customised post-trade analytics. Post-trade analysis demands the ability to process massive volumes of streaming tick data, compare it to trade data, and perform high-speed analysis and data handling. RTCE delivers these functions easily to an existing market data environment. Even more relevant to post-trade analysis is the RTCE persistence database and RTCE s scalability, which enables a customer to store almost any amount of market data for any amount of time. Many customers will need to look at the full order book for U.S. equities. This represents 30 gigabytes of data per day. Combining streaming market data from major market centres with corporate action data from Reuters DataScope Select creates a customerspecific tick-by-tick database suitable for any type of post-trade analysis. Market trends While the buy side can technically handle a portion of its order flow, the question then becomes: how well is it doing this? This is why firms are getting into transaction cost research and why there is a burgeoning business in predicting what a trade should cost and where to send it (pre-trade analytics). It s also why it is important today to examine how well that strategy was implemented by comparing the results to various benchmarks (post-trade analysis). As the buy side takes over more of the executions, are the traders on the buy side outperforming the sell-side traders? Are the buy-side traders outperforming the broker algorithms? The buy side needs to figure out the best execution strategies for reducing slippage. Post-trade analysis/transaction Cost Analysis user scenarios

9 Brokers: head of compliance, head of trading Buy side: head of compliance, head of portfolio trading concerned with best execution analysis. Compliance is the state of being in accordance with the relevant regulatory authorities and their requirements. In the context of financial services, compliance is a big topic with two major regulations relevant to trading being introduced soon. In Europe, the Markets in Financial Instruments Directive or MiFID comes info force in November 2007 and in the US, the regulation to modernize the national market system, or Reg NMS will be introduced in June Most large financial services companies have compliance teams whose role is to take an independent stance in making sure that the company is following all the necessary rules and regulations. RTCE benefit: It enables you to achieve best execution, analysis performance and store trade history for as long as required by regulation (see section How RTCE Meets These Needs ). Examples of compliance regulation 1. Reg NMS. Partial implementation of this regulation will begin in June 2006, with brokers required to demonstrate for 100 stocks that they achieved best execution. Trade Through requirements further mandate that exchanges pass the trade on to another exchange if they cannot get best price. For example, the NYSE may be required to pass a Microsoft trade to the NASDAQ for best price. Complete implementation is expected to be achieved by late 2006, early Customers will need to develop a comprehensive tick database covering these 100 securities initially, demonstrating where the market was at a given time and the price that was achieved. The market may move away from a broker in milliseconds simply due to a slow order management system. 2. MiFID. This covers all securities in the European markets and requires that investment firms take all reasonable steps to obtain, when executing orders, the best possible results for their clients taking into account price, costs, speed, likelihood of execution and settlement, size, nature, etc. Firms may need to prove that they are enforcing their best execution strategy and will be required to store transaction data for five years and real-time quote data for a currently undetermined period of time. Individual European countries will regulate and enforce MiFID according to the outcome of their legislative process, MiFID will appear in national legislation in October The following are specific scenarios that will need to be addressed: Reuters Tick Capture Engine (RTCE) supported by the Reuters Market Data System (RMDS) market data platform can help you meet these tough new requirements. RTCE captures and stores tick, order book and internal trade data for as long as you need, helping to build long and deep trade information suitable for compliance reporting. Combined with RMDS s powerful publishing capabilities, Reuters Tick Capture Engine helps meet your quote and trade data transparency obligations. With RTCE s extensible persistence database, multiple order books can be searched at once, scanning the market in real-time for best execution. A market-sweeping algorithm is employed to find the best price across different market centres. Its real-time order book analysis supports smart order routing. RTCE s Order Book Analyser for compliance uses level 2 data, providing a query-based, intra-day snapshot of where the markets were at a given time, and answering the question: Did I get the best price? It is an accurate and cost-effective means of producing execution performance analytics and transaction cost research for compliance and customer reporting purposes. We deliver RTCE pre-loaded with VWAP, the most widely used performance measurement of best execution. Customers can easily re-configure time frames and include or exclude any trade type. Easy integration with RMDS allows these calculated VWAPs, based upon streaming market data, to be used by other applications in a lowlatency environment. Market trends Reg NMS and MiFID These new regulations clearly will transform trading operations on both sides of the Atlantic. With its new pre- and post-trade transparency requirements for equity markets, MiFID will require European firms to store trading data for years. The market-structure reforms imposed by Reg NMS, meanwhile, could signal the beginning of the end for the auction trading system in the United States as it hastens the demise of floor trading. In both regions, meeting the new requirements will require firms to improve data storage, data integration and order management capabilities. Trading compliance user scenarios Best execution/data storage How can you get the best price and how can you prove that you did get the best price? Customers will need to capture and store real-time tick data and trades, and run post-trade analysis to satisfy the regulators and customers that they achieved best execution. Trading/execution algorithms Customers will need market-sweeping algorithms looking at the top of the book across major market centres for a given instrument; intraday snapshots looking at where the market was at a given time; and a record of whether they got the best price for internal queries and reporting purposes. And RTCE s ability to stream trade signals to an OMS through low-latency RMDS means that the risk of not getting best execution on a given security because of a slow OMS response time is reduced. Combined with low-latency Reuters Direct Feeds and low-latency analysis with RTCE s persistent database, achieving and reporting on best execution is a realistic goal.