1. InfoSphere Streams for Real Time Analytics in Financial Services Industry Krishna Mamidipaka, krishnag@us.ibm.com Roger Rea, rrea@us.ibm.com

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4. Agenda Financial Markets Business Challenges Industry Technical Challenges InfoSphere Streams Trend Calculator Financial Toolkit Data Mining in Real Time InfoSphere Streams Directions 4

5. Firms Must Capitalize on Drivers of Change Drivers Markets becoming electronic Implications Speed as source of Alpha Transparency is required Volume is a barrier Information availability Real-time data pressures Actions Accelerate the end-to-end marketplace connectivity and execution Store, retrieve and distribute comprehensive time series data in a timely manner Increase capacity to handle current and forecasted volumes Detailed analysis of trading process Transaction costs pressures Access to broader markets by accessing multiple markets 5

6. For US equity electronic trading brokerage 1 millisecond = $4M in annual revenue Source: Tabb Group We are in a technology arms race Latency reductions with a clear business value or cost associated Exponential increases in volumes Real time data pressures 6

7. The Volume, Complexity & Semantic Depth of data that to be analysed will increase significantly Market Data Risk Analytics Data Historical Trade Data Analytics & Insight Market Data Risk Analytics Data Video News Feeds Corporate Press Reports RSS Feeds Web Pages Weather Data Government Statistics Internal Message Bus Blogs & Commentary Historical Trade Data Analytics & Insight Real World Sensors Tomorrow? + Other Feeds Structured data Structured & Unstructured data Information overload Today 7

8. The Transaction Life Cycle or latency loop – end to end latency is the key to success and there are no prizes for coming second Investment / trading goals Market Data Trading Decision What to Buy/Sell Execution Algorithm VWAP,etc. Order Routing Decision Matching Transaction Cost Analysis latency measurement is a competitive advantage to deliver Alpha WAN Connectivity Middleware CEP Engines OMS/EMS Exchanges, End to end latency knowledge and a continuous performance road map is required Speed Speed  Current approaches reaching limits, based on x86 and networking technologies 8

9. RAM CPU DSK I/O Single Core Single Thread 100% Serial Programming Yesterday Core RAM Core RAM Core RAM Core RAM Core RAM Core RAM Core RAM Core RAM Core RAM Core RAM Core RAM Core RAM Core RAM Core RAM Core RAM Core RAM DSKI/ONET Multicore (2-16) Multithread (10s) 80/20 Serial/Parallel Programming Today DSKI/ONET Manycore (32-100s) 20/80 Serial/Parallel Programming Threading model breaks as complexity exceeds programmer capability Tomorrow The Manycore programming challenge Programmers cannot cope with thousands of threads and complex data flows using existing programming models 9

10. Options for exposing parallelism in a programming model  Full exposure of machine details  Only usable by experts  High performance  Low productivity Parallelism Fully Exposed Parallelism Implicit Partial Exposure  Limits exposure to machine details  Expands programmer community  High performance  Higher productivity for C/C++ class programmers -Bounds checks, pointer checks, strong typing, etc.  No exposure of machine details, e.g., Hadoop/map reduce, IBM Streams Processing Language  Usable by larger number of programmers  High Performance  High Productivity 10

11. Time is ripe for a new era of computing Emerging trends create need for new languages –Scientific programming  Fortran –Business programming  Cobol –Systems programming at higher level  C –Increased productivity  C++ –Web programming  Java Streaming data sources and multicore architectures –Streams Processing Language 11

12. Delivering ‘Continuous Intelligence’ with Powerful Analytics Automated Options Market Making: –Peak throughput of 10 million messages per second –Mean latency under 100 micro seconds across 28 dual quad core x86 blades Millions of events per second Microsecond Latency Traditional / Non-traditional data sources Real time delivery Powerful Analytics 12

13. IBM InfoSphere Streams v1.2 Development Environment Runtime Environment Toolkits & Adapters Front Office 3.0 RHEL v5.3 or v5.4 x86 multicore hardware InfiniBand support Up to 125 servers Eclipse IDE StreamSight Stream Debugger Connectors to data sources Operator Library Financial Toolkit Mining Toolkit 13

14. Scalable stream processing InfoSphere Streams provides –A programming model and IDE for defining data sources and software analytic modules called operators that are fused into process execution units (PEs) –infrastructure to support the composition of scalable stream processing applications from these components –deployment and operation of these applications across distributed x86 processing nodes, when scaled processing is required –stream connectivity between data sources and PEs of a stream processing application 14

15. Trend File 1 playback Trend File 2 playback Trend File 3 playback Up/down trend for Requested symbols Symbols to be output Algo Parameters Per Symbol Trend Calculator Example 15

16. Streams offers tremendous deployment flexibility With only a simple re-compile of application: All on one machine fused into one multi-threaded process All on one machine; each operator in its own process Each operator in its own process, each process on its own machine 16

17. Trend Calculator Example 17

18. Financial Services Toolkit Adapters layer used by top two layers and user-written apps Functions layer used by top layer and user-written apps Solution Frameworks are “starter” applications that target a particular use case Speeds development of Streams financial domain applications 18

19. Adapters, Functions, Utilities Financial Information Exchange (FIX) Adapters –fixInitiator Operator, fixAcceptor Operator, FixMessageToStream Operator, StreamToFixMessage Operator WebSphere Front Office for Financial Markets (WFO) Adapters –WFOSource Operator, WFOSink Operator WebSphere MQ Low-Latency Messaging (LLM) Adapters –MQRmmSink Operator Functions: –Coefficient of Correlation –“The Greeks” (Put/Call values, Delta, Theta, Rho, Charm, DualDelta, etc.) Operators: –Wrappering QuantLib financial analytics open source package. –Provides operators to compute theoretical value of an option: EuropeanOptionValue Operator – 11 different analytic pricing engines –e.g. Black Scholes, Integral, Finite Differences, Binomial, Monte Carlo, etc. AmericanOptionValue Operator - 11 different analytic pricing engines –e.g. Barone Adesi Whaley, Bjerksund Stensland, Additive Equiprobabilities, etc. 19

20. Equities Trading “Starter Application” Modular design Components are plug-replaceable – extend these or substitute your own Demonstrates how trading strategies may be swapped out at runtime, without stopping the rest of the application TradingStrategy module looks for opportunities that have specific quality values and trends OpportunityFinder module looks for opportunities and computes quality metrics SimpleVWAPCalculator module computes a running volume-weighted average price metric 20

21. Option Price Data Filtering and Preparation DataSources Stock Price Stock Information Risk Free Rate Pricing Decision Theoretical Price Computation Identification of Buying Opportunities OptionsPriceFeedData RiskFreeRate Stock OptionsValue Data Sinks Options Trading “Starter Application” DataSources module consumes incoming data; formats and maps for later use Pricing module computes theoretical put and call values Decision module matches theoretical values against incoming market values to identify buying opportunities 21

22. Multinational Mutual Funds Manager and Broker High speed market trend calculation system that can provide instant insights into the market behavior Improved development time from days to hours to add new features to the trend calculation system using the Streams programming model Customizable to run on one server or distributed across many servers to garner more compute power Visualization tools for effective live trade monitoring and risk assessment 22

23. Notional Information Supply Chain for Decision- making Transforming the Information Supply Chain to reduce the time to action! SOURCES Elapsed Time to Action WAREHOUSE Reports Ad-hoc Queries DATA INTEGRATION OPERATIONAL DATA STORES DATAMARTS Bus Process & Event Mgmt Operational Reports DashboardsPlanning Scorecarding Analytical Modeling & Information Typical information supply chain 23

24. Time to Action SOURCES WAREHOUSE Reports Ad-hoc Queries DATA INTEGRATION OPERATIONAL DATA STORES DATAMARTS Bus Process & Event Mgmt Operational Reports DashboardsPlanning Scorecarding Analytical Modeling & Information Stream Computing: Analytical Modeling & Information More context Reduces Time to Action Widens the aperture Reduces costs 24

25. Market Surveillance & Fraud applications Rule Parameters Market Feeds and Trade Data Historical Real time analysis processing Enrich ment Existing business rules PMML Model Scoring Additional sophisticated analytics Alerts Collected results Solution User Interface 25

26. What are key advantages of Streams? Compiling groups of operators into single processes enables: Efficient use of cores Distributed execution Very fast data exchange Can be automatic or tuned Can be scaled with the push of a button Language built for Streaming applications: Reusable operators Rapid application development Continuous “pipeline” processing Extremely flexible and high performance transport: Very low latency High data rates Easy to extend: Built in adaptors Extend with C++ and Java Extend running applications Use the data that gives you a competitive advantage: Can handle virtually any data type Use data that is too expensive and time sensitive for other approaches 26

27. IBM InfoSphere Streams directions WebSphere Business Events Existing business information Data in motion InfoSphere Warehouse IBM Mashup Hub 8BI Tools Streams Studio enhancements Video/audio analytics Text/unstructured analytics Streams Processing Language improvements Native XML support Runtime High Availability Expanded platform support Performance improvements Adapters WebSphere MQ RSS feeds Mashup Hub WebSphere Business Events Oracle SQL Server MySQL Millions of events per second Millisecond Latency Cognos Front Office All statements regarding IBM's plans, directions, and intent are subject to change or withdrawal without notice. Any reliance on these statements are at the relying party's sole risk and will not create any liability or obligation for IBM. 27

28. InfoSphere Streams sessions TimeSessionTitleLocation Thursday May 20 10:45 AM - 11:35 AM 3666AInfoSphere Streams for Real Time Analytics in Financial Services Industry Marriott Park Hotel, Room 14 Friday May 21 09:00 AM – 09:50 AM 3661AInfoSphere Streams helps Stockholm build Ver 2.0 Traffic Control System Marriott Park Hotel, Room 13 Friday May 21 11:30 AM - 12:30 PM 3692AInfoSphere Streams at Marine Institute of Ireland: Deep Dive Marriott Park Hotel, IOD Mini Theatre 3 Wednesday 10AM - 6PM Thursday 10AM - 5PM Friday 9AM - 2PM Demo Room InfoSphere Streams DemonstrationsMarriott Park Hotel, IOD Demo Room Station 19 Wednesday 10:30 – 11:30 Thursday 12:30 – 13:00 Thursday 16:30 – 17:00 Mini Theater on Expo Floor InfoSphere Streams in Telco InfoSphere Streams Business Insight Leverage Warehouse, SPSS with Streams Marriott Park Hotel, InfoSphere Mini Theater Expo Floor