1. © Copyright StreamBase®. Proprietary & Confidential. www.streambase.com1 StreamBase Case Study Automated Trading

2. © Copyright StreamBase®. Proprietary & Confidential. www.streambase.com2 I. The Problem  Background:  Successful Buy-side firm successful in conventional buy/hold strategies wanted to apply learnings to intraday trading  Business Drivers:  Making money: from short-lived trading opportunities in real-time market data feeds, and reducing transaction costs  Customer retention

3. © Copyright StreamBase®. Proprietary & Confidential. www.streambase.com3 The Approach: Application Overview  Data/events stream from real-time market data feeds  Data is filtered (watch-list) and processed  Trading rules/logic applied to real-time streams to make buy/sell decisions  Spread pairs, Bollinger bands, limit rules  Store and retrieve latest market data  Maintain execution state of trades, check continuously  Buy/sell orders sent to execution engine  Recent addition of block-trading and best execution application  Run algorithms across multiple liquidity sources to determine best price and optimize execution (price, transaction fees)

4. © Copyright StreamBase®. Proprietary & Confidential. www.streambase.com4 Event Sources, Types, Interfaces  Event sources:  NYSE Arca  Nasdaq  Instinet  15 other global exchanges  Event types:  Message format: contains string, int, datetime, Boolean, and decimal/float data types  Market data: e.g. Symbol, bid_price, ask_price, bid_size, ask_size, last_price, last_size, timestamp  Daily market condition data: symbol, market cap, sector, 52-week  Message rates:  Market Data providers: up to 10,000 messages per second.  < 20 ms from input to output  Interfaces:  Tibco EMS, MS SQL Server adapter. .Net adapter for EMS leveraging existing Microsoft/.Net development work

5. © Copyright StreamBase®. Proprietary & Confidential. www.streambase.com5 Example of Application Logic  Query table look-up and filter for watch-list  Calculate and store Bollinger Bands/moving average, (Aggregate operator)  Apply Bollinger rule: current price much reach lower band (Filter)  Apply 52-week rule: current price must reach 52-week low (Filter)  Apply daily volume rule: quote must reach 150% of daily volume (Filter)  Union all orders and add timestamp  Output stream with orders to submit

6. © Copyright StreamBase®. Proprietary & Confidential. www.streambase.com6 Application Module: Quote to Order

7. © Copyright StreamBase®. Proprietary & Confidential. www.streambase.com7 Example Code CREATE STREAM Low52WkOrders AS SELECT symbol, timestamp, watchlist_position_threshold as position_threshold, "off" AS new_order_type, bid_price AS new_order_price, int(watchlist_position_threshold / bid_price) AS new_order_size FROM QuoteAndMarketRef WHERE last_price < w52_low; Create order if the last_price on the QuoteAndMarketRef stream is less than the 52 week low.

8. © Copyright StreamBase®. Proprietary & Confidential. www.streambase.com8 III. Results, Costs and Benefits  Application in production  Built by in-house staff in 30 days (2-3 people, including QA/testing).  Estimated to take 8 months with team of 3-5 people via custom- coding  Easy for non-expert developer to build, understand, and modify  ROI  Trading profitability (not disclosed)  Customer retention and new acquisition  Deployed in 1/8 the time and resources vs custom-coding  Visibility to whole organization for event/application flow

9. © Copyright StreamBase®. Proprietary & Confidential. www.streambase.com9 IV. Conclusions  Alternative approaches would not have offered value of StreamBase  Custom-coding (too costly in terms of time/resources) to get high performance  Full-blown order management system (OMS) too expensive and too feature-rich  Lessons learned  Strong business drivers (not just an IT project)  Up-front architectural planning paid-off in time-to-deployment