1. The War of the Algorithms Emerging Trends in Algorithmic Trading Dr John Bates Co-Founder & Chief Technology Officer Apama VP Event Processing Products Progress Real-time Division

2. Agenda Emerging trends & requirements –Commoditization vs “Build Your Own” –Algorithmic War –Cross-Asset Class Algorithmic Trading Next Generation Algo Trading –Algo Trading Engines –Flexible Connectivity –Rapid Strategy Modelling Tools The Future –Self-evolving Algorithms Conclusions

3. The Interest in Algorithmic Trading Buyside –Competitive advantage Capitalize on opportunities before competitors –Leveraging Traders’ skills Scale each trader –Cost advantages Sellside –Increase trading volume –Attract & retain customers

4. Some Statistics TowerGroup –“…continued growth in total algorithmic trading, with volume doubling through 2006 and algorithmic trading initiated by the buy side tripling during the same period” ITG –“Algorithmic trading in use in 60% of US buy-side firms and this percentage is set to grow” –“The take up in Europe is currently thought to be about half of that in the US, but is expected to rise dramatically”

5. Black Box Strategies Most common way to algo trade Easily accessible through FIX and buy-side OMS Instrument Quantity Num slices Start time End time VWAP Buy/Sell

6. Risks of Commoditization Black Box Strategies –If everyone has the same black boxes = cancels out competitive advantage –Limited scope to use your skills – can only parameterize –Often there isn’t a module that offers exactly the algorithm required –An algorithm is tied to a particular broker –Can be expensive Pressure to differentiate –Hard for buy-side to understand what makes one broker’s strategies better than another’s –Fixed capability modules are too inflexible – pressure to offer cost-effective customization –Buy-side want to know “how it works”

7. Build your own –Takes a huge amount of time & effort (IT cycle) –Maintenance issues Markets are continually evolving –First mover gets the advantage –Lost opportunity cost of slow evolution Build Your Own

8. Algorithmic War Algorithms need to continually evolve –Competing with other algorithms over current opportunities –New opportunities emerging –Avoid being reverse engineered –Opportunities may disappear Evolve or perish!

9. Cross-Asset Class Strategies Interest is growing for algorithmic trading in multiple asset classes –Equities, Futures, Forex, Bonds –Trading and Market-making (e.g. bond pricing) Strategies should also be able to combine multiple asset classes –Example: Buy an equity, hedge with a future Wave trade the equity –Slice volumes based on historic volume profile –Time slices into market based on a slightly random wave period Take foreign exchange position if equity is in different currency

10. Summary of Emerging Requirements Desire for competitive differentiation through strategy customization Desire to achieve this customization rapidly to capitalize on opportunities –Survive in the algorithmic war Desire to support algorithmic trading across multiple asset classes

11. A Plug-and-Play Approach Some proven Pre-trade Analytic strategies –VWAP –Pairs –Index Arbitrage –Basket –Spread Combined with order management strategies –Wave Trading/Iceberg –Out of Market Limits –Active re-pricing –Timeout if not Filled –Smart Order Routing Feedback An institution’s strategy is likely to combine known analytic & order management strategies + their secret ingredient

12. Need Algorithm Hosting Environment Hosting environment for strategies –Describe a strategy & upload into algorithmic trading engine –Enables strategies to be easily & rapidly created and/or extended Efficient strategy execution –Exploit latest “complex event processing” logic –Plumbed directly into any number of market data feeds & order management systems Trading Strategies Data Feeds, e.g. Market Data, News Actions, e.g. Place Order

13. Access All Markets Need extensible integration architecture to plug into any Exchange, OMS, Middleware etc. Abstract underlying connections, enabling strategies to be –Exchange-Independent –Asset Class Neutral –Backtested with simulators or historical data Integration Framework FIX ReutersSonic MQGL

14. Business-Focussed Modelling Enable business user to compose, deploy, evolve and manage algorithmic strategies Business-focussed modelling of strategies – so users can “go inside” and customize Generate/evolve strategies in hours rather than weeks Upload directly to algorithm hosting environment

15. Modelling Trading Strategies Business User Orders Filled Orders Timed Out Orders Placed Monitor Spread Need to be able to define strategy process flow

16. Modelling Trading Strategies Business User VWAPEMA MACD P&L Basket Spread Price Feed Inst1 Inst2 MySpread Need to be able to plug in analytics & data sources Spread Orders Filled Orders Timed Out Orders Placed Monitor Spread

17. Modelling Trading Strategies Business User Orders Filled Orders Timed Out Orders Placed Monitor Spread Price Feed Inst1 Inst2 Need to be able to define trading rules Spread WHEN THEN Spread is greater than Threshold BuyLeg place order SellLeg place order Move to state [Order Placed] MySpread

18. Event Manager Reuters Adapter FIX Adapter ODBC Adapter Tier 1 Futures Broker + Apama Strategies+ Scenario ManagerExisting portal Database Server for Historic data access & storage and Back testing Fix Gateway “Advanced Trading” Client view Server side

19. UK Hedge Fund – Forex Trading Routing orders across multiple FX liquidity pools based on price and availability EBS Adapter Hotspot Adapter Risk Model FX Strategy user tools

20. Tier 1 Investment Bank - Bond Pricing Highly competitive pricing of bonds across inter-dealer and client networks Historic Client Performance Client-facing Bond Market Inter-dealer bond markets Inter-dealer market Adapter Real-time Trade Dashboards Internal middleware Adapter Bond Strategy Target Exposure Quoting Engine Start of day & intra-day positions Market data Price Adjustments Market data Price Adjustments

21. The Future Algorithms will not replace humans –They just help trading groups scale their own capabilities Self-Evolving Algorithms –1000s of permutations of the same algorithm All slightly different All with simulated P&L –Swap in most profitable

22. Summary To compete successfully in the “war of the algorithms”, algorithmic trading systems must support –Rapid evolution & customization of strategies –Cross-asset class strategies –Support for business users to do this Advantages can be realised with architectures involving –General purpose algorithm hosting engine –Integration to all data points –Business focussed environment to compose, deploy and manage strategies

23. Questions?