The Future of Algorithmic Trading

Transcription

1 The Future of Algorithmic Trading Apama Track Dan Hubscher, Principal Product Marketing Manager, Progress Apama Progress Exchange Brasil 2009 Progress Exchange Brasil de Outubro São Paulo Brasil O Mais Importante Evento para a Comunidade Progress no Brasil

2 Apama Background Progress Software (NASDAQ: PRGS) Apama Complex Event Processing Platform Focus on Capital Markets Algorithmic Trading Risk Management Market Making Best Execution Multiple Asset Classes 120+ Global Customers Including Regulators, Exchanges, Banks and Buy Side institutions FI FX Other F&O EQ 2

3 The Capital Markets Trading Community Today Global rise of the alternative venues Market fragmentation Price/speed competition Regulatory drivers RegNMS, MiFID, etc. Volume & volatility Technology arms race Increasing market data The need for speed Automated trading evolution 2004 Broker algorithms News-driven Algorithms Smart Order Routing Proprietary algorithms

4 Global Trends In Algorithmic Trading Source: Aite Group, Futures Trading Platforms OCTOBER 2008 Source: Aite Group, Futures Overview: From Front to Back DECEMBER 2008 "Looking at this from a long- term perspective, once the market stabilizes, we expect [our] projections to hold 1 1 Sang Lee, Aite Group, quoted in Crisis sparks reversal in electronic trading-study, Reuters, Wed Dec 3, 2008: ews/idusn

5 Recent Change & Reactions - Examples Demand For Aggressive Algorithms 2 Appetite remains strong for algorithms as prices of securities continue to swing widely on financial markets Use of algorithms that can move rapidly to grab liquidity is on the upswing Constant tweaking of the most aggressive algorithms to take advantage of changing markets Source: Aite Group, Futures Trading Platforms OCTOBER 2008 Demand for aggressive algorithms is growing primarily because of the markets' current volatility 2 Demand Stays Strong For Aggressive Algorithms, Securities Industry News, June 1, 2009, html 5

6 Algorithmic Trading What & Why Automated trading When to trade Objective: seek alpha Continuously re-calculate analytics Monitor thresholds/rules - Stat arb - Spread - Index arb How to trade Objective: best execution Order execution policy - Slice up orders (wave trade) - Route across liquidity pools (smart order routing) - In market limit + timeouts - Out of market limits ( electronic eye ) 6

7 4 Frontiers of Algorithmic Trading Competition Rapid customization Cross- Algorithmic Speed/latency geography Trading advantage Crossasset class 7

8 Algorithmic Trading Rule WHEN MSFT price moves outside 2% of MSFT Moving Average FOLLOWED-BY ( My Basket moves up by 0.5% AND ( HPQ s price moves up by 5% ALL WITHIN any 2 minute time period THEN BUY MSFT SELL HPQ multiple data streams temporal sequencing complex event sequences real-time constraints automated actions NASDAQ NYSE MSFT Moving Average My Basket 8

9 Modeling Trading Strategies Event Modeler Monitor Spread Orders Placed Orders Filled Orders Timed Out 9

10 Real-time, Event-based Platform for Building Applications Business & IT Tools - Apama Workbench Graphical Development Dashboard Builder Code-based Development Event Modeler Alpha-seeking & execution rules: Crossover Momentum trading Statistical arbitrage Index arbitrage Iceberg VWAP Market participation Order slicing...and more Inputs Market data Orders Any event Actions/Output Orders Derived data Dashboard interaction 10

11 Apama Solution Accelerators Enable rapid development, customization & deployment Rapid customization Specific to key applications Accelerate business value Algorithmic Trading Maintain flexibility 11

12 Apama Solution Accelerators!" Algorithmic Trading Market Surveillance FX Aggregator Smart Order Router Real-time Pricing #$ #... Sub-Systems & Components Market Data Trade Services Position Services Order Book Order Mgmt Status Utilities Risk Firewall % #& '" 12

13 Key Applications Applications Examples Solution Accelerators Customers Algorithmic Trading Alpha Seeking & Execution VWAP, TWAP, etc Multi-Asset, Cross Asset Buy Side, Sell Side, Prop Trading Algorithmic Trading Market Making & Aggregation Liquidity Discovery Market Making Smart Order Routing & Execution Auto Hedging FX Aggregation Surveillance Compliance Risk Management Market Abuse Detection Market Maker Monitoring Op Risk, Business Control Risk Firewall, Auto Hedging Market Surveillance Best Execution Smart Order Routing Aggregated Execution Compliance Smart Order Routing Market Making & Pricing Intelligent Pricing, Skewing, Tiering Market Making Real-Time Pricing 13 13

14 Algorithmic Trading Accelerator Sample Algorithms Alpha-seeking & Execution Cross-asset support Backtesting facility Strategy Design Business & IT Tools Dashboards Pre-built Customizable Integrated CMF Components Exchange simulator Risk & position management Trader P&L Broad Connectivity Market Data & Execution Infrastructure Customizable 14

15 Algorithmic Trading What & Why Automated trading When to trade Objective: seek alpha Continuously re-calculate analytics Monitor thresholds/rules - Stat arb - Spread - Index arb How to trade Objective: best execution Order execution policy - Slice up orders (wave trade) - Route across liquidity pools (smart order routing) - In market limit + timeouts - Out of market limits ( electronic eye ) 15

16 4 Frontiers of Algorithmic Trading Competition Rapid customization Cross- Algorithmic Speed/latency geography Trading advantage Crossasset class 16

17 Algorithmic Trading Rule WHEN MSFT price moves outside 2% of MSFT Moving Average FOLLOWED-BY ( My Basket moves up by 0.5% AND ( HPQ s price moves up by 5% ALL WITHIN any 2 minute time period THEN BUY MSFT SELL HPQ multiple data streams temporal sequencing complex event sequences real-time constraints automated actions NASDAQ NYSE MSFT Moving Average My Basket 17

18 Complex Event Processing Static Data Processing: How many orders were placed for a stock? time ()*! " # 18

19 Real-time, Event-based Platform for Building Applications Business & IT Tools - Apama Workbench Graphical Development Dashboard Builder Code-based Development Event Modeler Alpha-seeking & execution rules: Crossover Momentum trading Statistical arbitrage Index arbitrage Iceberg VWAP Market participation Order slicing...and more Inputs Market data Orders Any event Actions/Output Orders Derived data Dashboard interaction 19

20 Scalability Throughput & Latency Hypertree and Complex Event Sequencer - Millions of rules concurrently through smart algorithms - Determinism Parallel Correlator - Make optimal use of multiple cores Multiple Apama instances in a network Correlators can be plugged together in arbitrary CEP configurations (pipelines, meshes, grids) Event router - Large multi-cpu deployment Enterprise Monitoring & Management - Graphical configuration through a management console 20

21 Parallel Application Development Simple for users 21 High performance smart scheduler Multiple Execution Contexts Advanced Micro threading architecture

22 Apama Platform Performance Throughput & Latency Two test cases to demonstrate latency consistency with varying degrees of computation 1. Simple: Respond to every event with little processing 2. Work: Compute EWMA on price in every event, compare EWMA with price, calculate weighted price and submit order Event rate increased until application is CPU bound Host: Xeon X5570 (Nehalem), 2.93 GHz, Red Hat 5 64-Bit Testcases Event Generator Process 1 2 ) Event Receiver Process 22 Full Round trip (end-to-end) latency

23 Performance Results: Latency Behaviour One millisecond One millisecond Round-trip latency (to & from the Correlator process) is between 100 and 200 microseconds ( milliseconds) and independent of application load 23

24 Algorithmic Trading Buy Side Example Boutique Hedge Fund Limited IT resources Equities and Futures trading Monitors 1500 stock pairs Trades times daily Trade execution in 20 milliseconds Strategies built in Event Modeler Graphical tools put RAD in trader s hands 24 24

25 Algorithmic Trading Sell Side Example Major Scandinavian Bank Client access to Apama strategies Strategy design via Apama s Event Modeler Strategy execution - via Apama graphical dashboards - Via SEB Trading Station Integration with Ullink OMS Expanded Apama use Order flow monitoring Delivers trade compliance 25 25

26 Algorithmic Trading - Brazil Ágora The largest broker in Brazil, deployed the Apama platform to support multi-asset algorithmic trading, leveraging Apama s connectivity to BM&FBovespa Finabank Deployed the Progress Apama Algorithmic Trading Accelerator to supply buy-side clients with a platform for alpha-seeking, statistical arbitrage trading strategies and low latency execution on BM&FBovespa 26

27 Algorithmic Trading Evolution Algorithmic Trading Accelerator Out of the Box Customizable News trading Real-time risk management & market surveillance Trading-in-the-Cloud 27

28 Trading in the Cloud Exchange collocation enables competition in: Cross-geography Speed/latency Rapid customization Software-as-a-Service Quick time to market Minimal investment Crossgeography Algorithmic Trading Speed/latency advantage Cross-asset Hosted class Easy access Optional connectivity to any other trading venues and market data sources Apama Examples CQG Automated execution for commodity & FX futures BGC Algorithmic trading for Fixed Income cash & futures FFastFill Automated futures spread trading & global exchange collocation 28

29 Conclusions Fears of the Black Box If everyone has the same black boxes: no competitive advantage Limited scope to use your skills can only parameterise Trading in competitive markets requires SPEED Build quickly Run fast Markets are continually evolving First Mover Advantage Customization is now KING Create intellectual property for competitive differentiation Custom rules-based trading is critical Trading firms look to Apama for a platform Create trading strategies that exploit their unique trading ideas 29

30 THANK YOU Q&A SESSION CONTACT US Dan Hubscher Principal Product Marketing Manager, Progress Apama Web: Blog: 30