The PennLehman Automated Trading Project Michael Kearns Computer

The Penn-Lehman Automated Trading Project Michael Kearns Computer and Information Science University of Pennsylvania PLAT development team: Luis Ortiz, Berk Kapicioglu, Byoungjoon Kim, Rashid Tuweiq

What Is It? • A project in real-time, automated trading in financial markets • A realistic, multi-client market simulator using live and historical depth-of-market data • A competitive testbed for trading strategies • An investigation of order book strategies • An investigation of AI & ML methods for automated trading • A research and educational partnership between Penn and Lehman Brothers

Market Microstructure and Depth-of-Market • Consider an exchange for some specific security (e. g. MSFT stock) • Market order: specify number of shares desired, but leave price to “the market” • Limit order: specify number of shares and desired price (usually away from the market) • Limit orders are placed in a queue (or “book”) on the buy or sell side, ordered by price • Market orders are matched with the top of the book on the opposing side, giving price • Market orders “guaranteed” transaction but not price; limit orders guaranteed price but not transaction

Electronic Crossing Networks (ECNs) • Matching process as old as financial markets • Previously done manually (NYSE specialists) • Electronic Crossing Networks (ECNs): – Automate matching process – Now publishing order book data – Can be highly liquid • Examples: Island (17% of NASDAQ volume), Instinet, Archipelago, … • Consolidation efforts (Super. Montage) • OUCH and ITCH messaging protocols • Example: island. htm or http: //www. island. com/

The Penn Exchange Simulator (PXS) • A (conceptually) simple, real-time market simulator exploiting order book data • Core processes: – Data Gathering: frequent (live or historical) polling of any given stock from www. island. com – Client Order Processing: acceptance of connections and limit orders from automated clients via API – Market Simulation: simulation of matching process in a virtual market merging the Island client data; computation of profit and loss of clients

LAST MATCH Price 23. 7790 Time 9: 01: 55. 614 BUY ORDERS TODAY'S ACTIVITY Orders 1, 630 Volume 44, 839 PXS SELL ORDERS Client 1 SHA RES PRICE 1, 000 23. 7600 100 23. 7800 BUY SELL 3, 087 23. 7500 800 23. 7990 V 23. 80 100 23. 7400 500 23. 8000 V 23. 77 1, 720 23. 7280 1, 720 23. 8070 I 23. 76 2, 000 23. 7200 900 23. 8190 I 23. 75 1, 000 23. 7000 23. 8500 I 23. 75 100 23. 7000 1, 000 23. 8500 V 23. 74 800 23. 6970 1, 000 23. 8600 500 23. 6500 24. 0000 V 23. 74 3, 000 23. 6500 24. 0000 V 23. 73 4, 300 23. 6500 1, 000 24. 0300 I 23. 73 2, 000 23. 6500 24. 0300 I 23. 78 I 23. 80 V 23. 80 I 23. 81 V 23. 82 V 23. 83 V 23. 84 I 23. 85 I 23. 86 V 23. 89 V 23. 90 V 23. 97 V 24. 00 23. 6200 1, 100 24. 0400 500 24. 0500 (195 more) (219 more) I 23. 74 Client 2 Client 3 early-snap. txt later-snap. txt pxs. API 9. htm

Features of PXS • • Simulation merges real market data with client No guesses or models for limit order fills Permits investigation of order-book strategies Permits high-speed, high volume trading Forces real-time performance Sandbox for diverse strategies & interaction Execute “live” on real-time data or historical

Design Challenges • • • Data volume, network latency, performance Timing issues in live versus historical Partial observability of Island queues Client impact on market Future functionality: – – Multi-stock simulation External data feeds Web interface and GUI Multiple ECNs; other markets (futures, options)

The Project: Participants • Penn Development Team: – – PXS design, development, maintenance Client strategy design Education of student researchers/users Competition design and execution • Students: – Approximately 30 students designing 13 strategies – Many senior projects; many joint CIS-Wharton – Several external participants (UTexas and CMU) • Lehman: – Financial support from proprietary trading – Student mentorship and competition judging

Goals for the Trading Strategies and Competitions • Attempt to recreate Wall Street “biodiversity” • Market making, pairs trading, VWAP, block trading, technical strategies, etc. • Investigate predictive value of order book data • Investigate application of AI and ML methods • Create a library of strategies and competition structures for mix and match experimentation

Strategy Types • Many order-book strategies: – order book imbalance and variations obi. gif or Island Individual Investors • Market-making strategies • Traditional technical strategies: – various “breakout” strategies, trend spotting, etc. – often with order-book modulation • Machine learning strategies using order books – boosting – case-based learning – SVMs

The Competitions • All have share position limits • Fall 2002 Historical MSFT Competition hist 2002. html • Fall 2002 Live MSFT Competition (1 -day) Live. Comp. htm red-smooth 250. ps blue-smooth 250. ps • Order-book Face-off obonly. html • Currently: 3 -round, 3 -week MSFT Competition Feb. Comp. htm

Further Info • • General: www. cis. upenn. edu/~mkearns/ PLAT: www. cis. upenn. edu/~mkearns/projects/pat. html mkearns@cis. upenn. edu New participants welcome!