Trading of Complex Commodities

1. Trading of Complex Commodities Josh Johnson Committee: Eugene Fink Lawrence Hall Srinivas Katkoori

2. Introduction • Motivation • Build an automated • exchange for trading • goods and services

3. Introduction • Motivation • Build an automated • exchange for trading • goods and services • Combine the speed • and liquidity of the • stock exchange

4. Introduction • Motivation • Build an automated • exchange for trading • goods and services • Combine the speed • and liquidity of the • stock exchange with • the flexibility of eBay +

5. Outline • Main concepts • Previous work • Data structures • Performance

6. Concepts • Market • Orders • Matches • Fills

7. Market All items that can be traded form amarket. Example: All conceivable vehicles compose a car market.

8. Order example: Any Mustang or Corvette; Mustang for $38,000 or Corvette for $40,000 . Orders An order is asubsetof the market along with aprice function. , -$1 for every ten miles.

9. Buy and Sell Orders Price Price Buy order Sell order

10. Matching A buy order matches a sell order if: item buy-region  sell-region, buy-price(item) sell-price(item).

11. Matching Orders Sell order Price Buy order

12. Fill: Red Mustang $34,000 Fills Buy order: Any Color Sports Car $36,000 Sell order: Silver Limo $50,000 or Red Mustang $32,000

13. Implemented Exchange System Specific sell orders Good: Sell a red Mustang made in 1999. Bad: Sell any color Mustang made before 1999.

14. Outline • Main concepts • Previous work • Data structures • Performance

15. Previous Work • Auctions • Exchanges

16. Auctions • Complex commodities • Asymmetry between • buyers and sellers • Illiquid • Examples: • eBay, FreeMarkets, eMediator

17. Exchanges • Simple commodities • Symmetry between • buyers and sellers • Liquid • Examples: • Stocks, Futures

18. Outline • Main concepts • Previous work • Data structures • Performance

19. Main Structures • Tree of sell orders • List of buy orders

20. Tree of Sell Orders Model Mustang Corvette

21. Tree of Sell Orders Model Mustang Corvette Color Color Red Black Red White Grey

22. Tree of Sell Orders Model Mustang Corvette Color Color Red Black Red White Grey Year Year Year Year Year 1996 Red Mustang made in 1996

23. Camry Color Red Silver Grey Year Year Year 1992 2000 1998 Red Camry made in 1992 Silver Camry made in 2000 Grey Corvette made in 1998 Tree of Sell Orders Model Mustang Corvette Color Color Red Black White Year Year Year 1996 Red Mustang made in 1996

24. Node in the Tree Each node contains a red-black tree foroneattribute. If there arekvalues for an attribute, search within the node takes O(lg k).

25. Matching a Buy Order LetSbe the number of sell orders andmbe the number of matches. Best case:Time = O(m + lg S) Worse case:Time = O(m lg S) Worst case: Time = O(S)

26. Trading Cycle Process all new orders Re-match all old orders Stop trading? No Yes

27. Processing Steps Process Re-match Stop? For every new order: If it is a buy order, then search for matches; if not filled, add it to the list. If it is a sell order, then insert it into the tree.

28. Processing Time Process Re-match LetPbe the number of old orders, Nbe the number of new orders, and mbe the number of matches per order. Best case:Time = O(N (m +lg P)) Worse case:Time = O(N m lg P)) Stop?

29. Re-Matching Steps Process Re-match Stop? For each buy order, search for matches amongnewsell orders.

30. Re-Matching Time Process Re-match Let Pbe the number of old orders, Nbe the number of new orders, and m be the number of matches per order. Best case: Time = O(P (m +lg N)) Worse case: Time = O(P m lg N)) Stop?

31. Outline • Main concepts • Previous work • Data structures • Performance

32. Performance Extensive empirical evaluation: • 400 MHz CPU • 1,024 Mbyte memory • 100 MHz bus

33. Control Variables • Number of old orders • Number of new orders • Length of item description

34. Measurements • Processing time • Re-matching time • Response time • Throughput

35. Process Processing Time Re-match Stop? Logarithmic Scale Linear Scale 80 70 60 50 40 30 20 10 0 100 10 time (sec) 1 0.1 0.01 1 10 100 1000 10000 100000 50K 100K 150K 200K 250K number of old orders number of old orders 256,8,192, and 262,144 new orders

36. Process Re-Matching Time Re-match Stop? Logarithmic Scale Linear Scale 80 70 60 50 40 30 20 10 0 100 10 time (sec) 1 0.1 0.01 1 10 100 1000 10000 100000 50K 100K 150K 200K 250K number of old orders number of old orders 256,8,192, and 262,144 new orders

37. Process Total Time Re-match Stop? Logarithmic Scale Linear Scale 80 70 60 50 40 30 20 10 0 100 10 time (sec) 1 0.1 0.01 1 10 100 1000 10000 100000 50K 100K 150K 200K 250K number of old orders number of old orders 256,8,192, and 262,144 new orders

38. Response Time: Buy Orders Logarithmic Scale Linear Scale 80 70 60 50 40 30 20 10 0 100 10 time (sec) 1 0.1 0.01 1 10 100 1000 10000 100000 50K 100K 150K 200K 250K number of old orders number of old orders 256,8,192, and 262,144 new orders

39. Response Time: Sell Orders Logarithmic Scale Linear Scale 80 70 60 50 40 30 20 10 0 100 10 time (sec) 1 0.1 0.01 1 10 100 1000 10000 100000 50K 100K 150K 200K 250K number of old orders number of old orders 256,8,192, and 262,144 new orders

40. Throughput Market with ten attributes: 5,600 new orders per second.

41. Throughput Market with ten attributes: 5,600 new orders per second. 100000 10000 1000 100 10 orders per second 1 3 10 30 100 number of attributes

42. Main Results • Formal model of complex markets • Exchange system for limited order • semantics • Evaluation of its performance

43. Future Work • Short-term • Reducing response time • Improving data structures • Long-term • Extend order semantics • Search for optimal matches • Use multiple CPUs