trading of complex commodities committee: eugene fink lawrence hall srinivas katkoori josh johnson
TRANSCRIPT
Trading of Complex Commodities
Committee:Eugene FinkLawrence HallSrinivas Katkoori
Josh Johnson
IntroductionMotivation• Build an automated exchange for trading goods and services
IntroductionMotivation• Build an automated exchange for trading goods and services
• Combine the speed and liquidity of the stock exchange
IntroductionMotivation• Build an automated exchange for trading goods and services
• Combine the speed and liquidity of the stock exchange with the flexibility of eBay
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Outline
• Main concepts
• Previous work
• Data structures
• Performance
Concepts
• Market
• Orders
• Matches
• Fills
Market
All items that can be traded form a market.
Example: All conceivable vehicles compose a car market.
Orders
An order is a subset of the market along with a price function.
,-$1 for every ten miles.
Order example: Any Mustang or Corvette;Mustang for $38,000 or Corvette for $40,000.
Buy and Sell Orders
Price
Buy order
Price
Sell order
Matching
A buy order matches a sell order if:
item buy-region sell-region,buy-price(item) sell-price(item).
Matching Orders
Price
Buy order
Sell order
Fills
Buy order:Any Color Sports Car$36,000
Sell order:Silver Limo $50,000
or Red Mustang $32,000
Fill:Red Mustang
$34,000
Implemented Exchange System
Specific sell orders
Good:Sell a red Mustangmade in 1999.
Bad:Sell any color Mustangmade before 1999.
Outline
• Main concepts
• Previous work
• Data structures
• Performance
Previous Work
• Auctions
• Exchanges
Auctions
• Complex commodities
• Asymmetry between buyers and sellers
• Illiquid
Examples:eBay, FreeMarkets, eMediator
Exchanges
• Simple commodities
• Symmetry between buyers and sellers
• Liquid
Examples:Stocks, Futures
Outline
• Main concepts
• Previous work
• Data structures
• Performance
Main Structures
• Tree of sell orders
• List of buy orders
Tree of Sell Orders
Model
CorvetteMustang
Tree of Sell Orders
Model
Color Color
CorvetteMustang
Red Black GreyRed White
Tree of Sell Orders
Model
Year
Color Color
Year Year Year Year
CorvetteMustang
Red Black GreyRed
1996
White
Red Mustangmade in 1996
Tree of Sell Orders
Model
Year
Color Color
Year Year
CorvetteMustang
Red Black
1996
White
Red Mustangmade in 1996
Color
Camry
Red
Year
Red Camrymade in 1992
1992
Year
Grey
Grey Corvettemade in 1998
1998
Year
Silver
Silver Camrymade in 2000
2000
Node in the Tree
Each node contains a red-black treefor one attribute.
If there are k values for an attribute,search within the node takes O(lg k).
Matching a Buy Order
Let S be the number of sell orders and m be the number of matches.
Best case: Time = O(m + lg S)
Worse case: Time = O(m lg S)
Worst case: Time = O(S)
Trading Cycle
Process all new orders
Re-match all old orders
Stoptrading?
Yes
No
Processing Steps
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.
Process
Re-match
Stop?
Processing Time
Let P be the number of old orders, N be the number of new orders, andm be the number of matches per order.
Best case: Time = O(N (m + lg P))
Worse case: Time = O(N m lg P))
Process
Re-match
Stop?
Re-Matching Steps
For each buy order,search for matches among new sell orders.
Process
Re-match
Stop?
Let P be the number of old orders, N be the number of new orders, andm be the number of matches per order.
Best case: Time = O(P (m + lg N))
Worse case: Time = O(P m lg N))
Re-Matching Time
Process
Re-match
Stop?
Outline
• Main concepts
• Previous work
• Data structures
• Performance
Performance
• 400 MHz CPU
• 1,024 Mbyte memory
• 100 MHz bus
Extensive empirical evaluation:
Control Variables
• Number of old orders
• Number of new orders
• Length of item description
Measurements
• Processing time
• Re-matching time
• Response time
• Throughput
Processing Time
number of old orders
Logarithmic Scale
number of old orders
Linear Scale
256, 8,192, and 262,144 new orders
Process
Re-match
Stop?ti
me
(sec
)
1 10 100 1000 10000 100000
100
10
1
0.1
0.01
80
70
60
50
40
30
20
10
0 50K 100K 150K 200K 250K
Re-Matching Time
256, 8,192, and 262,144 new orders
Logarithmic Scale Linear Scale
Process
Re-match
Stop?ti
me
(sec
)
1 10 100 1000 10000 100000
100
10
1
0.1
0.01 50K 100K 150K 200K 250K
80
70
60
50
40
30
20
10
0
number of old orders number of old orders
Total Time
256, 8,192, and 262,144 new orders
Logarithmic Scale Linear Scale
Process
Re-match
Stop?ti
me
(sec
)
1 10 100 1000 10000 100000
100
10
1
0.1
0.01 50K 100K 150K 200K 250K
80
70
60
50
40
30
20
10
0
number of old orders number of old orders
Response Time: Buy Orders
256, 8,192, and 262,144 new orders
Logarithmic Scale Linear Scale
tim
e (s
ec)
1 10 100 1000 10000 100000
100
10
1
0.1
0.01 50K 100K 150K 200K 250K
80
70
60
50
40
30
20
10
0
number of old orders number of old orders
Response Time: Sell Orders
256, 8,192, and 262,144 new orders
Logarithmic Scale Linear Scale
tim
e (s
ec)
1 10 100 1000 10000 100000
100
10
1
0.1
0.01 50K 100K 150K 200K 250K
80
70
60
50
40
30
20
10
0
number of old orders number of old orders
Throughput
Market with ten attributes:5,600 new orders per second.
Throughput
Market with ten attributes:5,600 new orders per second.
number of attributes
orde
rs p
er s
econ
d
1 3 10 30 100
100000
10000
1000
100
10
Main Results
• Formal model of complex markets
• Exchange system for limited order semantics
• Evaluation of its performance
Future Work
Short-term
• Reducing response time
• Improving data structures
Long-term
• Extend order semantics
• Search for optimal matches
• Use multiple CPUs