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Trading of Complex Commodities. Josh Johnson. Committee: Eugene Fink Lawrence Hall Srinivas Katkoori. Introduction. Motivation Build an automated exchange for trading goods and services. Introduction. Motivation Build an automated exchange for trading

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Trading of complex commodities l.jpg

Trading of Complex Commodities

Josh Johnson

Committee:

Eugene Fink

Lawrence Hall

Srinivas Katkoori


Introduction l.jpg
Introduction

  • Motivation

  • Build an automated

  • exchange for trading

  • goods and services


Introduction3 l.jpg
Introduction

  • Motivation

  • Build an automated

  • exchange for trading

  • goods and services

  • Combine the speed

  • and liquidity of the

  • stock exchange


Introduction4 l.jpg
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

+


Outline l.jpg
Outline

  • Main concepts

  • Previous work

  • Data structures

  • Performance


Concepts l.jpg
Concepts

  • Market

  • Orders

  • Matches

  • Fills


Market l.jpg
Market

All items that can be

traded form amarket.

Example:

All conceivable vehicles

compose a car market.


Orders l.jpg

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.


Buy and sell orders l.jpg
Buy and Sell Orders

Price

Price

Buy order

Sell order


Matching l.jpg
Matching

A buy order matches a sell order if:

item buy-region  sell-region,

buy-price(item) sell-price(item).


Matching orders l.jpg
Matching Orders

Sell order

Price

Buy order


Fills l.jpg

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


Implemented exchange system l.jpg
Implemented Exchange System

Specific sell orders

Good:

Sell a red Mustang

made in 1999.

Bad:

Sell any color Mustang

made before 1999.


Outline14 l.jpg
Outline

  • Main concepts

  • Previous work

  • Data structures

  • Performance


Previous work l.jpg
Previous Work

  • Auctions

  • Exchanges


Auctions l.jpg
Auctions

  • Complex commodities

  • Asymmetry between

  • buyers and sellers

  • Illiquid

  • Examples:

  • eBay, FreeMarkets, eMediator


Exchanges l.jpg
Exchanges

  • Simple commodities

  • Symmetry between

  • buyers and sellers

  • Liquid

  • Examples:

  • Stocks, Futures


Outline18 l.jpg
Outline

  • Main concepts

  • Previous work

  • Data structures

  • Performance


Main structures l.jpg
Main Structures

  • Tree of sell orders

  • List of buy orders


Tree of sell orders l.jpg
Tree of Sell Orders

Model

Mustang

Corvette


Tree of sell orders21 l.jpg
Tree of Sell Orders

Model

Mustang

Corvette

Color

Color

Red

Black

Red

White

Grey


Tree of sell orders22 l.jpg
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


Tree of sell orders23 l.jpg

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


Node in the tree l.jpg
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).


Matching a buy order l.jpg
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)


Trading cycle l.jpg
Trading Cycle

Process all

new orders

Re-match all

old orders

Stop

trading?

No

Yes


Processing steps l.jpg
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.


Processing time l.jpg
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?


Re matching steps l.jpg
Re-Matching Steps

Process

Re-match

Stop?

For each buy order,

search for matches

amongnewsell orders.


Re matching time l.jpg
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?


Outline31 l.jpg
Outline

  • Main concepts

  • Previous work

  • Data structures

  • Performance


Performance l.jpg
Performance

Extensive empirical evaluation:

  • 400 MHz CPU

  • 1,024 Mbyte memory

  • 100 MHz bus


Control variables l.jpg
Control Variables

  • Number of old orders

  • Number of new orders

  • Length of item description


Measurements l.jpg
Measurements

  • Processing time

  • Re-matching time

  • Response time

  • Throughput


Processing time35 l.jpg

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


Re matching time36 l.jpg

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


Total time l.jpg

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


Response time buy orders l.jpg
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


Response time sell orders l.jpg
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


Throughput l.jpg
Throughput

Market with ten attributes:

5,600 new orders per second.


Throughput41 l.jpg
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


Main results l.jpg
Main Results

  • Formal model of complex markets

  • Exchange system for limited order

  • semantics

  • Evaluation of its performance


Future work l.jpg
Future Work

  • Short-term

  • Reducing response time

  • Improving data structures

  • Long-term

  • Extend order semantics

  • Search for optimal matches

  • Use multiple CPUs


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