Evolutionary Arbitrage For FTSE-100 Index Options
and Futures
S. Markose, E. Tsang, H. Er, and A. Salhi. Proceedings of the 2001 Congress on Evolutionary
Computation CEC2001, page 275--282. COEX, World Trade Center, 159 Samseong-dong,
Gangnam-gu, Seoul, Korea, IEEE Press, (27-30 May 2001)
DOI: doi:10.1109/CEC.2001.934401
Abstract
The objective in this paper is to develop and
implement FGP-2 (Financial Genetic Programming) on
intra daily tick data for stock index options and
futures arbitrage in a manner that is suitable for
online trading when windows of profitable arbitrage
opportunities exist for short periods from one to ten
minutes. Our benchmark for FGP-2 is the textbook rule
for detecting arbitrage profits. This rule has the
drawback that it awaits a contemporaneous profitable
signal to implement an arbitrage in the same direction.
A novel methodology of randomised sampling is used to
train FGP-2 to pick up the fundamental arbitrage
patterns. Care is taken to fine tune weights in the
fitness function to enhance performance. As arbitrage
opportunities are few, missed opportunities can be as
costly as wrong recommendations to trade. Unlike
conventional genetic programs, FGP-2 has a constraint
satisfaction feature supplementing the fitness function
that enables the user to train the FGP to specify a
minimum and a maximum number of profitable arbitrage
opportunities that are being sought. Historical sample
data on arbitrage opportunities enables the user to set
these minimum and maximum bounds. Good FGP rules for
arbitrage are found to make a 3-fold improvement in
profitability over the textbook rule. This application
demonstrates the success of FGP-2 in its interactive
capacity that allows experts to channel their knowledge
into machine discovery
COEX, World Trade Center, 159 Samseong-dong,
Gangnam-gu, Seoul, Korea
booktitle
Proceedings of the 2001 Congress on Evolutionary
Computation CEC2001
year
2001
month
27-30 May
pages
275--282
publisher
IEEE Press
organisation
IEEE Neural Network Council (NNC), Evolutionary
Programming Society (EPS), Institution of Electrical
Engineers (IEE)
publisher_address
445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
08855-1331, USA
isbn
0-7803-6658-1
notes
CEC-2001 - A joint meeting of the IEEE, Evolutionary
Programming Society, Galesia, and the IEE.
IEEE Catalog Number = 01TH8546C,
Library of Congress Number =
%0 Conference Paper
%1 markose:2001:eafiof
%A Markose, Sheri
%A Tsang, Edward
%A Er, Hakan
%A Salhi, Abdel
%B Proceedings of the 2001 Congress on Evolutionary
Computation CEC2001
%C COEX, World Trade Center, 159 Samseong-dong,
Gangnam-gu, Seoul, Korea
%D 2001
%I IEEE Press
%K Arbitrage, Discovery, FGP, Futures Machine Options, algorithms, genetic programming,
%P 275--282
%R doi:10.1109/CEC.2001.934401
%T Evolutionary Arbitrage For FTSE-100 Index Options
and Futures
%U http://privatewww.essex.ac.uk/~scher/eddieProj/TsangCEE2001.doc
%X The objective in this paper is to develop and
implement FGP-2 (Financial Genetic Programming) on
intra daily tick data for stock index options and
futures arbitrage in a manner that is suitable for
online trading when windows of profitable arbitrage
opportunities exist for short periods from one to ten
minutes. Our benchmark for FGP-2 is the textbook rule
for detecting arbitrage profits. This rule has the
drawback that it awaits a contemporaneous profitable
signal to implement an arbitrage in the same direction.
A novel methodology of randomised sampling is used to
train FGP-2 to pick up the fundamental arbitrage
patterns. Care is taken to fine tune weights in the
fitness function to enhance performance. As arbitrage
opportunities are few, missed opportunities can be as
costly as wrong recommendations to trade. Unlike
conventional genetic programs, FGP-2 has a constraint
satisfaction feature supplementing the fitness function
that enables the user to train the FGP to specify a
minimum and a maximum number of profitable arbitrage
opportunities that are being sought. Historical sample
data on arbitrage opportunities enables the user to set
these minimum and maximum bounds. Good FGP rules for
arbitrage are found to make a 3-fold improvement in
profitability over the textbook rule. This application
demonstrates the success of FGP-2 in its interactive
capacity that allows experts to channel their knowledge
into machine discovery
%@ 0-7803-6658-1
@inproceedings{markose:2001:eafiof,
abstract = {The objective in this paper is to develop and
implement FGP-2 (Financial Genetic Programming) on
intra daily tick data for stock index options and
futures arbitrage in a manner that is suitable for
online trading when windows of profitable arbitrage
opportunities exist for short periods from one to ten
minutes. Our benchmark for FGP-2 is the textbook rule
for detecting arbitrage profits. This rule has the
drawback that it awaits a contemporaneous profitable
signal to implement an arbitrage in the same direction.
A novel methodology of randomised sampling is used to
train FGP-2 to pick up the fundamental arbitrage
patterns. Care is taken to fine tune weights in the
fitness function to enhance performance. As arbitrage
opportunities are few, missed opportunities can be as
costly as wrong recommendations to trade. Unlike
conventional genetic programs, FGP-2 has a constraint
satisfaction feature supplementing the fitness function
that enables the user to train the FGP to specify a
minimum and a maximum number of profitable arbitrage
opportunities that are being sought. Historical sample
data on arbitrage opportunities enables the user to set
these minimum and maximum bounds. Good FGP rules for
arbitrage are found to make a 3-fold improvement in
profitability over the textbook rule. This application
demonstrates the success of FGP-2 in its interactive
capacity that allows experts to channel their knowledge
into machine discovery},
added-at = {2008-06-19T17:35:00.000+0200},
address = {COEX, World Trade Center, 159 Samseong-dong,
Gangnam-gu, Seoul, Korea},
author = {Markose, Sheri and Tsang, Edward and Er, Hakan and Salhi, Abdel},
biburl = {https://www.bibsonomy.org/bibtex/23ab6c192485b7d5a073994f5f957f3e0/brazovayeye},
booktitle = {Proceedings of the 2001 Congress on Evolutionary
Computation CEC2001},
doi = {doi:10.1109/CEC.2001.934401},
interhash = {72d9073798b31a0470b50040cd6bad9c},
intrahash = {3ab6c192485b7d5a073994f5f957f3e0},
isbn = {0-7803-6658-1},
keywords = {Arbitrage, Discovery, FGP, Futures Machine Options, algorithms, genetic programming,},
month = {27-30 May},
notes = {CEC-2001 - A joint meeting of the IEEE, Evolutionary
Programming Society, Galesia, and the IEE.
IEEE Catalog Number = 01TH8546C,
Library of Congress Number =},
organisation = {IEEE Neural Network Council (NNC), Evolutionary
Programming Society (EPS), Institution of Electrical
Engineers (IEE)},
pages = {275--282},
publisher = {IEEE Press},
publisher_address = {445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
08855-1331, USA},
timestamp = {2008-06-19T17:46:18.000+0200},
title = {Evolutionary Arbitrage For {FTSE}-100 Index Options
and Futures},
url = {http://privatewww.essex.ac.uk/~scher/eddieProj/TsangCEE2001.doc},
year = 2001
}