- author = "Harish Subramanian and Subramanian Ramamoorthy and Peter Stone and Benjamin J. Kuipers",
- title = "Designing safe, profitable automated stock trading agents using evolutionary algorithms",
- booktitle = "{GECCO 2006:} Proceedings of the 8th annual conference on Genetic and evolutionary computation",
- year = "2006",
- editor = "Maarten Keijzer and Mike Cattolico and Dirk Arnold and Vladan Babovic and Christian Blum and Peter Bosman and Martin V. Butz and Carlos {Coello Coello} and Dipankar Dasgupta and Sevan G. Ficici and James Foster and Arturo Hernandez-Aguirre and Greg Hornby and Hod Lipson and Phil McMinn and Jason Moore and Guenther Raidl and Franz Rothlauf and Conor Ryan and Dirk Thierens",
- volume = "2",
- ISBN = "1-59593-186-4",
- pages = "1777--1784",
- address = "Seattle, Washington, USA",
-
URL = "
http://gpbib.cs.ucl.ac.uk/gecco2006/docs/p1777.pdf",
-
DOI = "
doi:10.1145/1143997.1144285",
- publisher = "ACM Press",
- publisher_address = "New York, NY, 10286-1405, USA",
- month = "8-12 " # jul,
- organisation = "ACM SIGEVO (formerly ISGEC)",
- keywords = "genetic algorithms, genetic programming, Real-World Applications, finance, fitness evaluation",
- size = "8 pages",
- abstract = "Trading rules are widely used by practitioners as an effective means to mechanize aspects of their reasoning about stock price trends. However, due to the simplicity of these rules, each rule is susceptible to poor behaviour in specific types of adverse market conditions. Naive combinations of such rules are not very effective in mitigating the weaknesses of component rules. We demonstrate that sophisticated approaches to combining these trading rules enable us to overcome these problems and gainfully use them in autonomous agents. We achieve this combination through the use of genetic algorithms and genetic programs. Further, we show that it is possible to use qualitative characterizations of stochastic dynamics to improve the performance of these agents by delineating safe, or feasible, regions. We present the results of experiments conducted within the Penn-Lehman Automated Trading project. In this way we are able to demonstrate that autonomous agents can achieve consistent profitability in a variety of market conditions, in ways that are human competitive.",
-
notes = "GECCO-2006 A joint meeting of the fifteenth
international conference on genetic algorithms
(ICGA-2006) and the eleventh annual genetic programming
conference (GP-2006).
ACM Order Number 910060",
