Abstract

http://ssrn.com/abstract=938042
 
 

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A Boosting Approach for Automated Trading


Germán G. Creamer


Stevens Institute of Technology - Wesley J. Howe School of Technology Management

Yoav Freund


University of California, San Diego

2007

Journal of Trading, Vol. 2, No. 3, pp. 84-96.

Abstract:     
This paper describes an algorithm for short-term technical trading. The algorithm was tested in the context of the Penn-Lehman Automated Trading (PLAT) competition. The algorithm is based on three main ideas. The first idea is to use a combination of technical indicators to predict the daily trend of the stock, the combination is optimized using a boosting algorithm. The second idea is to use the constant rebalanced portfolios within the day in order to take advantage of market volatility without increasing risk. The third idea is to use limit orders rather than market orders in order to minimize transaction costs.

Number of Pages in PDF File: 10

Keywords: Automated trading, machine learning, algorithmic trading, boosting

JEL Classification: C49, C63, G24

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Date posted: October 17, 2006 ; Last revised: February 20, 2013

Suggested Citation

Creamer, Germán G. and Freund, Yoav, A Boosting Approach for Automated Trading (2007). Journal of Trading, Vol. 2, No. 3, pp. 84-96.. Available at SSRN: http://ssrn.com/abstract=938042

Contact Information

German G. Creamer (Contact Author)
Stevens Institute of Technology - Wesley J. Howe School of Technology Management ( email )
1 Castle Point on Hudson
Hoboken, NJ 07030
United States
2012168986 (Phone)
HOME PAGE: http://www.creamer-co.com

Yoav Freund
University of California, San Diego ( email )
9500 Gilman Drive
Mail Code 0502
La Jolla, CA 92093-0502
United States
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