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A Boosting Approach for Automated Trading

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

10 Pages Posted: 17 Oct 2006 Last revised: 20 Feb 2013

Germán G. Creamer

Stevens Institute of Technology

Yoav Freund

University of California, San Diego

Date Written: 2007

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.

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

JEL Classification: C49, C63, G24

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: https://ssrn.com/abstract=938042

Germán G. Creamer (Contact Author)

Stevens Institute of Technology ( 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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