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A Boosting Approach for Automated Trading
German G. Creamer Stevens Institute of Technology, Howe School and Systems and Enterprises; Columbia University - Department of Computer Science Yoav Freund University of California, San Diego October 2006 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 Classifications: C49, C63, G24 Working Paper SeriesDate posted: October 17, 2006 ; Last revised: January 16, 2007Suggested Citation |
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