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Primary Article

A Boosting Approach for Automated Trading

Germán Creamer and Yoav Freund
The Journal of Trading Summer 2007, 2 (3) 84-96; DOI: https://doi.org/10.3905/jot.2007.688953
Germán Creamer
An adjunct associate research scientist at the Center for Computational Learning Systems, Columbia University in New York, NY, and an affiliated professor at Centrum Católica, Pontificia Universidad Católica del Perú in Lima, Perú.
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  • For correspondence: ggc14@columbia.edu
Yoav Freund
A professor at the Department of Computer Science and Engineering, University of California in San Diego, CA.
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  • For correspondence: yfreund@cs.ucsd.edu
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Abstract

This article 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.

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The Journal of Trading
Vol. 2, Issue 3
Summer 2007
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A Boosting Approach for Automated Trading
Germán Creamer, Yoav Freund
The Journal of Trading Jun 2007, 2 (3) 84-96; DOI: 10.3905/jot.2007.688953

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A Boosting Approach for Automated Trading
Germán Creamer, Yoav Freund
The Journal of Trading Jun 2007, 2 (3) 84-96; DOI: 10.3905/jot.2007.688953
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  • Demystifying the Relationship between Trading-Level and Portfolio-Level Tracking Error
  • Dual Traders in Fully Anonymous Stock Markets, Iceberg Orders, and Liqudity Provision
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