Automated Trading with Boosting and Expert Weighting

Quantitative Finance, Vol. 4, No. 10, pp. 401–420

18 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: April 1, 2010


We propose a multi-stock automated trading system that relies on a layered structure consisting of a machine learning algorithm, an online learning utility, and a risk management overlay. Alternating decision tree (ADT), which is implemented with Logitboost, was chosen as the underlying algorithm. One of the strengths of our approach is that the algorithm is able to select the best combination of rules derived from well-known technical analysis indicators and is also able to select the best parameters of the technical indicators. Additionally, the online learning layer combines the output of several ADTs and suggests a short or long position. Finally, the risk management layer can validate the trading signal when it exceeds a specified non-zero threshold and limit the application of our trading strategy when it is not profitable. We test the expert weighting algorithm with data of 100 randomly selected companies of the S&P 500 index during the period 2003–2005. We find that this algorithm generates abnormal returns during the test period. Our experiments show that the boosting approach is able to improve the predictive capacity when indicators are combined and aggregated as a single predictor. Even more, the combination of indicators of different stocks demonstrated to be adequate in order to reduce the use of computational resources, and still maintain an adequate predictive capacity.

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

JEL Classification: C49, C63, G24

Suggested Citation

Creamer, Germán G. and Freund, Yoav, Automated Trading with Boosting and Expert Weighting (April 1, 2010). Quantitative Finance, Vol. 4, No. 10, pp. 401–420 . Available at SSRN:

Germán G. Creamer (Contact Author)

Stevens Institute of Technology ( email )

1 Castle Point on Hudson
Hoboken, NJ 07030
United States
2012168986 (Phone)


Yoav Freund

University of California, San Diego ( email )

9500 Gilman Drive
Mail Code 0502
La Jolla, CA 92093-0502
United States

Register to save articles to
your library


Paper statistics

Abstract Views