Automated Trading with Boosting and Expert Weighting
Stevens Institute of Technology - Wesley J. Howe School of Technology Management
University of California, San Diego
April 1, 2010
Quantitative Finance, Vol. 4, No. 10, pp. 401–420
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.
Number of Pages in PDF File: 18
Keywords: Automated trading, machine learning, algorithmic trading, boosting
JEL Classification: C49, C63, G24Accepted Paper Series
Date posted: October 17, 2006 ; Last revised: February 20, 2013
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