TY - JOUR
T1 - Automated trading with boosting and expert weighting
AU - Creamer, Germán
AU - Freund, Yoav
PY - 2010/4
Y1 - 2010/4
N2 - 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.
AB - 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.
KW - Algorithmic trading
KW - Automated trading
KW - Boosting
KW - Machine learning
UR - http://www.scopus.com/inward/record.url?scp=77951262368&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=77951262368&partnerID=8YFLogxK
U2 - 10.1080/14697680903104113
DO - 10.1080/14697680903104113
M3 - Article
AN - SCOPUS:77951262368
SN - 1469-7688
VL - 10
SP - 401
EP - 420
JO - Quantitative Finance
JF - Quantitative Finance
IS - 4
ER -