To get a feeling of SVM performance in trading, I run different setups on the S&P 500 historical data from … the 50s. The main motif behind using this decade was to decide what parameters to vary and what to keep steady prior to running the most important tests. Treat it as an “in-sample” test to avoid (further;)) over-fitting. First the performance chart:
Finally all the stars have aligned and I can confidently devote some time for back-testing of new trading systems, and Support Vector Machines (SVM) are the new “toy” which is going to keep me busy for a while.
SVMs are a well-known tool from the area of supervised Machine Learning, and they are used both for classification and regression. For more details refer to the literature.
It seems to me that the most intuitive application for trading is regression, so let’s start by building an SVM regression model.