After taking into consideration the designing of a backtesting model, one or more algorithms may be used to improve the model on a continuous basis. This section briefly covers some of the algorithmic techniques used in areas of backtesting, such as data mining and machine learning.
The k-means clustering algorithm is a method of clustering analysis in data mining. From the backtest results of n observations, the k-means algorithm is designed to classify the data into k clusters based on their relative distance from each other. The center point of each cluster is computed. The objective then is to find the within-cluster sum of squares that gives us a model averaged point. The model averaged point indicates the likely average performance of the model, which can be used for further comparison with the performance of other models.