Implementation of new strategies for the OLPS toolbox
New types of strategies for on-line portfolio selection in computational finance.
Our developers have developed new types of strategies for on-line portfolio selection in computational finance.
The first one is “Follow the leading history” (FLH) method described in “Efficient learning algorithms for changing environments” article by E. Hazan and C. Seshadhri. Core of the strategy lies in selection of an optimal solution among several ones suggested by several expert algorithms. Each of them uses the same optimization method but every expert makes calculations based on data from different periods. Selection of the optimal number and type of experts is specified in description of the strategy.
The second implemented strategy is Commission Avoidant Portfolio Ensembles (CAPE) described in “Online Learning of Commission Avoidant Portfolio Ensembles” article by G. Uziel and R. El-Yaniv. In this strategy along with existing expert algorithms there’s one more artificial expert added. This artificial expert allows to keep existing portfolio unchanged and to avoid expenses for making operations, maximizing profit in a long-term outlook.
Both strategies are developed in Matlab. We’ve also built wrappers that allow to use them in OLPS toolbox
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