Improved Particle Swarm Optimization method for investment strategies parameters computing

The paper studies the problem of computing the parameters for investment strategies. Proposed is an innovative modification of Particle Swarm Optimization algorithm for discrete and continuous data. The article shows how discrete and continuous version of the algorithm can be combined in order to achieve the best results. Moreover, the presented algorithm is expanded by a multi-swarm mechanism which allows to achieve better results in a fixed time. The proposed algorithm was tested on a simple investment strategy, based on one of the well known indicators Rate of Change (further referred as ROC) that uses a mixture of discrete and continuous parameters. All the tests were performed on a data gathered from one of the most important of currency pairs — EURUSD.

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