Combining Support Vector Machine with Genetic Algorithms to optimize investments in Forex markets with high leverage

This work proposes a new approach, based on Genetic Algorithms and Support Vector Machine to trade in the forex market. In this work, a new algorithm capable of generating technical rules to make investments with a given amount of leverage depending on the certainty of the prediction is presented. To forecast those predictions, a combination of a Support Vector Machine (SVM) algorithm - to identify and classify the market in three different stages-, and a Dynamic Genetic Algorithm- to optimize trading rules in each type of market, is used. The optimization of the trading rules is based on several technical indicators. Forex data for the EUR/USD currency pair, in a timeframe between the years of 2003 and 2016, is used as training and test data. The proposed architecture for the machine learning system, as well as the implementation and study of the proposed system is described in detail. The use of an hybrid system, combining a SVM and a GA with dynamic approaches such as hyper-mutation and adaptability approaches by training three different GA's for each type of market, provide a new approach for FOREX trading, where it is possible to classify trends using price sequences and therefore using the same classification for optimizing investment strategies with the most appropriate GA. Finally, the work shows promising results during the test period between the 2nd of January of 2015 until the 2nd of March of 2016, where the Return on Investment obtained is 83%. (C) 2018 Elsevier B.V. All rights reserved.