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

机译：结合支持向量机与遗传算法，以高杠杆优化外汇市场的投资

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摘要

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.
机译：这项工作提出了一种基于遗传算法和支持向量机在外汇市场交易的新方法。在这项工作中，提出了一种能够产生技术规则的新算法，以根据预测的确定性提供给定金属杠杆的投资。为了预测那些预测，支持向量机（SVM）算法的组合 - 以三种不同阶段识别和分类市场 - 以及动态遗传算法 - 以优化每种类型的市场中的交易规则。交易规则的优化基于几个技术指标。欧元/美元货币对的外汇数据在2003年和2016年之间的时间范围内被用作培训和测试数据。详细描述了用于机器学习系统的所提出的架构，以及所提出的系统的实现和研究。使用混合系统，将SVM和GA与动态方法相结合，如通过培训三种不同的GA为每种类型的市场训练三种不同的GA，为外汇交易提供了一种新方法，可以分类趋势使用价格序列，因此使用相同的分类来优化最合适的GA的投资策略。最后，该工作表明，2015年1月2日至2016年3月2日期间的测试期间有希望的结果，获得的投资回报率为83％。 （c）2018 Elsevier B.v.保留所有权利。

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