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Ensemble of machine learning algorithms for cryptocurrency investment with different data resampling methods
Applied Soft Computing ( IF 8.7 ) Pub Date : 2020-02-26 , DOI: 10.1016/j.asoc.2020.106187
Tomé Almeida Borges , Rui Ferreira Neves

This work proposes a system based on machine learning aimed at creating an investment strategy capable of trading on the cryptocurrency exchange markets. Additionally, with the goal of generating investments with higher returns and lower risk, rather than investing on predictions based on time sampled financial series, a novel method for resampling financial series was developed and employed in this work. For this purpose, the originally time sampled financial series are resampled according to a closing value threshold, thus creating a series prone to obtaining higher returns and lower risk than the original series. Out of these resampled series as well as the original, technical indicators are calculated and fed as inputs to four machine learning algorithms: Logistic Regression, Random Forest, Support Vector Classifier, and Gradient Tree Boosting. Each of these algorithms is responsible for generating a transaction signal. Afterwards, a fifth transaction signal is generated by simply calculating the unweighted average of the four trading signals outputted from the previous algorithms, to improve on their results. In the end, the investment results obtained with the resampled series are compared to the commonly utilized fixed time interval sampling. This work demonstrates that independently of using or not a resampling method, all learning algorithms outperform the Buy and Hold (B&H) strategy in the overwhelming majority of the 100 markets tested. Nevertheless, out of the learning algorithms, the unweighted average obtains the best overall results, namely accuracies up to 59.26% for time resampled series. But most importantly, it is concluded that both alternative resampling methods tested are capable of generating far greater returns and with lower risk relatively to time resampled data.



中文翻译:

具有不同数据重采样方法的用于加密货币投资的机器学习算法的集成

这项工作提出了一种基于机器学习的系统,旨在创建一种能够在加密货币交换市场上进行交易的投资策略。此外,为了产生具有较高回报和较低风险的投资,而不是根据基于时间采样的财务系列的预测进行投资,开发了一种对财务系列进行重采样的新方法,并将其用于这项工作。为此,根据收盘价阈值对原始时间采样的金融系列进行重新采样,从而创建一个比原始序列更容易获得更高收益和更低风险的序列。从这些重新采样的系列以及原始的技术指标中,计算技术指标并将其作为输入输入四种机器学习算法:逻辑回归,随机森林,支持向量分类器和梯度树增强。这些算法中的每一个都负责产生交易信号。然后,通过简单地计算从先前算法输出的四个交易信号的未加权平均值来生成第五交易信号,以改善其结果。最后,将通过重采样系列获得的投资结果与常用的固定时间间隔采样进行比较。这项工作表明,与使用或不使用重采样方法无关,在所测试的100个市场的绝大多数中,所有学习算法的性能均优于买入并持(B&H)策略。然而,在学习算法中,未加权平均值获得了最佳的总体结果,即时间重采样序列的精度高达59.26%。但最重要的是

更新日期:2020-02-26
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