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Optimizing Algorithmic Strategies for Trading Bitcoin

机译:优化交易比特币的算法策略

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摘要

This research tries to establish to what extent three popular algorithmic systems for trading financial assets: the relative strength index, the moving average convergence diversion (MACD) and the pivot reversal (PR), are suitable for Bitcoin trading. Using data about daily Bitcoin prices from the beginning of April 2013 until the end of October 2018, we explored these strategies through particle swarm optimization. Our results demonstrate that the relative strength index produced poorer results than the buy and hold strategy. In contrast, the MACD and PR strategies dramatically outperformed the buy and hold strategy. However, our optimizing process produced even better results.
机译:本研究试图建立三大流行算法系统的交易金融资产的程度:相对强度指数,移动平均收敛转移(MACD)和枢轴逆转(PR)适用于比特币交易。从2013年4月初的每日比特币价格使用数据到2018年10月底,我们通过粒子群优化探索了这些策略。我们的结果表明,相对强度指数产生较差的结果,而不是购买和保持战略。相比之下,MACD和PR策略显着优于买卖策略。但是,我们的优化过程产生了更好的结果。

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