This research has examined the ability of two forecasting methods to forecast Bitcoin’s price trends. The research is based on Bitcoin—USA dollar prices from the beginning of 2012 until the end of March 2020. Such a long period of time that includes volatile periods with strong up and downtrends introduces challenges to any forecasting system. We use particle swarm optimization to find the best forecasting combinations of setups. Results show that Bitcoin’s price changes do not follow the “Random Walk” efficient market hypothesis and that both Darvas Box and Linear Regression techniques can help traders to predict the bitcoin’s price trends. We also find that both methodologies work better predicting an uptrend than a downtrend. The best setup for the Darvas Box strategy is six days of formation. A Darvas box uptrend signal was found efficient predicting four sequential daily returns while a downtrend signal faded after two days on average. The best setup for the Linear Regression model is 42 days with 1 standard deviation.

中文翻译：

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使用算法学习系统预测比特币趋势

这项研究检验了两种预测方法预测比特币价格趋势的能力。该研究基于从 2012 年初到 2020 年 3 月底的比特币 - 美元价格。如此长的时间段包括具有强劲上涨和下跌趋势的波动期，这给任何预测系统带来了挑战。我们使用粒子群优化来找到设置的最佳预测组合。结果表明，比特币的价格变化并不遵循“随机游走”有效市场假设，Darvas Box 和线性回归技术都可以帮助交易者预测比特币的价格趋势。我们还发现，这两种方法比预测下降趋势更能预测上升趋势。Darvas Box 策略的最佳设置是六天的形成。发现 Darvas 盒上升趋势信号有效地预测了四个连续的每日回报，而下降趋势信号在平均两天后消退。线性回归模型的最佳设置是 42 天，标准差为 1。