Cohen Gil
Department of Management, Western Galilee Academic College, P.O.Box, 2125, Acre 2412101, Israel.
Entropy (Basel). 2020 Jul 30;22(8):838. doi: 10.3390/e22080838.
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.
本研究考察了两种预测方法预测比特币价格趋势的能力。该研究基于2012年初至2020年3月底的比特币兑美元价格。如此长的时间段，包括具有强劲上涨和下跌趋势的波动期，给任何预测系统都带来了挑战。我们使用粒子群优化算法来找到最佳的预测组合设置。结果表明，比特币的价格变化并不遵循“随机游走”有效市场假说，并且达尔瓦斯箱和线性回归技术都可以帮助交易员预测比特币的价格趋势。我们还发现，两种方法在预测上涨趋势时比预测下跌趋势时效果更好。达尔瓦斯箱策略的最佳设置是六天的形成期。发现达尔瓦斯箱上涨趋势信号在预测连续四个交易日回报时有效，而下跌趋势信号平均在两天后消失。线性回归模型的最佳设置是42天和1个标准差。
