An ensemble learning method for Bitcoin price prediction based on volatility indicators and trend

Predicting the price of Bitcoin poses a challenge for researchers, merchants, traders and investors alike. This paper delves into the analysis of a Bitcoin price and volume dataset, spanning from September 2014 to July 2023. The objective is to extract multiple features related to price volatility and employ them to forecast the Bitcoin price for the subsequent 7 days. To achieve this, an Ensemble Learning Method (ELM) is proposed, able to estimate prices in both bullish and bearish markets. For price prediction, we consider three categories of predictors: 1) Bitcoin historical data; 2) volatility indicators; 3) trend prediction (price up or down) obtained through binary classification. Further, we employ a combination of ensemble models (regressors and classifiers) to predict the price at the daily level. The predictions of these models are stacked and weighted by the proposed ELM to improve the forecast accuracy. The ELM is rigorously tested under various market scenarios, yielding results that demonstrate a noteworthy level of forecast accuracy. The period of 2021 stands out as particularly interesting for prediction due to several dramatic price swings. The ELM achieves a substantial 26% improvement in overall accuracy compared to the best-performing individual ensemble model. Throughout the entire year-2021, the Mean Absolute Error (MAE) stood at 319 USD, indicating a notably low MAE.