Cryptocurrency Price Forecasting Using XGBoost Regressor and Technical Indicators
It addresses the challenge of making profitable decisions for cryptocurrency traders and investors, but it is incremental as it applies an existing method to a specific domain.
This study tackled the problem of predicting cryptocurrency prices, which are highly volatile, by using technical indicators like EMA and MACD with an XGBoost regressor model, showing promising results in simulations for Bitcoin closing prices.
The rapid growth of the stock market has attracted many investors due to its potential for significant profits. However, predicting stock prices accurately is difficult because financial markets are complex and constantly changing. This is especially true for the cryptocurrency market, which is known for its extreme volatility, making it challenging for traders and investors to make wise and profitable decisions. This study introduces a machine learning approach to predict cryptocurrency prices. Specifically, we make use of important technical indicators such as Exponential Moving Average (EMA) and Moving Average Convergence Divergence (MACD) to train and feed the XGBoost regressor model. We demonstrate our approach through an analysis focusing on the closing prices of Bitcoin cryptocurrency. We evaluate the model's performance through various simulations, showing promising results that suggest its usefulness in aiding/guiding cryptocurrency traders and investors in dynamic market conditions.