Cryptocurrency Price Forecasting Using XGBoost Regressor and Technical Indicators

TL;DR

This paper presents a machine learning approach using XGBoost and technical indicators to predict Bitcoin prices, aiming to assist traders in the volatile cryptocurrency market.

Contribution

It introduces a novel combination of technical indicators with XGBoost for cryptocurrency price prediction, demonstrating its effectiveness on Bitcoin data.

Findings

The model achieved promising prediction accuracy.

Technical indicators like EMA and MACD improve model performance.

The approach can aid traders in decision-making during market volatility.

Abstract

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.