# Cryptocurrency Price Forecasting Using XGBoost Regressor and Technical Indicators

arXiv: https://arxiv.org/abs/2407.11786 · HTML: https://mlsanity.com/paper/2407.11786/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 35 references that are in the map.
- First posted on arXiv 16 Jul 2024.

## Builds on (most cited first, 4 of 4)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Financial Time-Series Forecasting: Towards Synergizing Performance And Interpretability Within a Hybrid Machine Learning Approach](https://mlsanity.com/paper/2401.00534/) (2023)
- [FinGAT: Financial Graph Attention Networks for Recommending Top-K Profitable Stocks](https://mlsanity.com/paper/2106.10159/) (2021)
- [Advancing DDoS Attack Detection: A Synergistic Approach Using Deep Residual Neural Networks and Synthetic Oversampling](https://mlsanity.com/paper/2401.03116/) (2024)

## Abstract (arXiv)

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.