Cryptocurrency Price Forecasting Using XGBoost Regressor and Technical Indicators

• URL: http://arxiv.org/abs/2407.11786v1
• Date: Tue, 16 Jul 2024 14:41:27 GMT
• Title: Cryptocurrency Price Forecasting Using XGBoost Regressor and Technical Indicators
• Authors: Abdelatif Hafid, Maad Ebrahim, Ali Alfatemi, Mohamed Rahouti, Diogo Oliveira,
• Abstract summary: This study introduces a machine learning approach to predict cryptocurrency prices. 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 evaluate the model's performance through various simulations, showing promising results.
• Score: 2.038893829552158
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• FinBERT-BiLSTM: A Deep Learning Model for Predicting Volatile Cryptocurrency Market Prices Using Market Sentiment Dynamics [3.6423651166048874]
  We propose a hybrid model that combines Bidirectional Long Short-Term Memory (Bi-LSTM) networks with FinBERT to enhance forecasting accuracy for cryptocurrencies. This approach fills a key gap in forecasting volatile financial markets by blending advanced time series models with sentiment analysis.
  arXiv  Detail & Related papers  (2024-11-02T14:43:06Z)
• Using Sentiment and Technical Analysis to Predict Bitcoin with Machine Learning [1.3053649021965603]
  This work represents a preliminary study on the importance of sentiment metrics in cryptocurrency forecasting. We present a novel approach for predicting Bitcoin price by combining the Fear & Greedy Index, a measure of market sentiment, Technical Analysis indicators, and the potential of Machine Learning algorithms.
  arXiv  Detail & Related papers  (2024-10-18T15:13:07Z)
• Predicting Bitcoin Market Trends with Enhanced Technical Indicator Integration and Classification Models [6.39158540499473]
  This study presents a machine learning model based on classification to forecast the direction of the cryptocurrency market. It is trained using historical data and important technical indicators such as the Moving Average Convergence Divergence, the Relative Strength Index, and Bollinger Bands. The results show a buy/sell signal accuracy of over 92%.
  arXiv  Detail & Related papers  (2024-10-09T14:29:50Z)
• When AI Meets Finance (StockAgent): Large Language Model-based Stock Trading in Simulated Real-world Environments [55.19252983108372]
  We have developed a multi-agent AI system called StockAgent, driven by LLMs. The StockAgent allows users to evaluate the impact of different external factors on investor trading. It avoids the test set leakage issue present in existing trading simulation systems based on AI Agents.
  arXiv  Detail & Related papers  (2024-07-15T06:49:30Z)
• Diffusion Variational Autoencoder for Tackling Stochasticity in Multi-Step Regression Stock Price Prediction [54.21695754082441]
  Multi-step stock price prediction over a long-term horizon is crucial for forecasting its volatility. Current solutions to multi-step stock price prediction are mostly designed for single-step, classification-based predictions. We combine a deep hierarchical variational-autoencoder (VAE) and diffusion probabilistic techniques to do seq2seq stock prediction. Our model is shown to outperform state-of-the-art solutions in terms of its prediction accuracy and variance.
  arXiv  Detail & Related papers  (2023-08-18T16:21:15Z)
• Dynamic Bayesian Networks for Predicting Cryptocurrency Price Directions: Uncovering Causal Relationships [1.4356611205757077]
  Six popular cryptocurrencies, Bitcoin, Coin, Litecoin, Ripple, and Tether are studied in this work. We propose a dynamic Bayesian network (DBN)-based approach to uncover potential causal relationships among various features including social media data, traditional financial market factors, and technical indicators. The results show that while DBN performance varies across cryptocurrencies, some cryptocurrencies exhibiting higher predictive accuracy than others, the DBN significantly outperforms the baseline models.
  arXiv  Detail & Related papers  (2023-06-13T22:07:51Z)
• Can ChatGPT Forecast Stock Price Movements? Return Predictability and Large Language Models [51.3422222472898]
  We document the capability of large language models (LLMs) like ChatGPT to predict stock price movements using news headlines. We develop a theoretical model incorporating information capacity constraints, underreaction, limits-to-arbitrage, and LLMs.
  arXiv  Detail & Related papers  (2023-04-15T19:22:37Z)
• Deep Q-Learning Market Makers in a Multi-Agent Simulated Stock Market [58.720142291102135]
  This paper focuses precisely on the study of these markets makers strategies from an agent-based perspective. We propose the application of Reinforcement Learning (RL) for the creation of intelligent market markers in simulated stock markets.
  arXiv  Detail & Related papers  (2021-12-08T14:55:21Z)
• The Doge of Wall Street: Analysis and Detection of Pump and Dump Cryptocurrency Manipulations [50.521292491613224]
  This paper performs an in-depth analysis of two market manipulations organized by communities over the Internet: The pump and dump and the crowd pump. The pump and dump scheme is a fraud as old as the stock market. Now, it got new vitality in the loosely regulated market of cryptocurrencies. We report on three case studies related to pump and dump groups.
  arXiv  Detail & Related papers  (2021-05-03T10:20:47Z)
• Pump and Dumps in the Bitcoin Era: Real Time Detection of Cryptocurrency Market Manipulations [50.521292491613224]
  We perform an in-depth analysis of pump and dump schemes organized by communities over the Internet. We observe how these communities are organized and how they carry out the fraud. We introduce an approach to detect the fraud in real time that outperforms the current state of the art.
  arXiv  Detail & Related papers  (2020-05-04T21:36:18Z)
• Ascertaining price formation in cryptocurrency markets with DeepLearning [8.413339060443878]
  This paper is inspired by the recent success of using deep learning for stock market prediction. We analyze and present the characteristics of the cryptocurrency market in a high-frequency setting. We achieve a consistent $78%$ accuracy on the prediction of the mid-price movement on live exchange rate of Bitcoins vs US dollars.
  arXiv  Detail & Related papers  (2020-02-09T20:23:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.