Predicting Bitcoin Market Trends with Enhanced Technical Indicator Integration and Classification Models

• URL: http://arxiv.org/abs/2410.06935v1
• Date: Wed, 9 Oct 2024 14:29:50 GMT
• Title: Predicting Bitcoin Market Trends with Enhanced Technical Indicator Integration and Classification Models
• Authors: Abdelatif Hafid, Mohamed Rahouti, Linglong Kong, Maad Ebrahim, Mohamed Adel Serhani,
• Abstract summary: 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%.
• Score: 6.39158540499473
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Thanks to the high potential for profit, trading has become increasingly attractive to investors as the cryptocurrency and stock markets rapidly expand. However, because financial markets are intricate and dynamic, accurately predicting prices remains a significant challenge. The volatile nature of the cryptocurrency market makes it even harder for traders and investors to make decisions. This study presents a machine learning model based on classification to forecast the direction of the cryptocurrency market, i.e., whether prices will increase or decrease. The model is trained using historical data and important technical indicators such as the Moving Average Convergence Divergence, the Relative Strength Index, and Bollinger Bands. We illustrate our approach with an empirical study of the closing price of Bitcoin. Several simulations, including a confusion matrix and Receiver Operating Characteristic curve, are used to assess the model's performance, and the results show a buy/sell signal accuracy of over 92%. These findings demonstrate how machine learning models can assist investors and traders of cryptocurrencies in making wise/informed decisions in a very volatile market.

Related papers

• 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)
• Cryptocurrency Price Forecasting Using XGBoost Regressor and Technical Indicators [2.038893829552158]
  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.
  arXiv  Detail & Related papers  (2024-07-16T14:41:27Z)
• 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)
• A Comprehensive Analysis of Machine Learning Models for Algorithmic Trading of Bitcoin [0.3069335774032178]
  This study evaluates the performance of 41 machine learning models, including 21 classifiers and 20 regressors, in predicting Bitcoin prices for algorithmic trading. Our comprehensive analysis reveals the strengths and limitations of each model, providing critical insights for developing effective trading strategies.
  arXiv  Detail & Related papers  (2024-07-09T13:07:43Z)
• 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)
• 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)
• Forecasting Bitcoin volatility spikes from whale transactions and CryptoQuant data using Synthesizer Transformer models [5.88864611435337]
  We propose a deep learning Synthesizer Transformer model for forecasting volatility. Our results show that the model outperforms existing state-of-the-art models. Our findings underscore that the proposed method is a useful tool for forecasting extreme volatility movements in the Bitcoin market.
  arXiv  Detail & Related papers  (2022-10-06T05:44:29Z)
• 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)
• A Sentiment Analysis Approach to the Prediction of Market Volatility [62.997667081978825]
  We have explored the relationship between sentiment extracted from financial news and tweets and FTSE100 movements. The sentiment captured from news headlines could be used as a signal to predict market returns; the same does not apply for volatility. We developed an accurate classifier for the prediction of market volatility in response to the arrival of new information.
  arXiv  Detail & Related papers  (2020-12-10T01:15:48Z)
• Taking Over the Stock Market: Adversarial Perturbations Against Algorithmic Traders [47.32228513808444]
  We present a realistic scenario in which an attacker influences algorithmic trading systems by using adversarial learning techniques. We show that when added to the input stream, our perturbation can fool the trading algorithms at future unseen data points.
  arXiv  Detail & Related papers  (2020-10-19T06:28:05Z)
• 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.