Cryptocurrencies have gained immense significance and popularity in recent times. With thousands of digital currencies available, selecting the right one can be challenging for users. In the financial sector, accurately predicting future prices is crucial for profitable investments in digital currencies. However, price prediction in this realm poses unique challenges, as it lacks physical goods or services as the basis, unlike stock prices. Machine learning emerges as a pivotal tool for addressing this challenge and plays a vital role in price prediction. This research analyzes five prominent currencies - Monero, Bitcoin, Ethereum, IOTA, and Zcash - employing five models: SVR, LRG, Huber, RANSAC, MLP, and AdaBoost. The experimental results demonstrate promising outcomes, showcasing the ability to predict digital currency prices with an impressive R2 score of 1.0 for specific machine learning algorithms. This advancement opens new avenues for informed decision-making and profitable ventures in the dynamic world of digital currencies
Predicting the prices of cryptocurrency owing to its volatility, instability, and other factors has been challenging; investors and traders especially in Nigeria have been on a constant look for a more reliable way of knowing market trends and prices and while there has been so many research conducted using deep learning, the results for which has fall short of what investors could called a strong predictor. This research reviewed the results of many works that had been done and proposed two types of recurrent neural network (RNNs) namely Long Short-Term Memory and Gated Recurrent Unit (GRU) for predicting the future prices of two of the most common crypto assets namely Bitcoin (BTC) and Ethereum (ETH), these two were selected based on their popularity, the volume traded and their market capitalization. The experiment was conducted in a GPU Jupyter Notebook environment and the performance of our experiment was evaluated on a test set using root mean square error (RMSE) and based on its values, the LSTM presented a better performance with rsme scores of 654.66, and 80.30 respectively for BTC and ETH as compared to GRU. The paper proceeds further to compare the results of this experiment with other related works and we discovered that its performance is a great improvement.
The stock market is a topic that is of interest to all sorts of people. It is a place where the prices change very drastically. So, something needs to be done to help the people risking their money on the stock market. The public's opinions are crucial for the stock market. Sentiment is a very powerful force that is constantly changing and having a significant impact. It is reflected on social media platforms, where almost the entire country is active, as well as in the daily news. Many projects have been done in the stock prediction genre, but since sentiments play a big part in the stock market, making predictions of prices without them would lead to inefficient predictions, and hence Sentiment analysis is very important for stock market price prediction. To predict stock market prices, we will combine sentiment analysis from various sources, including News and Twitter. Results are evaluated for two different cryptocurrencies: Ethereum and Solana. Random Forest achieved the best RMSE of 13.434 and MAE of 11.919 for Ethereum. Support Vector Machine achieved the best RMSE of 2.48 and MAE of 1.78 for Solana.
Quoc Minh Nguyen, Dat Tran, Juho Kanniainen, Alexandros Iosifidis · 5 authors
Many cryptocurrency brokers nowadays offer a va-riety of derivative assets that allow traders to perform hedging or speculation. This paper proposes an effective algorithm based on neural networks to take advantage of these investment products. The proposed algorithm constructs a portfolio that contains a pair of negatively correlated assets. A deep neural network, which outputs the allocation weight of each asset at a time interval, is trained to maximize the Sharpe ratio. A novel loss term is proposed to regulate the network's bias towards a specific asset, thus enforcing the network to learn an allocation strategy that is close to a minimum variance strategy. Extensive experiments were conducted using data collected from Binance spanning 19 months to evaluate the effectiveness of our approach. The backtest results show that the proposed algorithm can produce neural networks that are able to make profits in different market situations.
Cryptocurrency works similar to standard currency, however, virtual payments are made for goods and services without the intervention of any central authority. Many investors believe in and use Twitter tweets to guide their daily cryptocurrency trading. In this project, we investigated the feasibility of sentiment analysis and emotion for cryptocurrencies. For the study, we targeted (BTC) Bitcoin and collected related data. The data collection, cleaning were essential components of the study. Analysis of the sentiments about cryptocurrency is highly desirable to provide a holistic view of peoples' perceptions. In this regard, this study performs both sentiment analysis and emotion detection using the tweets related to the Bitcoin which are widely used for predicting the market prices of cryptocurrency. For increasing the efficacy of the analysis, a deep learning ensemble model LSTM-GRU is proposed that combines two recurrent neural networks applications. Comparatively, a larger number of people feel happy with the use of cryptocurrency, followed by fear and surprise emotions. The model achieves the highest performance for sentiment analysis with a 0.91 accuracy score and the highest emotion 0.83. Similarly, LSTM-GRU outperforms all other models in terms of correct and wrong predictions for both sentiment analysis 0.99 and emotion detection 0.98. Key Words: bitcoin, sentiment analysis, machine learning, cryptocurrencies, tweets
Cryptocurrency is a virtual payment instrument currently popular as an investment alternative. One type of cryptocurrency widely used as an investment is Bitcoin due to its high-profit potential and risk due to unstable exchange rate fluctuations. This high exchange rate fluctuation makes trading transactions in the crypto market speculative and highly volatile. To overcome this volatility factor, this research used the Generalized Autoregressive Conditional Heteroscedasticity forecasting method to describe the heteroscedasticity factor, as well as a Recurrent Neural Network (RNN) with long-short-term memory that has feedback in modeling sequential data for time series analysis. The two methods are combined to overcome the dependency of time series data in the long term and the heteroscedastic effect of the volatility of price changes. The results of the GARCH-LSTM hybrid model in this study show a Mean Absolute Percentage Error (MAPE) value of 15.69%. The accuracy value is obtained from the division of training data by 80% and testing data by 20%, with the number of neurons as many as three and epochs of 100 using the Adam optimizer. The MAPE accuracy results show a good prediction in predicting the value.
Purpose- This study aims to assess the effectiveness of technical analysis indicators used by investors in the cryptocurrency market for making informed decisions. Emphasizing the importance of accurate decision-making methods in financial markets, this research particularly focuses on the cryptocurrency market, which has gained significant attention among investors in recent years. Methodology- The study specifically examines technical analysis, a widely employed method in various financial markets, with a focus on its predictive capabilities concerning Bitcoin price forecasts. Leveraging advanced technologies, such as big data analysis and machine learning, the research utilizes daily trading data from January 1, 2017, to June 30, 2022, presenting technical indicators and their associated error margins. Findings- The study highlights the significance of using Weighted Moving Average (WMA) and Stochastic Oscillator (STO) indicators in combination, demonstrating that multiple indicators outperform individual ones. This research underscores the effectiveness of technical analysis methods in the cryptocurrency market, aiding the development of enhanced investment strategies. Conclusion- In conclusion, this study delves into the potency of technical analysis techniques employed by investors in cryptocurrency markets. The insights indicate that combining indicators and technical analysis methods holds promise for future investment strategies. It is essential to note that even the best method can lead to losses, as evidenced by the presence of error margins, and absolute profitability cannot be guaranteed through technical analysis methods. Keywords: Cryptocurrency, technical analysis, machine-learning, classification algorithms, investment. JEL Codes: C38, C55, G17
Muhammad Haziq Abdul Hadi, Nor Azuana Ramli, Qamar UI Islam
Predicting future prices of cryptocurrencies, including Bitcoin and Ethereum, presents a formidable challenge owing to their inherent volatility. This study applies Long Short-Term Memory (LSTM), a well-established recurrent neural network for time series forecasting, to predict Bitcoin and Ethereum values. Historical price data for both cryptocurrencies, sourced from Yahoo Finance, serves as the basis for analysis. The dataset undergoes an 80% training and 20% testing partition. Subsequently, LSTM models are developed and trained on both datasets. In parallel, the gated recurrent unit (GRU), recognized as an advanced variant of the LSTM model, is explored for comparative purposes. Performance evaluation utilizes fundamental metrics, including root mean squared error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). The results reveal an intriguing trend: both models exhibit superior performance when applied to the Ethereum dataset compared to the Bitcoin dataset. This observation suggests the potential presence of Ethereum-specific features or patterns that align more effectively with deep learning model architectures. Notably, the GRU model consistently outperforms the LSTM model across RMSE, MAE, and MAPE. These outcomes underscore the GRU model’s capacity as a robust tool for cryptocurrency value prediction. In summary, this study tackles the challenge of cryptocurrency price prediction while emphasizing the promising role of advanced neural network architectures, such as GRU, in enhancing prediction accuracy, thus offering valuable insights into financial forecasting.
Adhi Dharma Wibawa, M Sadewa Wicaksana, Yuri Pamungkas
Cryptocurrency has become one of the most widely used digital investment instruments worldwide. In simple terms, cryptocurrency is a digital currency. Cryptocurrencies are not available in physical forms, such as coins or cash, but are commonly used worldwide. In cryptocurrency, everything is completely virtual. Even so, this digital money has a fairly high value. In 2017, its market capitalization reached 18 billion USD. In addition, cryptocurrencies carry a very high risk due to the problem of very fast and irregular price changes. Therefore, a study on the link between cryptocurrency and other commodities is required. Thus, data with a strong correlation may be used to judge cryptocurrency transactions. As a result, in this study, we employ the Pearson Correlation to determine the association of cryptocurrencies with various datasets, including gold, oil and natural gas, and the Nasdaq stock market. Consequently, the Nasdaq stock market dataset outperforms the others in terms of correlation, with the greatest value on the Google index of 0.91 and 0.92 on bitcoin and Ethereum. Regarding oil and natural gas, Ethereum has the greatest value versus crude oil (at 0.74), while bitcoin is at 0.61.
Saachin Bhatt, Mustansar Ali Ghazanfar, Mohammad Hossein Amirhosseini
This research explores the impact of social media sentiments on predicting Bitcoin prices using machine learning models, integrating on-chain data, and applying a Multi Modal Fusion Model. Historical crypto market, on-chain, and Twitter data from 2014 to 2022 were used to train models including K-Nearest Neighbors, Logistic Regression, Gaussian Naive Bayes, Support Vector Machine, Extreme Gradient Boosting, and Multi Modal Fusion. Performance was compared with and without Twitter sentiment data which was analysed using the Twitter-roBERTa and VADAR models. Inclusion of sentiment data enhanced model performance, with Twitter-roBERTa-based models achieving an average accuracy score of 0.81. The best performing model was an optimised Multi Modal Fusion model using Twitter-roBERTa, with an accuracy score of 0.90. This research underscores the value of integrating social media sentiment analysis and onchain data in financial forecasting, providing a robust tool for informed decision-making in cryptocurrency trading.
Algorithmic trading enables the execution of orders using a set of rules determined by a computer program. Orders are submitted based on an asset’s expected price in the future, an approach well suited for high-volatility markets, such as those trading in cryptocurrencies. The goal of this study is to find a reliable and profitable model to predict the future direction of a crypto asset’s price based on publicly available historical data. We first develop a novel labeling scheme and map this problem into a Machine Learning classification problem. The model is then validated on three major cryptocurrencies through an extensive backtest over a bull, bear and flat market. Finally, the contribution of each feature to the classification output is analyzed.
Forecasting data and research on cryptocurrency price forecasting methods are increasing in importance. So far, methods based on LSTM deep learning architecture have shown the best results in forecasting cryptocurrency prices. In order to improve the accuracy of forecasting data, this paper investigates the application of a multivariate multistep forecasting method based on the LSTM deep learning model for the bitcoin price time series and evaluates its effectiveness. The variants of multivariate multistep forecasting implementation based on deep learning LSTM are analyzed, and a direct approach for building multistep forecasts is chosen. Time series of bitcoin price and cumulative stability and drawdowns are used as input data. Based on our research, we found that short-term predictions were most accurate using models trained on trading data. However, for long-term forecasts, incorporating stability features slightly improved accuracy.
Given the substantial volatility and non-stationarity of cryptocurrency prices, forecasting them has become a complex task within the realm of financial time series analysis. This study introduces an innovative hybrid prediction model, VMD-AGRU-RESVMD-LSTM, which amalgamates the disintegration–integration framework with deep learning techniques for accurate cryptocurrency price prediction. The process begins by decomposing the cryptocurrency price series into a finite number of subseries, each characterized by relatively simple volatility patterns, using the variational mode decomposition (VMD) method. Next, the gated recurrent unit (GRU) neural network, in combination with an attention mechanism, predicts each modal component’s sequence separately. Additionally, the residual sequence, obtained after decomposition, undergoes further decomposition. The resultant residual sequence components serve as input to an attentive GRU (AGRU) network, which predicts the residual sequence’s future values. Ultimately, the long short-term memory (LSTM) neural network integrates the predictions of modal components and residuals to yield the final forecasted price. Empirical results obtained for daily Bitcoin and Ethereum data exhibit promising performance metrics. The root mean square error (RMSE) is reported as 50.651 and 2.873, the mean absolute error (MAE) stands at 42.298 and 2.410, and the mean absolute percentage error (MAPE) is recorded at 0.394% and 0.757%, respectively. Notably, the predictive outcomes of the VMD-AGRU-RESVMD-LSTM model surpass those of standalone LSTM and GRU models, as well as other hybrid models, confirming its superior performance in cryptocurrency price forecasting.
Cryptocurrency prediction is important for a variety of stakeholders, from investors to businesses, as it enables them to make more informed decisions about the future of the digital asset market. This paper delves into the application of deep learning models for two of the most popular cryptocurrencies, Bitcoin and Ethereum, outlining how to effectively implement these methods. Our goal is to perform efficient deep learning structure based on the forecasting models specifically recurrent neural networks, convolutional neural network and long short-term memory to predict the Bitcoin and Ethereum prices. Our results include a comparison of these two cryptocurrencies according to the deep learning methods and their effectiveness in predicting the Bitcoin and Ethereum prices.
Kriptovalute su posljednja novost u razvoju novca koga ne emitira i regulira nijedna središnja banka ni institucija te nemaju pokriće u zlatu ili srebru već se zasnivaju na matematici i algoritmima, a smatra se rizičnim ulaganjem čija cijena ovisi o dobroj tehnologiji i sigurnosti koja stoji iza te kriptovalute. Ulagače u kriptovalute zanima je moguće na temelju povijesnih podataka kretanja tečaja kriptovalute predvidjeti cijenu u budućnosti pri čemu se koriste razne tehnike uključujući LSTM algoritam koji je korišten u ovom projektu. Za izradu projekta korišten je dataset koji sadrži povijesne podatke cijene Bitcoina u američkim dolarima od 2010. do 2019. godine, a ima za cilj stvoriti model koji će predvidjeti cijenu Bitcoina u USD-u na temelju tih povijesnih podataka. Da bi se utvrdila preciznost predviđanja modela uspoređujemo predviđene cijene s stvarnim cijenama iz dataseta
The complicated combination of long-term patterns, short-term seasonality, and uncertainty has made selecting an appropriate forecasting model for cryptocurrency prices challenging. To forecast the future prices of the cryptocurrencies, this study compared three machine learning models: the Seasonal Auto-Regressive Integrated Moving Average (SARIMA), the Long Short-Term Memory (LSTM), and the Facebook (Fb) Prophet for the period of 2017 till 2023. It was discovered that the FbProphet model works well in predicting the daily price forecasts of Bitcoin and Dogecoin with low mean squared error (MSE), using time series datasets from 2017 through 2023 as the training data to anticipate 12 months of unseen data. While predicting the future from 2023 to 2024, Fb Prophet stated that Dogecoin is most likely to remain stable while Bitcoin price is most likely to continue falling. The LSTM model performs better than the other models based on forecasting results.
We develop a general and practical framework to address the problem of the optimal design of dynamic fee mechanisms for multiple blockchain resources. Our framework allows to compute policies that optimally trade-off between adjusting resource prices to handle persistent demand shifts versus being robust to local noise in the observed block demand. In the general case with more than one resource, our optimal policies correctly handle cross-effects (complementarity and substitutability) in resource demands. We also show how these cross-effects can be used to inform resource design, i.e. combining resources into bundles that have low demand-side cross-effects can yield simpler and more efficient price-update rules. Our framework is also practical, we demonstrate how it can be used to refine or inform the design of heuristic fee update rules such as EIP-1559 or EIP-4844 with two case studies. We then estimate a uni-dimensional version of our model using real market data from the Ethereum blockchain and empirically compare the performance of our optimal policies to EIP-1559.
The paper presents a novel approach to developing a pair trading strategy for cryptocurrencies by employing a customized Rainbow DQN and an image encoding technique. This method transforms candlestick features of Bitcoin (BTC) and Ethereum (ETH) into images, which are then input into a Convolutional Neural Network (CNN) for feature extraction before being fed into the Rainbow DQN model for making trading decisions. Although the Rainbow DQN strategy outperforms RSI and correlation strategies, none of them are profitable after factoring in transaction costs.
The value of bitcoin as a financial asset is rising and due to its extreme volatility, accurate forecasts are necessary to guide investment choices. Adoption, regulatory developments, geopolitical events, and macroeconomic factors all have an impact on its price. Accordingly, a lot of researchers have looked into a variety of factors that influence the value of bitcoin including the trends that underlie its fluctuations, but limited studies have focused on applying various machine learning techniques in this domain. Therefore, the objective of this study is to analyze multiple algorithms used for machine learning regression model in order to identify the system which can estimate bitcoin values most effectively and accurately based on multiple attributes. To forecast the bitcoin price, the dataset has been analyzed and preprocessed meticulously and then multiple machine learning regression models such as XGBoosting, Gradient Boosting Regressor, Hist Gradient Boosting Regressor, Random Forest, Linear Regression, Support Vector Regressor, Neural Network Regressor, Decision Tree, Gaussian Process Regressor, and K-Nearest Neighbors Regressor. The top findings were 99.497 percent (almost 99.5 percent) R-squared (R2), 0.01281 (RMSE), and 0.005755 (MAE) scores employing gradient boosting regressor model.
Luis Miguel Jiménez Gómez, Erick Lambis-Alandete, Juan D. Velásquez-Henao
Debido al alto atractivo de las criptomonedas, los inversionistas y los investigadores han prestado mayor atención en la previsión de los precios de las criptomonedas. Con el desarrollo metodológico del Deep Learning, la previsión de las criptomonedas ha tenido mayor importancia en los últimos años. En este artículo, se evalúan cuatro modelos de Deep Learning: RNN, LSTM, GRU y CNN-LSTM con el objetivo de evaluar el desempeño en el pronóstico del precio de cierre diario de las dos criptomonedas más importantes: Bitcoin y Ethereum. Se utilizaron métricas de análisis de desempeño como MAE, RMSE, MSE y MAPE y como métrica de ajuste, el R2. Cada modelo de Deep Learning fue optimizado a partir de un conjunto de hiperparámetros y para diferentes ventanas de tiempo. Los resultados experimentales mostraron que el algoritmo RNN tuve un rendimiento superior en la predicción del precio de Bitcoin y el algoritmo LSTM en el precio de Ethereum. Incluso, ambos métodos presentaron mejor desempeño con dos modelos de la literatura evaluados. Finalmente, la confiabilidad del pronóstico de cada modelo se evaluó analizando la autocorrelación de los errores y se encontró que los dos modelos más eficientes tienen alto poder de generalización.
In this study, it was investigated which of the machine learning algorithms gave the most optimal results in determining the effect of news about cryptocurrencies on the prediction of Bitcoin (BTC) price. In this direction, three different data sets were created, and these data were tested separately in each machine learning algorithm examined. In this way, it is aimed to determine the effect of the data set on the success of machine learning algorithms. By doing web scraping with the script, we developed to determine the news data we used in the study, 684 news made by BBC, CNBC, Fox, Guardian, NY Post, Over News, Sun, USA Today, and VOA news sources were found and by sentiment analysis of them, 564 news related to cryptocurrencies were taken into account in the study. March 2020 and March 2021 were chosen as the dates of the selected news. (The period when the BTC price rose from 9Ks to 30Ks.). The reason for choosing a past time period is to precisely measure the consistency of the predictions made. While creating the data sets used in machine learning algorithms, high, low, volume, open, and close values in the dollar, Ethereum, and gold values were also taken into account, as well as news sources. In this way, it is aimed to make more realistic estimations by taking market data into account. Machine learning methods in the study; Decision tree, deep learning, random forest, support vector machine, gradient boosted trees, and a generalized linear model was used. The generalized linear model (model II), which gives the most successful result with a rate of 98.73%, is explained in detail in the study.