This study delves into the relationship between emotional trends from X platform data and the market dynamics of well-known cryptocurrencies Cardano, Binance, Fantom, Matic, and Ripple over the period from October 2022 to March 2023. Leveraging SenticNet, we identified emotions like Fear and Anxiety, Rage and Anger, Grief and Sadness, Delight and Pleasantness, Enthusiasm and Eagerness, and Delight and Joy. Following data extraction, we segmented each month into bi-weekly intervals, replicating this process for price data obtained from Finance-Yahoo. Consequently, a comparative analysis was conducted, establishing connections between emotional trends observed across bi-weekly intervals and cryptocurrency prices, uncovering significant correlations between emotional sentiments and coin valuations.
Now-a-days Crypto currencies are digital currency that can be used as long-term investments or for all kinds of transactions. Investor interest in it is high because of its fundamental values of security, transparency, and decentralization. Developing successful investing strategies requires an accurate price prediction of the crypto currency due to its unique qualities and price volatility. The price of Bitcoin, the most popular crypto currency, is predicted using this model. A multitude of factors will be considered in order to forecast the accurate prices. The change point detection technique is used to provide robust prediction performance in the unseen price range. Furthermore, unique records that exist in crypto currencies and are posted on the block chain, or âon-chain data,â are gathered and used as input variables to forecast values. Experiments with real world Bit coin price data and various methods have proven the proposed systemâs effectiveness in Bit coin price prediction. Crypto currency price trading is a huge demand of exchange right now. It is a great advantage for investors and day-to-day traders. In this work they will use latest Machine Learning algorithms for finding out the Price Prediction of Crypto currency.These algorithms assist in identifying price prediction. These algorithms are all contrasted with one another to provide precise results.
In cryptocurrency, where market volatility and unpredictability pose challenges to investors and businesses alike, there is a growing need for more accurate price prediction methods. This research paper introduces an innovative approach aimed at enhancing cryptocurrency price prediction using deep learning techniques. Focusing on 20 prominent cryptocurrencies, this study employs Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Bidirectional Long Short-Term Memory (Bi-LSTM), Hybrid LSTM-GRU models to harness the power of deep learning. Through the utilization of these advanced models, improved accuracy in price prediction is demonstrated. Additionally, a strategy is proposed to guide users on whether to buy, sell, or hold cryptocurrencies daily, enhancing decision-making in volatile markets. Integration of advanced models and strategic insights aims to facilitate informed financial decisions and business strategies in the cryptocurrency market. Preprocessing of data using the Min-Max scaler method, a technique that effectively scales features within a predetermined range, aiding in the convergence and optimization of deep learning models. The integration of such preprocessing techniques, coupled with the inherent strength of LSTM and GRU models, culminates in a predictive framework that significantly surpasses conventional methods of cryptocurrency price prediction. By outperforming traditional approaches, this research contributes to the advancement of cryptocurrency prediction, thereby facilitating more informed financial decision-making and business strategies.
Cryptocurrency markets have emerged as a dynamic and intriguing domain, with Bitcoin at the forefront, captivating the attention of investors, researchers, and enthusiasts alike. The volatile nature of Bitcoin prices presents both opportunities and challenges for market participants seeking to understand and anticipate its movements. In this study, we delve into the realm of time series analysis to explore the feasibility of predicting The research journey begins with meticulous data preprocessing steps to ensure the quality and integrity of the input data. Leveraging Python libraries such as pandas and NumPy, we cleanse and format the historical Bitcoin price data, laying the foundation for subsequent analysis. Key preprocessing tasks include handling missing values, normalization, and addressing any anomalies or outliers that may distort the underlying patterns. With the data prepared, our attention turns to assessing the stationarity of the Bitcoin price time seriesâa fundamental prerequisite for applying classical time series models. Through visual inspection and statistical tests such as the Augmented Dickey-Fuller (ADF) test, we ascertain the presence of trends or seasonality that could influence the modelling process. To mitigate such effects, we employ techniques such as differencing and transformations, including the Box-Cox transformation, to stabilize the variance of the data. Armed with a stationary time series, we embark on the core of our analysis: modelling Bitcoin prices using Autoregressive Integrated Moving Average (ARIMA) and Seasonal Autoregressive Integrated Moving Average with Exogenous Variables (SARIMAX) models. These models, renowned for their versatility and effectiveness in capturing temporal dependencies, offer a sophisticated framework for forecasting time series data. Guided by the principles of parsimony and model selection criteria such as the Akaike Information Criterion (AIC), we systematically explore the parameter space to identify the optimal specifications for our models. The efficacy of the chosen models is rigorously evaluated through diagnostic checks, encompassing residual analysis, model fit statistics, and out-of-sample validation. Insights gleaned from these assessments inform our confidence in the models' predictive capabilities and guide our interpretation of the forecasted outcomes. Finally, armed with a validated model, we turn our gaze to the future, employing it to generate forecasts of Bitcoin prices for forthcoming periods. Visualizations juxtaposing predicted prices against observed values provide a compelling narrative of the model's performance and offer stakeholders valuable insights into potential market trends and dynamics. In summary, this research contributes to the burgeoning field of cryptocurrency analytics by showcasing the application of time-tested statistical methodologies to forecast Bitcoin prices. By leveraging the power of ARIMA and SARIMAX models, we illuminate the intricate patterns underlying Bitcoin's price dynamics, empowering market participants with actionable intelligence for informed decision-making in an ever-evolving landscape. Keywords Cryptocurrency Markets, Bitcoin Price Prediction, Time Series Analysis, Python Programming, Data Preprocessing, Pandas, NumPy, Stationarity Testing, Augmented Dickey-Fuller (ADF) Test, Box-Cox Transformation, ARIMA Model, SARIMAX Model, Model Selection, Akaike Information Criterion (AIC), Diagnostic Checks, Residual Analysis, Out-of-Sample Validation, Forecasting, Visualization, Market Trends, Decision-Making, Cryptocurrency Analytics
ÎΔÏÏγία ÎÎżÏ ÏΜαÏÎ¶ÎŻÎŽÎżÏ , Ioannis Mallidis, Dimitrios Farazakis, Christos Floros
This paper provides a computationally efficient and novel four-step methodological approach for predicting volatility estimators derived from bitcoin prices. In the first step, open, high, low, and close bitcoin prices are transformed into volatility estimators using Brownian motion assumptions and logarithmic transformations. The second step determines the optimal number of time-series lags required for converting the series into an autoregressive model. This selection process utilizes random forest regression, evaluating the importance of each lag using the Mean Decrease in Impurity (MDI) criterion and optimizing the number of lags considering an 85% cumulative importance threshold. The third step of the developed methodological approach fits the Elastic Net Regression (ENR) to the volatility estimatorâs dataset, while the final fourth step assesses the predictive accuracy of ENR, compared to decision tree (DTR), random forest (RFR), and support vector regression (SVR). The results reveal that the ENR prevails in its predictive accuracy for open and close prices, as these prices may be linear and less susceptible to sudden, non-linear shifts typically seen during trading hours. On the other hand, SVR prevails for high and low prices as these prices often experience spikes and drops driven by transient news and intra-day market sentiments, forming complex patterns that do not align well with linear modelling.
Peter T. Yamak, Yujian Li, Ting Zhang, Pius Kwao Gadosey
In this paper, we introduce Wide-TSNet, a novel hybrid approach for predicting Bitcoin prices using time-series data transformed into images. The method involves converting time-series data into Markov transition fields (MTFs), enhancing them using histogram equalization, and classifying them using Wide ResNets, a type of convolutional neural network (CNN). We propose a tripartite classification system to accurately represent Bitcoin price trends. In addition, we demonstrate the effectiveness of Wide-TSNet through various experiments, in which it achieves an Accuracy of approximately 94% and an F1 score of 90%. It is also shown that lightweight CNN models, such as SqueezeNet and EfficientNet, can be as effective as complex models under certain conditions. Furthermore, we investigate the efficacy of other image transformation methods, such as Gramian angular fields, in capturing the trends and volatility of Bitcoin prices and revealing patterns that are not visible in the raw data. Moreover, we assess the effect of image resolution on model performance, emphasizing the importance of this factor in image-based time-series classification. Our findings explore the intersection between finance, image processing, and deep learning, providing a robust methodology for financial time-series classification.
Phumudzo Lloyd Seabe, Claude Rodrigue Bambe Moutsinga, Edson Pindza
This study explores cryptocurrency investment strategies by adapting the robust framework of factor investing, traditionally applied in equity markets, to the distinctive landscape of cryptocurrency assets. It conducts an in-depth examination of 31 prominent cryptocurrencies from December 2017 to December 2023, employing the FamaâMacBeth regression method and portfolio regressions to assess the predictive capabilities of market, size, value, and momentum factors, adjusted for the unique characteristics of the cryptocurrency market. These characteristics include high volatility and continuous trading, which differ markedly from those of traditional financial markets. To address the challenges posed by the perpetual operation of cryptocurrency trading, this study introduces an innovative rebalancing strategy that involves weekly adjustments to accommodate the marketâs constant fluctuations. Additionally, to mitigate issues like autocorrelation and heteroskedasticity in financial time series data, this research applies the NeweyâWest standard error approach, enhancing the robustness of regression analyses. The empirical results highlight the significant predictive power of momentum and value factors in forecasting cryptocurrency returns, underscoring the importance of tailoring conventional investment frameworks to the cryptocurrency context. This study not only investigates the applicability of factor investing in the rapidly evolving cryptocurrency market, but also enriches the financial literature by demonstrating the effectiveness of combining FamaâMacBeth cross-sectional analysis with portfolio regressions, supported by NeweyâWest standard errors, in mastering the complexities of digital asset investments.
Currently bitcoin is considered an investment tools, the value of bitcoin itself is unstable so it is difficult to predict which can cause losses for bitcoin traders. Some previous research shows that Long Short-Term Memory (LSTM) which is a deep learning approach as an improvement of RNN has the best performance in predicting stocks and cryptocurrencies compared to Support Vector Machine (SVM), Exponential Moving Average (EMA), and Moving Average (MA), and Seasonal Autoregressive Integrated Moving Average (SARIMA). LSTM has the disadvantage that it is difficult to understand in determining the best parameters and to obtain good results it needs strict hyperparameter adjustment. This study aims to find the best parameters in LSTM by selecting the amount of data, training data composition, batch size, epoch and the amount of prediction time and analyzing prediction performance. In this study, data collection was carried out in real time and was able to provide predictions for the next few days. The test results of the LSTM algorithm have a performance with an average accuracy of 93.69% with the parameters of the amount of bitcoin price data used is 3 years, with a percentage of train data of 85%, using 10 batch sizes, with a number of epochs 125, and the highest average accuracy rate for 7 days of prediction.
Bitcoin, the first cryptocurrency, has attracted much attention in the digital currency market, and its price volatility is affected by a variety of factors, posing a challenge to investors. For the sake of analytical feasibility, many studies have adopted simplified modeling assumptions. This may overlook certain key nonlinear features of the market, such as the complexity of investor behavior and the volatility of market sentiment. This study explores the application of Long Short-Term Memory (LSTM) networks and their derivative models in predicting Bitcoin prices. Recognizing the complex nature of Bitcoinâs market dynamics, the research delves into the effectiveness of LSTM in capturing the nonlinear patterns of cryptocurrency prices. Furthermore, it extends the analysis to derivative models like MSM-LSTM and Empirical Mode Decomposition (EMD) LSTM, evaluating their ability to enhance prediction accuracy by addressing the limitations of the standard LSTM. This study provides an in-depth analysis of the effectiveness of LSTM and its derived models in the practical application of Bitcoin price prediction, with a special focus on their ability to capture non-linear patterns in the market. In Bitcoin price prediction, LSTM models have limitations but also great potential in capturing nonlinear patterns in the market. Extended models based on their own can perform well in Bitcoin price prediction. This study contributes to the field by suggesting strategies to improve the accuracy of the model and by providing ideas for developing trading strategies based on the results of the analysis.
Time Series forecasting has been approached by a multiplicity of techniques including deep learning methods of various degrees of sophistication, showcasing notable advancements and improved performance over the past few years. More recently, there has been a sustained interest in the study of Transformers, a class of models renowned for their remarkable capacity to capture intricate long-range dependencies and interactions. This ability is perceived as particularly relevant and impactful in the context of time series modeling, reflecting a growing recognition of their potential in enhancing forecasting accuracy and understanding of complex temporal patterns. However, taking advantage of this principle to deploy successful forecasting methods is not yet clearly understood, and requires significant experimentation or engineering. Therefore, in this paper, we compare multiple variations of the Transformer model (standard Transformer, Autoformer, Informer), coupled with diverse combinations of embedding data. In particular, as the emphasis of our work is on forecasting, we investigate the relationship between Transformersâ input segment length and prediction performance in a multi-step time intervals framework. Our results suggest that the Autoformer outperforms both standard Transformer and Informer across various prediction steps. We also observe that shorter input lengths and shorter prediction lengths generally produce better model performance.
This study provides a comprehensive analysis of the existing body of work on predicting the price of Bitcoin using deep learning techniques. It discusses the fundamental concepts behind deep learning and Bitcoin, including recurrent neural networks, convolutional neural networks, and long short-term memory networks. The study also examines the data sources used in training these models, including historical Blockchain transaction data, social media sentiments, and Bitcoin prices. The report also highlights the importance of metrics like mean absolute error, mean squared error, and root mean squared error for evaluating the effectiveness of various models. It also discusses future research topics, such as incorporating external factors into prediction models. The article offers valuable insights for academics, practitioners, and policymakers interested in cryptocurrency prediction.
Since cryptocurrencies are among the most extensively traded financial instruments globally, predicting their price has become a crucial topic for investors. Our dataset, which includes fluctuations in Bitcoinâs hourly prices from 15 May 2018 to 19 January 2024, was gathered from Crypto Data Download. It is made up of over 50,000 hourly data points that provide a detailed view of the price behavior of Bitcoin over a five-year period. In this study, we used potent algorithms, including gradient descent, attention mechanisms, long short-term memory (LSTM), and artificial neural networks (ANNs). Furthermore, to estimate the price of Bitcoin, we first merged two deep learning algorithms, LSTM and attention mechanisms, and then combined LSTM-Attention with gradient-specific optimization to increase our modelâs performance. Then we integrated ANN-LSTM and included gradient-specific optimization for the same reason. Our results show that the hybrid model with gradient-specific optimization can be used to anticipate Bitcoin values with better accuracy. Indeed, the hybrid model combines the best features of both approaches, and gradient-specific optimization improves predictive performance through frequent analysis of pricing data changes.
Accurate prediction of the future movement of the financial instruments is a major factor that assures profitability and minimize risk in a volatile, unpredictable and complex financial and cryptocurrency market. Many researchers have proposed various ways to correctly forecast the future movements of these instruments with various technical indicators but losses are still in the increase.In this research, we have designed a price action based AI powered indicatorless algorithmic trading bot that can correctly predict the future direction of any financial instrument in the cryptocurrency and financial market with the capacity to ensure profitability while minimizing risk. The system is indicatorless, and uses price action and artificial intelligence algorithm for forecasting market movement and eventual execution of a buy or a sell trade with little or no human intervention.Our result shows an improved performance compared to others methods with a very low maximum risk drawdown of 1.06% with a profitability ratio of more than 297% over a 22 months period of back testing using EURO vs. USD (EURUSD) currency pair.
Due to cryptocurrencies' rising prices, like bitcoin, more and more people are becoming interested in them. Success in this business depends on a good price prediction. Several methods, including heuristic and machine-learning-based ones, can currently estimate the price with varied degrees of success. This study will use the Adaptive Neuro-Fuzzy Inference Systems (ANFIS) model to predict the price's general direction over the next 10 days. Along with popular traders' indicators, the previous day's price will be used. The findings demonstrated that, despite errors, price direction predictionsâan increase, a drop, or a stable priceâare typically accurate.
Bitcoin has become a prominent financial instrument in recent years, attracting increasing attention as a digital currency. Accurately forecasting the valuation of a financial asset carries substantial significance for both retail and institutional investors. The aim of this study is to evaluate and compare the predictive capabilities of various models, namely Support Vector Regression (SVR), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), a hybrid model combining CNN and Bidirectional LSTM (CNN-BiLSTM), and XGBoost, in the context of forecasting Bitcoin price. The main aim of this study is to ascertain the algorithm that demonstrates the most efficacy in forecasting the price of Bitcoin. This study utilizes the S&P500 index, Gold/Dollar exchange rate, West Texas Spot Oil Price, and Dollar Index as exogenous factors in order to forecast the price of Bitcoin. The dataset encompasses a consecutive time span of 2191 days, commencing on January 1, 2015 and concluding on September 18, 2023. The models outlined in the study undergo a two-stage procedure, including of training and testing. The assessment of the models' performance was carried out by utilizing several statistical measures, such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and R-squared (R2). The results indicate that the XGBoost algorithm had greater performance in projecting the price of Bitcoin, as evidenced by its consistently higher performance metrics across all evaluated aspects. The XGBoost model was succeeded by the CNN-BiLSTM, CNN, and LSTM models, which are hybrid methodologies, resulting in the most advantageous results. The SVR model demonstrated the least favorable performance..
P. Gayatri, Tiwari Ashish, B. Sankar, P. Evan Theodar - · 5 authors
Cryptocurrency has emerged as a transformative force in the financial realm, garnering widespread attention and acceptance. However, its dynamic nature and inherent uncertainties pose significant challenges for investors. This study delves into the factors shaping cryptocurrency value formation by harnessing the power of advanced artificial intelligence frameworks. Specifically, we employ fully connected Artificial Neural Network (ANN) and Long Short-Term Memory (LSTM) Recurrent Neural Network to analyze the price dynamics of prominent cryptocurrencies such as Bitcoin, Ethereum, and Ripple. Our research reveals that ANN tends to rely more heavily on long-term historical data, whereas LSTM exhibits a penchant for short-term dynamics. Interestingly, LSTM demonstrates superior efficiency in leveraging historical information, yet with adequate data, ANN can achieve comparable accuracy. Our findings shed light on the predictability of cryptocurrency market prices, albeit the interpretation may vary depending on the machine-learning model utilized. This study underscores the significance of leveraging artificial intelligence in comprehending and forecasting cryptocurrency market trends, thereby mitigating investment risks in this dynamic landscape. Keyword: Cryptocurrency, Artificial Intelligence, Market Trends, Price Dynamics, Bitcoin.
S. Venkatesh, B Rashmitha, S Manjunadha, Md Junaid
A type of digital currency known as a cryptocurrency allows all transactions to be completed online. There is no hard cash version of this soft currency. We highlight that a decentralized currency differs from a centralized currency in the any user of a virtual currency can purchase services without the need for third parties to get involved. Due to its extreme price volatility, using these cryptocurrencies has an impact on trade and international relations. Moreover, the constantly fluctuating oscillations indicate the urgent need for a more precise method of predicting this price. Deep learning techniques that use effective learning models for training data, including the LSTM, GRU, and Feedback Neural Network, can be used to do this. Benchmark datasets are used to test the suggested strategy. That brings us to the neural network, one of the clever data mining technologies that researchers in many domains have been using for the past ten years. In the current economy, stock market data is essential. There are two types of forecasting methodologies: nonlinear models (ARCH, GARCH, Neural Network) and linear models (AR, MA, ARIMA, ARMA). To forecast a company's stock price based on past prices, we employed the Box Jenkins Model also known as ARIMA, and Long Short-Term Memory (LSTM), and Feedback Neural Network also known as RNN.
This research paper aims to conduct a time series forecasting of the bitcoin mean weighted price using the data from Kaggle. The data has a one-minute resolution and includes the following variables: timestamp, open, high, low, close, volume (BTC), volume (currency), and weighted price. Data analysis was achieved using R, a statistical computing and graphics programming language. The main findings of this research paper were that the bitcoin mean weighted price had a strong upward trend and exhibited high volatility over time. The time series also had weak seasonal and significant random components, indicating periodic fluctuations and noise in the data. Four years of data were used to estimate the mean change for the following month. The results indicate that while the expected value may rise somewhat, it will do so with significant variability and unpredictability. The main implications of this research paper were that there was a potential for profit or loss depending on the timing and strategy of buying or selling bitcoins.
People are starting to see the cryptocurrency market as a viable source of income and investment, similar to the stock market, as the concept of cryptocurrencies continues to gain popularity. Predicting Bitcoin returns is related to financial machine learning, which uses time series to forecast price variance. This study starts with the daily close price of Bitcoin for its initial dataset. The price is transformed into percentages and binary classes, which categorize into âUpâ and âDownâ, after which a time series is applied to produce two datasets: a categorical dataset for classification and a numerical dataset for regression. For classification that represents a Binary classification in asset-price forecasting, k-fold cross-validation is applied to ensure that the best classifiers are selected for testing and analysis. Most of the regression analysis was based on visualization, which displayed the predicted prices by each regressor in front of the original values and helped analyse the modelsâ results more accurately. The outcomes of this study were achieved by anticipating bitcoin returns using classification and regression machine learning models, despite the approachesâ low accuracy and significant precision rate to the âUpâ class. At this stage, with a significant limitation regarding the dataset and a lack of other indicators, a model capable of predicting future variations is considered a beneficial addition for many trading tools or even for crypto market analysts.
Anomaly detection is critical in the financial sector, especially as financial environments evolve with increasing digitization, posing challenges for real -time anomaly detection. Recently, deep learning (DL) algorithms have emerged as promising solutions for this problem. This study presents a DL -based anomaly detection model utilizing various algorithms, including LSTM, GRU, and 1dCNN, applied to Tesla's stock market and Ethereum cryptocurrency data sets. Hyperparameter optimization is performed using grid search. Results show that the GRU algorithm achieves the highest prediction score in both datasets, while the 1dCNN algorithm performs the lowest. Additionally, anomaly values are graphically demonstrated using GRU for both datasets. Accurate bookkeeping is essential for legitimate business operations, yet the complexity of financial auditing requires new solutions. Supervised and unsupervised machine learning techniques are increasingly applied to detect fraud and anomalies in accounting data. This paper addresses the challenge of detecting financial misstatements in general ledger (GL) data, proposing seven supervised ML techniques, including deep learning, and two unsupervised ML techniques. Models are trained and evaluated on real -life GL datasets, demonstrating high potential in detecting predefined anomaly types and efficiently sampling data. Practical implications of these solutions in accounting and auditing contexts are discussed. The rapid development of computer networks brings both convenience and security challenges due to various abnormal flows. Traditional detection systems, like intrusion detection systems (IDS), have limitations, necessitating real -time updates to function effectively. With the advent of machine learning and data mining, new methods for abnormal network flow detection have emerged. This paper introduces the random forest algorithm for detecting abnormal samples, proposing the concept of an abnormal point scale to measure sample abnormality based on similarity. Simulation experiments demonstrate the superiority of random forest -based detection in terms of model accuracy and computing efficiency compared to other methods.
Abbas M. Ali, Abdullah Shah, Azaz Hassan Khan, Malik Umar Sharif · 8 authors
Software-based technical indicators have been widely used for the stock market forecasting, aiming to predict market direction. Even though many algorithms for the software based technical indicators are presented, there are almost no hardware implementations reported in the literature. In this paper, the hardware implementation is presented for three commonly used technical indicators: Moving Average Convergence/Divergence (MACD), Relative Strength Index (RSI), and Aroon. Latency evaluation is conducted for Bitcoin and Ethereum within a single-day timeframe, utilizing the Xilinx Zynq-7000 programmable SoC XC7Z020-CLG484-1 platform. Additionally, various hardware/software (HW/SW) partitioning strategies are explored to leverage the flexibility of software alongside the performance advantages of hardware via the Zynq SoC platform. The results show that the best performing technical indicator is MACD with a speedup of 30 times over its software only counterpart. Furthermore, a hybrid design integrating multiple technical indicators is proposed, pairing MACD with RSI due to their competitive throughput values, differing by only 0.38 microseconds. This hybrid approach capitalizes on the parallel processing capabilities of hardware, enabling multiple systems to operate simultaneously.