Harish Kundra, Sudhir Sharma, P. Nancy, D. Kalyani
Purpose Bitcoin has indeed been universally acknowledged as an investment asset in recent decades, after the boom-and-bust of cryptocurrency values. Because of its extreme volatility, it requires accurate forecasts to build economic decisions. Although prior research has utilized machine learning to improve Bitcoin price prediction accuracy, few have looked into the plausibility of using multiple modeling approaches on datasets containing varying data types and volumetric attributes. Thus, this paper aims to propose a bitcoin price prediction model. Design/methodology/approach In this research work, a bitcoin price prediction model is introduced by following three major phases: Data collection, feature extraction and price prediction. Initially, the collected Bitcoin time-series data will be preprocessed and the original features will be extracted. To make this work good-fit with a high level of accuracy, we have been extracting the second order technical indicator based features like average true range (ATR), modified-exponential moving average (M-EMA), relative strength index and rate of change and proposed decomposed inter-day difference. Subsequently, these extracted features along with the original features will be subjected to prediction phase, where the prediction of bitcoin price value is attained precisely from the constructed two-level ensemble classifier. The two-level ensemble classifier will be the amalgamation of two fabulous classifiers: optimized convolutional neural network (CNN) and bidirectional long/short-term memory (BiLSTM). To cope up with the volatility characteristics of bitcoin prices, it is planned to fine-tune the weight parameter of CNN by a new hybrid optimization model. The proposed hybrid optimization model referred as black widow updated rain optimization (BWURO) model will be conceptual blended of rain optimization algorithm and black widow optimization algorithm. Findings The proposed work is compared over the existing models in terms of convergence, MAE, MAPE, MARE, MSE, MSPE, MRSE, Root Mean Square Error (RMSE), RMSPE and RMSRE, respectively. These evaluations have been conducted for both algorithmic performance as well as classifier performance. At LP = 50, the MAE of the proposed work is 0.023372, which is 59.8%, 72.2%, 62.14% and 64.08% better than BWURO + Bi-LSTM, CNN + BWURO, NN + BWURO and SVM + BWURO, respectively. Originality/value In this research work, a new modified EMA feature is extracted, which makes the bitcoin price prediction more efficient. In this research work, a two-level ensemble classifier is constructed in the price prediction phase by blending the Bi-LSTM and optimized CNN, respectively. To deal with the volatility of bitcoin values, a novel hybrid optimization model is used to fine-tune the weight parameter of CNN.
Virtual currency has been greeted with an avalanche of attention these days. In this case, allocate investments into traditional assets and virtual currency properly seems very important. In this paper, we select gold and bitcoin as our research objects, and select a series of representative indicators in the financial field. After data preprocessing, XGBoost algorithm is used to sort the importance of indicators, thus eliminating some unimportant indicators. Next, LSTM is used to predict the price of gold and bitcoin respectively. Therefore, the portfolio can be built based on it. In reality, trades often come with transaction costs. So we improve the Mean-Variance model considering the transaction costs, so as to get the initial portfolio strategy. On this basis, taking investment potential into account, we propose Traffic Light Signal(TLS) model, and successfully increasing the gross profit rate from 11.582% to 13.614%. Finally, we prove our portfolio model earns the highest returns by comparing it to other traditional portfolio models in terms of metrics Cumulative Yield, Annual Yield, and Max Drawdown Ratio.
Market traders trade gold, and Bitcoin is aim to maximize their return. This paper utilizes the grey prediction model to explore the optimal trading strategy and optimize fund allocation based on dynamic programming. In addition, by comparing with other traditional trading strategies, we discover that the grey prediction model can more accurately estimate future prices, enabling the trader to gain steadily growing returns at a low-risk level.
With the progress of economic globalization, market transactions and investments occur frequently. For a long time, two assets, gold and bitcoin, have become the research hotspot due to payment means, circulation means, storage means, value scale, and world currency. To this end, we built two models. The model I: Diversified BP neural network model. Model II: Portfolio strategy based on mean-standard deviation model. For Model I, we have to predict the future prices of gold and Bitcoin based on past data. We can regard the past price data as a time series. There are relatively mature methods for predicting future values based on time series. For Model II, with future prices predicted, we can invest. It brings up three more questions: should I buy or sell or do nothing now? Buy or sell only gold and bitcoin or both gold and bitcoin? When buying, how should the cash for gold and bitcoin be distributed? Finally, the specific investment strategy is determined by linear programming. Then, we analyzed the effect of commissions on final earnings and derived the relationship between total assets held last and commissions.
The main contribution of this research is to investigate whether an Artificial Neural Network is an option to predict Ethereum cryptocurrency close price on a time constrained scenario. The ANN training time and time lagged data availability are considered as constraints on finding the fastest and the most accurate regression model using ARIMA results as a baseline. As part of the study, hourly aggregated data is processed to generate a step-ahead forecast and then processing time is compared for each architecture. Previous work related to cryptocurrency forecasting usually focus the analysis only on accuracy, and use coarser data granularity. Results have shown that convolutional neural networks over performed other architectures for accuracy and time objectives.
Suneetha Rani R, Sri Vinithri Chowdary D, Uday Kiran C H, K Deekshith ยท 5 authors

 
 
 Forecasting can be used in many fields such as crypto currency prediction, financial entities, supermarkets etc. We get the time series date which we use to feed the data into the algorithm is given by Y finance with this we get refreshed data every day. The stock market prediction or forecasting helps customers and brokers get a brief view of how the market behaves for the coming years. Many models are currently in use Like Regression techniques, Long Short-Term Memory algorithm etc. FB Prophet is proven to perform better than most other Algorithms with better accuracy. From the proposed research and references we have determined Facebook's Prophet algorithm as our forecasting algorithm because it is predicting at better accuracy, low error rate, handles messy data, doesnโt bother for null values and better fitting.
 
 
<p>Market traders frequently buy and sell volatile assets with the goal of maximizing total returns. In the complex market, how to predict the general trend of the market more accurately, how to determine the buying point or selling point, to maximize the target income, is the primary consideration of investors more scientifically and reasonably. In this paper, based on the closing price data of bitcoin and gold on Nasdaq from September 11, 2016 to September 10, 2021,<sup>[1]</sup> we first build a LSTM neural network prediction model for sliding sequence prediction, on this basis, we build a trading strategy selection model based on nonlinear programming, and introduce Monte Carlo algorithm to optimize the solution.</p>
In recent years, machine learning and deep learning techniques have been frequently used in Algorithmic Trading. Algorithmic Trading means trading Forex, stock market, commodities, and many markets with the help of computers using systems created with various technical analysis indicators. The BTC/USD market is a market that allows buying and selling of products. People aim to profit by buying and selling in the Bitcoin market. Reinforcement Learning (RL) was also helpful in achieving those kinds of goals. Reinforcement learning is a sub-topic of machine learning. RL addresses the problem of a computational agent learning to make decisions by trial and error. For our application, it is aimed to make as much profit as possible. This study focuses on developing a novel tool to automate currency trading like a BTC/USD in a simulated market with maximum profit and minimum loss. RL technique with a modified version of the Collective Decision Optimization Algorithm is used to implement the proposed model. Feature engineering is also performed to create features that improve the result.
Aiming at the portfolio problem of gold and bitcoin with a given linear trading commission, this paper puts forward the stage implementation forecast and optimal portfolio model. In the aspect of data prediction, SMA is used to predict the initial data, LSTM is used to predict the price trend of long-term data, and daily updated real-time price data is predicted. Considering the risk aversion of investors, the heuristic algorithm is used to solve the daily trading strategy of maximizing utility from September 12th, 2016 to September 12th, 2021. The simulation analysis of the sliding window shows that the algorithm can realize reasonable prediction, which verifies the effectiveness of the algorithm.
Cryptocurrencies are nowadays getting popular for investment due to its various benefits such as low transaction cost, blockchain secured platform, profit, etc. Bitcoin being top of the market capitalization currency, gained more popularity during covid-19 pandemic. This study focuses on bitcoin price prediction with covid-19 sentiment. Here Long Short Term Memory Deep learning model based on machine learning is used for price prediction. At the end both results i.e., with covid-19 sentiment and without it are compared which shows model performs better by adding sentiments.
Umair Khalid, Syeda Asnia Arif, Muhammad Umair Khan
Purpose: The research aims to analyze the log-returns of Bitcoin exchange rates against the US Dollar and Chinese Yuan by applying parametric distributions for understanding behavior and suggesting a best-fitted distribution.
 Design/Methodology/Approach: Methodology involves the volatility risk analysis using the GARCH model for analyzing the behavior of Bitcoin Exchange rates of USD and CNY.
 Findings: The results showed that the Weibull distribution gives the best fit to both of the currenciesโ exchange rates
 Implications/Originality/Value: The exchange rates of Bitcoin analyzed in this study in midst of myriad other cryptocurrencies using parametric distributions thereby encouraging the application of nonparametric and semiparametric distributions in similar scenarios. The application of this study would enable not only individual investors but also institutional investors and venture capital firms to stay informed of alternating trends and movements through distributions for predicting future returns.
The present research is reviewing the requirement of a novel approach in the case of cryptocurrency price prediction. Several types of research in the area of cryptocurrency and price prediction have been considered. Moreover, researches that are related to machine learning and deep learning are considered. The research paper considers the role of Artificial intelligence and machine learning in the price prediction of cryptocurrency. The issues in the case of previous research and the need for research in the area of cryptocurrency price prediction have been considered. The future scope of BTC, Wave, Ethereum, and Made cryptocurrency value prediction is also discussed.
Quantitative trading replaces the traditional subjective judgment mode based on modern digital model, so as to avoid irrational investment decisions under extreme conditions. This processing method has important research and application value in many fields such as stock and foreign exchange. Especially in the context of increasingly strong research on deep learning algorithms, judgment analysis on Cryptocurrency trading strategies and profitability has also received attention from all walks of life. Therefore, on the basis of understanding the current development of the stock market and the overall state of China's economy, this paper combines the stock prediction model with deep learning as the core, and constructs corresponding trading strategies on the basis of understanding the prediction results to improve the actual profitability.
Cryptocurrency is a new buzz in the financial sector. This study helps user to evaluate the price of cryptocurrency and user can decide and invest on the current crypto coin based on predicted price graphs. We are proposing a system to evaluate daily price of five coins i.e. Bitcoin, XRP, Ethereum, Dogecoin and SHIBA INU with the help of two Machine Learning algorithm models ARIMA (auto-regressive integrated moving average model) and LSTM (long-short-term memory networks) where the database is protected by Blockchain technology. User will be able to trade in the current financial cryptocurrency market by selecting the coin and prediction date, system will display data using Smart Contract coding and the visualized graph with Time Series Forecasting. The system will improve the crypto coin evaluation as it will get trained and dataset will get updated using Machine learning algorithm.
Cryptocurrencies are becoming increasingly popular day by day among people. It is providing more features than traditional banking or money can provide, hence attracting investors all around the world. But people are still hesitant to use this technology because of the high price volatility are variable markets. The majority of the forecasting solutions reported have some level of error and cannot predict the price accurately due to randomness. The proposed model consists of using parent coins as a parameter for price prediction to overcome market volatility. Moving average is used as a data preprocessing technique for the effective prediction of Litecoin and Dogecoin prices. Also, in this, we are using Decision Tree, Random Forest, Extra- Tree-Regressor and Ridge regressor models for predicting the close price of cryptocurrencies. From the result, we can see that our proposed model performs better than predicting without using the parent coin feature.
Jacques Fleischer, Gregor von Laszewski, Carlos Theran, Yohn Jairo Parra Bautista
Digitization is changing our world, creating innovative finance channels and emerging technology such as cryptocurrencies, which are applications of blockchain technology. However, cryptocurrency price volatility is one of this technologyโs main trade-offs. In this paper, we explore a time series analysis using deep learning to study the volatility and to understand this behavior. We apply a long short-term memory model to learn the patterns within cryptocurrency close prices and to predict future prices. The proposed model learns from the close values. The performance of this model is evaluated using the root-mean-squared error and by comparing it to an ARIMA model.
In this study, cryptocurrency is a type of digital money that uses cryptography to protect transactions, limit the production of new units, and verify asset transfers. The focus of this research is to see how volatile Bitcoin exchange rates and returns. The standard deviation of logarithmic returns is calculated to gauge volatility. The finding of the results was based on the Shapiro-Wilk test that was employed to predict normality in this investigation. In addition, the box-whisker plot and statistical process control chart were used to find high volatility. Volatility is now regarded to be a high value. Eventually, Because of the current high level of volatility, investing in Bitcoin is seen as a high-risk endeavor. The purpose of this study is to assist investors in developing a strategy that maximizes returns while minimizing risk.
Objective: The purpose of this paper is to demonstrate the effectiveness of the nonparametric GARCH model for the prediction of future Bitcoin prices. Methodology: The parametric GARCH models to characterize the volatility of Bitcoin returns are widely used in the empirical literature. Alternatively, we consider a non-parametric approach to model and forecast the volatility of Bitcoin returns. Results: We show that the volatility forecast of the nonparametric GARCH model yields superior performance compared to an extended class of parametric GARCH models. Originality / relevance: The improved accuracy of forecasting the volatility of Bitcoin returns based on the nonparametric GARCH model suggests that this method offers an attractive and viable alternative to commonly used GARCH parametric models.
It has been emphasized in many studies that the developments in the crypto money markets have a serious impact on the world stock markets. Due to these effects, the fluctuations in the world stock markets have increased, and it has become necessary for investors to follow these markets more closely and determine their strategies according to these developments. In this study, it was examined whether the developments in the crypto money market have an effect on Borsa Istanbul (BIST) indices. For this purpose, data of the three most popular cryptocurrencies Bitcoin, Ethereum and Ripple were used, and their spillover effects on BIST100, BIST30 and banking (XBANK) indices were investigated. Oil prices (WTI) and fear index (VIX) variables were also used as control variables in the study. The findings obtained from the analyses in our study carried out for the period 01/01/2014-31/12/2021 showed that there is a positive spillover effect from the crypto money markets to the indices we examined. While oil prices were found to be statistically significant in all models among the control variables, different results were obtained on the effect of the fear index. The findings show that it is imperative for stock market investors to closely monitor the developments in the crypto money market in addition to track various economic variables, in their investment decisions.
Due to its growing popularity and commercial acceptance, cryptocurrency is playing an increasingly essential role in altering the financial system. While many people are investing in cryptocurrency, the dynamic characteristics and predictability of cryptocurrency are still largely unknown, putting investments at risk. In this paper, we attempt to anticipate the Bitcoin price by taking into account a variety of factors that influence its value with the highest possible accuracy using (LSTM) Recurrent Neural Network. The data we use in this work includes updated daily records of many aspects of Bitcoin pricing over a five-year period. Since the cryptocurrency (Bitcoin) data is so volatile, we implement an effective pre-processing of the data in order to have a better prediction result. With this solution, we gain accuracy of 95.7% and RMSE of 0.05. Furthermore, we compare this work with other existing methods based on performance and accuracy. This comparison demonstrates that utilizing LSTM with adequate hyperparameter tweaking is one of the most efficient ways for cryptocurrency price prediction.