In the last decade, cryptocurrency trading has attracted the attention of private and professional traders and investors. To forecast the financial markets, algorithmic trading systems based on Artificial Intelligence (AI) models are becoming more and more established. However, they suffer from the lack of transparency, thus hindering domain experts from directly monitoring the fundamentals behind market movements. This is particularly critical for cryptocurrency investors, because the study of the main factors influencing cryptocurrency prices, including the characteristics of the blockchain infrastructure, is crucial for driving experts’ decisions. This paper proposes a new visual analytics tool to support domain experts in the explanation of AI-based cryptocurrency trading systems. To describe the rationale behind AI models, it exploits an established method, namely SHapley Additive exPlanations, which allows experts to identify the most discriminating features and provides them with an interactive and easy-to-use graphical interface. The simulations carried out on 21 cryptocurrencies over a 8-year period demonstrate the usability of the proposed tool.
This study aims to establish the model of the cryptocurrency price trend based on a financial theory using the Long Short-Term Memory (LSTM) networks model with multiple combinations between the window length and the predicting horizons. The Random Walk model is also applied with different parameter settings. The object of this study is the cryptocurrency and medical issues, primarily the Bitcoin and Ethereum and the COVID-19. Quantitative analysis is adopted as the method of this dissertation. The research tool is Python programming language, and the TensorFlow package is employed to model and analyze research topics. The results of this study show the limitations of the LSTM and Random Walk model for price prediction while demonstrating the different characteristics of both models with different parameter settings, providing a balance between the model's accuracy and the model's practicality.
With the prosperity of the financial market, more and more people are involved in securities trading. How to combine investment in bitcoin and gold to achieve the highest profit is one of the issues that market traders think about. To solve this problem, we build a model that predicts future prices in order to better help investors. We constructed an ARIMA model through differential stationarity processing, AEC, white noise test and other methods, and used the data of the current day and the previous day to predict the price of the next day. At the same time, we use the model to predict the average for the next N days. If it is predicted that the price of the asset will decrease in the future, it will be sold on the same day. If the price of the asset will increase in the future, and the income obtained is greater than the transaction cost and the expected income of the investor, the purchase will continue until the end. Last until the last day $323841.52. Finally, it can be verified that the accuracy of the ARIMA model is the highest by comparing other mainstream machine learning models. In other words, the ARIMA model is the best strategy for this problem.
Mustafa Shabbir Bhavanagarwala, K N Nagarjun, Tanzim Abbas Charolia, Mr Sonawane Vishal · 5 authors
In our project, the point is to anticipate long term esteem of the money related stocks of a company and crypto coins individually with fine precision. The future prices of stock and cryptocurrency are predicted by using the past available values. “Buy low, sell high" is a good saying but it is not a good choice for making speculations. Investment is best stock or crypto currency in awful time can have bad results, while investment in best stock or cryptocurrency at right time can have best benefits. Prediction for long term values is easy as compared to day-to-day basis as prices fluctuate a lot. So, our model predicts the price of stocks and cryptocurrencies, which helps the investors to invest in appropriate stocks and cryptocoins. The dataset used is taken from yahoo finance and twelve data using web scraping. The dataset retrieved is in raw format. It consists of collection of values of stock market data of various companies, and also data of various cryptocurrencies. First, raw data is converted into processed data, which is done using feature extraction. Then the dataset is splitted into training and test sets. We use the training dataset to train the model, and use test dataset to predict the future prices of stocks and cryptocurrencies. Now user can gain best knowledge about stock price trends of various companies and also cryptocurrency price trends, and can decide on for best investments in respective fields and gain best benefits.
As a popular cryptocurrency, Bitcoin has been an important investment tool in recent years. This study aims to analyze the factors that affect the Bitcoin price to help investors make better investment choices. Applying the simple linear regression model and Granger causality test to the data from January 2015 to December 2021, the research first examines the stationary of the data and then studies the relationships between Bitcoin price and other factors including Dow Jones Industrial Index, U.S. currency in circulation, U.S. disposable income. According to the result, all three factors have a positive effect on the price of Bitcoin and the Bitcoin price will in turn influence the Dow Jones Industrial Index and U.S. disposable income. This finding helps explain how certain economic indicators and Bitcoin prices interact. Since investment is always risky, investors must consider certain factors like the trend of DOW, M2, or PCI in advance to make a reasonable investment decision.
Cryptocurrencies are now the most popular investment instruments among millenials. Crypto offers great returns in a short period of time. Prior to COVID-19, Crypto experienced significant price fluctuations accompanied by an increase in the number of high transaction volumes. This situation was disrupted by the presence of the COVID-19 which made the world economy devastated, marked by the decline of stock prices in the world, especially in Indonesia. A paired test was conducted in this study to compare the state of Crypto before and during COVID-19 with the variables of Risk, Transaction Volume, Return, and Sharpe Performance. The results showed that there was a significant difference in the variables of Transaction Volume and Return. However, there was no significant difference in the Risk and Sharpe performance before and during COVID-19. This study shows that despite the COVID-19 pandemic, the enthusiasm of investors who transact crypto assets is not affected and they still get returns in accordance with the investments made. The high risk will be followed by a high standard deviation, so that the Sharpe Performance is small. Cryptocurrencies still have many gaps to research, such as regulation, so that many countries have not legalized Crypto transactions. If there is no regulation for Crypto, it is certain that an increase in cybercrime harms crypto investors and threatens global financial stability. Nevertheles, with or without COVID-19, investment transactions gain and lose based on confidence in the limited market. Therefore, the success of confidence fluctuations in crypto encourages the emergence of alternative coins created by investors to conduct an Initial Coin Offering (ICO).
In the topic selection, we need to estimate the prices of bitcoin and gold according to the data given from 2001 to 2012. According to the estimated price, the initial amount is set as $1000, which is used as the principal for financial investment for a period of five years from 2016. In the whole modeling process, the main problems we need to solve are the following four points: the task 1 is the best investment strategy is given through the established model, and the investment value on October 9, 2021 is calculated. The task 2 is the best strategy of the model is proved. The task 3 is Determine the impact of transaction costs on transaction results. The last task is to Complete a memo with strategies, models, and results. In the whole modeling process, we first preprocess the data, which is arranged and classified in chronological order, and fill the data by interpolation fitting. LSMT algorithm is a neural network algorithm, which is suitable for the calculation of various long-term processes. The investment problem we study is a good application field. In the calculation process of the basic model, it is necessary to set the initial value, complete a series of processing, and process the hidden layer of LSTM unit. Take x as the output value, set the temporary hidden layer and new hidden layer, and verify that the size of the final output result is consistent with the label size. The hidden layer is transformed according to the sigmoid function proposed above. After calculating the hidden layer conversion, the error is back propagated, the input derivative of the file is obtained, and the overall error and record hidden layer are obtained. Then the calculated hidden layer difference is used to calculate the change of parameters and update parameters. Since bitcoin can be traded on any trading day, gold can only be traded on weekly trading days. For the convenience of calculation, we fix the transactions of bitcoin and gold as trading days every Friday. After receiving the benefits, the total assets of the cash flow as of the trading day are obtained by deducting the Commission to be paid. However, the model ignores the impact of bitcoin mining with different software and the fact that gold and bitcoin are not fixed on the same trading day, so there will be errors.
Cryptocurrency has evolved from a fringe phenomenon to a far more popular method of investing and financing. For investors and traders, predicting the price of bitcoin is critical. Several machine learning algorithms are utilized to anticipate the price of digital money in this research paper. The analysis employed Decision Trees, Light Gradient Boosting Machines, and Neural Networks. The purpose of this study is to look at the predicted accuracy of each machine learning method. According to the analysis, decision tree, lightGBM, and neural networks have a very high accuracy rate when it comes to forecasting cryptocurrencies. These results shed light on guiding further exploration to help investors in building an appropriate digital currency portfolio and reducing risks.
To be or not to be is the question that Hamlet thinks about day and night. Gold or Bitcoins is an inescapable choice for investors. With the ever rising and falling price of gold and bitcoin, making good trading decisions is of paramount importance. In this paper, we systematically investigate how data can be used to quantify the factors that influence trading and make the final decision. We build time series with the prices of gold and bitcoin for the past five years. We obtained forecast curves with excellent fit by seasonality analysis and ARIMA time series model forecasts.
Cryptocurrencies are rising in importance as an investment option and alternative currency. Thus, investors are keen on finding timely market movement insights. One such source is Twitter due to its live feed of information on cryptocurrencies and emotional information from investors expressing their sentiments. This article examines the extent to which Twitter sentiments can be used to predict price and transaction volume changes for the nine largest cryptocurrencies for the period of June 2021 to September 2021. This study was conducted using a lexicon-based approach through the VADER algorithm for sentiment analysis, while applying the Granger causality method to analyze the two-way predictive capabilities of each cryptocurrency&s;s sentiments toward their respective price and transaction volume changes. Past studies have shown that sentiment analysis may work for several cryptocurrencies, while this study only found predictive capabilities in transaction volume changes and not in price movement.
Digital currencies such as Ethereum and XRP allow for all transactions to be carried out online. To emphasize the decentralized nature of fiat currency, we can refer, for example, to the fact that all virtual currency users may access services without third-party involvement. Cryptocurrency price swings are non-stationary and highly erratic, similarly to the price changes of conventional stocks. Owing to the appeal of cryptocurrencies, both investors and researchers have paid more attention to cryptocurrency price forecasts. With the rise of deep learning, cryptocurrency forecasting has gained great importance. In this study, we present a long short-term memory (LSTM) algorithm that can be used to forecast the values of four types of cryptocurrencies: AMP, Ethereum, Electro-Optical System, and XRP. Mean square error (MSE), root mean square error (RMSE), and normalize root mean square error (NRMSE) analyses were used to evaluate the LSTM model. The findings obtained from these models showed that the LSTM algorithm had superior performance in predicting all forms of cryptocurrencies. Thus, it can be regarded as the most effective algorithm. The LSTM model provided promising and accurate forecasts for all cryptocurrencies. The model was applied to forecast the future closing prices of cryptocurrencies over a period of 180 days. The Pearson correlation metric was applied to assess the correlation between the prediction and target values in the training and testing processes. The LSTM algorithm achieved the highest correlation values in training (R = 96.73%) and in testing (96.09%) in predicting XRP currency prices. Cryptocurrency prices could be accurately predicted using the established LSTM model, which displayed highly efficient performance. The relevance of applying these models is that they may have huge repercussions for the economy by assisting investors and traders in identifying trends in the sales and purchases of different types of cryptocurrencies. The results of the LSTM model were compared with those of existing systems. The results of this study demonstrate that the proposed model showed superior accuracy based on the low prediction errors of the proposed system.
Alfonso Guarino, Luca Grilli, Domenico Santoro, Francesco Messina · 5 authors
Abstract Financial bubbles represent a severe problem for investors. In particular, the cryptocurrency market has witnessed the bursting of different bubbles in the last decade, which in turn have had spillovers on all the markets and real economies of countries. These kinds of markets and their unique characteristics are of great interest to researchers. Generally, investors and financial operators study market trends to understand when bubbles might occur using technical analysis tools. Such tools, which have been historically used, resulted in being precious allies at the basis of more advanced systems. In this regard, different autonomous, adaptive and automated trading agents have been introduced in the literature to study several kinds of markets. Among these, we can distinguish between agents with Zero/Minimal Intelligence (ZI/MI) and Computational Intelligence (CI) -based agents. The first ones typically trade on the market without resorting to complex learning strategies; the second ones usually use (deep) reinforcement learning mechanisms. However, these trading agents have never been tested on the cryptocurrencies market and related financial bubbles, which are still mostly overlooked in the literature. It is unclear how these agents can make profits/losses before, during, and after a bubble to adjust their strategy and avoid critical situations. This paper compares a broad set of trading agents (between ZI/MI and CI ones) and evaluates them with well-known financial indicators (e.g., volatility, returns Sharpe ratio , drawdown, Sortino and Omega ratio ). Among the experiment’s outcomes, ZI/MI agents were more explainable than CI ones. Based on the results obtained above, we introduce GGSMZ , a trading agent relying on a neuro-fuzzy mechanism. The neuro-fuzzy system is able to learn from the trades performed by the agents adopted in the previous stage. GGSMZ ’s performances overcome those of other tested agents. We argue that GGSMZ could be used by investors as a decision support tool.
Jul 20, 2022·Proceedings of the 15th International Conference on Computer Graphics, Visualization, Computer Vision and Image Processing (CGVCVIP 2021), the 7th International Conference on Connected Smart Cities (CSC 2021) and 6th International Conference on Big Data Analytics, Data Mining and Computational Intelligence (BigDaCI’21)
Ningbo Zhu, Fei Yang, Mingzhi Zhu, Xinyao Sun and Irene Cheng
The cryptocurrency industry has evolved rapidly in recent years, and it is increasingly popular as a convenient tool tocomplement the traditional stock and futures exchanges. Accurate market research enables traders to make moreinformed decisions
The cryptocurrency market is generally accepted in the world, and its price has soared and plummeted sharply. Meanwhile, skewness is an index reflecting the rapid rise and fall of asset prices in a short period. Studying the relationship between the skewness of cryptocurrency and its returns can help risk evaders expect bad news and provide a reference for risk enthusiasts to make investment decisions. Therefore, through univariate combination analysis, this paper groups cryptocurrency according to the skewness of the previous month before buying and holding it in the next week. Moreover, the excess return series are calculated to make statistical tests on it. Then we construct a three-factor model of cryptocurrency and adjust the return series. To enhance the robustness of the conclusion, we also use other measures of skewness such as idiosyncratic skewness to conduct a univariate combination analysis. The results show that a positive correlation between cryptocurrency skewness and its returns exists, which can be used as a reference index of the returns.
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 · 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, doesn’t 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>
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.