Marek Zatwarnicki, Krzysztof Zatwarnicki, Piotr Stolarski
In 2020 and 2021, the cryptocurrency market attracted millions of new traders and investors. Lack of regulation, high liquidity, and modern exchanges significantly lowered the entry threshold for new market participants. In 2021, over 5 million Americans were regularly involved in cryptocurrency trading. At that time, the interest in market indicators and trading strategies remained low, leading to the conclusion that most investors did not use decision-support indicators. The correct and backtested use of technical analysis signals can give the trader a significant advantage over most market participants. This work introduces an algorithmic approach to examining the effectiveness of the signals generated by one of the most popular market indicators, the Relative Strength Index (RSI). A model corresponding to an actual cryptocurrency exchange was used to backtest the strategies. The results show that the RSI as a momentum indicator in the cryptocurrency market involves high risk. Using alternative RSI applications can allow traders to gain an advantage in the cryptocurrency market. Comparing the results with the traditional buy and hold strategy shows the credible potential of the indicated method and the usage of signals generated by the technical analysis indicators.
Kate Murray, Andrea Rossi, Diego Carraro, Andrea Visentin
Traders and investors are interested in accurately predicting cryptocurrency prices to increase returns and minimize risk. However, due to their uncertainty, volatility, and dynamism, forecasting crypto prices is a challenging time series analysis task. Researchers have proposed predictors based on statistical, machine learning (ML), and deep learning (DL) approaches, but the literature is limited. Indeed, it is narrow because it focuses on predicting only the prices of the few most famous cryptos. In addition, it is scattered because it compares different models on different cryptos inconsistently, and it lacks generality because solutions are overly complex and hard to reproduce in practice. The main goal of this paper is to provide a comparison framework that overcomes these limitations. We use this framework to run extensive experiments where we compare the performances of widely used statistical, ML, and DL approaches in the literature for predicting the price of five popular cryptocurrencies, i.e., XRP, Bitcoin (BTC), Litecoin (LTC), Ethereum (ETH), and Monero (XMR). To the best of our knowledge, we are also the first to propose using the temporal fusion transformer (TFT) on this task. Moreover, we extend our investigation to hybrid models and ensembles to assess whether combining single models boosts prediction accuracy. Our evaluation shows that DL approaches are the best predictors, particularly the LSTM, and this is consistently true across all the cryptos examined. LSTM reaches an average RMSE of 0.0222 and MAE of 0.0173, respectively, 2.7% and 1.7% better than the second-best model. To ensure reproducibility and stimulate future research contribution, we share the dataset and the code of the experiments.
Numerous research have been done to predict cryptocurrency prices since cryptocurrency prices affect global economic and monetary systems. However, investigations using linear connection approaches and technical analysis indicators frequently fall short of providing an explanation for changes in the pattern of BitCoin pricing. This paper is proposed to study time-varying parameters with long short-term memory (LSTM). The study is investigated on a dataset retrieved from Binance from March 2022 to April 2022. The proposed LSTM used a variety of hyperparameter settings, particularly time parameters, to predict the cryptocurrency price (BTC/USDT) on the dataset. Additionally, it is evaluated in terms of mean absolute percentage error (MAPE) in comparison to smooth moving average (SMA), weighted moving average (WMA), and exponential moving averages (EMA). From the investigation, using the previous 3 days for prediction gives the lowest of the MAPE values and the proposed LSTM outperformed the other models. When considering the last three days' value of pricing, the indicated LSTM offers the best accurate prediction, with a MAPE percentage of 0.0927%.
Ashikur Rahaman, Abu Kowshir Bitto, Khalid Been Md. Badruzzaman Biplob, Md. Hasan Imam Bijoy · 6 authors
As opposed to other fiat currencies, bitcoin has no relationship with banks. Its price fluctuation is largely influenced by fresh blocks, news, mining information, support or resistance levels, and public opinion. Therefore, a machine-learning model will be fantastic if it learns from data and tells or indicates if we need to purchase or sell for a little period. In this study, we attempted to create a tool or indicator that can gather tweets in real-time using tweepy and the Twitter application programming interface (API) and report the sentiment at the time. Using the renowned Python module "FBProphet," we developed a model in the second phase that can gather historical price data for the bitcoin to US dollar (BTCUSD) pair and project the price of bitcoin. In order to provide guidance for an intelligent forex trader, we finally merged all of the models into one form. We traded with various models for a very little number of days to validate our bitcoin trading indicator (BTI), and we discovered that the combined version of this tool is more profitable. With the combined version of the instrument, we quickly and with little error root mean square error (RMSE: 1,480.58) generated a profit of $1,000.71 USD.
Abstract Mean-variance portfolio optimization models are sensitive to uncertainty in risk-return estimates, which may result in poor out-of-sample performance. In particular, the estimates may suffer when the number of assets considered is high and the length of the return time series is not sufficiently long. This is precisely the case in the cryptocurrency market, where there are hundreds of crypto assets that have been traded for a few years. We propose enhancing the mean-variance (MV) model with a pre-selection stage that uses a prototype-based clustering algorithm to reduce the number of crypto assets considered at each investment period. In the pre-selection stage, we run a prototype-based clustering algorithm where the assets are described by variables representing the profit-risk duality. The prototypes of the clustering partition are automatically examined and the one that best suits our risk-aversion preference is selected. We then run the MV portfolio optimization with the crypto assets of the selected cluster. The proposed approach is tested for a period of 17 months in the whole cryptocurrency market and two selections of the cryptocurrencies with the higher market capitalization (175 and 250 cryptos). We compare the results against three methods applied to the whole market: classic MV, risk parity, and hierarchical risk parity methods. We also compare our results with those from investing in the market index . The simulation results generally favor our proposal in terms of profit and risk-profit financial indicators. This result reaffirms the convenience of using machine learning methods to guide financial investments in complex and highly-volatile environments such as the cryptocurrency market.
The research purpose of this paper is to obtain an algorithm model with high prediction accuracy for the price of Bitcoin on the next day through random forest regression and LSTM, and to explain which variables have influence on the price of Bitcoin. There is much prior literature on Bitcoin price prediction research, and the research methods mainly revolve around the ARMA model of time series and the LSTM algorithm of deep learning. Although it cannot be proved by the Diebold–Mariano test that the prediction accuracy of random forest regression is significantly better than that of LSTM, the prediction errors RMSE and MAPE of random forest regression are better than those of LSTM. The changes in the variables that determine the price of Bitcoin in each period are also obtained through random forest regression. From 2015 to 2018, three US stock market indexes, NASDAQ, DJI, and S&P500 and oil price, and ETH price have impact on Bitcoin prices. Since 2018, the important variables have become ETH price and Japanese stock market index JP225. The relationship between accuracy and the number of periods of explanatory variables brought into the model shows that for predicting the price of Bitcoin for the next day, the model with only one lag of the explanatory variables has the best prediction accuracy.
Machine learning and deep learning algorithms produce very different results with different examples of their hyperparameters. Algorithm parameters require optimization because they aren't specific for all problems. In this paper Long Short-Term Memory (LSTM), eight different hyperparameters (go-backward, epoch, batch size, dropout, activation function, optimizer, learning rate and, number of layers) were used to examine to daily and hourly Bitcoin datasets. The effects of each parameter on the daily dataset on the results were evaluated and explained These parameters were examined with hparam properties of Tensorboard. As a result, it was seen that examining all combinations of parameters with hparam produced the best test Mean Square Error (MSE) values with hourly dataset 0.000043633 and daily dataset 0.00073843. Both datasets produced better results with the tanh activation function. Finally, when the results are interpreted, the daily dataset produces better results with a small learning rate and small dropout values, whereas the hourly dataset produces better results with a large learning rate and large dropout values.
In the last decade, the techniques of news aggregation and summarization have been increasingly gaining relevance for providing users on the web with condensed and unbiased information. Indeed, the recent development of successful machine learning algorithms, such as those based on the transformers architecture, have made it possible to create effective tools for capturing and elaborating news from the Internet. In this regard, this work proposes, for the first time in the literature to the best of the authors’ knowledge, a methodology for the application of such techniques in news related to cryptocurrencies and the blockchain, whose quick reading can be deemed as extremely useful to operators in the financial sector. Specifically, cutting-edge solutions in the field of natural language processing were employed to cluster news by topic and summarize the corresponding articles published by different newspapers. The results achieved on 22,282 news articles show the effectiveness of the proposed methodology in most of the cases, with 86.8% of the examined summaries being considered as coherent and 95.7% of the corresponding articles correctly aggregated. This methodology was implemented in a freely accessible web application.
Bitcoin was the first cryptocurrency introduced as a cryptographic proof-based electronic payment system in 2009. Till now approximately more than 10,000 digital coins are active in the crypto market. Cryptocurrency is a virtual digital asset that uses cryptography and blockchain technology for transaction verification and records maintenance. Its trading is gaining attention due to volatile behavior, decentralized nature, and liquidity in this digital asset. Trading this digital asset provides anonymity and security in transactions. Groundless fluctuations in its price contribute to making its trade risky. Market Prediction of the cryptocurrency is trending because it can reduce the trade loss risk. Data related to this market is vast and publicly available on the internet. It is nearly impossible to infer the market by simple data analysis. Statistical price prediction approaches are less effective due to the absence of seasonality in cryptocurrency market data. Therefore researchers proposed efficient price prediction techniques utilizing statistical, algorithmic, and neural network-based Machine Learning models. This paper provides a detailed literature survey related to the state-of-the-art Machine learning-based prediction methodologies for the market prediction of the digital asset from 2014 to 2022. This research will categorize, summarize, and review the existing research in cryptocurrency market prediction using Machine Learning classifiers. This paper will benefit researchers to be productive in the right direction in the future.
Machine learning has a wide range of applications to meet the complexity of data and various expectations for prediction types.In this study, a comprehensive review of various machine learning approaches for Bitcoin price prediction will be proposed.After examining previous research on cryptocurrency prediction using Long-Short Term Memory (LSTM), Multi-layer Perceptions (MLP), and Support Vector Machine (SVM), with the focus on LSTM, it can be found that LSTM is a widely employed method in Bitcoin price prediction because of its advantages in incorporating both long-term and short-term dependencies.This paper reviews a series of research papers by comparing the differences between the methods they implemented, to a limited extent, based on their predictive power, replicability, and model limitations.Furthermore, some potential improvements and explored innovations for future studies also be discussed.
Jan 1, 2023·Proceedings of the 4th Management Science Informatization and Economic Innovation Development Conference, MSIEID 2022, December 9-11, 2022, Chongqing, China
Quantitative portfolio of gold and bitcoin investment can be determined by synthesized quantitative model, with the help of various quantifying indicators. Previous prediction models labor to disperse risk of the investment portfolio as well as maximize the return. To handle it, this essay digs into
After the invention of Bitcoin by a man named Satoshi Nakamoto along with other blockchain-based person-to-person payment systems, the cryptocurrency market has instantly gained popularity. Because of this, that is, the volatility of the various cryptocurrency prices. This attracts much attention from both the investors and the researchers. The task of forecasting the prices of crypto-currencies because of the static prices and the arbitrary effects in the market is quite challenging. Cryptocurrency price forecasting models that are available now mainly focus on analyzing extrinsic factors, like macro-financial indicators, data linked to the blockchain, and data from social media – with the goal of enhancing the prediction accuracy. However, the intrinsic noise present in the raw data, caused by market and political conditions worldwide, is complex to interpret. In our research we propose a multiple input convolutional neural network model, specifically a convolutional neural network model for the prediction of future cryptocurrency price. Generally, RNNs and LSTMs are used for problems dealing with timeseries data. We used the concept of residual networks on 1-Dimensional convolutional networks to solve the problem of predicting the price of Bitcoin, the most popular cryptocurrency out there at the moment. Furthermore, we conduct additional experiments on ether, the cryptocurrency of Ethereum to further confirm that even CNNs can work equally well, if not better in comparison to the widely used LSTM neural network models.
Jan 1, 2023·Proceedings of the International Conference on Financial Innovation, FinTech and Information Technology, FFIT 2022, October 28-30, 2022, Shenzhen, China
Price forecasting is pretty crucial in the asset management and allocation and quantitative trading industries. With the development of the global economic situation, decentralized finance has gradually entered people's field of vision, and cryptocurrency and cryptocurrency finance have become the r
The purpose of the research is to study the cryptocurrency data listed on Binance, and design a profitable strategy based on the findings. The data covers over 150 selected cryptocurrencies. The study aims to detect anomalies in the volume and number of transactions and apply an investment strategy based on deviations and sudden price fluctuations. An autoencoder and LSTM-based neural network have been used. Based on the results of the present research, it can be concluded that the model successfully identified anomalies in the data regarding the volume and number of transactions carried out. I it was also observed that price volatility in the period close to the detected anomaly was significantly higher than average volatility for the sample.
In the dynamic world of financial markets, the prediction of stock performance and bitcoin trading is undergoing a significant transformation due to the integration of advanced technologies and novel methodologies.The incorporation of Transformer models alongside Time Embeddings significantly improves the precision of stock market predictions by effectively capturing intricate temporal relationships and mitigating the presence of overly simplistic assumptions.The integration of real-time social media data with sentiment analysis based on BERT provides significant value in understanding investor sentiment.Additionally, the application of language model pre-training, as exemplified by BERT, brings about a transformative impact on text classification for predicting stock prices.Within the domain of cryptocurrency, sophisticated algorithms such as Transformers, Long Short-Term Memory (LSTM), Deep Convolutional LSTM (DC-LSTM), and Neural Networks (NN) have demonstrated enhanced capabilities in predicting price movements.These algorithms are further bolstered by the implementation of a comprehensive trading strategy.Automated systems for bitcoin trading introduce elements of personalization and adaptability to the trading process, thereby facilitating broader access to a diverse group of traders.The progress highlights the significant importance of the integration of technology and methodologies in the field of financial analysis.This integration enables investors and traders to possess the necessary resources for making well-informed choices within the ever-changing landscape of financial markets.
Jan 1, 2023·Proceedings of the International Conference on Financial Innovation, FinTech and Information Technology, FFIT 2022, October 28-30, 2022, Shenzhen, China
The market for cryptocurrency has thrived for more than 10 years and has experienced a drastic change. The success of cryptocurrencies was concerned and analyzed worldwide. This research discusses the way to build machine learning and statistical models to predict the future price of the cryptocurre
The question of how to benefit from an organic combination of gold and bitcoin has become a prominent topic in the contemporary society. Hence, we've built the time series forecasting models and target planning models of gold and bitcoin, providing the best gold and bitcoin rotation investing strategy based on our methodology. We consider the connection between gold and bitcoin price fluctuations by creating the SVM-GARCH Combination Model, and at the same time, data-based nonlinear feature extraction and heteroscedasticity processing give a more accurate and dependable foundation for investment decision making.In terms of investment planning, We first utilized VaR to clarify our quantitative investment risk indicators, and then built a VaRY Model to organically integrate and balance investment returns and risks. At the same time, we include Risk Adjustment Parameters into the planning model so that, by dynamic weight adjustment, our target planning model can match the wealth utility propensity of investors with diverse risk preferences, therefore improving the model's application and flexibility. Finally, in view of the differences in trading restrictions between Trading Days and Non-trading Days, we formulate different dynamic weights - Multi-objective Programming Models for trading and non trading periods, so that our best investment decision can be more comprehensive and targeted.We present proof for the brilliance of our investment strategy in four dimensions by merging and assessing the forecasting model and the planning model: Accuracy, Rationality, Flexibility, and High Return.