0.0212 btc 4o usd
Balance. 0 BTC USD ; Total received. BTC ; Total sent. BTC ; Transactions. Inputs: 2. Outputs: 2. Total Transactions: 4 ; Omni Tokens. 0. Account Balance Internal Deposits - Internal Withdrawls Bitcoin Price Current Balance & Net vs Block Height Block Height Amount (BTC) Price BTC/USD. The currency converter shows the conversion of Bitcoins to US Dollars as of today price. Current tool convert BTC in USD using live average market. STRATEGY PROFIT FOREX SIGNAL
ETHBTC — Daily Chart However, on the upside, a possible bullish movement may likely push the market above the channel, when this is done, the resistance level of 0. The stochastic RSI is recovering from the oversold zone and this indicates that the market may continue to fall.
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SVM algorithms were applied in [ 27 ] to investigate the profitability of the cryptocurrency market and explore how high they would be. The authors showed that the SVM technique could outperform the benchmark strategies in terms of return-risk relation.
Note that traditional econometric and time-series models, such as the ARMA and GARCH models, might not provide helpful results in predicting cryptocurrency returns due to the impact of risk and uncertainty on return volatility. Although several SVM techniques have already been used in numerous applications, very few have been done in the cryptocurrency market.
To the best of our knowledge, the SVM has never been employed to predict the daily classification of cryptocurrency returns according to the quantiles of the gold price the next day. Our objective is to propose a new approach for predicting cryptocurrency returns by using the SVC technique.
Specifically, instead of forecasting the movement of the cryptocurrency return indicating whether it would go up or down, we forecast the type of this return reporting whether it would be in the first, second, third quartile, or any quantile of the gold price the next day. Therefore, our investigation fills this gap by proposing a new methodology for predicting the daily classification of cryptocurrency returns according to the quantiles of the gold price the next day.
The rest of this article is organized as follows. In Section 2 , we propose an algorithm that uses a sensor in the database to predict the return of cryptocurrencies considering COVID data and the price of gold. Section 3 defines the methodology based on the SVM models employed in this study. In Section 4 , we present the data and results of our investigation, whereas in Section 5 our conclusions are reported. In addition, we design an algorithm that allows us to sense changes in the data and show updated results.
The database management system permits the configuration of a digital sensor. This sensor can detect if new data are entered into the database, if the data are modified, or if data are deleted. In this work, a sensor that detects the presence of new data is utilized.
As a response to new data, a trigger is implemented, which enables instructions to be registered and executed when the new data arrive. This sensor permits the software that performs the calculations to be notified that new records were entered in the database and therefore that the computations must be updated. In summary, the local database is updated daily with external data websites. A sensor in the local database sends a signal to the developed software to perform the calculations considering the new data.
The software receives the signal and executes the corresponding processing. Lastly, the software delivers the results. Next, we describe Algorithm 1 that senses changes in the data and shows updated results. For the sake of better understanding, we relate some steps of this algorithm to our case study in Section 4. Algorithm 1 Approach to predict the daily classification of cryptocurrencies returns relating them to gold price and COVID data using sensors.
Note that the database has a digital sensor with the ability to detect when new data are entered. Methodology SVM with binary classification is a powerful learning algorithm that aims to differentiate between two classes by finding an optimal hyperplane that maximizes the separation between the two-dimensional space points [ 23 ].
An algorithm that maximizes the margin between the training patterns and the decision boundary is a helpful tool that can be applied to a wide variety of classifiers. In this tool, the number of parameters must be fit automatically to match the complexity of the problem, with the solution being formulated as a linear combination of support patterns, which are a subset of training patterns that are closest to the decision boundary.
Next, we provide a background of the method used in the present investigation. For detailed mathematical derivations of the results presented in this section, we refer the readers to [ 34 ].
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