Bitcoin Assets Research Articles

The best choice between gold and bitcoin

In this paper, we use Analytic Hierarchy Process (AHP) to determine the appropriate weights and establish a bull market and bear market judgment indicator and investment risk models. Secondly, the Autoregressive Integrated Moving Average model (ARIMA) is constructed to make the expected trend for the next five years, and the optimal asset portfolio of gold assets and bitcoin assets is constructed through quadratic programming, and the Dynamic Programming (DP) model is used to compare strategies between two trades. Finally, the rationality of the model calculation is verified by the test of Long-Short-Term Memory (LSTM).

A Quantitative Trading Strategy Based on A Position Management Model

With the rapid development of economic globalization, various financial products have appeared in the domestic and foreign financial markets. How to adopt ideal trading strategies to meet the demands of market traders for high returns and low risks has become the focus of investors and the research goal of scholars. In order to solve this problem, this paper establishes a quantitative trading strategy based on the position management model.First, we performed price forecasting for gold and bitcoin based on the Time-series ARIMA method. A differential autoregressive moving average model was developed for the price data of gold and bitcoin at different cycle times, respectively, and used to predict the price of the next trading day. Error analysis was performed with the actual prices, and it was found that the prediction error was the smallest when the data was 60 days, and the relative error of the average prediction value (APV) could be controlled at 0.003016.Then, we establish a quantitative trading strategy based on the position management model. We use the Apriori algorithm of Association rules to study the rising and falling rules of gold and bitcoin assets. According to the rule of " High throw bargain - hunting" in the investment market, we established a position management model and achieved dynamic and stable returns. After the model is established, we continue to introduce the evaluation indexes of the investment value of financial assets, among which the first-class indexes are profitability and safety. We use the Analytic Hierarchy Process (AHP) to determine the weight of each evaluation index, and allocate the daily trading investment through the ratio of two asset evaluation indexes. This quantitative trading strategy, based on the position management model of AHP, can not only stabilize the income but also avoid risks, reaching a quantitative trading strategy with an annualized rate of return of 25%. On September 10th, 2021, the accumulated income could reach 223,640.58 USD.Further, we evaluate the profitability and risk resistance of the strategy using Principal component analysis. Model validation was performed by varying the parameter values and selecting the parameters that yielded a locally optimal solution, which was found to be consistent with our initial parameters and was proof of the optimal solution of the model.Finally, we conducted a sensitivity analysis of the model. The two variable parameters of initial commission fluctuation and investment principal of gold and bitcoin are varied up and down respectively, and the results show that as the initial commission increases or the principal decreases, the number of trades under this strategy gradually decreases and the trading return gradually decreases, and the sensitivity curve shows that the model is sensitive and meets expectations.

The Features of Building a Portfolio of Trading Strategies Using the SAS OPTMODEL Procedure

The article describes the original information technology of the algorithmic trading, designed to solve the problem of forming the optimal portfolio of trade strategies. The methodology of robust optimization, using the Ledoit–Wolf shrinkage method for obtaining stable estimates of the covariance matrix of algorithmic strategies, was used for the formation of a portfolio of trade strategies. The corresponding software was implemented by SAS OPTMODEL Procedure. The paper deals with a portfolio of trade strategies built for highly-profitable, but also highly risky financial tools—cryptocurrencies. Available bitcoin assets were divided into a corresponding proportion for each of the recommended portfolio strategies, and during the selected period (one calendar month) were used for this research. The portfolio of trade strategies is rebuilt at the end of the period (every month) based on the results of trade during the period, in accordance with the conditions of risk minimizing or income maximizing. Trading strategies work in parallel, being in a state of waiting for a relevant trading signal. Strategies can be changed by moving the parameters in accordance with the current state of the financial market, removed if ineffective, and replaced where necessary. The efficiency of using a robust decision-making method in the context of uncertainty regarding cryptocurrency trading was confirmed by the results of real trading for the Bitcoin/Dollar pair. Implementation of the offered information technology in electronic trading systems will allow risk reduction as a result of making incorrect decisions or delays in making decisions in a systemic trading.

Backpropagation neural network prediction for cryptocurrency bitcoin prices

The value of bitcoin currency is very volatile, hard to guess for every hour, so many of the bitcoin traders suffer losses because they are wrong in managing their bitcoin assets. Changes in the price of bitcoin itself are influenced by many things such as the closing of the bitcoin market in a country, the occurrence of hacker attacks on the bitcoin blockchain and the emergence of new coins that use technology similar to bitcoin. But when a stable market situation changes the price of bitcoin is purely influenced by market forces. By implementing an artificial neural network using backpropagation method, it will be able to predict the price of bitcoin by giving a form of predictive results that are strengthened with a fairly good value of accuracy. This research begins by determining prediction variables with target values that can be determined based on previous bitcoin prices. This artificial neural network process is able to conduct training and testing of data based on network patterns that have been formed, then the results of training and testing of the network will be analysed again, so that at the last stage the best network patterns will be used in the prediction process.