Abstract
Cryptocurrency trade is now a popular type of investment. Cryptocurrency market has been treated similar to foreign exchange and stock market. The Characteristics of Bitcoin have made Bitcoin keep rising In the last few years. Bitcoin exchange rate to American Dollar (USD) is $3990 USD on November 2018, with daily pice fluctuations could reach 4.55%2. It is important to able to predict value to ensure profitable investment. However, because of its volatility, there’s a need for a prediction tool for investors to help them consider investment decisions for cryptocurrency trade. Nowadays, computing based tools are commonly used in stock and foreign exchange market predictions. There has been much research about SVM prediction on stocks and foreign exchange as case studies but none on cryptocurrency. Therefore, this research studied method to predict the market value of one of the most used cryptocurrency, Bitcoin. The preditct methods will be used on this research is regime prediction to develop model to predict the close value of Bitcoin and use Support vector classifier algorithm to predict the current day’s trend at the opening of the market
Highlights
The one of unique characteristics of bitcoin is keep rising in the last few years
To solve the problem above, there’s a need a tool for predictoon to help investors decide for bitcoin or other cryptocurrency market investment
Market technical analysis can determine the trend of the market in by using historical market price [2]
Summary
The one of unique characteristics of bitcoin is keep rising in the last few years. Bitcoin exchange rate (USD) is $3990 USD on November 2018 and daily fluctuations could reach 4.55%2 [1]. To solve the problem above , there’s a need a tool for predictoon to help investors decide for bitcoin or other cryptocurrency market investment. Market technical analysis can determine the trend of the market in by using historical market price [2]. They give the candle graph and market indicator to help the analysis. Even market technical analysis is useful, another automation method is needed[3], because There has been much research about SVM prediction, Machine learning provides capability produce results of predictions more accurately without expert. Vol 1, No 1, March 2019 p-ISSN: 2656-5935 http://journal-isi.org/index.php/isi e-ISSN: 2656-4882
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