Cryptocurrency Trading Using Machine Learning

Abstract

Education on cryptocurrency is essential for individuals to make informed decisions regarding foreign investment in digital assets. In recent years there is an exponential increase in the price of cryptocurrency due to its easy trading especially in developing countries so the trend of financial institutions buying cryptocurrency into their portfolios has grown in the past decade which results in economic growth. The first completely digital assets that asset managers have included are cryptocurrencies. Traders have a unique opportunity to forecast price swings due to social media’s impact on cryptocurrency prices. Trading using Al and Machine Learning has drawn more attention in recent years. One could investigate the above hypothesis to determine if it is feasible to capitalize on the Bitcoin market’s inefficiency for the purpose of generating unusually high profits. The advanced machine-learning techniques enable straightforward trading strategies to exceed conventional benchmarks. The findings demonstrate how basic computational processes might assist predict the near-term development of the bitcoin market. Further, there are prediction and comparison prices using SVM and Random Forest algorithms on the basics of efficiency while changing the number of days.