Abstract
Cryptocurrencies have displayed promising potential to all types of investors since their inception in 2009. Cryptocurrencies have been designed with the intention of providing individual users with a verified, decentralized, and anonymous digital currency. A major appeal to it is that the currencies are based on a secure ledger, the blockchain. Cryptocurrencies can be used as a form of payment to buy or sell goods and services or be traded for a profit. This thesis focuses on the technical analysis of different cryptocurrency algorithmic trading methods and the processes as well as the components that enable a maximum profit. First discussed is the proposed method, an algorithm that trades cryptocurrency based on technical market indicators that tend to repeat themselves despite the high volatility of the respective cryptocurrency. Following this is the technical analysis of the AI powered trading algorithms that can be developed or purchased for use. This focuses on the needed processing power, scalability, and the general success of AI when trading cryptocurrency. The comparative performance analysis takes the proposed method and compares it to known artificial intelligence algorithms that are capable of producing results of imitating current cryptocurrency trading charts or predicting future developments in a trading chart. The performance of each algorithm provides in depth analysis into the best approach for investors to trade cryptocurrency for a profit.