Automated trading system using machine learning, stream mining and technical analysis of trading

Digital trading of securities is beginning to dominate over classical trading and the trading exchanges are rapidly migrating to the cloud. Computer is not only present in the exchange process but is also capable of making trading decisions on human's behalf. Automated trading system is a computer system, capable of trading without human interaction. The benefits of such an approach to trading are objectivity and fast execution of orders, which are often crucial for success. In this thesis we examine automated trading systems and their structure. We study the field of technical analysis which quantifies market price movements. We define trading as a supervised machine learning and stream mining problem and examine the k-nearest neighbours algorithm, naive Bayes classifier and artificial neural networks. Based on our research we design an automated trading system. We evaluate its performance on actual market data of cryptocurrencies Bitcoin and Litecoin using a simulated environment. Automated trading turns out to be a difficult machine learning problem, but with the use of the k-nearest neighbours algorithm and artificial neural networks we manage to achieve decent success in our predictions and profitability