Knowledge Graphs, Automatic Feature Engineering and Machine Learning in Algorithmic Trading for Financial Markets

Thesis consists of novel ideas and practical implementations 1.Automatic Features extractions using knowledge graph, proceeds new dataset with Au- toML and Standard Machine Learning Pipeline for prediction. These new features enhance the predictive power of Machine Learning Algorithms to improve the accuracy. 2.Realtime price prediction of Cryptocurrency using ML algorithms. Existing Studies only predict the price of Cryptocurrency for static datasets. The proposed AI signal Machine predicts price of BTC for four timeframes of 1Minute,3 Minute ,5 Minute and 15 Minute simulta- neously.3.Developed AI Automated Trading Algorithm which can be used for trading any instrument like Gold, Stock not limited to crypto currency. The algorithm is based on Feature Engineering, the features extracted using Automatic Features Engineering using knowledge graph.