Trading algorithms for high-frequency currency trading

Abstract: This thesis uses modern portfolio theory together with machine learning techniques to generate stable portfolio returns over eleven currency pairs with spreads included. The backtests show that support vector machine predicted future returns better than neural network and linear regression. Principal component analysis and data smoothing combined with the local outlier factor further improved the performance of the trading algorithm. However, the ensemble of the top performed predictor performed below the individual predictors. Also, the use of different error estimates showed the criticality of mean arctangent absolute percentage error over mean absolute error and over mean squared error for profitability. For obtaining sensible results in a transaction costless setting, adopting risk adjusted leverage proved necessary. Otherwise, the profit-maximizing leverage surpassed the risk adjusted in a spread setting.