Currency pair portfolio optimization through the use of algorithmic coding

Abstract

The winds of change have been felt in the financial world – humans no longer monopolize financial markets. We now live in a world where humans are becoming less important, and are quickly being replaced by machines. The key players in modern finance today are cyborgs: part machine, part human. Machines have allowed markets to move faster and more efficiently, and to become more globally connected, effectively changing the way trading works. This dissertation begins by providing a brief introduction to the use of electronic and algorithmic trading in modern finance, shedding light on its importance and advantages to the new investor. Next, the text explores the different mathematical models and statistical tools that can be used to trade on the market. The study then provides detailed analysis into the use of algorithms for technical analysis. This study investigates the use of two major mathematical models, specifically Hilbert and Fibonacci, as predictive indicators. These models were applied to an equally weighted portfolio created from the major currency pairs. The portfolio was adjusted according to the results observed from the algorithms, and the excess return was invested in the risk free asset. Next, the text examines the success of using these models conjointly, by comparing results to a traditional method of trading, namely, a moving average cross over strategy. In particular, this study compares the return on investment (ROI). The study concludes with the observation that a better ROI was achieved when using the Hilbert and Fibonacci models simultaneously to predict future price movements, compared to that achieved using a traditional method.