A Study on Algorithmic Trading

Sammanfattning: Algorithms have been used in finance since the early 2000s and accounted for 25% of the market around 2005. In this research, algorithms account for approximately 85% of the market. The challenge faced by many investors and fund managers is beating the Swedish market index OMXS30. This research investigates publicly available algorithms and their potential for implementation and modification to outperform the market. There is a lot of research done on the subject and most of the research found was mostly at a high academic level. Although few algorithms were found in the search, some algorithms that managed to beat other markets caught interest. The market data for this research was obtained from Nordnets closed API, specifically the historical price data of various financial securities. The algorithms use the historical price data to generate buy and sell signals which represents a trade. These trades were then used to calculate performance metrics such as the geometric mean and the sharpe ratio. The performance metrics are used to measure and compare performance with the OMXS30 using a quantitative method. On average, the algorithms did not perform well on the chosen securities, although some securities stood out in all cases. Beating the market is considered a difficult task, and this research reflects some of the challenges involved. The chosen method highlights the importance of the stocks the algorithms trade, emphasizing that stocks cannot be chosen randomly. Building a fully automated unsupervised trading system is challenging and requires extensive work. Some strategies tend to require human supervision to maximize returns and limit losses, while others yield low returns for low risk.