This thesis investigates whether quantitative trading strategies can outperform the general market and if they would serve as a better investment than a traditional buy and hold strategy of Indices when applied to the same stock portfolio. Three relatively simple algorithmic trading strategies were selected and backtested on historical stock prices over a 20 year and 5 months trading period (January 1st 2000 – May 1st 2020). The chosen algorithmic trading strategies were a pairs trading strategy, a Bollinger band trading strategy, and a loss-decreasing trading strategy. The algorithms were written in the Python programming language, the data was downloaded from Yahoo! Finance and the trading strategies were simulated within the Jupyter Notebook environment. The benchmarks to represent the overall market performance were the Dow Jones Industrial Average Index and the Standard & Poor's 500 Index. To make the comparison between the benchmarks and the algorithms as fair as possible, the stock portfolios that the algorithms used were built upon the composition of each index (except for the pairs trading strategy). The tested algorithmic trading strategies outperformed the general market and generated superior returns concerning their respective benchmarks. The algorithms also produced these superior returns while taking on less risk than the benchmark and as such, also returned higher risk-adjusted returns than the benchmarks.