This project investigated different algorithmic trading strategies. Algorithmic trading is a method to automate trading by using computers programmed to follow a defined set of instructions for placing trades to generate profits at a speed, frequency and volume which are beyond a human trader’s capability. The aim was to backtest the strategies on historical data to gauge how they will work in current conditions, optimise, evaluate and combine different strategies to create safe and profitable strategies. During the parameter optimisation, different theories and myths about strategy parameters or the strategy itself could be accepted or rejected. A simple and effective way of combining strategies was found to reduce volatility in the VAMI (Value Added Monthly Index) and the monthly return.