Using Random Forests in conintegrated pairs trading

In this thesis we will use Random Forests to define a trading strategy. Using this powerful machine learning technique, we will try to predict the daily price changes of financial products that move similarly over the long term, so-called cointegrated pairs. We propose a way to adjust our portfolio based on these prediction, while limiting our risk. Firstly, we test our strategy on data generated from a model that mimics these kinds of financial products. After promising results, we test our strategy on the Dutch AEX index and the German DAX index. From our backtests we see that our strategy outperforms both indices in terms of Sharpe ratio. Using a backtesting period of 10 year up to mid 2017 we find an annualized Sharpe ratio of about 0.7, before transaction costs and ignoring the riskfree return rate.