Single asset trading: a recurrent reinforcement learning approach

Abstract: Asset trading using machine learning has become popular within the financial industry in the recent years. This can for instance be seen in the large number of daily trading volume which are defined by an automatic algorithm. This thesis presents a recurrent reinforcement learning model to trade an asset. The benefits, drawdowns and the derivations of the model are presented. Different parameters of the model are calibrated and tuned considering a traditional division between training and testing data set and also with the help of nested cross validation. The results of the single asset trading model are compared to the benchmark strategy, which consists of buying the underlying asset and hold it for a long period of time regardless of the asset volatility. The proposed model outperforms the buy and hold strategy on three out of four stocks selected for the experiment. Additionally, returns of the model are sensitive to changes in epoch, m, learning rate and training/test ratio.