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The use of machine learning in algorithmic trading systems is increasinglycommon. In a typical set-up, supervised learning is used to predict the futureprices of assets, and those predictions drive a simple trading and executionstrategy. This is quite effective when the predictions have sufficient signal,markets are liquid, and transaction costs are low. However, those conditionsoften do not hold in thinly traded financial markets and markets fordifferentiated assets such as real estate or vehicles. In these markets, thetrading strategy must consider the long-term effects of taking positions thatare relatively more difficult to change. In this work, we propose aReinforcement Learning (RL) algorithm that trades based on signals from alearned predictive model and addresses these challenges. We test our algorithmon 20+ years of equity data from Bursa Malaysia.

Data Cross-Segmentation for Improved Generalization in Reinforcement Learning Based Algorithmic Trading