Data Cross-Segmentation for Improved Generalization in Reinforcement
  Learning Based Algorithmic Trading
Vikram Duvvur Aashay Mehta Edward Sun Bo Wu Ken Yew Chan Jeff Schneider
The use of machine learning in algorithmic trading systems is increasingly
common. In a typical set-up, supervised learning is used to predict the future
prices of assets, and those predictions drive a simple trading and execution
strategy. This is quite effective when the predictions have sufficient signal,
markets are liquid, and transaction costs are low. However, those conditions
often do not hold in thinly traded financial markets and markets for
differentiated assets such as real estate or vehicles. In these markets, the
trading strategy must consider the long-term effects of taking positions that
are relatively more difficult to change. In this work, we propose a
Reinforcement Learning (RL) algorithm that trades based on signals from a
learned predictive model and addresses these challenges. We test our algorithm
on 20+ years of equity data from Bursa Malaysia.