Reinforcement Learning for Asset Allocation

Reinforcement Learning (RL) has drawn a lot of attention thanks to its successful applications in many fields, most notably to playing games. In this work, we have designed and implemented an RL framework for the task of tactical asset allocation, given a portfolio of equity and fixed income assets. Our approach based on Policy Gradient made use of a particular reward function accounting not only for P&L but also for diversification and stability.

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