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Deep Reinforcement Learning for Online Optimal Execution Strategies

By Alessandro Micheli and Mélodie Monod
This paper tackles the challenge of learning non-Markovian optimal execution strategies in dynamic financial markets. We introduce a novel actor-critic algorithm based on Deep Deterministic Policy Gradient (DDPG) to address this issue, with a focus on transient price impact modeled by a general decay kernel. Through numerical experiments with various... Show more
October 17, 2024
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Deep Reinforcement Learning for Online Optimal Execution Strategies
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