Deep Reinforcement Learning for Online Optimal Execution Strategies
This addresses the problem of inefficient human intervention in financial execution tasks, but it appears incremental as it builds on existing DDPG methods.
The paper tackled learning non-Markovian optimal execution strategies in dynamic financial markets by introducing a novel actor-critic algorithm based on DDPG, and showed that it successfully approximates the optimal strategy and adapts to evolving market conditions.
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 decay kernels, we show that our algorithm successfully approximates the optimal execution strategy. Additionally, the proposed algorithm demonstrates adaptability to evolving market conditions, where parameters fluctuate over time. Our findings also show that modern reinforcement learning algorithms can provide a solution that reduces the need for frequent and inefficient human intervention in optimal execution tasks.