Reinforcement Learning for Optimal Execution when Liquidity is Time-Varying

By Andrea Macrì, Fabrizio Lillo

Published 2024-02-21

Everscope rating

1477.1

Relevance to quantitative trading

9 / 10

Implementation complexity

7 / 10

Reproducibility

4 / 5

About this paper

Methodology: Double Deep Q-Learning (DDQL). Problem types: Algorithmic Execution, Optimization.

arXiv:2402.12049 · Paper rankings

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