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Optimizing Trade Execution with RL by Tañedo Jeremy Joseph Rodriguez is a document available to read on EtoBox.
What is Optimizing Trade Execution with RL about?
This document proposes a framework called Offline Reinforcement Learning with Dynamic Context (ORDC) to model the problem of optimized trade execution. It finds that existing RL methods for trade execution suffer from overfitting issues that prevent real-world deployment. The ORDC framework highlights that overfitting occurs due to the large context space of market variables and limited samples of context sequences available during offline training. To address overfitting, the document proposes learning com
- Author
- Tañedo Jeremy Joseph Rodriguez
- Language
- EN