Decomposable Reward Modeling and Realistic Environment Design for Reinforcement Learning-Based Forex Trading

By Nabeel Ahmad Saidd

Published 2026-03-20

Everscope rating

1354

Relevance to quantitative trading

9 / 10

Implementation complexity

8 / 10

Reproducibility

4 / 5

About this paper

Methodology: Modular RL Framework with Decomposable Reward and Legal-Action Masking. Problem types: Reinforcement Learning, Algorithmic Execution, Risk Management, Portfolio Optimization.

arXiv:2604.00031 · Code · Paper rankings

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