[2604.00031] Decomposable Reward Modeling and Realistic Environment Design for Reinforcement Learning-Based Forex Trading

Quantitative Finance > General Finance arXiv:2604.00031 (q-fin) [Submitted on 20 Mar 2026] Title:Decomposable Reward Modeling and Realistic Environment Design for Reinforcement Learning-Based Forex Trading Authors:Nabeel Ahmad Saidd View a PDF of the paper titled Decomposable Reward Modeling and Realistic Environment Design for Reinforcement Learning-Based Forex Trading, by Nabeel Ahmad Saidd View PDF HTML (experimental) Abstract:Applying reinforcement learning (RL) to foreign exchange (Forex) trading remains challenging because realistic environments, well-defined reward functions, and expressive action spaces must be satisfied simultaneously, yet many prior studies rely on simplified simulators, single scalar rewards, and restricted action representations, limiting both interpretability and practical relevance. This paper presents a modular RL framework designed to address these limitations through three tightly integrated components: a friction-aware execution engine that enforces strict anti-lookahead semantics, with observations at time t, execution at time t+1, and mark-to-market at time t+1, while incorporating realistic costs such as spread, commission, slippage, rollover financing, and margin-triggered liquidation; a decomposable 11-component reward architecture with fixed weights and per-step diagnostic logging to enable systematic ablation and component-level attribution; and a 10-action discrete interface with legal-action masking that encodes explicit trading ...