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

TL;DR

This paper introduces a modular RL framework for Forex trading that emphasizes realistic environment modeling, decomposable rewards, and explicit trading actions, leading to improved training dynamics and performance.

Contribution

The paper presents a novel RL framework with a friction-aware environment, a decomposable reward architecture, and a discrete action interface tailored for Forex trading applications.

Findings

Full reward configuration achieves highest Sharpe ratio and cumulative return.

Expanded action space increases returns but also turnover and reduces Sharpe ratio.

Scaling variants reduce drawdown and improve endpoint performance.

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 primitives while enforcing margin-aware feasibility constraints. Empirical evaluation on EURUSD focuses on learning dynamics rather than generalization and reveals strongly non-monotonic reward interactions, where additional penalties do not reliably improve outcomes; the full reward configuration achieves the highest training Sharpe (0.765) and cumulative return (57.09 percent). The expanded action space increases return but also turnover and reduces Sharpe relative to a conservative 3-action baseline, indicating a return-activity trade-off under a fixed training budget, while scaling-enabled variants consistently reduce drawdown, with the combined configuration achieving the strongest endpoint performance.