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

Nabeel Ahmad Saidd

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.

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

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.

Paper Structure

This paper contains 85 sections, 7 equations, 13 figures, 12 tables, 3 algorithms.

Table of Contents

  1. Introduction
  2. Contributions.
  3. Research questions.
  4. Related Work
  5. Reinforcement Learning for Financial Trading: What Has Been Solved and What Has Not
  6. Environment Fidelity and Temporal Causality
  7. Reward Design and Risk-Aware Objectives
  8. Action-Space Modeling and Legality Constraints
  9. Algorithmic Stability and Replay Under Financial Non-Stationarity
  10. Forex-Specific Evidence and Evaluation Standards
  11. Novelty and Contributions
  12. Methodology
  13. Design Principles and System Overview
  14. Data Pipeline, Temporal Partitioning, and Leakage Control
  15. Chronological processing policy.
  16. ...and 70 more sections

Figures (13)

  • Figure 1: System architecture of the RL trading framework, linking configuration, data processing, training, deterministic backtesting, and reproducibility logging in one end-to-end pipeline.
  • Figure 2: Causality-correct environment step timing: the environment emits $\mathbf{s}_t$, receives $a_t$, execution is driven by market $\mathrm{open}_{t+1}$, portfolio state is marked at $\mathrm{close}_{t+1}$, reward engine returns only $r_t$ to the environment, and the environment emits the full transition $(\mathbf{s}_t,a_t,r_t,\mathbf{s}_{t+1})$ without any Reward$\rightarrow$Agent bypass.
  • Figure 3: Reward taxonomy and computation flow for the RL trading environment, from agent--environment interaction through component aggregation and normalization to the final scalar reward.
  • Figure 4: Training-loop schematic with mask-aware interaction, complete transition construction, replay learning, and periodic target synchronization.
  • Figure 5: Multi-panel dashboard of endpoint metrics across retained experiment families, summarizing return, drawdown, risk-adjusted performance, and activity.
  • ...and 8 more figures

Theorems & Definitions (1)

  • Definition 1: Decision--Execution Separation