Realistic Market Impact Modeling for Reinforcement Learning Trading Environments

TL;DR

This paper introduces three RL trading environments with realistic market impact models, demonstrating significant effects on trading behavior and algorithm performance, and provides an open-source toolkit for more accurate backtesting.

Contribution

It develops three new trading environments incorporating nonlinear market impact models, enabling more realistic RL trading research and evaluation.

Findings

Market impact models significantly alter algorithm performance and trading costs.

The Almgren-Chriss impact model reduces costs and turnover dramatically.

Hyperparameter tuning is crucial for realistic trading behavior and cost reduction.

Abstract

Reinforcement learning (RL) has shown promise for trading, yet most open-source backtesting environments assume negligible or fixed transaction costs, causing agents to learn trading behaviors that fail under realistic execution. We introduce three Gymnasium-compatible trading environments -- MACE (Market-Adjusted Cost Execution) stock trading, margin trading, and portfolio optimization -- that integrate nonlinear market impact models grounded in the Almgren-Chriss framework and the empirically validated square-root impact law. Each environment provides pluggable cost models, permanent impact tracking with exponential decay, and comprehensive trade-level logging. We evaluate five DRL algorithms (A2C, PPO, DDPG, SAC, TD3) on the NASDAQ-100, comparing a fixed 10 bps baseline against the AC model with Optuna-tuned hyperparameters. Our results show that (i) the cost model materially changes both absolute performance and the relative ranking of algorithms across all three environments; (ii) the AC model produces dramatically different trading behavior, e.g., daily costs dropping from $200k to $8k with turnover falling from 19% to 1%; (iii) hyperparameter optimization is essential for constraining pathological trading, with costs dropping up to 82%; and (iv) algorithm-cost model interactions are strongly environment-specific, e.g., DDPG's OOS Sharpe jumps from -2.1 to 0.3 under AC in margin trading while SAC's drops from -0.5 to -1.2. We release the full suite as an open-source extension to FinRL-Meta.